discussion about hypothesised link between the menopause and Alzheimer disease

Posted comment on ´Changing your mind`  by J. Hamzelou and published in New Scientist 3141 2nd September 2017 p.36.


Hamzelou began her article by describing some of the symptoms experienced by some women going through menopause. She stated that the cognitive changes observed in menopause, eg. migraines, mood swings, anxiety, short-temper, forgetfulness and insomnia, resemble the presenting symptoms observed with sufferers of Alzheimer disease and may in fact signal the start of that disease. In order to support her view Hamzelou quoted work by Brinton, a Californian scientist who studies the hypothesised link between the menopause and Alzheimer disease. Brinton hopes to develop therapies that artificially boost hormone levels that would lead to protecting the brain from the detrimental changes that could lead to dementia later in life.

Hamzelou continued her article by describing the biological basis of menopause and listed the common non-cognitive symptoms observed, eg. fatigue and weight gain. She said that in comparison to those obvious symptoms, cognitive symptoms are often overlooked since they occur at a time when other reasons can be given to their appearance eg. ageing and also because society demands a level of expectation and endurance when considering mental health problems. However, research given by Hamzelou as being carried out in the last decade, has shown that a decrease in oestrogen level has effects on memory, mood and even what has been termed the  ´brain health` of men and women. Research by Brinton and others has shown that reduced levels of oestrogen are correlated to alterations in the type of energy the brain cell uses and to a reduction in the production of energy. Under normal conditions, oestradiol increases the activity of the mitochondria in brain cells involved in normal cellular energy production and therefore, it helps cells recover from damage associated with normal ageing. Grimm of the University of Queensland, Australia supports Brinton`s view and was quoted in the article as saying that the drop in oestrogen makes the brain more sensitive to damage that could lead to death of neurons. Brinton believes that the fall in oestrogen that occurs in the menopause causes the brain to produce less energy and to change the type of energy it uses. Glucose is the normal energy source of brain and this is reduced by 25% in tissues of menopausal sufferers. To overcome the shortage of glucose the cells, according to Hamzelou and Brinton, begin a ´starvation` response and use fats as their energy source instead. They are also believed to use myelin as well which can be found in the protective shield around the neurons themselves. Although their studies were carried out on mice, Brinton suggested that the results could also apply to humans and some research supports this. A decrease in glucose metabolism, a change in white matter volume and grey matter volume and an increase in beta amyloid production relative to men have been observed. The switch in energy source was also suggested by Brinton to provide an explanation for some of the other symptoms of menopause. For example, the metabolism of fat because it is a less efficient energy source than glucose creates more heat and this excess heat in the brain was suggested in some animal studies as possibly triggering the menopausal non-cognitive symptom of  hot flushes.

Hamzelou continued her article by describing why some researchers link the supposed protective effect of oestrogen on cognitive function and hence why the menopause and its cognitive symptoms can be linked with symptoms observed in Alzheimer sufferers. Brinton investigates why women are more susceptible to Alzheimer`s illness and thinks that the hormonal transition occurring  in the perimenopause stage and full menopause may be the cause and start of Alzheimer illness in some women. Studies have shown that two thirds of people with Alzheimer`s illness are women and even though the disease is diagnosed when they are in their seventies, the disease actually starts around 15-20 years earlier when the natural menopause occurs.  The link to energy production during the hormonal transitions occurring in menopause was supported by work from others. For example brain scans measuring how much glucose is being metabolised across different brain regions were carried out in 2005 by Mosconi and colleagues of the New York University and they observed reduced glucose metabolism with Alzheimer sufferers and women who were in perimenopausal or postmenopausal stages. These observations compared favourably to Brinton`s observations in mice and suggested a link between a decline in glucose metabolism in the menopause, ageing and Alzheimer illness.

Hamzelou then went on to describe the natural progression of such results – if oestrogen has a brain effect when it falls, then what happens when it is replaced? Some studies suggested that hormone replacement therapy (HRT) could prevent dementia, but a trial of 7500 women in 2005 by the Women`s Health Initiative Memory Study found that HRT actually quickened cognitive decline and increased the risk of not only dementia, but also breast cancer and cardiovascular disease.  Hamzelou quotes researchers who believe that the study was flawed and describes the study by Pinkerton at the University of Virginia who looked at women given conjugated equine oestrogens. They stated that the negative link between HRT and cognition was incorrect since the administered oestrogen was obtained from pregnant horses and therefore, not an appropriate hormone source for premenopausal women and that the women taking part in the study were already over 65 and were therefore, too old to be described as suitable menopause subjects. They said that their brains had already adapted to low oestrogen levels and that the number of relevant receptors had already decreased. Pinkerton went on to say that there appeared to be an optimum time for HRT treatment (termed ´window of opportunity`) and that this time period was limited to between the appearance of the menopausal symptoms and the time when the brain was still responsive to treatment. They said that oestrogen can work better on healthy cells and therefore, HRT works better when women take it around the time of the menopause. In response to the increase of detrimental side effects observed with HRT administration, Pinkerton said that in the case of breast cancer, administration of HRT was linked to only an increase in breast cancer of under one case in a thousand. Pinkerton concluded by saying that HRT should be used only if women experience unpleasant symptoms, but the view of ´lowest dose for shortest amount of time` should be replaced by the caveat of ´making sure that the treatment is appropriate`. The determination of what is appropriate has not yet been made. Hamzelou continued by suggesting that the better solution may be to use oestrogens that only work on specific organs eg. one that works on brain, but by-passes breast tissue. She quoted in her article work by Raber of Oregon Health and Science University in Portland who reports that drugs of this nature are already in development. Hamzelou also quotes Brinton who suggests a nutritional approach to protect the brain from the effects of hormone loss. This view is linked to food obtained from the diet and brain function. For example, ketogenic diets appear to benefit epilepsy sufferers. In the case of the menopause, a high fat diet is not advised for people at risk of weight gain and against the view of a healthy diet rich in fruit, vegetables and grains being good for brain health. She also recommended exercise and keeping active, which has been shown to boost mood and cognition and can increase bone mass.

The article concluded with Brinton describing the future with individually tailored hormone therapies given at the right time to treat menopause symptoms and prevent Alzheimer`s illness.


The menopause can be regarded as a ´sensitive` topic at the best of times particularly with women, but when it is linked in scientific research to the appearance of Alzheimer disease then the feelings it evokes are intensified. Therefore, any research into the association between these two topics should be rigorously examined because unlike other factors causing changes in memory and cognitive capability (eg. the administration of certain drugs or a stroke) the natural decline of a hormone due to increasing age is something that transcends effects under the control of the person herself. Experimentation into the menopause in humans is beset with problems. For example, because the onset is variable and the occurrence of relevant symptoms is individual. We know that natural occurring menopause is clearly defined as existing one year after the last menstrual period, but definition of the ´last menstrual period` is difficult to define itself since women experience differing forms of menstrual periods in the perimenopausal phase. The definition of the beginning of menopause is therefore easier to establish when it occurs through surgical intervention eg. hysterectomy or also through disease such as polycystic ovarian syndrome. Even if the beginning of menopause can be determined accurately time-wise the variation in symptoms whether physiological, cognitive or emotional makes interpretation of results difficult in humans. Physiological symptoms such as hot flushes or loss of sleep are probably easier to see and measure, but the cognitive symptoms (eg. irritability, loss of spatial memory) on which this Blog is focussed are more difficult since they are in part ascertained through self-reporting which can be unreliable and are subjective with daily variations and differences depending on personal situations. However, we can say that the menopause is a physiological condition or state brought about by decreased levels of circulating oestrogen/oestradiol and therefore, we can assume that whatever symptoms are observed then they occur as a result of this decrease in circulating hormone.

Oestrogen is produced from progesterone by the ovaries and instigates a wide variety of effects in the whole body. However, it is also produced in the brain, blood vessels and bone synthesised from cholesterol to various intermediate compounds eventually to pregnenolone which then is converted to 17alpha-hydroxyprogesterone then to androstendione (to estrone),  to testosterone and eventually to oestradiol. Since this Blog focusses on the brain and neurochemical processes we shall concentrate here in this post on effects of oestrogen in the brain and on neurons. It can be said that the presence of oestrogen in this organ and on these types of cells has a general effect on neuronal firing and is said to elicit intracellular effects associated with changes in the DNA, membranes and from the article reviewed here on cellular energy production. This general positive synaptic effect translates into an influence on firing and is said to provide a protective effect on neurons and their functions. Exposure to oestrogen or oestradiol can mean that cells are more likely to survive hypoxia, oxidative stress and exposure to neurotoxins for example and hence, also elicit a protective effect against the development of certain mental illnesses such as multiple sclerosis, Parkinson´s disease and dementia.

When considering the effect of oestrogen on brain cell firing we should assume that the effect is not major since for example there are other systems in play which have far more wide-ranging effects (eg. NMDA concentration, glial cell functioning) and that there is a natural variation in oestrogen level anyway with the menstrual cycle with no major signal transmission shut down when oestrogen is at a low level. Therefore, we should probably consider oestrogen more as an instrument of ´fine tuning` of the neurobiological system in the same vein as the emotional system (eg. a positive influence from dopamine on the emotional system and neuromodulation of prefrontal cortex firing) or like the effect of tiredness and sleep deprivation. In order that such an influence can occur the cells in question must have oestrogen ´acceptor` capability and this will be described in more detail later on. The possession or absence of such a capability could explain why some brain areas are affected by oestrogen and why some are not and hence, why some cognitive functions are affected and others independent from oestrogen influence.

For now in the context of a positive effect on synaptic firing, oestrogen has been shown to increase neuronal firing due to the growth of neurites (increases cell viability) and an increased number of dendritic spines. For example in the case of the hippocampus, the number of spines varies with the level of oestradiol in vivo with both peaking together. The presence of oestradiol also shows that the area grows more excitatory synapses and the new spines have more NMDA receptors on them. Hence, the long-term plasticity of the hippocampus is increased in the presence of oestrogen. Also oestrogen can initiate its effect directly in the hippocampus by depressing the synaptic inhibition mechanism. Oestrogen receptors have been found on the inhibitory interneurons in the area which do not grow more spines on exposure.  The oestradiol causes the inhibitory cells to produce less GABA so there is less inhibition of firing and hence greater general neural activity which somehow triggers an increase in spine growth in the area and increases the number of excitatory synapses on the pyramidal cells. In the presence of low oestrogen then decreased spine density and a decreased number of NMDA receptors is observed as expected, but also increased acetylcholinesterase activity is seen. This implies that an effect on the cholinergic firing mechanism in the area is also influenced. These effects on the hippocampus give an explanation in part as to why certain memory systems are said to be affected in menopause since the hippocampus is believed to be responsible for the relay of information in the brain and with the neighbouring entorhinal cortex area responsible for the binding of information together. Hence, effects on object recognition and verbal memory in menopause where there is a reduced level of circulating oestrogen are seen.

Another brain area said to be affected by oestrogen is the prefrontal cortex. It has been found that dopamine activity in this area is enhanced by oestradiol and in its presence then bigger synapses are observed. The effect is associated with the presence of oestrogen receptors of the alpha type. Therefore, in this case oestrogen could influence the neuromodulatory control associated with this area and dopamine, thus explaining in part the observed cognitive symptoms in menopause linked to the emotional pathway eg. irritability, and lower decision-making capability eg. assessment of values of events.

The synaptic and firing effects observed in the presence of oestrogen are brought about by intracellular processes involving the hormone. These are believed to be associated with DNA binding and/or cellular membrane effects and also as suggested by the authors in the article reviewed in this blog, by changes in the energy producing mechanisms taking place in the cell`s mitochondria. The DNA effect is well documented and begins with the transfer of the hormone through the cell`s membrane – a process that is simple due to its non-polar molecular structure. Once inside the cell it binds to a highly specific soluble receptor protein in the cell`s cytosol. These oestrogen receptors are of the alpha or beta type and are known as nuclear oestrogen receptors (ERalpha, ERbeta). It is thought that it is the alpha type in the hippocampal CA1 area that is linked to the increased synaptic plasticity described above. The hormone/receptor complex then interacts directly with specific binding sites on the DNA called oestrogen response elements (EREs) and ultimately, this binding modulates gene transcription. The DNA binding domain is highly conserved with 9 cysteine residues, 8 of which bind zinc ions which stabilise the structure of the domain (called zinc finger domains). The ligand binding site exists at the carboxyl end and is comprised of alpha helices. Ligand binding in a hydrophobic pocket in the centre leads to conformational changes that allow the recruitment of a coactivator protein such as SRC-1, GRIP -1 or NcoA-1. These have a common modular structure and bind to the ligand binding domain of the receptor dimer. Binding to the DNA ultimately changes gene transcription so that certain proteins are either down- or up-regulated so that the oestrogen influence on the cell is realised.

The other known cellular effect of oestrogen is its binding to membrane-bound receptors (mERs) eg. GPER (GPR30), ER-X and Gg-mER. These receptors can be rapidly activated on exposure to oestrogen and their effects are believed to be associated through the attachment of caveolin-1. Complexes are formed mostly with G protein coupled receptors, striatin, receptor tyrosine kinases (eg. EGFR, IGF-1) or non-receptor kinases (eg. Src) and each causes different effects. Although binding through G protein coupled receptors eg. GPR30 has an unknown role, binding to other structures cause cellular effects eg. through striatin – some of the membrane bound oestrogen receptor complex may lead to increased levels of calcium ions and nitric oxide; through receptor kinases – signals sent to the nucleus via the mitogen activated protein kinase MAPK/ERK pathway and the phosphoinositide 3 kinase (PI3K/AKT) pathway; and finally through glycogen synthase kinase 3 (GSK- 3beta) which inhibits transcription by the nuclear oestrogen receptor by inhibiting phosphorylation of serine 118 of the nuclear oestrogen alpha receptor. Phosphorylation of the GSK-3beta removes its inhibitory effect and this is achieved by PI3K/AKT pathway and MAPK/ERK pathway via rsk.

Another possible mechanism involving the membrane is the oestrogen receptor complex`s effect on the lipid domain as a whole and the subsequent increased or decreased action of other neurotransmitter complexes existing in that same lipid domain. It is known that oestrogen elicits an effect on NMDA receptors in the hippocampus, but it also affects acetylcholine binding to the M2 acetylcholine receptor. In general, however it is likely that the overall effect of oestrogen by binding to membrane-bound receptors is increased firing activity as described above.

The third action of oestrogen at the intracellular level is that suggested by Hamzelou and researchers such as Brinton who hypothesise that the presence of oestrogen supports the use of glucose as fuel source in the cell in its energy production mechanisms, but its absence causes a change in fuel source to fats and even myelin and a decrease in mitochondrial function. What does this actually mean? In the brain, the sole source of fuel for cells is glucose under normal circumstances and we have to assume that there are normal circumstances even in the low levels of oestrogen in parts of the menstrual cycle because of diet and that the glucose transport into the cells is below maximum capacity and hence, an increase in brain cell activity will still keep glucose transport into the cell within its limits. As already described in another Blog post, cell energy production mechanisms can change according to certain conditions eg. conditions of low oxygen/high altitude. In this case, the lack of oxygen means cellular adaptation of the biochemical processes supplying energy to the cell occurs.  In low oxygen conditions, the normal mechanism of energy production means that glucose is still being metabolised by a chain of enzymatic reactions (called glycolysis) to produce pyruvate just as that occurring in aerobic respiration (ie. in the presence of oxygen),  but the second stage of the process is altered. This stage is where the pyruvate is converted by another chain of reactions into the energy molecules, ATP.  If oxygen is not present at the level required for this aerobic mechanism, then a process called lactic acid fermentation is initiated (anaerobic respiration). Lactic acid fermentation means that pyruvate is then converted to lactate by the enzyme lactate dehydrogenase (LDH). However, this anaerobic process does not produce the same number of ATP molecules as normal aerobic mechanisms, but it does provide some. The other potential problem of this scenario is the build-up of lactate which is observed in muscle cells. However, it is likely that in the brain which is dependent on a constant supply of glucose and energy that a safeguarding mechanism is in place called the Cori cycle which transports the lactate out of the cell, back to the liver where it is converted into glucose by a process known as gluconeogenesis. Again the LDH enzyme is involved and this conversion could explain the lack of appetite experienced by some when undergoing rising altitude.

In the case of the menopause, Hamzelou and researchers such as Brinton suggest that the source of fuel in the brain cell changes from glucose to fats when oestrogen levels are low. Normally, fatty acids are bound to albumin in the blood and cannot cross the blood brain barrier, but under conditions such as starvation for example,  ketone bodies are generated by the liver and transported in the blood across the blood brain barrier to partly replace the glucose as fuel in the brain cells. Therefore, Hamzelou and Brinton suggest that ketone bodies are used as fuel source. The acetyl coA formed in fatty acid oxidation enters the citric acid cycle only if levels of fat and carbohydrate degradation are balanced. This is because of the availability of the substrate oxalocitrate which forms citrate, the next substrate in the cycle. Oxalocitrate concentration is lower if carbohydrates are not available.  In fasting or diabetes, oxalocitrate is used to form glucose by the gluconeogenic pathway and therefore the substrate is not available for acetyl coA production. Therefore, acetyl coA is converted to acetoacetate (by a 3 step mechanism) and D-3-hydroxybutyrate (formed by reduction of acetoacetate in mitochondrial matrix) which with acetone (formed from slow spontaneous decarboxylation of acetoacetate) forms compounds known as ketone bodies. The major site of production of ketone bodies is in liver mitochondria and these are transported via the blood to other tissues. They are used as fuel sources in the muscle, renal cortex and brain in cases of starvation (75% of fuel in prolonged starvation) and insulin-dependent diabetes mellitus. In the latter, the absence of insulin means that the liver cannot absorb glucose and as a result cannot provide oxaloacetate for the fatty acid derived acetyl coA process and cannot prevent fatty acid mobilisation by the adipose tissue. Therefore, the liver produces large amounts of ketone bodies which are strong acids and the presence of such high levels causes severe acidosis. This results in a decrease in intracellular pH which impairs tissue function – a condition already described in a previous Blog post when considering cell function in high altitude conditions. The brain begins to use acetoacetate after 3 days of starvation (a third of energy needs met), but after several weeks it is a major source. The advantage is that ketone bodies are built from released fat and this preferable to breaking down muscle instead.

Although the hypothesis by Hamzelou, Brinton and supporters about the switch from glucose to fat and even myelin may be true and that glucose metabolism is reduced in the brain in low oestrogen, then if this hypothesis is correct, then we must assume that in menopause, the brain cells are not getting their normal fuel source because of the lack of oestrogen. Therefore, under normal conditions oestrogen would then aid the transport of glucose into the cell by affecting the insulin signal on the glucose transporters, or by directly effecting the glucose transporters themselves. Is there any proof of this? There are no reports of significant effects on insulin sensitivity or levels or glucose levels in the menopause. However, there is a report of the change in insulin metabolism. Therefore, the cause of effect could be indirect through reported changes in diet in menopausal women where diet is altered to counteract the increased weight gain and fat deposits observed around the middle. A strict diet could translate into starvation conditions and hence, changes in fuel sources as indicated above could be observed. It is likely that if a normal diet is maintained then such an effect on fuel source would not be seen.

Therefore, the overall conclusion about the action of oestrogen in the brain is that it is a molecular compound that affects cell functioning of susceptible cells by either binding to the cell membrane or by internally binding to receptors which bind directly to the DNA and affect gene transcription. This can result in either a negative or positive effect on cell functioning. If the cell has oestrogen acceptor capability then oestrogen can affect that cell, that area and ultimately have an effect on cognitive function of some sort linked to that brain area eg. oestrogen influences the activity of the hippocampus by inhibiting the interneurons and hence, increasing synaptic firing and increased plasticity of area in question. An absence of the hormone will lead to observed changes in verbal memory, object recognition, spatial memory (only rats), short term memory, learning new associations, long term memory, working memory plus depression. However, the action of oestrogen should be considered more in terms of ´fine tuning` systems and mechanisms already in place rather like the effects of tiredness. This would in part explain why there appears to be a neuroprotective effect with oestrogen ie. cells are more likely to survive hypoxia, oxidative stress, exposure to neurotoxins for example or protection against diseases such as multiple sclerosis, Parkinson`s disease and dementia if exposed to oestrogen or oestradiol. The positive oestrogen effect on gene transcription and synaptic firing would counter-balance the negative effects caused by the cellular stresses.

This leads on to the hypothesis proposed by Brinton and others and explained by Hamzelou in her article about a link between the menopause and Alzheimer`s disease. It is said that there are several similarities between the two conditions eg. the start of menopause is considered to be linked to the same time as the start of Alzheimer`s disease; women are far more susceptible than men; and the presenting symptoms relating to cognition appear to be the same or similar. Therefore, we must question whether this is just circumstantial or whether there is a real link. With regards to timing, the menopause or reduction in oestrogen as described above could initiate some minor temporary changes in physiology which could lead on to changes in sleep patterns, depression and anxiety and small changes in performance of some cognitive functions. Although the physiological changes seen with Alzheimer`s disease are known for times later on in the disease progression, the physiological changes associated with the early stages are to date not clearly defined. It could be that these are actually the same changes as those observed in menopause ie. changes in sleep patterns, susceptibility to depression and anxiety, reduced levels of interest and hence, lower levels of mental stimulation etc. and therefore, the timing of the menopause and onset of Alzheimer disease would appear to be the same. Of course, it should be remembered that not all women who experience the menopause go on to develop Alzheimer disease and menopause and Alzheimer disease  are associated with more elderly people and hence, timing could be a reflection of the normal ageing process and the changes in life style, aspirations, emotional stability that could accompany this particular life period.

The second association between the menopause and Alzheimer disease according to Hamzelou, Brinton and others is the observation that Alzheimer disease is more prevalent in women and understandably, the menopause is a female condition. Since brain neurochemical mechanisms are independent of gender then we must assume that the difference is due to either physiological differences between the female and male brain, or possibly could the reflect the way in which men and women mentally approach and carry out events. The latter is probably a product of the former and therefore, the observation that oestrogen level has an effect on the performance of the hippocampus (described above) could explain why there is a gender difference in the appearance of Alzheimer disease. The hippocampus is an important brain area with multiple roles in cognitive functions such as information intake and binding, memory mechanisms, working memory and decision-making and is known to be progressively and extensively negatively affected as Alzheimer disease progresses. Women appear to have naturally larger hippocampal areas and therefore, this could provide a possible reason why women appear to suffer from Alzheimer disease more than their male counterparts.

The third similarity proposed by Hamzelou, Brinton and others linking menopause to Alzheimer disease is that the cognitive symptoms of the menopause are similar to those seen with sufferers of Alzheimer disease. Both appear to be a collection of cognitive symptoms linked to relaying information taken in, binding of information together, value assessment for example and hence, the similarity of symptoms of for example lack of memory, decision-making problems and emotional status changes are understandable. As stated above, circulating oestrogen appears to affect synaptic functioning and as with the timing association physiological changes would instigate observable performance changes. Since both produce to some extent permanent changes in physiology eg. menopause causes minor changes due to its ´fine tuning` role and Alzheimer disease massive changes because of amyloid deposits and abnormally high apoptosis of neurons then symptoms would be appear to be the same.

There is however a difference between menopause and Alzheimer disease in relation to whether cognitive performance can be restored by treatment of oestrogen replacement therapies. With Alzheimer disease in the later stages of the disease, administration of oestrogen replacement therapies appears to have no beneficial effect. In the former however, treatment can reverse some of the symptoms eg. verbal memory, short term memory are improved and there are positive effects on sleep and emotional state disturbances, eg. depression is reduced. This is understandable since falling levels of circulating oestrogen are being boosted by the administered oestrogen compounds and hence, the positive effects on DNA transcription and synaptic firing are being restored. Increased expression of the oestrogen alpha receptor in hippocampal CA1 area and increased NMDAR synaptic transmission have been observed with the administration of oestrogen compounds to mice menopausal models. However, most research appears to suggest that the positive effect of this oestrogen administration appears to be limited to only a short period when falling levels are minimal (called the ´window of opportunity`) which implies that in the long-term other changes are occurring in the synapse and brain areas that are not associated with the fine tuning mechanism brought about by the presence of the oestrogen hormone. These physiological changes could be those linked with natural ageing for example or instigated through life-style changes brought about by a variety of reasons. This may be important because other things appear to be beneficial for reduction of menopausal cognitive symptoms eg. exercise, proper diet, social contact, mental stimulation, appropriate sleep patterns. These can possibly restore the balance or counteract the loss of oestrogen experienced in the menopause. One factor that should be considered relating to this is the importance of zinc in brain cell functioning. Zinc deficiency is known to cause anorexia, lethargy, diarrhoea, impaired immune system, growth restriction, intellectual disability, depression, loss of appetite and disorders of fear conditioning. There is a range of effects because zinc ions have important functions in general in nerve conduction in the brain, roles in correct enzyme functioning such as carbonic anhydrase, aspartate transcarboamylase, aminoacyl –tRNA synthase, metalloproteases and in neurons in particular an important role in the phospholipid cell membrane signal and in relation to menopause in  steroid binding to the receptor as seen in the case of oestrogen. It is possible that menopausal women could suffer from zinc deficiency due to dieting and/or poor diet. Food stuffs containing zinc are bread, eggs, oysters, liver, meat, dairy products and pulses and weight gain associated with falling oestrogen levels may mean that the diet is restricted of these zinc containing foods. This deficiency could lead to the wide range of effects attributed to the multiple cellular roles of zinc.

Therefore, can we definitively say that oestrogen reduction in menopause is linked with Alzheimer disease? It is likely that oestrogen is not a major player in neuron function rather it provides a ´fine tuning` mechanism for synaptic physiology and function in the same way as tiredness or emotional state changes can. It appears that its effect in the brain is limited to particular areas such as hippocampus and prefrontal cortex which play important roles in cognitive functions such as memory and decision-making. Therefore, the presence of oestrogen may provide a neuroprotective effect on certain neurons which allows these cells to more likely survive extreme negative conditions such as those seen with  hypoxia, oxidative stress and exposure to neurotoxins. This of course naturally translates then into positive changes on cognitive functions so that oestrogen is said to have a protective effect against certain mental illnesses such as multiple sclerosis, Parkinson`s disease and dementia. It is then understandable that conditions where there is an absence of oestrogen or where levels are low such as the menopause lead to minor effects on cognitive performance. However, the effect could be also attributed to normal ageing processes being experienced at that time. The association between menopause and Alzheimer illness, although symptoms appear similar, is likely to be indirect with general ageing, certain conditions such as stroke and lifestyle changes being the main causes of the appearance of the disease. Therefore, it is understandable that boosting the level of oestrogen when it is naturally falling can provide some positive effect on certain cognitive functions, but only temporarily. Probably of more benefit to women experiencing the menopause is the continuation and maintenance of good life style practices.

Since we`re talking about the topic………..

…..if synaptic firing is enhanced by the presence of oestrogen because of the inhibition of hippocampal interneurons can we assume that the administration of a GABA antagonist preferably targeted to the hippocampal area simultaneously with the administration of oestrogen to ovariectomised mice will block this positive effect? Would the expected behavioural changes relating to restored spatial memory also be absent?

…..using real-time functional MRI would it be possible to chart connectivity between certain brain areas eg. hippocampus, amygdala and prefrontal cortex during the course of a problem-solving type task using menopausal subjects and to monitor the effects that the administration of either oestrogen or progesterone pre-testing would make on those connectivity patterns?

…..performance of place recognition tasks was found to be reduced in female rats who were in the proestrus (high oestrogen) phase of their oestrous cycle. An excessive consumption of sugar sweetened drinks daily beginning 14 days before testing was found to protect the rats from this negative change. This was attributed to the sugar consumption causing functional changes in the hippocampus. Object recognition appeared not to be effected. Can we assume that the same pattern of results would be unlikely to be observed with human females because of the effect of insulin, but may produce a problem in those that suffer from diabetes?

Posted in Alzheimer disease, glucose, menopause, neuronal firing, oestrogen, Uncategorized | Tagged , , , ,

bioelectric signals and membrane ion channels

Posted comment on ´Bioelectrical Signals and Ion Channels in the Modelling of Multicellular Patterns and Cancer Biophysics` by J. Cervera, A. Alcaraz and S. Mafe published online in Nature Science Reports February 4th 2016 6:20403 doi 10.1038/srep20403


Cervera, Alcaraz and Mafe state in their article that bioelectrical signals and ion channels are central to spatial patterning in cell assemblies (ensembles) and are therefore, important in determining cell positioning and the organisation of multicellular groups. Defects or deficiencies of the bioelectric signal are also implicated in cancer. The research group proposed a model or an approach to a bioelectrical network of non-neural cells where the biochemical coupling of the assembly is brought about by the action of an ion channel blocker. Their approach was based on three concepts: that a cell`s electrical state was characterised by the membrane potential of that cell which was regulated by voltage gated channels that had depolarising and hyperpolarising capabilities; that the long-range electrical coupling between neighbouring cells of multicellular assemblies was brought about by gap junctions; and that the electrical state of the whole multicellular assembly could be changed by the administration of a biochemical agent locally in the cell`s microenvironment and this could alter the conductivity potential of the membrane`s ion channels. Using their model, Cervera, Alcaraz and Mafe investigated the electrical effects of small neuronal assemblies in spatial patterning, the role of ion channels in cancer biophysics and the distribution of charged nanoparticles over neuronal assemblies. They found that spatial patterns arising from their model are characterised by a map of cell potentials which are ultimately regulated by the voltage gated channels on each cell. The spatiotemporal patterns could normalise areas where there were abnormal cell electrical states ie. from the administration of charged nanoparticles and these findings indicated that bioelectric signals of multicellular groupings provide a new insight into the biophysics of cancer.

Cervera, Alcaraz and Mafe began their article by describing the connectivity between cells of multicellular groupings as not just biochemical in nature, but also bioelectrical, a fact they said is often disregarded. They cited examples of where tissue morphology and morphostatic fields are important in building and maintaining cell group architecture and where cancer results from the destruction of this architecture. Cancer cells have been observed to have an overexpression of specific ion channels (ie. an up-regulation of sodium ion channels and a down-regulation of potassium ion channels) and so tend to exhibit sustained depolarisation (ie. have a low membrane potential in absolute value). According to Cervera, Alcaraz and Mafe, depolarisation of the cell leads to a significant level of spatial distribution of negatively charged lipids and a significant effect on their interactions with positively charged proteins which may lead to the activation of intracellular biochemical pathways that result in cell proliferation.

The authors continued in their article with a description of the cell electrical state. They said that the electrical state of the cell is described by the membrane potential (Vmem) being less than zero and this is defined as the potential difference between the cell cytoplasm and the extracellular microenvironment under zero current conditions. This potential difference regulates the entry of sodium, potassium, calcium ions and other biologically-relevant molecules into the cell. It is a dynamic system transitioning between low (depolarized, abnormal) and high (hyperpolarized, normal) electrical states. Cervera, Alcaraz and Mafe said that the cells of their model exhibit bioelectric bistability (observed with for example neural cells) and others have demonstrated it also in their experiments. Hence, there are 3 values for Vmem: one for the hyperpolarised state, one for the depolarised state and one for the intermediate unstable transition state. Transitions are induced by modifying the equilibrium potential (Ein) which is dependent on ionic concentrations within the cell. It may also be associated with changes in biological parameters such as the pH and the ionic concentrations of the salt solution regulating conductance ratio (Gout/Gin). Other ion channels aside from those of sodium and potassium are also likely to be involved.

Cervera, Alcaraz and Mafe went on in their article to describe the case of cancer cells with relation to membrane potential and conductance ratios. In the case of cancer, anomalous inward-rectifying potassium channels were found in tumour cell lines giving values of Vmem different to those found in normal cells. It was also hypothesised that voltage-gated sodium channels are associated with depolarized values for Vmem in cancer cells. Abnormally low absolute values of Vmem correspond to plastic (not yet differentiated) cells while high absolute values of Vmem are found in terminally differentiated cells. Therefore, it was concluded that there is in the case of cancer cells simultaneous up-regulation of sodium ion channels (and hence, increased sodium ion inward currents) and down-regulation of potassium ion channels (leading to a decreased potassium ion outward current) giving a conductance ratio for Gout to Gin greater than 1. This implies that the membrane potential Vmem decouples from the normal hyperpolarized value Ein, bistability and cell depolarization regime. Therefore, depolarization is seen as a characteristic of abnormal cells that promotes the initiation of biochemical signal cascades. The addition to the external microenvironment of an ion channel blocker which would act on the outward rectifying channel in these circumstances could then decrease the channel conductance ratio Gout to Gin to less than 1 and have an effect on the existing bioelectrical state of the cell.

The authors then went on to extend their model (using long-range electrical coupling simulated by effective conductance ratios (G) and capacitances (C) arranged in parallel) to describe electrical states within multicellular groupings. This topic was of interest to the authors since abnormal tissues appear to have defective intercellular communication. Using their model, Cervera, Alcaraz and Mafe described the bioelectrical states of the cells in terms of protein channels acting as gap junctions between neighbouring cells of the cellular assembly. External ionic concentrations were ignored in the model with potential changes by the cell only occurring because of the gap junctions present.

Having described the role of non-functional junctions and defective intercellular communication in uncontrolled growth regulation, Cervera, Alcaraz and Mafe went on to report on the anticipated normalisation of small regions of the cell`s membrane of abnormal, depolarised cells brought about by modulation of the ion channels present. This would have the potential for restoring long-range gap junctions occurring in cell patterning and result in the removal of the conditions associated with tumorigenesis. The authors hypothesised that it would be possible to do this if the conductance ratios of coupling cells were high enough. The non-uniform distribution of gap junctions was suggested as allowing the coexistence of spatial regions of cells having increased intercellular communication (ie. having high G values – strong coupling and hyperpolarised state) acting as an electrical buffer with other cellular regions having decreased communication (low G values – depolarised state and abnormal). However, this was suggested as not being possible if there were a high number of abnormal cells within the multicellular assembly. Another reason given for the unlikelihood of this scenario was that low values of G produce cell isolation with inhibitory electrical signals characteristic of hyperpolarised potential neighbouring cells. This could be modulated however and reversed by the administration of particular agents in the external cell microenvironment. The authors did suggest that this was unlikely in this scenario because of the slow speed of diffusion of the agent and the biochemical reactions that may result.

A second scenario discussed in the article was the effect of the upregulation of the ion channels which leads to abnormal cell functioning and stimulates uncontrolled proliferation. In some cases, this causes persistent depolarisation and modifies the spatial distribution of negatively charged lipids in the cell membrane leading to the clustering of signalling proteins with positive residues around them. This can result in the initiation of biochemical pathways promoting cell proliferation. Cervera, Alcaraz and Mafe gave the example of upregulation of the outward rectifying channel by increasing conductance Gout to Gin thus promoting depolarisation. They said that this condition could not be normalised, but instead the region could expand and invade the normal cell region. Blocking of the outward rectifying channels by the administration of an external ion channel blocker would have the opposite effect with the conductance ratio Gout to Gin decreasing locally. The authors described this effect as immediate and coupling between the cells means that the effect on a single cell would spread to the multicellular assembly as a whole. Therefore, there would be a gradual transition from the depolarised abnormal state to the normal hyperpolarised cell state. This scenario would, according to the authors, require specific blockers for specific ion channels. If instead channels associated with membrane hyperpolarisation were blocked then the depolarised area would be extended and the authors gave as an example the situation in CHO and HeLa cells where the blocking of the potassium ion channels that are responsible for maintaining the membrane potential by cation nanoparticles would lead to significant cell depolarization.

Cervera, Alcaraz and Mafe then discussed in their article the association between bioelectric cell status and cellular patterning. They said that the coordinated action of external effects along defined spatial directions could produce cellular patterning providing it was a fast channel blocking action and the spatial map of the electric potentials closely followed the concentration of the blocking agent. In the example they gave in their article, the patterning required no anisotropic electrical coupling between the neighbouring cells and so they stated that the positional information from the individual cell could be determined by its electrical potential value plus individual cell properties such as its ion channels and the charged lipids and proteins in the cell membrane. The multicellular assembly was regarded as a bioelectrically coupled network of cells supported by intercellular gap junctions allowing the transmission of bioelectric signals. Interactions among the cells were crucial for positional information in addition to the presence of specific morphogens.

This led the authors to discuss the distribution of charged nanoparticles on multicellular assemblies. With the number of particles scaled to an exponential function of local electrical potential, they found that positive nanoparticles concentrated around negatively charged cells. Therefore, the spatial map of potentials would in their opinion influence the local uptake of charged nanoparticles over the tissue as a whole. They also discovered that the binding of the nanoparticles could also disrupt the cell membrane potential leading to modified intercellular communication. This indicated to them that the administration of nanoparticles would be a method for altering maps of potentials for multicellular groups by external means.

Cervera, Alcaraz and Mafe concluded their article by summarising the importance of bioelectrical signals in cancer. They suggested that the cancer cell`s microenvironment may show long-range bioelectrical signals and gap junctional cell insulation which may be involved in tumorigenesis and that the membrane depolarization caused may trigger gene transcriptional changes that regulate morphogen transport. Changes in single cell membrane potential and polarization states can occur when specific ion channels are up-regulated, down-regulated, or physically blocked. These modulations change the activity of those particular ion channels and possibly lead on to a promotion of depolarisation and normalisation. However, modification of the bioelectric state can also be brought about by modifying the gap junction conductance ratio by shifting the balance between the outward- and inward-rectifying channels. This can be achieved by blocking specific ion channels. Although the role of ion channels in cancer biophysics is already known, the authors reminded their readers that studying them in vivo in humans with ion channel blocking agents has problems since for example, these agents can cause cardiac arrhythmias. The authors also concluded that the bioelectrical characteristics associated with the spatial distribution of external morphogens over the multicellular assembly can be of significance for cellular patterning. The map of bioelectric potentials resulting locally from cell coupling regulates the experimental uptake of charged nanoparticles over the tissues themselves. These nanoparticles can disrupt the cell membrane potential and modify intercellular communication which is suspected of having a role in the biophysics of cancer.


What makes this article interesting is that it links together cell outer membrane potential (transmembrane potential, or Vmem), bioelectrical cell status and cell connectivity within multicellular groupings and describes dysfunctioning of these mechanisms in terms of ´limiting causes` for problems of cell proliferation, cell organisation and patterning and possibly to the disease, cancer.  Cervera, Alcaraz and Mafe stated that what they called the bioelectric status and bioelectric signaling of the cell are often disregarded in research on diseases with emphasis usually on physiology and biochemical systems reactions. This is probably not completely correct since the source of the topic being considered, that of the change in electric potential of cells through membrane channel functioning, is an enormously important topic in neurochemistry and forms the basis of neuronal firing mechanisms, neuronal firing patterns and cell connectivity and these are associated with cognitive functioning and mental health. Although this type of research may be centered on brain tissue, bioelectric signals or the movement of charged particles whether simple ions, charged molecules, protons or electrons underpin a huge number of biochemical reactions in the whole body and are always in consideration when looking at cell functioning.

That said we must consider what the authors are saying in their article. They concluded from their simplified model of bioelectric signals relating to outer cell membranes that the bioelectric status of the cell in question comes about by the presence and functioning of cell membrane ion channels. Through an imbalance of concentration of charge across a membrane due to their action then a voltage difference across the membrane would exist (just like that seen in neurons) called the transmembrane potential (TMP). The passage of ions through these ion channels, according to Cervera, Alcaraz and Mafe, could lead to cellular depolarisation (inward flow of sodium ions and up-regulation of channels leading to abnormal cell functioning) or hyperpolarisation (outward flow of potassium ions and down-regulation of channels leading to normalisation of function). The authors also concluded that connectivity between multiple cells within a group could be achieved by shared bioelectric signals through gap junctions that require functioning ion channels between neighbouring cells and this connectivity could lead to cell positioning and organisation within the cell`s grouping. In addition, another conclusion made was a logical extension of the two and that was that modulation of ion channels by a number of different measures (eg. administration of ion channel blockers, surface distribution of externally administered nanoparticles) could lead to changes in individual cell functioning and multicellular group functioning some of which could be detrimental to the organism, for example in the case of cancer.

Cervera and colleagues first conclusion ie. that the bioelectric status of the cell relies on cell membrane ion channels and the passage of ions through them results in the electrical potential of the cell (designated Vmem) or the voltage of the cell, is supported by neurochemical studies and is well-known for neurons and neuronal firing. It is also known in the case of non-neural cells in the brain, eg. glial cells where neurotransmitter binding can cause the opening and closing of associated ion channels. The authors link their ion channel functioning to the electrical status of the cell in non-neural cells and formulated a two-channel model. One type of channel was the voltage gated sodium ion channel which was associated with the inward flow of the ion and subsequent depolarisation of the cell. An upregulation of this type of ion channel and resulting persistent depolarisation was proposed by the authors and others to be linked to cell dysfunction observed in cancer. The other type of channel was a voltage gated potassium ion channel which was associated with the outward flow of potassium ions and hyperpolarisation leading to the ´normalisation` of cell functioning in the case of, according to the authors, cancer cells. If we look at these conclusions, we can say that there is support for the ´limiting cause` of cancer being at the level of the membrane since essentially if there is going to be a root cause then the cell outer membrane is the logical first step, the first encounter, the protector of cell functioning from external influences. The ion channels also span from outside to inside (or vice versa) of the cell and therefore, provide a route between external causal event and internal effect (or vice versa). Describing the effect as a signal is also supported since the mechanism relies on the transfer of charged particles eg. electrons from one side of the cell to another thus promoting a change in the voltage across the membrane and this acts as the message/signal being transmitted from external sources to internal mechanisms (or vice versa). It should be noted however, that according to Levin and colleagues the Vmem of non neural cells is itself important and not how it is achieved, ie. ion transfer must occur but not linked to any type of channel in particular. This is not the case of neural cells where specific cell surface receptors and channels are involved in signal transfer. However, the idea that the voltage difference across a membrane is a signal is further supported by evolutionary evidence linking change in bioelectrical status with cell movement as with amoeba, cell functioning as with paramecium, regeneration as with frogs and salamanders, and voltage differences observed between organs as in the so-called life fields in humans, plus on a smaller scale in correct muscle functioning and bone repair.

However, a change in bioelectric status is only a signal. It may lead to a change in proteins or molecules at the membrane level possibly leading to conformational changes that result in a change in reactions or function of the cell internally. To keep this comment relating to cancer, we should look at what effects this bioelectric signal could have on cell differentiation and proliferation, both systems appearing to be defective/abnormal in cancer. If we take a look at the observations made in life fields research we see that the bioelectric signal is believed to be associated with the health and well-being of an organism and is thought to be associated with growth and repair of an organism. In the case of new bone growth an application of an electric current is reported to lead to new growth around the negative electrode implying that the bioelectric signal was negative on the outside of the cell and positive on the inside. (This is opposite to what is known about the resting potential of the neuron which has a negative charge inside the cell relative to the positive charge on the outside.) The observation was explained by looking at bone stress and the so-called pizoelectric effect where the stressed bone acts as a matrix biphasic semiconductor with positive apatite outside (P) and collagen inside (N negative). With the PN junction and P to N current flow then the current rectifies the charge on the compressed internal side leaving it with a negative signal that stimulates cells to grow to form new bone.

Continual cell proliferation relating to the bioelectric signal and life fields was described in the case of the frog and its ´current of injury` reported with leg amputation and limb regeneration. Early ideas of ´electric currents coming from a wound` (Galvani) developed into ideas about changes in transmembrane potentials across the cell membrane dictating repair. In the frog it was reported that a stream of electrons (negative current) came from the wound from an amputated limb as a result of cell damage. With the transmembrane potential ions leaking out this was said to lead to the reversal of the polarity of the cell to positive. This only occurred on the wound surface and the current of injury was found to be proportional to the amount of nerve present. The change in current was associated with regrowth. In the case of the salamander a similar experiment was reported where a limb was amputated and the wound washed to remove blood clots. An immediate fall of membrane potential from minus 10 to plus 22 was observed. However, after about 3 days there was no membrane potential and no blastema (a mass of undifferentiated cells capable of proliferation and differentiation). After 6-10 days the Vmem rose to negative 30 and a blastema formed. This indicates that an external negative charge promoted cell proliferation. However, the potential was recorded as changing relative to regrowth since the external negative charge dropped to around minus 10 up to 22 days and the limb regenerated over that time. The view that cell proliferation is linked to bioelectric signals was further supported by studies where an external application of an electric current (AC current) or field was applied to tissues or tissue samples and this caused them to divide in a manner similar to cancer.

The implication from the observations of life fields researchers and cancer is that cell differentiation and cell proliferation mechanisms involve in some form bioelectric signals relating to the external membrane (ie. via the transmembrane potential). They may have an indirect effect through influencing gene transcription and the release of factors in response to signal changes. We know that differentiation is guided by transcription factors and morphogens and that differentiation is changed by a bioelectric signal. Therefore, we should ask whether the bioelectric signal could act as a ´transcription factor`. This is unlikely since the conditions arising from the signal are more like a ´state` rather than a specific factor. Transcription factors bring about DNA methylation and modification of the histone structure, but the bioelectric signal instigates changes in molecules to bring about these reactions and therefore, cannot itself be described as a transcription factor, but as a factor that modulates indirectly transcription via another party.  More likely is the consideration of it as a ´morphogen` with the signal arising from its own cell and/or from others and beginning at the level of the outer membrane (ie. differentiation`s  ´limiting cause`). Since a morphogen usually acts by a general mechanism of ligand binding to a receptor (possible bioelectric signal involvement here through conformational changes of the receptor induced by the presence of ions) leading to active ligand-receptor complexes which result in the catalysation of reactions through phosphorylation type reactions (processes requiring proton exchanges and conformational changes of molecules involving ionic charge changes also indicating bioelectric signal involvement). These can lead to the activation of dormant transcription factors (the direct intervention) or cytoskeletal protein changes (indirect intervention via the action of actin and myosin in microfilament and microtubule functioning – also involving the bioelectric signal in their mechanism and direction of movement) that result in changes to the differentiation process. Alternatively, cell differentiation can be affected by matrix elasticity which is also dependent on an actin-based intracellular microstructure reacting to the tugging motion of the cell`s contents during the cell`s division. This acts as a signal and hence, the bioelectric signal is again involved through molecular motor molecules and their reliance on the bioelectric signal for their correct functioning.

Therefore, Cervera, Alcaraz and Mafe`s model that bioelectric signals relating to outer cell membranes may play a role in cell differentiation and dysfunctioning of that system in the form of cancer may be valid. However, although there is support, there are also factors that count against such an easy and plausible cause. We must consider that in the case of neuronal depolarisation, persistent depolarisation arising from only a change in membrane function is unlikely to occur since under normal circumstances there are feedback mechanisms that switch off the mechanism, eg. the refractory period for neuronal cell recovery after sustained firing. Also, nothing is ever simple regarding cell functioning. In neuronal cells there are multiple receptors in play and multiple stages in any mechanism with multiple areas for feedback mechanisms to possibly right any wrong.  For example, it is unlikely that any cell has just two types of membrane bound ion channel and their actions are often linked to the performance of a third (eg. calcium channels with depolarisation or GABA channels with hyperpolarisation) or changes in intracellular conditions eg. pH changes counteract enzymatic mechanisms. Therefore, the bioelectric status of cells is likely to be highly controlled and to override this control there must be continual gene transcription changes and possibly involving many cells. This is seen in the case of cancer cells where transcription changes induce continual division of the cells against normal instructions.

Cervera, Alcaraz and Mafe also made a hypothesis in their article about the bioelectric signal and physical connectivity between multiple cells in a functional grouping. Their view is that connectivity occurs through one type of cell signaling mechanism, that of gap junctions and that disruptions of that connectivity can have catastrophic effects on the cell, the grouping or even the organism itself. Support for connectivity between cells of a group is clear, eg. from neuronal cell assemblies in memory, consciousness (connectivity supported by the observations of  brain wave synchronicity between participating cells), and cancer with connectivity between tumour cells. There is also support for connectivity between long-range parts of a total organism, for example: Planarium and organism polarity; cell positioning and growth alignment (eg. matrix arrangements of Planarium, hydra, salamander and frog); ´current of injury` in regeneration (frogs and salamanders); and more controversially humans and the so-called chi meridians. Although support is there the situation is more complicated than just having coupling via gap junctions since some examples exhibit long-range connectivity and the gap junction is a close-quarter communication mechanism with connexon proteins spanning physical connections between neighbouring cells. Membrane ion channels effects may explain the bioelectric state of the cell at the level of a single cell, but it must apply to a whole number of cells to get to the large scale connectivity required to explain positioning, polarity etc. A possible way of approaching this is to view connectivity more like Jenga or dominos ie. a multitude of single pieces making up the shape of the whole or the shape is made by the hole that is left (template vs stencil). In this way, maybe only certain cells in close quarters need to have gap junctions as the signaling mechanism and these can produce other signals that act long-range by alternative transmission mechanisms, eg. by exuding neurotransmitters and/or hormones. However, either way assumes that each single cell has a property that makes it special from one that does not experience the ´switched on/active` bioelectric state and the whole takes on that ´quality`. (Salt describes this as the ´E` quality meaning ´energised state`.) This property must be apparent for other cells within the group and guesses could be made as to what the cell with the ´switched on` bioelectric state  does eg. does it vibrate, shine, hum etc whereas the non-active cell remain indistinguishable from other non-active cells. This question still remains to be answered. However, continuing with the view that the multitude of cells within a grouping (Cervera and colleagues` ensemble or in the case of memory, the neuronal cell assembly) has a particular electrical status then the authors` view that a quantity of abnormal cells in the multitude grouping determines the overall status of the group is also plausible. When the number of abnormal (or non-switched on) cells is low then the ´quality` of the grouping is likely to be minimally effected, but the effect would increase in some manner (a threshold and then total dysfunction or a sliding scale effect for example) as the number of abnormal cells rises.

Observations on life fields lend support to bioelectric signals and electric fields having a role in connectivity between cells. The overall view is that they act as a signal for new growth as described above and then as a mould or matrix for that cell growth and in the organisation and maintenance of positioning. For example, life field studies have shown that specific voltage potentials reflect the arrangement of the nervous system of worms and fish. One area of positive potential in the brain is reported to match the one major nerve ganglion and in the case of the salamander there is a report of strong positive charges in three areas (the brain, brachial plexus and lumbar nerve ganglions) and negative charges (8-10millivolts) towards the extremities and tail. Organisation based on voltage potentials was also proposed for humans with voltage potential differences shown to be just like with the salamander (head and spinal cord, brachial plexus between the shoulder blades, lumbar arrangement at the base of spine dictated as positive and hands and feet as negative). There are also reports of the human brain having direct current coming from the reticular activating system and flowing from the back to the front of the brain through the middle with the olfactory lobes (positioned at the front) several millivolts more negative than the occipital lobe located at the rear. Current was assumed to flow up from the brain stem. Naturally, the automatic arrangement of the neurons with its polarised positive end at the input dendrite end and negative at the axon output end lent support to the idea of the bioelectric signal being used to organise cells and connect them.

As indicated by Cervera, Alcaraz and Mafe, the bioelectric status of a multicellular grouping associated with connectivity achieved through gap junctions dictates the functioning capability of that grouping. Supporters of the life fields and bioelectric signal views suggest that the bioelectric signal is linked to correct functioning and conversely abnormal changes in fields or bioelectric status can be an indicator of dysfunction and possibly disease eg. voltage differences have been observed between certain organs with cancer. The bioelectric signal and field exhibits normal changes according to the function assigned to that individual cell or grouping of cells, eg. changes of voltage potential in salamanders is associated with sleep status. In this case, it was reported that negative potentials are linked to frontal regions of the salamander brain and the periphery of the nervous system and these potentials are associated with wakefulness, sensory stimuli and muscle movements. The greater the shift to the negative, the greater the activity recorded whereas a shift to positive charge was found to be associated with rest and sleep. Studies on anaesthesia revealed that the negative charge vanished from the extremities and a reversing of the voltage occurred with the limbs and tails becoming positive and the brain and spinal cord negative. The administration of minute currents from the front of the salamander`s brain to the back was found to render the animal unconscious. An examination of brain waves during the process showed the presence of delta brain waves with the unconscious salamander and these increased in size as the current grew bigger. This was plausible since delta brain waves are believed to coordinate brain area connectivity during slow wave sleep. As the anaesthesia wore off, it was found that the normal potential differences returned to normal. A similar pattern was observed in humans with the brain becoming more negative during physical activity, declining during sleep and reversing to positive under general anaesthesia. Therefore, the association between bioelectric status of individual cells linked to others within the grouping via gap junctions (or as suggested by other means) and overall function appears plausible and may give an alternative approach to explaining how certain cognitive functions that rely on connectivity of large areas, eg. consciousness and memory, may occur. Applying knowledge obtained from studies on cell growth (eg. proliferation, organisation and positioning) of non-neural cells and cancer relating to the bioelectric status of a cell achieved by its ´limiting cause` the ion channels present at the cell membrane, the effect of this on neighbouring cells and the corresponding connectivity of those cells of the groupings may give an indication of how these cognitive functions could occur.

The third area explored by Cervera, Alcaraz and Mafe leads on from the other two points that they made, and that is that deliberate modulation of the outer membrane ion channels and hence, modification of the bioelectric signal, leads to changes in bioelectric status and changes in cell functioning. This can be positive for the organism as in for example the life field`s observed ´current of injury` and regeneration of a missing limb, or they can be negative as in cancer where there is a upregulation of sodium ion channels resulting in unwanted changes in cell proliferation, differentiation and organisation for example. We can assume that modulation is achieved in the same way as that for neurons at the first level (´limiting cause`) at the outer membrane stage, eg. by changing the number of  cell membrane receptors or ion channels, or by changing the secondary messenger systems (eg. by a change in conformation of molecules). In their article, Cervera, Alcaraz and Mafe describe direct methods for changing the bioelectric signal at the outer membrane level. This was achieved by the administration of blockers that act directly at the ion channels or receptors or nanoparticles that produce positive or negative changes in the microenvironment of the membrane where the relevant ion channels/receptor molecules are located. Other methods observed to have effects at this level and known to influence the bioelectric status of a cell is the administration of silver (eg. silver electrodes are shown to bring about the destruction of bacteria in close vicinity) and bleach or a weak acid. It is thought that these methods produce a change in the phospholipid basis of the cell`s membrane physiology and hence, affect the functioning of the ion channels and other membrane proteins by changing the conformation of the proteins within their phospholipid microdomains. With all methods the likely result of such a modulation is a detrimental end-effect on gene transcription, which could have positive or negative consequences for the cell. For example, stripping the outer cell membrane of its bioelectric charge using diluted bleach leads to changes in cell growth and proliferation and as reported here a change in transcription rate and cancer. Similarly, leaving cells bathed in diluted bleach takes electrons off a molecule that activates NF- Kb transcription factor resulting in an inhibition of the inflammatory pathway and faster healing of burns and increased skin cell production. Studies have also shown that a weak acid bath can lead to the reprogramming (de-differentiation) of lymphocytes to STAP cells.

In the context of voltage, bioelectric signal and transmission of charged molecules other researchers report the effect of external electromagnetic fields whether electric, magnetic, geomagnetic, or EMF on cell functioning. Cell alignment and cell positioning changes have been reported for applied EMF as well as changes in cell-to-cell communication, membrane physiology and change in cell division for magnetic fields. If such influences on gene transcription rates should occur as the authors suggest through the starting point of the electrical voltage differences at the inner and outer membrane surfaces of the cell, then administration of electric currents or magnetic fields can have probably effects at the single cell level. A problem may arise in explaining how this can influence the whole organism since there are multiple cells present. Again the hypothesis that the bioelectric status of one cell can translate to an effect on a multitude of cells through the connectivity mechanism of gap junctions may provide an answer, but the situation is made more complex by an organism being made up of many different materials within close vicinity to each other all having different physical characteristics eg. fluids, gases, bone.

To summarise, the bioelectric status of a cell through voltage differences across a the outer membrane dependent on two ion channels acting in opposite polarising directions as proposed by Cervera, Alcaraz and Mafe is likely to be an over-simplification of what is actually occurring, but the knowledge obtained from the studies can be applied to the areas of cognitive function like consciousness and memory which have yet to be fully explained. From what is known it can be said that bioelectric signaling is likely to be the first step (the ´limiting cause`) in the action of many cell types. It can also be said that this type of mechanism can provide a means of signaling from the external environment to the inner workings of the cell and hence, is a mechanism of cell-to-cell communication providing connectivity between a multitude of cells. It is unlikely that gap junctions are the sole mechanism of such connectivity as suggested by Cervera and colleagues since this method in neural cells is intended only for close neighbouring cells. Instead a mixed system may be more plausible with gap-junctions and a longer ranging signaling system eg. exudation of another transmitter or signaling molecule such as oestrogen. Cervera, Alacaraz and Mafe suggest that the bioelectric status of a cell from the membrane voltage differences attributed to ion channels present may provide a link to cancer and since it has been reported that bioelectric signals attributed to life field observations are linked with cell proliferation, cell organisation and cell positioning from other species such as the frog and salamander this may be plausible. It definitely needs further investigation, but in the case of humans, the mechanism may not be so applicable. It may be the case early on in development and later on in some areas where pluripotent differentiation and growth occurs normally eg. hippocampus and neurogenesis, skin cells and skin repair, but it may not be applicable in all organs and in all situations. The area needs more research especially in its application to explain the mechanisms behind memory and consciousness and also to the fields of specific targeting of electrical ´charge-busting` molecules on to cell membranes in the hope of normalising cell dysfunction, but maybe more emphasis should be placed on looking at whole organisms or larger groupings of cells rather than single cells in culture.

Since we`re talking about the topic……………………………

…..calcium ion channels have been implicated in the growth of some fish cells. What effect on the bioelectric status of the cell is there if calcium blockers are used? Would the use of specific calcium blockers at each stage of the intracellular calcium signaling mechanism show a dependency for growth on the calcium ion channel at the outer membrane and hence a link to bioelectric status?

……does stripping cells of their external electrical charge with diluted bleach cause a change in structure and performance of the sodium ion channel or potassium ion channel? Could this be observed with fluorescent imaging techniques?

…..would the cooling of cells or the use of specific blockers of phospholipids definitively show that microdomains of the cell outer membrane and particular phospholipids are important in ion channel functioning and hence, bioelectric status of the cell?

….reversine is said to de-differentiate cells by inhibiting the phosphorylation of a histone and preventing the activation of a specific cell differentiation kinase. Can we assume that the dual administration of a targeting cell membrane sodium ion channel blocker and reversine would have no greater effect on cell regeneration than the former alone because the signal for cell growth would have been prevented at the level of the cell membrane and not at the level of gene transcription?

…would the use of voltage sensitive fluorescent dyes to track non-invasively ionic gradients allow the changes in Vmem to be measured for each cell type and a comparison to cell function and cell division and proliferation be made?


Posted in bioelectric signals, cancer, ion channels, neuronal connectivity, neuronal firing, Uncategorized | Tagged , , , ,

effect of high altitude on cognitive functions

Posted comment on ´Acute and Chronic Altitude-Induced Cognitive Dysfunction in Children and Adolescents` by S.F. Rimoldi, E. Rexhaj, H. Duplain, S. Urben, J. Billieux, Y. Allemann, C. Romero, A. Ayaviri, C. Salinas, M. Villena, U. Scherrer and C. Sartori and published on website Researchgate.net/publication/282245481


The investigation carried out by Rimoldi and colleagues was to see whether short-term and long-term exposure to high altitude (around 3450m) induces cognitive dysfunction in children and adolescents. They found that short-term hypoxia had a significant negative effect on executive functions (inhibition, shifting and working memory), some memory functions (verbal short-term and verbal episodic memory), but not visuospatial memory and processing speed. The impairments observed were found to be even more severe for those individuals living at high altitudes permanently. A return to lower altitude for one group of study participants gave performance values back in the control range.

Test subjects consisted of 3 groups of healthy European children and adolescents. The control group consisted of 14 children and adolescents (aged between 10 and 17) living at low altitude (less than 800m).The acute, short-term high-altitude group consisted of 48 children and adolescents who were tested 24 hours after arrival at high altitude (Switzerland, 3450m) and 3 months after their return to low altitude (46 living at an altitude less than 800m, 2 at 1100m). The chronic, long-term high altitude group consisted of 21 matched subjects who had lived for longer than 3 years at high altitude (La Paz, Bolivia 3500m). The general cognitive abilities of each subject was assessed before testing using Raven`s Progressive Matrices (a non-verbal reasoning task) and it was found that subjects of all groups performed similarly. The cognitive tests given assessed each subject`s executive function, memory and processing capabilities. Testing of executive functions was carried out using the Attentional Network Task (tested inhibition),   the Trial Making Test Part B (TMT- tested shifting) and the backward Digit Span Task (tested working memory). Memory functions were tested using the  Forward Digit Span Task (tested verbal short term memory), the California Verbal Learning Test (tested verbal episodic memory) and the Corsi Block Tapping Test (tested short-term visuospatial memory).  Speed processing ability was tested using the TMT Part A. Paired student t tests were carried out for all results to indicate statistical significance.

According to Rimoldi and colleagues their study showed that short-term exposure to high altitude produced significant negative effects on all executive and memory performances except for visuospatial memory (tested by the Corsi Block Tapping Test) and processing speed (TMT part A). The value for inhibition was found to be 30% higher (a rise in value from 92 to 129) indicating an increase in reaction time and a decrease in attentional performance. The test of shifting capability (Trial Making Test Part B) showed that the subjects required on average 20% longer (63 to 74) to complete the task demonstrating that cognitive flexibility was reduced and the value of the working memory test (Backward Digit Span Task) was 10% smaller (4.9 to 4.5)indicating that working memory performance had decreased. On return to low altitude the same group of subjects was said to show performances in all of these tests similar to control values, ie. inhibition (Attentional Network Task) demonstrated a shift from 129 to 93 indicating a quicker reaction time and increased performance;  shifting (Trial Making Test Part B) with a shift from 74 to 66 also indicating a quicker reaction time and increased performance;  and working memory (Backward Digit Span Task) with a shift from 4.5 to 4.8 indicating according to the authors an improvement in working memory performance.

In the case of long-term exposure to high altitude, Rimoldi and colleagues said that the detrimental effect on these executive functions were the same or even more severe as those observed for acute exposure compared to the control, ie.  inhibition (Attentional Network Task) had a significantly longer reaction time (92 to 148);  shifting (Trial Making Test Part B) an approx. 20% longer reaction time (63 to 71); and working memory (Backward Digit Span Task) demonstrating an approx. 10% lower capability (4.9 to 4.6).

Rimoldi and colleagues found for the tests of memory capability that both verbal short-term memory and verbal episodic memory were impaired by acute exposure to high altitude. Verbal short term memory tested by the Forward Digit Span Task produced a decrease in length of word series remembered compared to the control from an average of 6.1 to 5.6. In the verbal episodic memory test (California Verbal Learning Test) the number of correct words recalled fell from an average of 12.4 to 11.9 compared to the control. However, in the case of short-term visuospatial memory measured by the Corsi Block Tapping Test, acute short-term exposure to high altitude was said to produce no effect (values recorded of average number of blocks remembered was 6.1 for the control and 6.6 for exposure). The authors concluded that the recall of both numbers and words were significantly impaired with short-term exposure to high altitude, but visuospatial memory capability was not. After return to low altitude the performance of the acute exposure group was said to improve back to the control values (verbal short term memory by a recorded value shift from 5.6 to 5.9 and verbal episodic memory by a recorded value shift from 11.9 to 13.5 and short term visuospatial memory by a recorded shift from 6.6 to 6.8).

In their investigation of the effects of long-term exposure to high altitude, Rimoldi and colleagues  stated that verbal short term memory determined by the Forward Digit Span Task and the verbal episodic memory test determined by the California Verbal Learning Test were no different to those experienced with only short-term exposure. Values recorded were shifts from 6.1to 6.3 in the former and 12.4 to 10.8 for the latter. However, the results of the visuospatial memory test (Corsi Block Tapping Test) was said to show more severe impairment in capability with long-term exposure in comparison to short-term (a recorded negative shift in performance from an average of 6.1 blocks remembered to 5.6) . Therefore, the authors concluded that the recall of words and numbers was not affected by long-term exposure to high altitude compared to short-term, but visuospatial processing impairment was even more severe.

The authors also tested their subjects for speed processing capability using the TMT Part A test. They said that on short-term exposure to high altitude there was no detectable effect on speed of processing, but recorded a shift in average performance time for joining the dots or letters from 25.3 to 27.6. On returning to low altitude, this value was decreased to 26.0. On long-term exposure to high altitude speed processing ability was said to be reduced by more than 25% since the subjects needed longer to complete the task (average recorded values of 25.3 for the control and 32.5 for subjects exposed long-term to high altitude).

Therefore, the authors concluded that changes in some cognitive areas were observed with exposure to high altitude. These changes it was said could not be attributed to acute mountain sickness (AMS) since the neuropsychological changes observed with hypoxia were different to those seen with this condition. In the case of hypoxia, acute short-term exposure to high altitude was found to induce marked cognitive deficiencies in healthy children in the areas of verbal short term memory, episodic memory and executive functions such as inhibition, shifting and working memory. Impairments were said not to be observed in visuospatial memory capability and speed processing for acute exposure, but longer exposure would lead to deficiencies not only in these areas, but also to a more severe level. A return to lower altitude after acute exposure led, according to the authors, to impairments being no longer discernible. The causes of the impairments observed for the executive functions were attributed by the authors to dysfunction of white cerebral matter, perhaps at the level of the prefrontal cortex and anterior cingulate cortex and this dysfunction had been induced by hypoxia. The differences observed between acute- and long-term exposure in the cases of visuospatial memory and processing speed indicated to them that children and adolescents may have a greater resistance to a hypoxia effect at neuronal level in these areas than adults. Since semantic cues were of no help in recall in the verbal episodic memory task the authors hypothesised that the defect in the memory process was at the level of information encoding. This observation was found to contradict the results observed for adults who do not present with episodic deficits with high altitude. Therefore, the authors suggested that the hippocampus which is involved in episodic memory is more sensitive to effects of hypoxia in children than adults. With reference to speed processing problems seen with long-term exposure in children, the authors reported that other research shows that this processing deficit has been reported even at lower altitudes (2500m) for adults and with short-term hypoxia exposure. Hence, they surmised that speed processing capability is better preserved in children than adults.

The authors concluded their article by saying that the results of their investigation into high altitude and executive, memory and processing capabilities were important not only because major tourist destinations are located at high altitudes that would expose tourists to hypoxic conditions that could have effects on their cognitive capabilities, but also that more than 15 million people live permanently at high altitudes and appear to have little or no neurophysiological functional adaptation to living in these conditions.


What makes Rimoldi and colleagues` article interesting is that it indicates an element of influence on cognitive processes that is frequently ignored or forgotten:  that is, the influence of the body`s hormonal system.  Emphasis is often placed on changes to cognitive performance through personal thinking, our attitudes, effects of our experiences and other mental reasons, but this is not the sole explanation for differences in capabilities observed.  The article commented on in this Blog post describes the effect of one example of hormonal influence on cognitive capability. It describes how hormonal changes brought about by low environmental oxygen affects the attentional and memory systems. In this case, low environmental oxygen is achieved by the individuals enduring either a short-term or long-term stay at high altitude (above 3500m). Low oxygen causes the body to experience what is termed ´environmental stress` and researchers have shown that this induces physiological changes that lead to cognitive and emotional effects and they can be different for altitude, duration of stay and age of individual.

Before discussing the cognitive effects observed by Rimoldi and team, we must answer the question as to what influence does high altitude and low environmental oxygen have on cell physiology. One of the most important and obvious is the change in energy production mechanisms. We are all familiar with the processes involved in muscles during intense and prolonged exercise – a situation where cells cannot get enough oxygen. Hence, the normal biochemical mechanisms of energy production cannot continue and other pathways are used instead which leads to the formation of lactate. Continued production means a build-up of lactate in the muscles and a fall in intracellular pH until this inhibits even this energy producing pathway. A similar problem occurs with the energy production in brain cells exposed to high altitude. High altitude is characterised biochemically as ´environmental stress` since the fall in external oxygen leads to a decrease in inhaled oxygen pressure (50% at 3000m of that at sea level) and this results in reduced driving pressure for gas exchange in the lungs. Therefore, the individual has less oxygen intake for each breath and so blood is not fully oxygenated at the alveolar level and ultimately, circulating blood level. Hence, brain cells are unable to obtain enough oxygen for their normal biochemical processes relying on it and as a result cellular adaptation has to occur.

In the case of the energy-producing systems in low oxygen conditions, the normal mechanism of glucose being metabolised by a chain of enzymatic reactions (called glycolysis) to produce pyruvate that occurs in aerobic respiration (ie. in the presence of oxygen) still takes place, but the second stage of the process is altered. This stage is where the pyruvate is converted by another chain of reactions into the energy molecules ATP.  If oxygen is not present at the level required for this aerobic mechanism, then a process called lactic acid fermentation is initiated (anaerobic respiration). Lactic acid fermentation means that pyruvate is then converted to lactate by the enzyme lactate dehydrogenase (LDH). However, this anaerobic process does not produce the same number of ATP molecules as normal aerobic mechanisms, but it does provide some.

The other potential problem is the build-up of lactate which is observed in muscle cells. However, it is likely that in the brain which is dependent on a constant supply of glucose and energy that a safeguarding mechanism is in place called the Cori cycle which transports the lactate out of the cell, back to the liver where it is converted into glucose by a process known as gluconeogenesis. Again the LDH enzyme is involved and this conversion could explain the lack of appetite experienced by some when undergoing rising altitude.

The processes of this energy switch due to the rise in altitude plus the short fall of ATP production are made easier by increasing the production of a cellular factor that influences gene expression. The production of hypoxia-inducible transcription factor (HIF-1) is induced as the oxygen concentration of the blood falls. This transcription factor leads to increased gene expression of many glycolytic enzymes eg. hexokinase, aldolase, phosphoglycerate kinase (ie. those taking part in the first stage of the energy producing cycle), plus LDH (which we have seen converts lactate to ATP) plus the glucose transporter molecules  GLUT1 and GLUT3, which transport glucose across cell membranes. Therefore, the energy producing mechanisms that are required for any metabolic energy changes due to lower oxygen availability have been optimised.

Changes to the blood and circulatory system can also occur to alleviate the absence of oxygen.  This is understandable since oxygen is transported via the blood from the lungs to the brain and the brain has a high need for oxygen and for glucose. Therefore, increased ventilation has been observed from altitudes of 3000m and this response varies with individuals and is not related particularly to performance. There is also an increased level of cardiac output initially leading to normal values with time. However, there is increased heart rate and decreased stroke volume since hypoxia acts as a vasodilator in systemic circulation and a vasoconstrictor in pulmonary areas which can lead to a high risk of pulmonary hypertension and pulmonary oedema. There is also increased viscosity of the blood and increased coagulability which can result in an increased risk of stroke and venous thromboembolism. Blood components can also adapt in travelling to high altitude leading to increased haemoglobin concentrations by the decrease in blood volume due to dehydration. Later on, hypoxia leads to an increased production of erythropoetin and increased haemoglobin production to compensate.

Although clearly the production of energy molecules for the cell is very important, the presence of oxygen in the blood is also required for a number of other biochemical processes that could also influence cognitive function, eg. the degradation of aromatic amino-acids by oxygenases, the formation of steroid hormones by hydroxylation, oxidative phosphorylation, the elongation of unsaturated fatty acids in endoplasmic reticulum of cells, the formation of nitric oxide, the synthesis of plasmologens and the degradation of nucleotides. Systems influenced by low oxygen availability affecting in particular brain cells and cognitive functions are not specifically known, but certain paths could be possible. For example, the degradation of aromatic amino-acids by oxygenases could in the case of the amino acid phenylalanine mean reduced tyrosine levels and tyrosine is used in the brain to form dopamine, a major brain neurotransmitter and instrumental in inducing hypoxic changes as we will describe later. Another example is the elongation of unsaturated fatty acids in the endoplasmic reticulum of cells could influence cell membrane structure ie. affect the formation of membrane lipid rafts and affect the cell surface receptors and other membrane proteins and molecular functioning. Also, an example of an effect on the synthesis of plasmologens could mean that the plasmologen phosphatidal choline which corresponds to the phospholipid, phosphatidyl choline, which is important for cell membrane structure, is not formed.

So we have seen how environmental stress from low oxygen availability causes physical adaptations and these can as with all other influences include effects on brain physiology and because of that, on the functioning of cognitive and emotional systems. For example, increased vigilance and arousal is observed with high altitude exposure and these are associated with increased activity in the ventral tegmentum area (effects linked with increased dopamine availability), raphe nuclei (increased 5HT), locus coeruleus (increased noradrenaline) and other brain stem and basal forebrain areas. The increased neurotransmitter production and increased functioning of these areas in response to high altitude exposure all lead to increased firing of the thalamus and cortical areas which result in changes to the input, processing and storage of information.  High altitude exposure also causes the activation of the autonomic nervous system (ANS) which leads to the production by the adrenal cortex of adrenaline (associated with the ´fight or flight` response) and noradrenaline (associated with the activation of the locus coeruleus and the ´fight or flight` response). Also, there is an observed release of cortisol from the adrenal glands and this has an important role in promoting changes to cognitive capabilities since it leads to the activation of the hypothalamus-pituitary-adrenal axis (HPA). HPA activation from cortisol release leads to increased amygdala responses and decreased hippocampal activity (inhibits corticotropin-releasing hormone CRH release and hippocampal cells can wither and die during chronic stress).  Cortisol can bind to receptors in the cytoplasm of neurons and travel to the nucleus where it stimulates gene transcription. It can also promote calcium ion entry into neuronal cells by either changing ion channel function or by changing energy metabolism in the cell, but independent of method both cause cell depolarisation. The effects on cellular firing and neuronal firing connectivity between brain areas can be linked to performance of attention and memory systems and hence, high altitude exposure can have multiple effects on these dependent cognitive systems.

Rimoldi and colleagues`  investigation looked at certain cognitive functions in relation to exposure to high altitude in European children. We must say at this point that our interpretations of their results were not always in exact agreement with the interpretations by the authors, but in this comment we attempt to understand what is being observed and provide an explanation of the neurochemical mechanisms involved. The first area we will discuss is that of attention. Rimoldi and colleagues say that there is decreased attention capability with exposure to high altitude and with duration of that exposure. We disagree about the interpretation of the level by which this occurs since the authors say acute exposure produces a 30% decrease with chronic exposure greater, but our interpretation gives a greater negative influence at 40% on acute exposure and more severe at chronic (60% decrease in capability). The neurochemical explanation for such a large decrease in capability (whether at the authors´ level or ours) could be that the brain areas associated with attention (ie. of the fear system, the association between the cingulate cortex and ventral tegmentum and the dopamine activation observed) are affected by the physiological changes brought about by hypoxia. Lower levels of firing seen in low-oxygen conditions could result in the level of connectivity between brain areas not being achieved and hence, decreased performance occurs. For example, neuronal cell group connectivity is required as shown by alpha brain waves between that group`s  members to maintain informational items and therefore, if connectivity is disrupted to an extent that the items cannot be held in the working memory, performance on the task (in this case the assessment of arrow direction in relation to a given example) would be reduced.

However, this hypothesis conflicts with the view that increased dopamine would lead to increased attention and processing and increased hippocampal functioning. Therefore, it is possible that the decrease in attention seen with high altitude exposure can be explained by the mechanisms involved being more akin to those observed with increased dopamine seen in individuals with attention-deficit disorder (ADHD). In this case, there is a fear state observed with increased prefrontal cortical and cingulate cortical activities which would lead to an increase in task relevant information and task irrelevant material. The result of this is that the tasks given in Rimoldi and colleagues` study are more difficult to perform since a decision has to be made between whether the arrow points in the same direction as the example or not. If there is an increase in task irrelevant material then it is more likely that errors would increase and reaction time decrease and hence, the overall performance level observed would drop. In Rimoldi and colleagues` attention experiment only correct congruent and incongruent results were measured and the level of errors was not considered. It should be noted however, that decreased blood flow is seen in the frontal lobes of ADHD patients akin to the symptoms observed with high-altitude exposure, but there is also decreased glucose availability and no prefrontal cortex activation of the amygdala, both of which are not observed under hypoxic conditions.

Under chronic environmental stress conditions decreased attention is still measured since there is continued increased dopamine release from the production of cortisol and there is decreased hippocampus functioning via feedback inhibition of the HPA. There is also an increase in amygdala activity because of this continued HPA activation. This could explain why the decrease in attentional capability is more severe with longer duration of high altitude exposure. Long term changes occur at the neuronal level due to over-excitability of the amygdala. Apoptosis of neuronal cells can occur as a result of over-excitability of certain areas and decreased functioning and reduced area size is observed. However, there must be in the areas the capability of readjusting once the stressor is removed since the subject`s return to low altitude is met with a reversion back to control values for certain cognitive functions. Therefore, long-term changes such as the overly large thalamus and decreased size of the hippocampus as seen with full-blown ADHD are not observed in the case of exposure to high altitude. Readjustment of an area`s capability occurs in response to a reduction in HPA activation and cortisol production.

Another cognitive capability investigated by Rimoldi and team was working memory. The authors saw a lower decrease in capability after acute high altitude exposure compared to attention (10% as seen with increased reaction time with our interpretation slightly less at 8%). A greater change was measured (20%) as expected with the Trail Making Test Part B (shifting task) since this task measures a combination of attention and working memory capabilities. Although an association between dopamine and working memory is not definitive (working memory is thought more of a GABA process), an explanation for the negative effect is likely to be decreased hippocampal function (glucocorticoid receptor effects) and increased amygdala function as a result of increased HPA activation and cortisol production. The changes in firing of these areas lead just like with attention to alterations in connectivity of the brain areas involved in the working memory function.  Working memory requires cell group connectivity with theta brain waves (gives temporal order) and alpha (maintains relevant items and keeps non relevant information away) for correct performance and hence, changes in individual brain area functioning could lead to the working memory conditions of binding and presentation of information for the correct completion of the task not possible. Chronic exposure to high altitude appears to bring, according to Rimoldi and team, no change to the level of impairment observed from the acute (although our interpretation is that there is slight improvement with chronic exposure) and therefore, negative influences on the physiology occur on immediate exposure and there is no long-term adaptation to the systems. This is supported by the observation that once the stressor is removed (ie. there is a return to low altitude) the mechanism returns to normal and therefore, any element effected is only temporary and only in the presence of the stressor.

Another capability investigated by Rimoldi and team is the cognitive capability of visuospatial memory. They found that acute exposure to high altitude had no effect on visuospatial memory, but long-term exposure did. Our interpretation is slightly different in that a slight increase (8%) in capability was observed with acute exposure, but chronic exposure led to a slight decrease (8%). An explanation for the observations could be that there is a ´fight or flight` response to acute exposure due to increased amydala activity induced by the activation of the HPA and production of cortisol. The change is amygdala function would lead to both increased levels of task-relevant and task irrelevant material, but the low effect could be due to the task (the Corsi Block Tapping Test) not being complicated enough.  Therefore, what would be observed would be the increased performance from the initial high dopamine concentration which induces ventral tegmentum activity and greater cortex and thalamus activities. Since there is greater activity and greater connectivity, better decision making and higher end-performance would be observed. The effect still remains to some extent (our interpretation 3% increase above control) once the stressor is removed (ie. by returning to low altitude) which implies that some physiological changes are not readily reversed.

However, chronic exposure to high altitude decreases visuospatial capability and this is consistent with the idea that long-term stress causes permanent physiological changes. Other researchers have already shown that there is an age-related decrease in spatial working memory that is exacerbated by increasing HPA activity therefore, it is likely that the chronic effect on the HPA,  downregulation of hippocampal activity, upregulation of amygdala activity and long-term cortisol production leads to decreased capability observed through permanent changes in brain area functioning and connectivity.  In the case of spatial memory, other researchers have said that the dorsal lateral prefrontal cortex is essential (although this is disputed) and this area and the ventral hippocampus and medial prefrontal cortex form a pattern of area connectivity that sets up a theta brain wave for correct functioning. A parieto-temporal circuit is also involved which integrates valuable sensory information. Dopamine receptor stimulation appears to demonstrate an inverted dose activation plot. Therefore, it is assumed that if visuospatial memory capability is decreased with chronic exposure to high altitude then all of these essential areas are affected by long-term administration of the stressor. This also applies to the workings of the visual working memory system where connectivity between the prefrontal cortex, cingulate cortex and hippocampus is involved. Therefore, it is thought that increased dopamine production leads to decreased connectivity patterns resulting in poor visuospatial performance.

Rimoldi and colleagues also looked at the cognitive capability of verbal memory.  They said that exposure to high altitude caused a decrease in capability independent of duration for both verbal working memory and episodic verbal memory. Our interpretation is slightly different with short-term verbal memory demonstrating an 8% decrease in capability, but only 3% after long-term exposure and episodic memory with decreased capability with long-term exposure even more severe (8% to 3% acute).  Researchers have already stated that hypoxic conditions lead to problems with memory encoding, retrieval and retention. In this case, it is likely that there is a decrease in encoding in the acute exposure situation as expected because of decreased working memory and attentional capabilities described above, but there is readjustment with time (long-term exposure leads to a decrease and return to values observed at low altitude albeit that the recovery is not at the same level as control). The decrease in episodic memory capability can be explained by decreased prefrontal cortex functioning as observed and decreased hippocampal activity (through the sustained HPA activation) causing problems with encoding (input and binding), retention and retrieval of information. It has also been reported that glucocorticoid receptors activity associated with hypoxic exposure eliminates newly formed cortical spines and disrupts previously acquired memories. Hence, episodic memory performance would be diminished. Again, hypotheses that cortisol production and HPA activation due to exposure to high altitude would lead to disruption of the brain area connectivity patterns required for memory mechanisms to function normally (ie. adequate and correct cellular connectivity within the groups with theta brain wave patterns required for encoding and gamma brain waves for learning) would apply. This is supported by other research that memory mechanisms under stress when the HPA axis is stimulated are different to those when not.

Although Rimoldi and colleagues provide adequate evidence that physiological changes are induced on exposure to high altitude and these can change according to duration, one factor not investigated by them, but which provides support for their conclusions, is the effect of high altitude exposure on the emotional system and mood. Mood changes are associated with high altitude exposure and duration. Initial environmental stress causes euphoria and arousal, a system which is dopamine dominated and hence, provides support to the observation that there is a rise in dopamine levels on stress exposure.  Corticotrophic releasing factor (CRF) is produced in response to hypoxia and this leads to cortisol production that is known to act on the brain area ventral tegmentum to cause increased dopamine function. As given above, the increased activity of this area leads to firing of axons connected to the frontal cortex and to parts of limbic system such as the nucleus accumbens (known to fire with unpredicted rewards plus known for its role in encoding anticipated rewards) and striatum (increased activity also seen with increased environmental stress). Both systems are thought to be associated to the pleasure system and reward and hence, this supports changes in mood observed. There is also release of other neurotransmitters eg. 5HT and NA recognised as being linked with cortisol and HPA axis activation and both of these can lead to increased dopamine release in the prefrontal cortex by the action of the raphe nuclei.

Continued exposure to stress (ie. high altitude) leads to a change in mood from euphoria to depression. This condition is normally associated with decreased serotonin levels and has symptoms such as a lack of concentration, disinterest, insomnia and loss of appetite. There are two ways of looking at this. The first is that there is decreased serotonin because there is physical acclimatisation to the environmental stress as physiological systems such as the energy production mechanism adjust and therefore, the lack of oxygen is seen as a ´controllable stressor`. This leads to inhibition of the raphe nuclei and decreased ventral medial prefrontal cortical activity which is linked to the System 1 automatic, fast decision-making system and has a role in assessing value. Or, secondly, that there is a down-regulation of the dopamine receptor system in response to its high exposure to its own ligand neurotransmitter as seen in drug addiction for example. Excessive dopamine levels leads to decreased prefrontal cortex activity, decreased numbers of D1, D3 and D4 receptors, decreased glutamate receptors and decreased working memory capability. 5HT follows suit with the down- regulation of its receptors and down- regulation of prefrontal cortical activity leading to the symptoms of depression.  Again, the neurochemical systems readjust to this state and with time will lead to another mood change that of anxiety, irritability and belligerence which are all symptoms normally attributed to increased amygdala activity. This type of mood response supports the documented increase in amygdala activity that HPA axis activation is said to lead to.

Therefore, what can we conclude about the effect of exposure to high altitude on brain functioning? What we have seen is that there are physiological effects at the cellular level due to the low oxygen availability and at the area level because of the activation of the HPA and cortisol production and these translate into effects on cognitive functioning and performance and mood. These effects change in response to duration of exposure and level and naturally, to individual`s own physiological levels.  What makes this topic interesting is that here is another element that has to be considered when we are talking about cognitive capability and bringing about change to cognitive capability. We know about sleep and oestrogen and now we have the HPA system and the action of cortisol and glucocorticoids. It also reinforces the view that changes to physiology and biochemical functioning can have repercussions on how and what we think and even things like environmental stress should not to be underestimated. The results recorded in this investigation with exposure to high altitude shows that individuals should consider it with concern and perhaps for some preventative measures, eg. cognitive training could be advantageous.

Since we`re talking about the topic………

…..can changes in pH in brain cells be observed with exposure to high altitude and do these change with time?

…..would the use of serotonin receptor knock-out mice show definitively that the serotonin system is involved in the effects on working memory and attentional performance caused by exposure to high altitude?

…would administration of serotonin selective uptake inhibitors and other antidepressants lead to a decreased level of anxiety and counteract the depressive mood change observed with longer term exposure to altitude? Would the administration of ketamine also lead to protection from depression as observed with stressed mice?

….what would be the effect of the administration of the ADHD treatment, ritalin? Would there be a shift to normal prefrontal activity as measured by neuroimaging techniques that could accompany the return to normal performance levels for working memory, attention and memory capabilities?

….would there be an increased number of glucocorticoid receptors observed in the hippocampus on acute and chronic exposure to high altitude?

Posted in altitude, attention, memory recall, Uncategorized, working memory | Tagged , , ,

lithium in drinking water and dementia incidence

Posted comment on ´Association of Lithium in Drinking Water with the Incidence of Dementia` by L.V. Kessing, T.A. Gerds, N.N. Knudsen, L.F. Jorgensen, S.M. Kristiansen, D. Voutchkova, V. Ernsten, J. Schulllehner, B. Hansen, P.K. Andersen and A.K. Ersboll and published in JAMA Psychiatry 2017 74(10) 1005-1010 doi 10.1001/jamapsychiatry.2017.2362


Kessing and colleagues` article addresses the question of whether the incidence of dementia in the general population of Denmark co-varies with long-term exposure to micro-levels of lithium in the drinking water. Their study suggested that higher levels of lithium could be associated with a decreased incidence of dementia.

A nationwide, population-based nested case control study with 733,653 control individuals and 73,731 dementia sufferers was performed in Denmark. Dementia sufferers were aged between 50 and 90 years of age and had received a diagnosis of dementia from 1st January 1970 to 31st December 2013 and had hospital contact either as an in- or  out-patient. Each dementia sufferer was matched by both age (median age 80.3 yrs old) and sex (approx. 60% female and 40% male) to 10 controls. All control subjects had to be alive and have no diagnosis of dementia when their matched subject had been diagnosed. The residence of each study subject was recorded from 1986 and each location was cross-referenced to the level of lithium in the drinking water at that time measured according to municipality between 2000 and 2010. The lithium level was assumed to be stable within the study period.  Data was analysed from 1st January 1995 to 31st December and Kessing and colleagues looked at 4 levels of lithium exposure (2.0uG/L to 5.0; 5.01 to 10; 10.1 to 15; and greater than 15) and recorded the incidence of dementia in their test groups.

Kessing and colleagues found in their study that the level of lithium exposure was lower for patients with a diagnosis of dementia than for the controls (dementia – median 11.5uG/L whereas controls -median 12.2 uG/L). Also, this association between exposure and dementia was found to be non-linear. A comparison of incidence rate ratio (IRR) for dementia for lithium exposure of 2.0 to 5.0uG/L to exposure greater than 15.0uG/L showed a decreased value for the higher exposure (IRR 0.83, 95% CI, 0.81-0.85 P>0.001). However, the ratio value was the same for a comparison of exposures between 10.1 to 15.0 uG/L (IRR 0.98, 95% CI, 0.96 – 1.01; P= 0.17), but the value increased when it was compared to 5.1 to 10.0uG/L (IRR 1.22, CI 95%, 1.19-1.25 P>.001. Similar patterns were obtained for both Alzheimer and vascular dementia sufferers.

Therefore, Kessing and colleagues summarised that their results suggested that increased lithium exposure in drinking water may be associated with a lower incidence of dementia in a non-linear manner. However, they described the association as not being definitive since the nature of their study set-up meant that patterns and links could be identified between factors, ie. lithium exposure and incidence of dementia, but no definite conclusion could be made because other factors may have influenced  their results.


What makes this article interesting is that it looks at the topic of dementia not from cause or treatment, but from protection. This article hints that naturally occurring high levels of lithium in drinking water can have a protective effect from dementia, ie. there is a slightly lower risk of dementia occurring with exposure to high levels of naturally occurring lithium in this case sourced in the drinking water. Others have jumped to suggesting that adding lithium to drinking water may provide some form of protection from developing this disease, but it should be said that Kessing and colleagues indicate that their results are not definitive and the incidence of dementia could be influenced by other unknown factors. Unfortunately, because of the nature of the disease and the fear that it induces, the field of dementia research and non-academic thinking is awash with different hypotheses about causes, treatment and protection and also unfortunately, although we may be gradually explaining its biochemical basis, we are still not far enough forward to finding a ´one-tablet` cure. The problem with dementia is it`s elusiveness – many causes and triggers, many possible culminations of causes and its wide-ranging physiological effects that may be individual in both degree and nature. The value of this article is that it provides another element to studying this disease in the hope that every fragment of information is another piece of the puzzle.

The article commented on here in this blog is about the effect of lithium exposure on dementia. The beneficial health effects of lithium comes about from studies on bipolar disorder which is where sufferers experience periods of mania and periods of depression.  Sufferers of this particular disorder exhibit associated negative cognitive effects with both of the two phases and it often develops later into dementia. Treatment with chronic administration (but not acute) lithium helps the sufferers by stabilising moods so that the periods of mania and depression are reduced and has also been found to delay the onset of dementia if occurring.

The first point about lithium exposure and dementia is that cognitive disorders, especially those not genetically predetermined and where the emotional status and system are involved, seem to appear over a period of time and require treatments that are also needed over a period of time before reduction of symptoms is observed. This delay between administration and effect means that the long-term ´fix` is not reliant on instantaneous changes of biological processes. There may be observable influences on processes after single administrations, but these may not be the same as those occurring after long-term administration, or even be related to symptoms.  The exploration of the effects of lithium exposure on dementia hence requires two approaches – the instantaneous effects observed perhaps in the ´test-tube` although more advisable on the whole body after single exposure and secondly, what is happening at the biological and physiological levels after a longer period with or without further administration of the ligand or sustained administration. This type of exploration mirrors the approaches to research on depression where we know that the beneficial effects of antidepressant administration require 3 weeks to occur whereas certain biochemical changes are instantaneous eg. the immediate effect on neuronal adenyl cyclase activity compared to long-term effects on neuronal connectivity and neurotransmitter receptor number . The therapeutic effect of any drug (or therapy) has to either reverse these changes in order that treatment is deemed successful, or provide alternative means by which correct neuronal functioning may occur. In the case of some treatments this reversal is also temporary and administration must continue. Without sustained administration the biological processes and systems can fall back into ´dysfunctioning status` resulting in the reappearance of symptoms and an example of this is lithium itself and its role in the treatment of bipolar disorder.

If we are to understand what is happening in dementia we have to look at the conditions that cause it and the mechanisms that are subsequently put into play. Kessing and colleagues` article prompts an investigation of the metal, lithium and its supposed effect on the incidence of dementia. We have to assume that the biological mechanisms involved in dementia and associated with cell death and destruction of neuronal pathways are independent of the cause of the disorder, ie. the biological mechanisms with abnormal functioning are the same whether the dementia observed is a result of long-term bipolar disorder or injury for example. In the same vein, as given above treatments can either normalise the dysfunctioning processes or provide alternatives that compensate for those dysfunctions caused by the disease or injury. If we apply this hypothesis to lithium and dementia, the biochemical effects of lithium which lead to the proposed neuroprotective effect can then be grouped according to whether they affect neuronal firing (eg. reducing levels of over-excitation in neuronal firing in certain brain areas as observed in dementia) or by reducing cell apoptosis (ie. reducing the mechanisms employed in cell degradation and cell death, also perceived as important in dementia). Therefore, lithium could be said to promote a reduction in susceptibility to dementia by affecting one or both of these groups of functions.

If we look at the first group of functions relating to lithium exposure that of effects on neuronal cell firing we can see that the action of short term administered lithium acts at many different neuronal sites and cellular functions. Biochemically, neuronal over-excitation results in excessive firing in individual cells or in groups of cells within brain areas at a level which is outside normal expectations for that cell or area. For lithium to have an effect on this mechanism then it must interact with normal firing mechanisms. (Here in this comment we concentrate just on the brain and neuronal systems since we are looking in general at possible mechanisms for cognitive failure, emotional system upset and dementia.) Lithium ions due to their size and electronic charge can act where two other common ions in the brain act. These are sodium ions (a cation like lithium with a single charge) and magnesium ions (also a cation, but with an electronic charge of two, however having the same ionic and hydrated radius as the lithium ion). Since a lithium compound is medically administered then the lithium is already in ionic form and so is already able to accept an electron. Therefore, electronically lithium ions can replace sodium ions in firing mechanisms.  One way in which it can do this is to transfer through the sodium channels of the neuronal cell membrane. The possible result of this action is cell depolarisation and this is observed in brain areas where hyperpolarisation occurs. Researchers have also found that lithium can regulate the expression of different isoforms of sodium channel and therefore, effect on firing through increased presence of sodium channels is possible. Cellular firing levels can also be affected by lithium causing the release of the neurotransmitters, serotonin (5HT) and noradrenaline (NA) which can result in cells that are activated by these molecules actually depolarising. This is important since in some brain areas, 5HT and NA have an inhibitory effect on cell activity, or have an inhibitory effect on cells further down the pathway.  In the absence of these neurotransmitters for whatever reason, the administration of lithium ions could substitute for their loss and firing whether excitatory or inhibitory results. It should be noted however, that under normal firing circumstances, lithium ions do not cause excessive firing and cellular depolarisation.

Whereas lithium ions can directly replace sodium ions in neuronal firing mechanisms, their action relating to magnesium ions is a little more complicated since they disrupt processes important in cellular firing that rely on the involvement of magnesium ions for correct molecular conformational structure.  For example, lithium ions can have an effect on cell firing via its action on sodium potassium ATPase ( Na+K+ATPase) which is magnesium ion sensitive and responsible for ionic gradients across the neuronal cell membrane during firing and another transport system, the mitochondrial sodium-calcium exchanger. The sodium-calcium exchanger is important in the removal of calcium ions from the mitochondrial matrix. The activity of this depends on previous action of the Na+K+ATPase which pumps sodium ions out of the cell and potassium ions in during firing. This action allows the entry of calcium ions into the cell in the presence of high concentrations of sodium ions (ie. at depolarisation). Hence, failure of Na+K+ATPase action (as observed as impaired in bipolar disorder) leads to potassium ion depletion inside the cell and sodium ion accumulation. Therefore, the sodium calcium exchanger begins to pump calcium ions in leading to an increase in the cells hyper-excitability (also observed in bipolar disorder). Lithium ions in this case activate Na+K+ATPase, hence normalising cellular ion concentrations and depolarising the cells where hyper-excitability was previously observed.  It was found that lithium ions at high doses actually replace the magnesium ions in the complex. The presence of the metal ion is important because of its role in the nucleotide phosphorylation stage of the process eg. in the breakdown of ATP (the transport of ions requires energy) in this case. Nucleoside triphosphates require magnesium ions or a manganese complex to be active since the magnesium ions neutralise some of the negative charges present on the physical polyphosphate chains of the molecules, hence reducing non-specific ionic interactions between the enzymes and the polyphosphate groups of the nucleotide.  This link with nucleotides, magnesium function and lithium action as a substitute ion can also be observed with cellular cyclic nucleotides, eg. in the case of adenyl cyclase and the production of cAMP. Here the enzyme interacts indirectly with the magnesium ion through hydrogen bonds to coordinated water molecules. The inhibitory effects or activation effects of lithium ions on adenyl cyclase (cAMP levels are reduced in depression and increased in mania) relates to G protein binding (likely to be inhibited) and hence, this is another example where lithium has an effect dependent on what the normal functioning of that area or system is.

Therefore, we have seen how lithium ions can work at the level of cell firing, but how does it reduce levels of neuronal firing over-excitation and ultimately, levels of excitotoxicity where cells begin to die? Both over-excitation and increased excitotoxicity have already been reported as involved in dementia and occur in areas such as the entorhinal cortex and hippocampus. The end effect can be local cell death and in dementia there appears to be a natural progression of cellular degradation as inactivity and death in one area leads to underactivity and cellular death in another and so on. It should be remembered that only the hippocampus appears to be capable of high levels of neurogenesis (new cell formation functionally linked to memory) and hence, any loss of cells in other areas will have serious effects on firing of the system as a whole.

So, how can lithium ions reduce over-excitation where needed? The different mechanisms influenced by lithium ions will lead to different effects on neuronal cell functioning restoring normality. The administration of lithium ions can lead to neuronal firing in areas which would normally have an inhibitory effect or could force depolarisation in areas which are normally hyperpolarised. Neuronal firing forced by lithium ion presence could be by substitution with sodium ions in membrane-bound sodium channels leading to cellular depolarisation directly or through its action on neurotransmitters 5HT and NA release. Normalisation of the other ion transfer systems required such as Na+K+ATPase and sodium-calcium exchangers can also be induced. The overall effect is that lithium ions substitute for dysfunctional firing systems and cause neuronal activation which leads to inhibition of cells further down the pathway. This knock-on effect on others could remove the higher levels of excitation seen in some conditions that lead to excitotoxicity and cell death. The overall inhibition of hyper-excitable cells may also explain the activation effects of lithium on adenyl cyclase (cAMP levels are reduced in depression and increased in mania) where G protein binding is inhibited and also reports where lithium administration causes an increase in calcium ions through likely activation of the PI3 / Akt pathway as observed with action against alpha-bungarotoxin and muscarinic receptor binding. Therefore, a reduction in firing occurs and since areas work together and firing is interconnected then functioning patterns of firing will also change and long term alterations ensue. Since in the case of lithium ions and bipolar disorder, mood is stabilised on long-term administration the physiological changes that occur after the sustained period of administration of the metal ion result in a likely ´normalising` action of individual cell firing and ultimately, connectivity patterns seen in the disorder.

The second area of functioning where lithium ions may have their effect is on reducing cell apoptosis or death.  Cell death methods can be intrinsic (eg. requires transcription factor effects and subsequent DNA transcription changes) or extrinsic (eg. requires extracellular receptor binding and caspase cascades), but independent of cause (eg. injury, cell membrane signalling detrimental changes, neurotrophic signal changes) they appear essentially to have the same mechanisms. A change in apoptosis because of lithium treatment leads to a change in mitochondrial/ER dysfunction, reduction of negative epigenetic effects, reduction of glial dysfunction, reduction of oxidative stress and inhibition of the enzyme, glycogen synthase kinase- 3 (GSK-3). For example it has been reported that there is a change in IP3 in bipolar disorder which results in a change in calcium ion signalling in offending cells. The rise in intracellular calcium (also reported in bipolar disorder) results in dysfunction of the mitochondria (observed by increased Bcl-2 levels and decreased Bax levels) and increased oxidative stress of the cell (an effect that can be decreased by glutathione administration also observed in bipolar disorder). The increased level of oxidative stress results in cellular apoptosis. Lithium is reported to inhibit GSK-3 which is one of the factors controlling cellular apoptosis and it can either increase activity or decrease it depending on circumstances. This again could be interpreted as lithium ions ´normalising` function in dysfunctioning cells, or having no effect if the cells are functioning normally.

Increasing apoptosis occurs by the disruption of correct mitochondrial functioning and affects the regulation of the expression of the mitochondrial transcription factor, Bcl-2. Decreased apoptosis occurs in the case of lithium administration when GSK-3 inhibits the early phase of the caspase cascade (caspase 8) or by having an active PI3-AKT pathway which leads to a rise in calcium ion release or an increased beta-catenin/wnt pathway. The inhibition of the GSK3 enzyme has wide ranging effects since the enzyme has multiple functions such as phosphorylation of glycogen synthase and ultimately regulation of glucose metabolism, effects on transcription factors such as cJun and cell cycle mediators such as cyclin D. There are also observations that GSK-3 affects proteins bound to microtubules and this could relate to an observed build-up of beta-amyloid protein in the cells – a process which is linked to the observation of dementia.

Therefore, the administration of lithium ions can result in decreased apoptosis or a protective effect  against apoptosis. The latter may be the case if the cell senses that the normal firing function is defective and another unnatural ion has taken the system over as suspected in the case of lithium ions actions on cellular functioning described above. The result is that there is a normalisation of function in the relevant areas which in bipolar disorder may exhibit extensive induced apoptosis. The increase in cell number and functioning cells is supported also by the observation that lithium administration leads to an increase in volume of the hippocampus, an area whose function is observed to be reduced in dementia. Renewing cells is essential particularly for the hippocampus as stated above since neurogenesis here is linked to the cognitive input of new experiences, binding of information and memory. An increase in growth of the hippocampus has been observed with lithium ion administration and this counteracts the area`s shrinkage which has been reported in depression. An increase in volume implies the production of new cells.

Therefore, lithium ions exhibit a wide range of cellular effects, but their action appears to be linked to only those brain areas whose neuronal functioning is abnormal, ie. areas which exhibit over-excitation or are subjected to over-inhibition. Exposure to the lithium ions can ´normalise` firing and also ´switch off` the apoptosis mechanism so that cells can return to normal functioning levels even if not by natural mechanisms. Cells that are functioning normally appear not to be affected and this is supported for example by the action of lithium ions on guanylyl cyclase which is part of the photo-reduction mechanism in humans and important in visual processing.  Guanylyl cyclase activity leads to an increase in cGMP keeping sodium ion channels open whereas light reduces the level of cGMP causing the sodium channels to close and the cell to hyperpolarise. Sufferers of bipolar disorder report reduced visual motion perception, but lithium has no effect on photo-reduction or on ocular functioning even though it is a potent inhibitor of guanylyl cyclase activity. Therefore, just like with neuronal firing, this brings about a discrepancy since the biochemical effects described for lithium ions are independent of cell status and therefore should occur whether the cell is in either an over-excited/over-inhibited state or not and the action should be on all cells and not just those exhibiting excitotoxicity. The action of lithium ions therefore, implies that those cells exhibiting excitotoxicity or lack of inhibition have already different functioning or different physiological characteristics that favour lithium ion action and binding in preference to its action on normal cells. It could mean that the propensity of binding of lithium ions is lower on these abnormally functioning cells than the cells own natural substrates and hence, it only works when these natural substrates are not available. Since the first port of call of the lithium ion is the neuronal cell membrane then it is possible that the over-excited or over-inhibited cell already has a different cell membrane structure or functioning and it is here where the natural substrates are normally favoured, but when absent, then the presence of lithium ions after administration will have an effect. This hypothesis is supported by the biochemical property of magnesium ions which is that they coordinate groups of ions or molecules so that the correct arrangement and correct conformation of the molecule is attained. This could be of importance when looking at lipid rafts and protein conformations as part of the cell membrane physiological structure and functioning. Lithium ions could replace magnesium ions if absent to re-stabilise the neuronal cell membrane lipid raft structure so that normal firing mechanisms can be induced.

Therefore, a look at the biochemical basis of lithium ion action in bipolar disorder and the mechanisms behind its mood stabilising effect gives an indication to the complexity of dementia and to the problem of defining biochemically what causes it and what can be done about it. Lithium ion administration can be described as a ´blanket` treatment affecting many different systems even if the overall affect is a reduction of emotional upsets and a stabilisation of mood. Its possible mode of action is likely to be through normalising cellular firing in areas experiencing over-excitation or lack of firing and this could be caused by imbalances of sodium ions and magnesium ions. It could also act by affecting the cellular apoptosis processes that may be called into play if cell death is ordered due to cell dysfunction or to prevent cell death ordered because of the abnormal lithium ion effects on the neuronal firing mechanisms. Lithium ion exposure also does provide some insight into a possible initial ´limiting` cause for dysfunctional cell firing and this appears to be the cell`s exterior membrane.

Since we`re talking about the topic….

…would it be possible to use radioactive lithium to map areas of neuronal cell action with time using mouse or rat models of disorders such as dementia? Would such studies give an idea of where the ´limiting` brain area for dementia is?

……lithium is said to be an important inhibitor of glycogen synthase kinase 3 which is known to phosphorylate the voltage gated potassium channel type, KCNQ2. Phosphorylation of the channel decreases its activity and ultimately affects the transport of potassium ions important in depolarisation. Therefore, can we assume that lithium ion action ultimately has an effect on KCNQ2  channels` number or function and this can be observed by measuring potassium ion transport and concentration in the neuronal cells using inhibitors or blockers of KCNQ2?

……prolonged lithium ion treatment is said to lead to probably indirect inhibition of PARP-1 (poly-(ADP-ribose) polymerase) by inhibiting 3´5´-phosphoadenosine phosphatase (pAp-phosphotase) function. This causes a progressive accumulation of pAp in the cell that binds to the PARP-1 inhibiting it. However, the hypothesis is under dispute. Can we assume that deletion of pAp phosphatase gene could clarify the observation since in its absence poly -ADP-ribosylation activity inside the cell would be normal and PARP-1 inhibition would not be observed?


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network reorganisation of procedural memory during sleep

Posted comment on ´Network-wide reorganisation of procedural memory during NREM sleep revealed by fMRI` by S. Vahdat, S. Fogel, H. Benali and J. Dovon and published in eLife 2017;6:e24987 doi 10.7554/eLife.2498


Vahdat and colleagues described in their article their investigation of how learnt motor sequences formed memory traces during sleep and how there was a gradual shift of memory representations from a temporarily activated cortical pattern to a downscaled, more interconnected subcortical pattern.

For their experiments, Vahdat`s team used 13 healthy volunteers and recorded the development of memory traces of their subjects in real-time using the techniques of  functional magnetic resonance imaging (fMRI) and electroencephalography (ECG). The subjects performed two different finger movement tasks one week apart. In the motor sequence learning task (MSL), they practiced a self-paced, known 5-item finger sequence task and this was compared to another task in which the subjects were asked to produce simultaneous movements of all four fingers at the same average frequency. This was termed the motor control task (CTL). The initial training session was held at 22.30 (termed ´learning session`, S1) and this was followed by a retest session at 09.00 the following morning (termed the ´retest session`, S2). Resting state conditions were met by the subjects staying awake with their eyes open and these were carried out before and after each practice session in the evening (termed RS1 and RS2) and the following morning (RS3 and RS4). A simultaneous EEG-fMRI recording scan was made lasting a maximum of 2.5 hr while the subjects slept in the scanner. Offline BOLD-fMRI imaging was carried out during the task learning, sleep and repetition of the task. Functional connectivity of brain areas was estimated using the overall co-activation of brain areas within a network during the different fMRI sessions.

The results of Vahdat and colleagues` experiments showed that specific neuronal firing activation patterns representing the learning of the motor sequences occurred in brain areas during the learning practice session (S1). This sequence related pattern was termed the ´learning pattern`. It was also observed that there was greater activation with the MSL rather than the CTL and although the speed of performance appeared faster for the MSL than the CTL, when the speeds of the MSL and CTL were intentionally matched, then no difference in average speed was observed. The summary of the activation peaks observed during the learning pattern was found to be related to the pattern observed representing consolidation. When the subjects were retested after sleep (S2), the authors found that the performance of the MSL had improved. They interpreted this as evidence of memory consolidation taking place during sleep. No such improvement was observed with the CTL. They also measured the accuracy of the performance of the MSL by looking at the percentage of incorrect key presses and it was found that in general there were a very low number of errors even after the overnight sleep. This was explained by the motor sequence being explicitly known by the subjects. An investigation into performance variability also found a significant decrease only in the MSL.

Vahdat and colleagues then looked at the pattern of activation of brain areas associated with learning and retesting of the motor sequence and found that the pattern of activation was similar for the learning session (learning pattern) and the retesting (consolidated pattern).  Each had similar patterns of activity in the sensorimotor core areas, but there were significant differences between the cortical and subcortical areas. It was found that the consolidated pattern had increased activity (approx. 4 times as much) in sub-cortical areas (mostly basal ganglia and cerebellar areas) and decreased activity (approx. half as much) in cortical areas (mostly fronto-parietal sensorimotor regions). A volume-based analysis of the activated areas showed that two cortical clusters (including superior parietal lobule and anterior intraparietal sulcus) had greater activation during the learning session than during retesting and conversely, subcortical regions (including putamen and cerebellar regions) had greater activation during retesting. An estimate of functional connectivity (where strength was given as the connectivity index, CI) showed that there was a significant effect for both learning and retesting patterns with the latter being greater. A significant effect was also observed for the CTL. The authors showed that the effect was specific for consolidation by observing four recognised brain networks: the default mode, visual network, and left and right fronto-parietal. The CIs calculated for the resting states and during NREM sleep for both conditions showed no change in CI values. Therefore it was concluded that the changes observed were not due to overall changes caused by time, learning or sleep, but were specific for the tasks being undertaken.

Investigation of the CI analyses carried out using dual regression analysis of the fMRI allowed the authors to identify specific brain areas within the connectivity patterns that were recruited for each condition during the learning session and the Stage 2 NREM sleep period. Vahdat and colleagues found that transient reactivation in a cortically dominant pattern formed during learning was followed by downscaling of the functional connectivity in these areas followed by a gradual reorganisation of the representation towards a subcortically dominant consolidated trace during NREM sleep. The functional connectivity of the ventrolateral putamen area was significantly increased within the consolidated network during NREM sleep in MSL compared to CTL. A gradual increase in the strength of connectivity was observed and this was significantly related to the amount of gain seen with the motor performance. However, the pattern of the brain area connectivity during learning did not show significant change during the NREM periods with either task. Therefore, although the consolidated patterns were not increased immediately after training it was already significantly elevated before the retesting session indicating that the memory consolidation process took place during the sleep phase and was not caused by the practice during the retesting session after sleep.

Vahdat and colleagues also found that the V-VI areas of the cerebellar cortex also showed increased functional connectivity for both tasks during NREM sleep and the retesting session and this was also associated with the overnight behavioural gains observed. The functional connectivity within the posterior parietal cortex (more activated during the learning session) also demonstrated increased values in the resting state condition following the MSL.

Therefore, the authors concluded from their investigations that there is a gradual shift of the patterns of neuronal activation representing the motor sequences during the memory process from a transiently activated cortical pattern in learning to a downscaled, more interconnected and more subcortically dominant  one during NREM sleep and after during recall. This indicated that NREM sleep is necessary for two complementary processes to consolidate human motor memory. They are: the suppression of the initial memory trace formed during learning; the restoration and reorganisation of the newly-learnt information in a more stable state following. The ventrolateral putamen appears to play a central role in the emergence of the consolidated pattern during NREM sleep. The authors concluded by indicating why sleep deprivation leads to impairment of motor skills and motor memory plus also indicated the possibility of enhancing newly learned skills by manipulating brain circuits during NREM.


What makes this article interesting is that it supports the view that sleep plays a beneficial, some might say even essential, role in the formation of long-term memories not only for explicit and implicit memories, but also as shown here for motor memories. Vahdat and colleagues demonstrated in their experiments that the learning of a five-finger sequence of movements produced as expected a specific pattern of neuronal activation of appropriate brain areas relating to motor memory. This activity pattern at learning represents the different complementary functions required for such tasks eg. control of muscle contraction and expansion and movement plus cognitive functions associated with visual pathways, attention, reasoning etc. that all occur simultaneously during the learning period. During the sleep phase (in particular the NREM phase) consolidation of the memory formed leads to greater performance of the motor sequence during the retesting phase. This was accompanied by reduced levels of variability in the performance and lower levels of recall error. With regards to the neuronal firing patterns, not only did the authors notice an increase in connectivity between the relevant areas in the retesting phase, but they also observed a shift of level of activity from the higher cortical areas (50% of the activity observed earlier for frontoparietal sensorimotor areas) to increased levels of activity in the subcortical areas (4 times increase in the basal ganglia areas and cerebellar V-V1) and particularly in the area of the ventrolateral putamen. Therefore, in order to understand  their observations we need to look further at two areas in particular: the brain regions involved in motor movements and memory of those motor movements; and the role of sleep in the memory consolidation process.

Brain region activity related to motor learning and memory

Vahdat and colleagues in their article showed that there was a change in area activity between the learning and consolidation (retesting) phases of their experiment with a shift from cortical motor areas to subcortical structures eg. striatum and a gradual increase in connectivity between all participating areas. Area activity relates to the different functions occurring during the learning and retesting stages and these can be divided into two sets of processes: those relating to motor movements which one can assume are the brain areas associated with muscle movements and the second set which are those areas associated with cognitive functions such as visual processing, learning, reasoning, monitoring etc. plus those associated with emotional status and conscious awareness. This division is supported by McDougle, Bond and Taylor who separated the cognitive demands of motor memory into explicit and implicit learning all sub-serving the task performance. They described fast and slow processes with fast being associated with the explicit memories involved.

With regards to motor movements and motor memory of motor movements, it is known that the neuronal activation pattern follows area activity associated with what is known as the ´motor loop` – a group of interacting, sometimes interrelated brain areas where some areas influence excitatory effects on others in the loop and some inhibitory effects. Therefore, the greater the activity of some brain areas means other areas exhibit higher levels of inhibition and so, a change in activity of one area alone can have an overall effect on the performance of the motor loop as a whole. Another feature of this system is that specific activity leads to specific movement eg. force and direction since somatotopic maps are in present and also that  because of the population coding scheme, the larger the active population, the finer the control of the movement. In the experiments discussed here the neuronal activation patterns representing the motor movements alone can be assumed to be the same whether the individual is in the learning phase or retesting phase.

Vahdat and colleagues found in their experiments that the brain areas, ventrolateral putamen and lobule VI of the cerebellar cortex, were mainly involved in the reorganization process following the learning phase of the motor sequence. This change in bias of function was linked to a change in functional connectivity of the putamen within the consolidated pattern during the NREM sleep phase, as well as during the post-sleep resting-state periods, and was thought to be related to the level of improved behavioural performance in the retesting stage following sleep. These results support the important role of the putamen in the motor loop and the observations that abnormal activity of this area is involved in certain disorders/diseases linked with abnormal or restricted movements eg. Parkinson`s disease, Tourette syndrome and stroke. Activation of the putamen is affected by the other member areas of the motor loop.

The motor loop consists of a group of brain areas whose activation patterns create the conditions required to perform motor movements. There is a group controlling strategy (eg. the higher levels of the brain such as the neocortex and basal ganglia), another controlling the tactics such as the movement of the muscles, the contractions, the organisation of sequences all based on previous experience (eg. the motor cortex and cerebellum) and the last group, the actual execution which involves the activation of the muscles (eg. the spinal cord and brain stem). Therefore, specific activation of the frontal (sensory cortex) or prefrontal, parietal or motor cortex areas leads to excitatory firing of the appropriate areas of the basal ganglia caudate region (through the somatotopic map condition). These activated caudate neurons then cause specific firing of areas of the globus pallidus or more importantly relating to the investigation described here, areas of the putamen, which then causes firing of the global pallidus. The globus pallidus is connected to the ventral lateral nucleus of thalamus (VLo), but global pallidus firing is inhibitory and therefore, any activation of the putamen increases the inhibitory effect on the VLo. The VLo is connected to the dorsal lateral thalamus which at rest is inhibited and hence, activity of this area undergoes excitation as the putamen activity relaxes the enforced inhibition. Át this point, the signal carries on through the motor loop to the higher cortical areas of supplementary motor cortex (area 6) and the premotor area. Areas 4 and 6 then send excitatory axons to the corticospinal tract which then activates the spinal cord. The other area involved in the motor loop is the M1 motor area whose layer V pyramidal cells receive input from other cortical areas and the thalamic ventral lateral nucleus. The output from such pyramidal cells activates the lower motor neurons such as those of the spinal cord and the subcortical sites for example associated with motor processing (brain stem). Hence, firing of the motor loop goes from the strategic areas to the execution areas.

Both of the two areas of the motor loop investigated by Vahdat and colleagues, that of the putamen and cerebellum, are capable of their wide-ranging involvement in motor movements and motor memory due to their complex physiology. The putamen has extensive connectivity to other brain areas, for example to the substantia nigra (an area linked with Parkinson`s disease), the globus pallidus, the thalamus (inhibitory effect from the putamen and excitatory effect to the putamen), and to the cortex (sends information to the putamen in multi-fibre pathways plus the area has numerous parallel circuits for cortico-subcortico-cortico communication loops). A look at the area at the cellular level shows a wide range of axons and dendrites which are highly arborized and exhibit a topographical organisation eg. anterior to posterior, and lateral to medial for functional and somatotropic gradients, diffuse terminal outputs, segregated terminals from adjacent regions and finely interconnected terminals from distal cortical regions in an overlapping function. It also has a varying range of active neurotransmitters, eg. GABA, which controls the inhibitory effect on the thalamus and dopamine, which acts presynaptically and influences the substantia nigra.

The functioning of the motor loop areas relates to each of their roles in motor movement and motor learning and memory and therefore, specific movements produce specific patterns of neuronal activation whether in learning or retesting. The other group of brain areas where activation could be observed relate to the cognitive demands placed on the individual because of the task. These include visual processing, and areas associated with cognitive functions such as learning, reasoning, monitoring etc. plus those associated with emotional status and conscious awareness. Even after sleep in the retesting session activity of this group of higher brain areas constitutes 50% of that reached during the learning cycle. Therefore, some cognitive processes are still active and repetition of the activation pattern is appropriate to memory recall of the learnt sequence. With regards to the putamen and its increased activity in retesting said to be linked to consolidation of the motor memory during sleep, we have already seen that learning is linked to putamen activity. This area integrates with other areas to complete tasks and an injection of muscimol (a GABA agonist) leads to a decrease in learning. In the case of reinforcement learning, then cholinergic interneurons in the area fire during stimulus with an impulse rate of 0.5 -3 and with regards to rule based tasks, then lesions of the area disrupt the recall due to the damage of the required hypothesis testing system.

The cognitive demands of the task not only relate to memory input, formation and recall they are also linked to emotional state which undergoes many changes during the learning and recall stages with each stage associated with different brain area activation. This means that the overall neural representation of the sequenced task will vary according to the emotional state observed at the time. However, only the overriding emotional status will be recorded in the form of an emotional tag alongside the information and this is the emotional status recalled along with that information. The concurrent emotional status observed due to real-time system activation will be expected as relaxed during re-testing due to successful recall. The changes in emotional status will also be mirrored by a shift in level of conscious awareness between the learning and retesting stages which will also be reflected by the activation pattern of the appropriate areas. As the learning of the motor sequence progresses then it is likely that there is a shift in conscious awareness to subconscious. Both conscious awareness and emotional status will be subconscious and neutral with any changes initiated through feedback (eg. monitoring for errors in the recalled sequences), a lack of the continuation of the sequence initiating the required prompts/reminders and increased speed although essentially the task sequence is order dependent and not time-dependent. The retesting session although proceeding with recall of learnt material with no variability demanded still requires visual input and other sensory input eg. body positioning, balance and error monitoring and therefore, increased activation in these areas should be observed.

Therefore, the activation of brain areas during the task given by Vahdat and colleagues reflects the requirements for motor movements plus for the cognitive demands placed on the individual due to the nature of the task. Whereas in other types of memory, the more connectivity observed the better the recall, ie. more details, more associations, in the case of motor memory, the more the connectivity then the finer the motor control.

Role of sleep in motor memory implied from the investigation

Sleep is believed to aid the consolidation of the learning neuronal firing patterns and this can have a beneficial effect on recall. For example, researchers have shown that a 10 minute nap can lead to a better recall of a story. In the experiments performed by Vahdat and colleagues regarding motor memory, sleep aids the consolidation process of the motor memory and a shift of strength of area activity is observed with lower levels observed for higher brain areas eg. the reasoning, strategy areas to the lower areas that of the putamen and cerebellum. There are many studies on sleep and memory and there are various hypotheses that cover the area of memory consolidation, including the trace reactivation, synaptic homeostasis, and systems consolidation hypotheses. The Trace Reactivation Hypothesis assumes that the repeated reactivation of a recently formed memory representation during sleep replayed in the slow-wave-sleep phase leads to a gradual strengthening of the learning-related connections (Guidotti and colleagues) especially in the hippocampus (an area known to exhibit activity even if the movement is not being carried out) and hence, to long-term storage of the memory trace.

Procedural memory has been found to be positively linked to sleep both in the sleep spindles and slow wave phases of sleep. This hypothesis is in contrast to the Systems Consolidation Hypothesis that proposes that sleep engages an active reorganization process that stabilizes the unstable neural representations formed of a new skill into a consolidated memory trace. In this hypothesis, protein synthesis is required. In the third hypothesis, the Synaptic Homeostasis Hypothesis it is proposed that local neuronal networks are potentiated and eventually become saturated during learning. In order for new information to be encoded, sleep is involved in the restoration of these local networks by downscaling the strength of synaptic connections. The authors of this article support this view by showing that there is a shift from the higher cortical areas to lower brain areas.

All three hypotheses however can be linked to the process involved in the consolidation of the memory traces even if the processes may be more complicated than suggested. The repetition of the sequence leading to the learnt sequence and then its recall in the retesting stage means that the temporary changes occurring in the initial learning stages cause physiological changes that mirror the translation of the memory from short-term to long-term memory. Consolidation comes through recall and retesting and it is disputed whether protein synthesis is required for this process or not, but it is known that histone acetylation is required.

In the experiments described here retesting occurs after sleep and the changes in connectivity imply that the consolidation process during sleep is the same as that which occurs when repetition is carried out whilst the individual is awake. The advantage of the consolidation process occurring during sleep is that the cognitive resources of the brain are minimised during sleep by the reduced sensory input, eg. visual pathway shut-down. Therefore, sleep is linked to reducing cognitive load allowing resources to be focused on the consolidation of memories formed from events recently experienced during the waking period. This implies that there is a point at which the cognitive demand outweighs the available resources and priorities have to be set. This point should be explored. We know at what point activity is not high enough for conscious awareness (through default network observations and minimally conscious individuals) or cognition (through dementia or coma sufferers), but the point at which the cognitive load is so great that the brain shuts down or sets its own cognitive priorities gives another angle to how the functioning of the brain could be manipulated.


Therefore, to conclude motor movements and learning of these movements rely on the activity of certain brain areas in a particular pattern. Motor memory just like episodic memory is dependent on learning and recall processes and the consolidation of memories can occur during sleep with replaying of the activation patterns specific to the movements during the SWS sleep phase. Vahdat and colleagues have shown that in the case of motor memories there is a shift of neuronal firing strength from the cortical areas to the putamen and cerebellum during sleep and hence, consolidation is linked to some extent to these shifts. This can be explained by the role of the putamen and cerebellum in the motor loop, the reduction of involvement of the higher areas in the formation of strategy and tactics of the motor movements required for the task in the recall phase compared to the learning phase and the decreased levels of conscious awareness and relaxed emotional status exhibited during the retesting phase. Learning impairments said to occur due to the lack of sleep can be caused by the inhibition of the replay process observed in the SWS and sleep spindle phases and are likely to affect both episodic and motor memories. They are likely to be linked to failed changes in area activation strength from the higher cortical areas to the subcortical as observed by Vahdat and colleagues in their experiments.

Since we`re talking about the topic……………………

…..it is said that new information can be acquired by an individual during sleep. Would a repetition of the above experiment when new information is presented during the sleep phases (eg. introduction of a conditioning sound or smell) have an effect on the observed shift of activation pattern strength from the higher cortical areas to the putamen and cerebellum as observed by Vahdat and colleagues?

…caffeine is said to enhance memory consolidation, but has an adverse effect on sleep. Can we assume that the connectivity changes observed in this experiment would still occur, but there may be increased performance levels due to better memory retention?

…would an intervening task have an effect on the activation patterns observed by Vahdat and colleagues and the shift of activity strength to the putamen would still be present since stimuli are said to be more familiar after an intervening task?

…it is said that mindful meditation leads to less reliable memory recall. Does it have an effect on motor memory and would mindful mediation cause the same shift in activation strength of the cortical and basal ganglia areas observed by Vahdat and colleagues in their experiments?

Posted in neuronal connectivity, neuronal firing, procedural memory, recall, sleep, Uncategorized | Tagged , , , ,

dorsolateral prefrontal cortex role in decision-making

Posted comment on ´Stimulation of dorsolateral prefrontal cortex enhances adaptive cognitive control: a high-definition transcranial direct current stimulation study` by O. Gbadeyan, K. McMahon, M. Steinhauser and M. Meinzer and published in Journal of Neuroscience 2016 36 (50) p.12530; doi.org/10.1523/JNEUROSCI.2450-16.2016


Gbadeyan and colleagues investigated in their experiments the role of the dorsolateral prefrontal cortex (DLPFC) in the adjustment of strategic control to real-time conflict caused by previously experiences of conflict. The authors support conflict monitoring theory where the anterior cingulate cortex (ACC) adjusts attentional resources to goal-directed information and ignores irrelevant information – a process believed to involve the lateral prefrontal cortex (LPFC). Their research centred on the role of the dorsolateral prefrontal cortex in strategic control in particular since most research using human subjects was said to concentrate on the activity of the ACC and any findings on the role of the DLPFC appears to be contradictory, eg. different views on the contributions made by the two hemispheres.

In their experiments, Gbadeyan and colleagues used 120 healthy participants and performed high-definition transcranial direct current stimulation (HD-tDCS) under 4 conditions – left or right DLPFC, or left or right primary motor cortex (M1). The M1 area was used to assess the specificity of the HD-tDCS performed since the area is thought to play no part in the response to conflict. Active and sham HD-tDCS were performed on each group in crossover and double blind tests. The adaptation to conflict was assessed during a visual flanker test ie. assessment of modulation of the flanker effect as a function of previous response to conflict. The participants were required to respond to a centrally presented target (a centre row of arrows) for 100ms after being presented with first a fixation cross (300ms) and then the distracting material. This was 4 flanker arrows which were presented (100ms) as either pointing in the same direction as the target stimulus (congruent), or in the opposite direction (incongruent). The participants were required to ignore these flanker arrows and to respond by indicating the direction of the target row arrows by pressing either the left or right response key. Each subject performed 5 blocks of trials.  The HD-tDCS used was a concentric set up with a 20 min long administration before ramping down. Nine potential side effects were recorded by all participants. Data analysis involved the mean response times (RTs) and varying ANOVA. Adverse effects between the active and sham trials were compared using paired t tests.

The experiments were performed to measure the conflict adaptation effect. The authors expected that there would be a flanker effect because responses are slower for incongruent trials than congruent. The responses were also expected to be slower due to the modulation of the response from the directly preceding trials and this is the so-called conflict adaptation effect to anticipated levels of conflict. On analysis of the mean RTs results of their experiments, Gbadeyan and colleagues found that there was a sizeable conflict adaptation effect in all groups and in all conditions (greater than 30%). ANOVA showed a significant 2 way interaction between current congruency and previous congruency indicating a strong conflict adaptation effect. There were also significant results for an ANOVA 4 way interaction between region, stimulation, current congruency and previous congruency. Separate ANOVAs between stimulation, current congruency and previous congruency found the largest conflict adaptation effect in the active condition than in the sham group, but the effect was not further modulated by laterality ie. both DLPFC groups had the same influence. With the M1 directed HD-tDCS then the conflict adaptation effect was reflected by significant interaction between current congruency and previous congruency, but was not further influenced by stimulation.

Gbadeyan and colleagues reanalysed the DLPFC with additional variable response repetition (repetition, switch) to see if the effects of active stimulation on conflict adaptation were larger for, or were restricted to response repetitions. This would indicate a priming effect. The authors found that the conflict adaptation effects were larger on response repetition trials than on response switches, but did not further interact with stimulation. This indicated that the effects did not rely on priming. To see if the effect of stimulation significantly changed the course of the experiment the researchers used an additional variable Block. No significant results were obtained under these conditions.

The authors also investigated whether the conflict adaptation effect demonstrated regional specificity (left vs right hemisphere) and motor cortex involvement. A 5 way ANOVA showed that there was significant interaction between region and laterality for DLPFC. It was found that the overall mean RTs were all higher when stimulation was applied to the right hemisphere as compared to left, whereas the opposite was observed for the M1 brain area.

Gbadeyan and authors concluded from their investigations that the DLPFC has a causal role in adaptive cognitive control. Their observations support previous evidence obtained from BOLD responses and also reports about DLPFC activity of current trials matching the activity of ACC in previous trials.  The activity appeared not to be restricted to either the left or right hemisphere which was contrary to reports from other researchers who had linked adaptive control to activity in one hemisphere or another. The authors concluded their article by continuing to describe the beneficial effects of tDCS stimulation on cognition generally and how HD-tDCS in particular could modulate cognition in a regionally specific manner.


What makes this article interesting is it further describes the subtleties of the decision-making process and the various roles that the brain area, the prefrontal cortex (PFC), plays in it. In the last Neurochitchat Blog post, we described the ventral medial prefrontal cortex (VMPFC – or orbitofrontal area, OFC) area and its multiple roles in various cognitive processes such as memory, conditioning, attention, emotions and consciousness. That blog post also described the PFC`s roles in general and the OFC roles in particular in the decision- making System 1 mechanism (the rapid decision-making mechanism) with a link to the striatum and also in System 2 (the slow mechanism) relating to values, conscious choice and the effect that values play on that choice. Research demonstrating these various roles include for example: the orbitofrontal area-basal amygdala pathway and the personal assessment of values given to events and the later recording of updates to that value; comparison of values as in choosing which action to be followed (Rich); the alternating process between two options, also responsibility of the frontal cortex-basal amygdala pathway (Rich); the switching of attention;  the link to reward with encoding predictions of reward values (Zhou); the allocation of rewards, the responsibility of the orbitofrontal cortex-anterior cingulate cortex pathway (Chang); the awareness of choice, involving the VMPFC and DLPFC (De Martino); and the calculation of delay in respect to expected reward/risk (Rudebeck) with feedback of the event responsibility of the VMPFC and rostrolateral prefrontal cortex connectivity.

In this article, Gbadeyan and colleagues discuss the role of a further part of the PFC brain area, that of the dorsolateral prefrontal cortex (DLPFC) and its role in particular in decision-making. This area (designated Brodmann areas 9 and 46) is connected to the post-parietal cortex and is known to be involved in updating information and maintaining the working memory state. Research has shown its activity in working memory is dependent on connectivity with the frontoparietal area (Ekman) and that activity is NMDA dependent. However, the DLPFC area is also known for its effects on decision-making – a function predominantly known to be linked to the VMPFC – with activity not vital to decision-making since it can be carried out without it, but influential.

Therefore, the question is ´what roles does the DLPFC play then in decision-making?` We suggest here that in this context DLPFC acts as a neuronal ´signal amplifier` that allows differences in event values to be more easily distinguished by the sender region, the VMPFC. What do we mean by that? Imagine hitting a ball against a wall where the wall doubles the force of that ball so that when it rebounds back it comes back at double the speed. If we hit several balls with different forces at the wall then the returning balls would be more easily distinguished if the wall can alter the return speed. Doubling numbers increases the difference between consecutive numbers more than between consecutive ones eg. 2 squared is 4, 3 squared is 9 so the difference between the two is 5 compared to just 1 if consecutive numbers are only considered. If a brain area receives a neuronal firing signal and amplifies it to double or triple its original firing strength and then returns it back to its sender region then distinguishing between competing signals by that sender is improved. An alternative option is that the ´amplifying cell` could dismiss the weakest signals and only return the stronger ones. If the signals sent are the values of the various options in the decision-making task and the sender is the ventromedial prefrontal cortex then by amplifying the received signals the dorsolateral prefrontal cortex would have aided the ventromedial prefrontal cortex in its choice of the optimal course of action. The DLPFC area is itself not responsible for the formation of event values or their assessment, both of which are jobs of the VMPFC and other brain areas, it just makes the task of the next stage in the decision-making physiological mechanism, that of choice, by these other decision-making areas easier.

There are several areas of support for such an action of the DLPFC and these are:

  • It is known that there is reciprocal connectivity between the VMPFC and DLPFC areas. Therefore, the VMPFC could send its signal to the DLPFC and the DLPFC could return it – the strength of the signal is not measurable so it does not directly prove that the DLPFC acts as an amplifier. This could explain why low motivational decisions lead to earlier activity in the DLPFC – the DLPFC strengthens the signal even though it is not warranted by the normal valuation process since it is of low motivation. It could also explain why high rewards are favoured since the signal is strong when it is sent to the DLPFC so amplifying would mean further strengthening which is then received and re-registered by the VMPFC.
  • It is also known that there is increased activity in this DLPFC area when actions are selected and initiated (Spence). Therefore, the DLPFC is involved in decision-making, but the exact stage is not determined. It is also known that the DLPFC is involved in reasoning tasks (D`Espito) with the right hemisphere thought to be involved in plan generation and the left in plan execution. A role in strategic control is also reported with the VMPFC known for comparing values as in choosing which action to be followed (Rich), but the awareness of choice the responsibility of the VMPFC and linked DLPFC (De Martino) and feedback of the event the responsibility of the VMPFC and rostrolateral PFC connectivity. Therefore, it would seem that DLPFC plays a role in strategic control.
  • Support also comes from the observations that the activity of the DLPFC leads to irrelevant information being ignored. The lateral intraparietal area (LIP) is also involved in ignoring distracting information, but this could relate to the functioning of the attentional system only. The role of the DLPFC in this function could relate to the other informational situations, eg. sensory input and memory formation. Repetition leads to a shift away from task relevance. In the case of DLPFC, then distracting information is part of the decision-making process since it can be considered as what forms the other options before the choice is made. Since we know that DLPFC activity suppresses distracting information (Gbadeyan) then this implies that the suggestion that the DLPFC only sends back the strongest option is likely.
  • It is also known that activity of this area is linked to conscious awareness. If this suggestion is valid then the incoming signal is amplified and strengthened by the DLPFC so that the information reaches conscious awareness. This information takes priority over the distracting, irrelevant information which according to point (3) above could be suppressed by the DLPFC.

Therefore, the suggested role of signal amplification by the DLPFC implies that this area is part of the decision-making process and does not act just as assurance or verification that the process carried out by other brain areas particularly the VMPFC is correct. It implies that the DLPFC can play a role in both the slow decision-making System 2 (here, strengthens the best value option) and the fast System 1 ´magic answer` decision-making process (here, strengthens the strongest firing option). It can also explain the disputed role of DLPFC in spatial working memory (eg. Platke – yes, Mackay – no with roles in accuracy only and a requirement for precentral sulcus activity). If we look at spatial working memory as being an example of part of a decision-making process undertaken when the route is followed and a decision is required as to the next step, if the above hypothesis is true then the options (ie. turn left or turn right) require the retrieved memory of the correct option to be a stronger firing assembly compared to the unreal option. Amplification of the firing of the possible options by the DLPFC would mean that the correct route learnt from previous experiences would be chosen. This could explain why Mackay said the DLPFC is involved in accuracy only. If the DLPFC is not involved then previous experience would still present the option with the highest value, but it would be more difficult to recognise it from other options presented that represent previous unsuccessful and successful attempts carried out during the learning process. This supports the idea that DLPFC is part of the strategic control mechanism.

This proposed signal amplification role of the DLPFC means that the area is fully functioning for all aspects of decision-making, but at different levels for different tasks. It is therefore, necessary to investigate the relationship between the two for optimal efficiency in all circumstances. The rebound nature of the area may mean that its efficiency can be affected by factors such as tiredness or stress which may imply that only the simple ´magic answer` decision-making mechanism can be used and not the more complicated option assessment and choice mechanism. Therefore, factors influencing the activity of the area may be important and certainly it highlights the need for care in interpreting brain area connectivity and cognitive functioning.

Since we`re talking about the topic………………

……it is said that mindful meditation increases awareness of unexpected distractors. If the role of the DLPFC in amplification of neuronal signals is valid, would an effect on activity of this area in decision-making be observed if mindful meditation is carried out during or before problem solving?

….tDCS is known to decrease stress, but has a negative effect on working memory. What happens to DLPFC activity in an anxiety situation? Would more errors be observed as the ability to suppress distractors cannot be overcome?

…..spatial memory performance is said to be impaired in a rat model of neuropathic pain and this is associated with a reduced hippocampus-prefrontal cortex connectivity? What would happen to DLPFC activity in this situation? Does the pain cause the DLPFC to preferentially strengthen and send back signals relevant to the removal of the pain stimulus whether they are relevant to the decision-making task at hand or not?

…….the administration of a drug that temporarily prevents the opening of K+ channels in the PFC leads to a restoration of working memory activity in ageing (Laubach). Also, administration of estrogen is known to restore multi-synaptic boutons in the DLPFC area in ageing and hence, an increase in working memory is observed. Do these factors have an effect on DLPFC functioning in decision-making?

Posted in decision-making, emotions, prefrontal cortex, Uncategorized | Tagged , ,

choice changes connectivity between prefrontal cortex and striatum

Posted comment on ´Human choice strategy varies with anatomical projections from ventromedial prefrontal cortex to medial striatum` by P. Piray, I. Toni and R. Cools and published in Journal of Neuroscience 2016, 36 (10) p. 2857; doi.org/10.1523/JNEUROSCI.2033-15.2016


Piray, Toni and Cools` article begins with a description of the two decision-making systems: one goal directed (model-based, flexible, and cognitively expensive) and the other habitual (model-free, rigid and more rapid). They state that these systems could arise from two computational mechanisms representing reinforcement learning and associated with firing in the frontostriatal circuits which are believed to be responsible for learning and behaviour. The authors investigated how the two systems relate to the specific connectivity of neural circuits particularly of the dorsomedial striatum and the ventromedial prefrontal cortex (vmpfc) for the model-based system and dorsolateral striatum and vmpfc for the model-free system and how structural differences in these circuits could account for the individual differences observed in choosing which decision-making system would be used in any situation.

In their experiments over 30 healthy volunteers underwent probabilistic tractography diffusion tensor imaging (DTI), connectivity based parcellation of the frontal lobe and  computational explicit learning associated with a multistep decision task.  In each trial of the task the subject had to make a choice between two fractal stimuli (70% chance) leading to one of two different second-stage sets represented by different colours. Then the subjects had to make another choice between two stimuli presented in the second-stage set (30%). Each stimulus at this second stage was associated with a specific probability of delivering a monetary reward and the probabilities were changed in order to keep the motivation to participate high. In this way, the authors could distinguish between model-based and model-free choices.

In all 120 trials were performed and the results were analysed by logistic regression using MATLAB. Piray, Toni and Cools used a 2 by 2 factorial design with transition (common or rare) against reward delivery on the previous trial (rewarded or unrewarded). The degree of model-free and model-based decision-making used was quantified as the main effect of the delivery of the reward and the interaction effect between reward delivery and transition respectively. They also fitted the results to reinforcement learning models for model-based, model-free learning plus a hybrid account. Structural and diffusion images were recorded using MRI and DTI and a connectivity matrix was computed between the striatum and frontal cortex after striatum-based parcellation of the frontal cortex had been completed. The authors associated 2-8 clusters for each group and demonstrated the stability of the clusters and as a result identified 5 clusters as targets. Also, as part of their experiments any influence from bottom-up systems was discounted by lesioning the anterior limb of the internal capsule thus destroying all the fibres going from the striatum to the frontal lobe along the striatal-thalamocortical pathway.

The premise of the authors in their experiments was that the probability of transition from the first to the second stage set was different. Each first stage choice led 70% of the time to one of the two second state sets (termed by the authors as common transition) or for the remaining 30% to the other choice (termed rare transition). Therefore, in the model-based system then the first stage choice associated with a rewarded second-stage choice was reinforced. Hence, the probability of choosing a first stage action that was ultimately rewarded after a rare transition decreased. When the model-free system applied then the transition probabilities were not observed and the first stage choice was reinforced regardedless of the second stage. The probability of repeating the actions in the subsequent trials (termed stay probability) was a function of the current trial. If the model-free system was applied then repeating the first stage choice in a subsequent trial became a function of reward delivery regardless of transition occurring. Therefore, according to Piray, Toni and Cools model-free and model-based behaviour was quantified to 2 key effects: the main effect of the reward and the interaction effect of the  reward and transition.

When the stay probability was analysed from the test results, the experiment showed a significant main effect of reward delivery ie. the model-free system was employed as well as an interaction between reward delivery and transition (ie. model-based system). Piray, Toni and Cools also showed that there were large differences in the degree of model-based control between individuals. In half of the subjects, the characteristics of this type of control were clearly observed whereas in the other half there was no evidence of a reliance on it. The authors also looked at a hybrid model combining the learned values of model-based and model-free strategies according to the task. They found that at an individual level then the hybrid model outperformed the model-based results (all 31 participants) whereas comparing it to the model-free situation then its use was low (only 6 out of the 31). It was therefore, suggested that in analysis of reward-by-transition then the model-based decision-making strategy was not evident in half of subjects, but was evident in the other half. This confirmed that participants consistently used model-free control whereas the use of a model-based strategy depended on the subject.

Through parcellation of the striatum and cortex and DTI, the structural interconnectivity of the striatum was investigated to see if the differences in model-based strategy use were dependent on structural differences. In this investigation, the authors looked at the 5 clusters common to all participants which overlapped with other brain areas areas (precentral cluster – with frontal lobe motor areas; posterior prefrontal – with presupplementary motor area and frontal gyrus; dorsal prefrontal – with anterior cingulate gyrus and para-cingulate gyrus; anterior prefrontal – para-cingulate gyrus and anterior cingulate cortex plus dorsal parts of frontal pole;  and vmpfc – including frontal orbital cortex and ventral parts of the frontal lobe). A large variability was observed with 3 out of the 5 clusters, but not the precentral area and the vmpfc. It was found that only individuals relying on the model-based strategy had stronger interconnectivity between the vmpfc and medial striatum and this was also observed with the hybrid model. Piray, Toni and Cools also found using lesioning the bottom-up tracts that the effect was top-down and that these afferences went from the vmpfc to the striatum. A similar analysis with the model-free strategy found no significant correlation between strength of connectivity and frontal clusters with striatum. However, lower structural connectivity between the right dorsal prefrontal and right medial caudate nucleus was observed.

Therefore, Piray, Toni and Cools concluded that the connectivity between the frontal cortex and striatum predicted differences in the use of the model-based strategy between individuals. Other researchers also implicated areas such as amygdala, hippocampus, lateral prefrontal cortex and the default mode network (DMN). They also tested other areas and found that there were marginal effects between the left posterior cingulate cortex (the socalled DMN hub) and the vmpfc so the authors positively associated this observed connectivity to model-based strategy control.

Using a tensor model and fractional anisotrophy, activity was investigated relating to white matter microstructure integrity. The investigation was carried out on the use of the white matter tract and model-based strategy. The authors looked at the strength of the vmpfc tract which consists of 4 major bundles arising from it (the uncinate fascicle, the corpus callosum, the superior longitudinal fascicle and the cingulus bundle).  A significant correlation between tract integrity and model-based use was observed only in the cingulum bundle and it reflected the degree of model-based control employed. This supported observations made by other researchers. Piray, Toni and Cools also found that the dorsomedial striatal/vmpfc tracts went through the cingulate cortex bundle. No change in grey matter was observed between individuals in these areas and therefore, it was concluded that the correlation between model-based control and vmpfc-striatum tract strength was not accompanied by changes in grey matter.

Therefore, the Piray, Toni and Cools concluded in their article that it appears that there is a mechanism for the instrumental action control through which the medial striatum determines at least partly the relative contribution of model-based and model-free systems during decision-making according to the top-down model-based information from the vmpfc. This was described as being important in the understanding of neural connectivity that could be influenced in impulsive or compulsive psychiatric disorders.


What makes this article interesting is that not only does it provide more proof that the physiology of the brain is adaptable and responds to the demands placed on it from the side of information, but also that neuronal physiology can be adapted in response to the use of particular cognitive methods. The authors of the article commented upon here, Piray, Toni and Cools showed with their experiments that the neuronal connectivity between the ventral medial prefrontal cortex (vmpfc) and the basal ganglial striatum was thicker (hence, implying greater efficiency) when a certain decision-making strategy had been employed frequently. The cognitive decision-making method employed in this case required informational input from the beginning to the reward (the end-point of the process) as well as processing relating to optimising strategy and assessment of the overall process according to the success of reward attainment. Therefore, the authors looked at particular brain areas known to be associated with decision-making, real-time information processing and the emotional system relating to reward and value.

In general, decision-making can be said to be a multiple stage neurochemical system leading to a specified action or outcome. There are many circumstances when decision-making can be employed, eg. when there is a lack of stored information to interpret the real-time stimulus, or the stimulus provides no clear course of action dictated by previous experience but provides equal ranking options, or the stimulus is so far removed from the task (the end-point) that multiple cognitive steps are needed to bring the two together (such as that seen in problem solving), or the stimulus and task (the endpoint) are ´unreal` and any stages inbetween are based on conjecture and/or reality (such as that seen in forward planning). What we can say is that: it is highly individual; occurs in real-time; the process matches the task at hand; and it requires the attentional, emotional and working memory systems.

The general decision-making process can be said to consist of 7 cognitive and neurochemical stages (Salt, 2012). Stage 1 involves defining the purpose of the task, ie. I know where I am, is not where I want to be. In Piray, Toni and Cools experiments, the definition of the purpose is based on instructions, but it can in other situations be elicited by ´cues` in the stimulus. Therefore, both rely on informational input and requires the formation of an ´unreal` image representing the sought after goal. Stage 2, the input stage, also occurs in real-time and is again related to the task demands or is stimulated by the same cues used in defining the purpose. This stage is the acknowledgement that there could be more than one option and, hence points of access ie. the cues set to define the decision-making task are set. Stage 3 can be termed the questions or solutions stage and occurs if the so-called ´magic answer` (ie. the answer that just ´pops into the head` after presentation of certain cues and thoughts) fails to appear. This stage requires ´reframing` of the information so that a decision can be made in the future based on presented information and therefore, it requires a certain amount of previous experience as well as possibly the application of creativity to be successful. This stage is also likely to involve the attentional system in its monitoring role for conflict and acceptable process timing, the emotional system which will respond to unacceptable timing (dictated by activity of the cingulate cortex) as well as possibly conscious awareness. The following stage, Stage 4, is where the various options available for making the decision are constructed. Strategies are applied and these strategies reflect invidividual experience and favourites, eg. Consequence and Sequel, Other Peoples Views, Consider All Factors, Plus and Minus Points.  It is clear that the better this stage is the more likely that the resulting decision is the most optimal. This stage is also where construction errors can occur, for example because of the use of false facts or biasness of facts, a misunderstanding of the problem, habit, dominance of particular emotional values and constricting time factors.

The theoretical end-stage for the processing of information before the action is taken is Stage 5 where a decision is made from the options constructed in Stage 4. According to Salt 2012 the decision making stage means taking either the ´simplest path` or where more complex assessment is made. The ´simplest path` is the easiest solution and involves taking the option which shows the greatest firing strength or has the highest rating for self-interest which means taking into consideration emotional values and/or priorities. Emotional values mean the value of the event to the individual from the point of the two defining emotions, pleasure and pain with pleasure being a graded value. Priorities on the other hand are entirely personal with some perhaps reflecting an individual`s physiological needs such as hunger or sleep and others more cognitive and subjective such as personal long-term goals or sense of morality.

The other decision-making path in Stage 5 is much more complex and can be further split into two defining criteria based on either emotional factors ie. ´heart` or logic and facts  ie. ´head`. As indicated by the name the ´heart` decision-making stage requires emotional values of the options to take priority to determine which decision is taken. The biochemical mechanism probably involves the comparison of emotional values for each of the options described in Stage 4 and the option which demonstrates the strongest firing of the emotional value is the path followed. This type of mechanism involves the brain areas associated with emotional values and storage of emotional values eg. the prefrontal cortex, striatum, nucleus accumbens. The other decision-making method termed ´head` involves making decisions based on facts and logic and instead of comparing the emotional worth of the available options then a comparison of the factual ´worth` of the different options is made. Hence, there is a ´mathematical-type` basis to this type of decision-making process and this may not be so visible or so instantaneous as decisions based on emotional values. Salt (2012) suggests that the psychologists` views on decision-making can be divided ultimately into three neurochemical techniques for distinguishing the firing patterns of the different options and these are based on frequency, utility and risk with the ideal solution demonstrating high probability, high utility and low risk/high reward.

In the case of decisions being made as a result of looking at ´frequency`, Salt (2012) suggested that this is based on the strength of neuronal activation within the neuronal cell assembly representing that option. This means that the choice of option will be that neuronal cell assembly that demonstrates the strongest connections between its firing cells and overall firing and will be caused by the neuronal cell assembly representing the largest amount of relevant detail or will be that most strongly consolidated achieved by frequent activation. It should be noted that firing strength here means in all cases not exact mathematical numbers, but more rough approximations like, for example a show of hands, degree of lighting, or an overall impression. Therefore, in the case of decision-making made according to ´frequency` the firing neuronal cell assembly demonstrating the strongest connections between its participating cells will be chosen when comparing the different options constructed in Stage 4.

This is different to the second ´head` method that could be used that of ´utility` which represents the strength of the similarity of the characteristics of the various options and hence, the degree of usefulness in obtaining the purpose defined in Stage 1. This method involves the matching of  ´characteristics` between the reactivated stored neuronal cell assemblies of each option (a process requiring the attentional system) with the ´unreal` image stored of the purpose (Stage 1) so that the option with the greatest number of characteristics shared with the goal is considered the most likely to be useful. Some strategies for the construction of options favour this criterion eg. Consequence and Sequel.

The Prospect theory (Kahneman and Tversky, 1979) combined two types of decision-making mechanism, that of utility (the quality of characteristics described above) and risk and according to Salt (2012) risk forms the third ´head` type mathematical strategy suggested here. Risk can be calculated using the strength of emotional response and can be re-defined as the assessment of the chance of reward or loss being received. Natural decision-making seeks to maximise reward (happiness) and minimise loss (stress, pain) and therefore, the emotional strength of each option is calculated according to personal values and priorities. The values obtained from each option are compared and that option producing the highest value (reward or loss) indicates the most ideal solution.

Therefore, Stage 5 of the decision-making process determines the ´strongest` option chosen to be followed to the end-stage of the mechanism whatever this is and this is carried out in Stage 6 (the Action stage). Completion of this stage leads to shift downwards of the cognitive system and attention is switched again to external events if not taken up with the monitoring task. The emotional system also shifts to a relaxed status as the decided action takes place. The final stage of the decision-making process is the acknowledgment of the outcome relative to what is expected and for this the measure of conflict between the two is recorded by the cingulate cortex in its monitoring role. Neurochemically, this feedback requires the incoming information to be matched against the firing neuronal cell assembly representing the purpose (as defined in Stage 1). The attentional system which probably monitors the comparison ´relaxes` the cognitive systems if the two match, but if unexpected results are obtained it will shift the brain areas activities to a fear state to answer why.

Now that we have described the basic decision-making mechanism we can see how Piray, Toni an Cools`s two types of decision-making can relate to the suggested neurochemical mechanism. The first type, the model-free scheme was described as rapid, rigid, and conforms essentially to the ´magic answer` decision-making mechanism given above. This is where the decision made is based on tried and tested previous experience and there is a strong reliance on taking the established route. The authors` second type, the model-based scheme, is here still a multiple stage decision-making process, but demands less time and less cognitive resources than other tasks because the experimental method used by Piray, Toni and Cools essentially indicates a ´magic answer` type mechanism. The subject knows instinctively the correct decision because repetition and practice quickens the decision-making process to a point of automaticity. Therefore, how does this affect the various stages of decision-making process? Both Stages 1 (defining purpose) and Stage 2 (input stage) are unaltered, but Stage 3 (questions or solutions stage) probably becomes easier or less complicated because of the previous experimental experience ie. the subject knows where the points of access are and what to look for. Stage 4, the construction of options, is also likely to be easier since the subject would know which strategy to apply. Stage 5 (the information gathering end-stage) is likely in Piray, Toni and Cools experiments to begin more slowly with the use of the ´head-based` decision-making strategy because the subject would be learning to apply knowledge of the experiment and its demands and would be busy assessing for high probability, high utility and low risk to maximise high reward frequency. The method chosen is likely to be based on strength of activation (high probability of route choice most frequently giving reward) with assessment of the reward and its value occurring through the emotional system response. The strength of similarity of characteristics method would not be applicable since the experimental conditions were not changed during the trials only certain features. Stage 7, the feedback stage, would be important because the monitoring of the decision and result (reward) would lead to learning of strategy so that the choice of path next time could be positively manipulated. As the subject participates in more and more trials the speed at which the decisions could be made would increase and strategy and feedback would simplify the decision-making process.

As seen by the complexity of the decision-making neurochemical mechanism, multiple physiological systems are in play during the whole process (eg. attention, working memory, visual pathways, motor pathways as well as the emotional system) and hence, activation can be observed in multiple brain areas. No single area is defined as the seat of decision-making, seat of recall, seat of emotions etc. with cognitive demands relying on simultaneous activity in multiple areas. Piray, Toni and Cools showed that their experiments frequent use of their so-called model-based strategy ie. the ´head-based`  decision-making system given above had an effect on the connectivity of two brain areas, the ventralmedial prefrontal cortex (vmpfc) and the striatum. This corresponded to the increased cognitive demand when more involved ´thinking and analysis` was required, more strategies employed and feedback was taken into account in comparison to the model-free system when decision-making was quicker, simpler in terms of that the answer appears more readily (the so-called ´magic answer`) without serious, multi-staged analysis. The resulting effects of such computation implies that the connectivity between the vmpfc and striatum is needed in this type of decision-making and this is supported by other researchers who have found that effective decision-making requires the strength of this pathway (Chung-Chuan and colleagues) – a process requiring the dopamine system. It has been described that a decision requires and brings about ´value` with value described in terms of reward/loss. This supports the views that the value of goal-directed actions are the responsibility of the striatum and prefrontal cortex in the emotional pathway and access to emotional tag ie. the pfc stored memory of the emotional value placed on the stored information. Piray, Toni and Cools found that the vmpfc to striatum tract strengthened with the model-based decision-making of their experiments and that there was a correlation between the model-based control and individual differences in strength of this pfc/striatum tract. Both brain areas have roles in real-time processing and in emotional values in decision-making.

Therefore, does this observation support what we know about neurochemistry of decision-making and the emotional system? We know that there are 2 ways in which the emotional system is involved in the decision-making process. The first is that the real-time emotional status affects the performance of the mechanism at that time and secondly, that neurochemical representations of personal emotional values are part of the decision-making process. In the first case, the current status of the emotional system at the time of the decision-making process means that activation of the striatum is linked with the other brain areas playing a role in the emotional pathway particularly that of pleasure. The striatum may not have as strong links as other basal ganglia areas such as nucleus accumbens, but it is involved. A signal is sent from the prefrontal cortex to the hippocampus and cingulus and also a signal is sent to the globus pallidus of the basal ganglia which spreads to the putamen, caudate and striatum with activation of these areas having an inhibitory effect on the thalamus. Therefore, in terms of pleasure, the full extent of the neuronal capability of the thalamus is not used. The prefrontal cortex also sends an inhibitory signal direct to the amygdala so that firing of this area is effectively ´switched off`. This real-time emotional status of the subject having an effect on decision-making, however, is not likely to be the main reason here for the observed stronger connectivity between the vmpfc and striatum associated with the frequently-used model-based decision-making strategy since both strategies are likely to have the same emotional pathway demands during the performance of the experiment.

Therefore, the strengthened vmpfc to striatum connectivity must be part of the decision-making process itself. This can be explained by the use of values to determine decision options, ie. this type of decision-making process requires the comparison of emotional tags/values and these are hypothesised as stored in the prefrontal cortex. Each option constructed in Stage 3 of the decision-making process from each point of access (the cue) activates the appropriate stored neuronal cell assembly and these contain the relevant information relative to the purpose of the process as well as a connection to the ´emotional tag` which is the memory store of the emotional value of that option and neurochemically stored at its point of origin possibly in the prefrontal cortex. Therefore, when the stored neuronal assembly is fired the informational content is activated and the value of that information is also registered and here in Piray, Toni and Cools` experiments that is the amount of the reward received in past experiments.

Supporting evidence for this role of the prefrontal cortex and striatum in the value part of the decision-making mechanism is abundant. For example, Bechara in 2005 described two separate, but interacting neural systems that control decision-making: an impulsive amygdala system for signalling pain or pleasure of immediate prospects and a reflective prefrontal cortex system for signalling pain or pleasure of future prospects. At the neurotransmitter level then Bechara proposed that drugs which can alter neurotransmitter levels, can modulate, bias or even hijack the goal-driven cognitive resources that are needed for the normal operation of the reflective system, thus providing support for the drug induced influences on brain memory input. Morris, Brandford and Franks`s 1977 study showed that the activity of dopamine neurons in a decision-making situation was modulated according to the value of the upcoming action and that the activity determined behaviour and not the reward. They concluded that the decisions were generated elsewhere and conveyed to the dopamine neurons, which then played their role in decision-making by affecting the basal ganglia efficacy. The exact connectivity demonstrated here between the vmpfc and striatum was described by Daw, Niv and Dayan in 2005 who showed that competition between dorsolateral striatal areas and the prefrontal cortex system affected decision-making.

Even though both the vmpfc and striatum are involved therefore, in model-based decision-making their roles in the process are not necessarily the same. The prefrontal cortex is linked to various cognitive functions eg. it has roles in processing (Baddeley`s central executive), the emotional system (sliding switch location and emotional tag site) and the attentional system (perceptual load capacity and task relevancy). Both Stanovich and West (2000) and Evans (2003) identified two decision-making systems with different functions and different locations involving the prefrontal cortex. System 1 was described as having belief-based processes, which are rapid, parallel and automatic in nature and involve the activation of the ventral medial prefrontal cortex and System 2 which involves the activation of the right inferior prefrontal cortex and is linked to slow and sequential thinking and uses the central working memory system (here model-based). It should be noted that the terminology of these systems do not agree with those of Piray, Toni and Cools who described the System 1 as the model-free system and System 2 as model-based, but was the one which showed activation of the vmpfc. The observation could be explained by the nature of the experiment itself because its repetitive nature meant that the experimental process is learnt, becomes more automatic and mathematical analysis of the best route to get maximum reward is carried out early on. Also, the experiment means that emotional system activation is not associated to just one or the other decision-making system eg. a gut reaction of the model-free system involves the emotional system as well as its involvement in the model-based system with its value comparisons for the various strategies.

The role of the ventromedial prefrontal cortex area in model-based decision-making as proposed by the authors is supported by others who have also shown its link to emotional values. The vmpfc (anatomically synonymous with orbitofrontal cortex) is thought to be responsible for subjective value of outcome (work by Winecott, Kringelbach and Wallis and teams), increased activity in encoding goal values (Hare and colleagues) and with activation in predicted success ie. value (Kao and colleagues). However, the wmpfc was considered by some (eg.Wunderlich) to be unlikely to be responsible for the comparison of emotional values since activity did not depend on what options were available at any one time, but instead depending on true values (Paduo-Schiappa and Assad). Since it is thought that some comparison has to be made in decision-making and self-evidence shows that one selects by choosing which one is ´liked` the most, it is likely that a comparison of emotional values stored in the emotional tag located likely in the vmpfc, then this makes it a more likely that this is the area assigned this function. The choosing of the option based on emotional value will then initiate the running through of the information linked to the stored memory and neuronal cell assembly so that the appropriate action could be made. This would explain the observation that areas involved in decision-making are the parietal cortex, basal ganglia and motor structures (Kaini, Hanks and Shadlen, 2006).

Whereas the role of the vmpfc is not only linked to option values, but also information processing, the role of the striatum is likely to be purely related to the emotional system involvement. This particular area is part of the emotional system relating to the emotion pleasure and is reliant primarily on the action of the dopamine neurotransmitter. Two effects are seen in the model-based system, which indicate the area involved is the vmpfc. One is in real-time and is the response to the cingulate cortex in its assessment of the situation and strategies. For example, relating to experimental timing issues, the subject is happy if the timing of the process is as expected and hence, the emotional system relating to the expressed emotion, pleasure is dominant. However, if the subject considers the process to be too slow then there is a shift to activate the amygdala and the fear system responds with anxiousness and fear emotions expressed. The other role is in the decision-making mechanism itself as described above and the striatum has been found to be responsible for the trade-off of strategies. Therefore, experience and computational simplicity is traded-off against flexibility. The strategies are computed in the caudate and posterior putamen and the vmpfc integrates these computations. The value of the goal directed actions also plays a role in feedback which directs future decision-making and activity of the striatum appears to be important in this final stage of the decision-making process. Anticipatory signals in the striatum was found to lead to encoding success in the hippocampus (Wang and colleagues) and the striatum is responsible for reward prediction error, an effect that is weaker when the episodic memory ie. the stored past experience, is stronger (Wimmer and colleagues).

Therefore, the observation of strengthened connectivity between the vmpfc and striatum with frequent model-based decision-making observed by Piray, Toni and Cools has a valid explanation. However, there are two notes of caution attached to the interpretation of their results. The first relates to the process of decision-making itself. We have already discussed in the comment on this article how activity of the emotional system is important to decision-making not only through the provision of values and their use with the various strategies, but also through the emotional status of the subject at the time of the process. However, it has also been shown that not all decision-making is performed using conscious strategies and this system is described by the authors as ´model-free` or by others as the ´magic answer`. In this case, conscious application of values does not occur even though automatic application does. Therefore, the increased connectivity between vmpfc and striatum may apply to both systems relating to values, but only one in relation to conscious awareness.

The second note of caution should be attached to Piray, Toni and Cools` suggestion that this adaptation of the brain could be used as a link to the causes and possible therapeutic routes of certain psychiatric disorders such as impulse control. The importance of brain functioning and cognition means that fail-safe physiological mechanisms are in place so that even if brain areas are negatively affected, the brain can adapt to a certain extent to allow functioning to continue if possible. Means of adaptation are for example physiological in the form of changes in brain area function via mass action, equipotentiality and distributed control capability, the benefits of cognitive reserve, the presence of right and left hemispheres, and via processing safeguards such as shifting of focus, changing perception, changing levels associated with arousal and sleep states and even the capability to shut-down under extreme conditions such as that seen in coma. Therefore, any observation connected with a psychiatric disorder may not be the direct cause of that disorder, but instead a mere adaptation to it and rebalancing that may not be cognitively beneficial in the end.

Since we`re talking about the topic,

…..factors such as stress and anxiety are known to have an effect on decision-making. Anxiety is known to lead to decreased neural coding of the subjective value of risky options in the vmpfc and the striatum and a decreased prediction of observed choices and decreased functional coupling of other areas. Can we assume that if anxiety is induced in the subject by perhaps linking reward to performance expectations would we see the same strengthening of connectivity of the vmpfc and striatum if the experiment was repeated?

……is it possible that the effect of more changes in reward or a sudden change from reward to risk would see a shift from strengthening of connectivity from the vmpfc to the striatum to strengthening the connectivity between the vmpfc and the amygdala in line with the activation of the fear emotional system instead?

……it is known that the administration of L-Dopa increases the concentration of dopamine in the prefrontal cortex and other dopaminergic pathways and results in an increase in the number of risky options when the reward is a gain, but not in the case of losses (Routledge and colleagues). Therefore, would the administration of L-Dopa in a repeat of the experiments described in this article increase the number of attempts at rare transition in order to increase the overall performance of the experiment?

….since an age-related decline in attention and executive function (eg. choice accuracy, increase in omissions, increased response latency) is reported due to decreased glutaminergic transmission in the medial prefrontal cortex (Guidi and colleagues), if the experiment is repeated with elderly subjects would the increased strengthening between the vmpfc and striatum still be observed?

….lesions of the vmpfc means that there is a decreased sensitivity to fairness and an increased likelihood of acceptance of unfair offers (Gu and colleagues). Would vmpfc lesions in an animal model cause a difference in vmpfc and striatum connectivity and increase the number of errors in a mouse model replica of the experiment?

…. it is known that cognitive training can increase attention and working memory. Would such training affect the performance of the subject in the experiment and would the effect on connectivity between the vmpfc and striatum be observed earlier?


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