Posts tagged science

April 13th, 2012
By Kay H. Brodersen 

Researchers at ETH Zurich and the University of Zurich identify a new method of unerringly detecting the presence of pathophysiological changes in the brain.

Brain model (left) depicting brain activity stimulated by speech processing (yellow). The new method allows for the mathematical modeling of interactions between regions within the brain (right). The prism represents the transition or “Generative Embedding.” Image adapted from pr image by Brodersen KH/ ETH Zurich.

The new method was developed in order to gain a mechanistic understanding of schizophrenia and other spectrum disorders, which will lead to more accurate diagnoses and more effective treatments.

When mathematical genius John Nash was diagnosed with schizophrenia, the chance for recovery was slim. Medicine in the 1960’s simply had no convincing explanations for his condition. Alarmingly, things don’t look much better nowadays: depression, addiction, schizophrenia, and other spectrum disorders remain among the toughest challenges for medicine. This is because they are caused by complicated and largely unknown interactions between genes and the environment. Different disease mechanisms may underlie similar, or even identical, symptoms. This means that the effect of any given drug may vary hugely across individuals, resulting in trial-and-error treatment. In addition, conditions whose biological basis is not well-understood may be perceived as particularly stigmatizing.

Most spectrum disorders lack a physiological definition altogether; they are simply described in terms of particular symptoms. This is problematic when these symptoms are caused by different disease mechanisms. Conversely, existing disease classifications frequently group patients with disjoint symptoms under the same label: a person with delusions and disorganized thought, for instance, can be diagnosed with schizophrenia, just as somebody else suffering from hallucinations and movement problems. Examples such as this one show that the development of more specific diagnoses and more effective treatment will require a mechanistic understanding of the pathophysiological mechanisms underlying spectrum disorders.

One step in this direction has recently been made by Kay Henning Brodersen and Klaas Enno Stephan at ETH Zurich and the University of Zurich. Within the framework of the SystemsX.ch project ‘Neurochoice’, the two researchers investigate how insights gained from mathematical models of decision making and underlying brain function can be translated into clinical applications. “Put simply, we develop ‘mathematical microscopes’ that allow us to estimate physiological or computational quantities that cannot be measured directly,” says Klaas Enno Stephan, director of the newly founded Translational Neuromodeling Unit (TNU) in Zurich. “This allows us to obtain more accurate classifications and gain deeper mechanistic insights into the underlying condition than previous attempts.”

To demonstrate the plausibility of their idea, the two scientists collaborated with a clinical team led by Alex Leff at University College London. They analysed brain activity from two groups of participants: one group of stroke patients that suffered from language impairments; and one group of healthy volunteers. While undergoing functional magnetic resonance imaging (fMRI), participants were asked to passively listen to speech. A mathematical model was then used to assess, separately within each participant, how brain regions involved in speech processing interacted. Notably, none of the brain regions included in the model had been affected by the stroke in the patients.

The researchers then asked whether it was possible to automatically detect the presence of a remote lesion from patterns of brain connectivity in the healthy part of the brain. “Using our model of brain function, we were able to diagnose patients with an accuracy of 98%,” says Brodersen, first author of the study. “This became possible by tying together dynamic causal models of neuronal dynamics with mathematical techniques from machine learning and Bayesian inference.” In contrast to subtle spectrum disorders, of course, this initial proof-of-principle study concerned a rather salient clinical condition, that is, language impairments caused by a stroke. In the future, Stephan and Brodersen therefore plan to investigate whether their approach might work equally well for those diseases where contemporary medicine is struggling, such as schizophrenia, depression, and addiction. The two researchers hope that their approach will help dissect these spectrum disorders into pathophysiologically well-defined subgroups. Identifying such subgroups would provide an important step towards more specific diagnoses and may eventually predict the most effective treatment for an individual patient.

Source: Neuroscience News

April 11, 2012

A vast majority of cells in the brain are glial, yet our understanding of how they are generated, a process called gliogenesis, has remained enigmatic. Researchers at Baylor College of Medicine have identified a novel transcripitonal cascade that controls these formative stages of gliogenesis and answered the longstanding question of how glial cells are generated from neural stem cells.

The findings appear in the current edition of Neuron.

"Most people are familiar with neurons, cells that process and transmit information in the brain. Glial cells, on the other hand, make-up about 80 percent of the cells in the brain and function by providing trophic support to neruons, participating in neurotransmission, myelin sheaths for axons, and comprise the blood brain barrier," said Dr. Benjamin Deneen, assistant professor of neuroscience at BCM. "Importantly, glia have been linked to numerous CNS pathologies, from brain tumors and spinal cord injury and several neurological disorders including, Retts Syndrome, ALS, and Multiple Sclerosis. Therefore deciphering how glial cells are generated is key to understanding brain function during health and disease."

As researchers began investigating glial development in chicks they started by going backwards – examing what steps were needed before the glial cells matured. They discovered that glial cells are specified in neural stem cells when the transcription factor NFIA is induced.

Taking another step back in the transcriptional cascade, they looked for what triggered NFIA induction.

"By comparing mouse and chick regulatory sequences we were able to perform enhancer screening in the chick to identify regulatory elements with activity that resembled NFIA induction. This method allowed us to pinpoint Sox9," said Peng Kang, postdoctoral associate in the Center for Stem Cell and Regenerative Medicine at BCM. "Subsequently, we found that Sox9 doesn’t just induce NFIA expression, it also associates with NFIA, forming a complex."

Just after the initiation of gliogenesis this complex was discovered to co-regulate a subset of genes that play important roles in mitochondria energy metabolism and glial precursor migration.

"Sox9 induces NFIA expression during glial initiation and then binds NFIA to drive lineage progression by cooperatively regulating a genetic program that controls cell migration and energy metabolism, two key processes associated with cellular differentiation," said Deneen. "We now need to ask what other proteins contribute to this process, and how does the nature of this complex evolve during astro-glial lineage progression."

Additionally, these findings may also help researchers to understand how certain brain tumors might begin to form, as these same developmental processes and proteins are found in both adult and pediatric brain tumors. A more comprehensive understanding how this regulatory cascade operates during development, could eventually lead to better treatment targets for brain tumors.

Provided by Baylor College of Medicine

Source: medicalxpress.com

April 11, 2012

No matter what novel objects we come to behold, our brains effortlessly take us from an initial “What’s that?” to “Oh, that old thing” after a few casual encounters. In research that helps shed light on the malleability of this recognition process, Brown University neuroscientists have teased apart the potentially different roles that two distinct cell types may play.

In a study published in the journal Neuron, the researchers document that this kind of learning is based in the inferior temporal cortex (ITC), a brain area buried deep in the skull. Scientists already knew the area was important for visual recognition of familiar items, but they hadn’t figured out the steps required to move from novelty to familiarity, a process they refer to as “plasticity.”

"We know little about that because of the level at which this plasticity is taking place," said senior author David Sheinberg, professor of neuroscience and a member of the Brown Institute for Brain Science. "The inner workings made up of individual neurons make it very hard to actually track what’s going on at that level."

Working with two monkeys, in whom they monitored single neuron activity using tiny microelectrodes, Sheinberg and graduate student Luke Woloszyn tracked the firing patterns of individual neurons in the ITC while monkeys viewed 125 objects they had been trained to recognize and 125 others that they had never seen before.

The scientists found that the two major classes of cells found in the brain, excitatory and inhibitory, responded differently depending on what the monkeys saw. Excitatory neurons were especially active when the monkeys saw a preferred familiar object — the familiar image, out of the 125 such images, that the cell “liked” best. Although the particular preferred familiar image varied across the sample of neurons, almost every excitatory cell had at least one familiar image to which it responded more robustly than its preferred novel image, Sheinberg said. Inhibitory neurons, meanwhile, were much more active when the monkeys saw any novel image, independent of the object’s actual identity. 

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April 11, 2012

(Medical Xpress) — An abnormally low level of a protein in certain nerve cells is linked to movement problems that characterize the deadly childhood disorder spinal muscular atrophy, new research in animals suggests.

Spinal muscular atrophy, or SMA, is caused when a child’s motor neurons – nerve cells that send signals from the spinal cord to muscles – produce insufficient amounts of what is called survival motor neuron protein, or SMN. This causes motor neurons to die, leading to muscle weakness and the inability to move.

Though previous research has established the disease’s genetic link to SMN in motor neurons, scientists haven’t yet uncovered how this lack of SMN does so much damage. Some children with the most severe form of the disease die before age 2.

A research team led by Ohio State University scientists showed in zebrafish that when SMN is missing – in cells throughout the body as well as in motor neurons specifically – levels of a protein called plastin 3 also decrease.

When the researchers added plastin 3 back to motor neurons in zebrafish that were genetically altered so they couldn’t produce SMN, the zebrafish regained most of their swimming abilities movement that had been severely limited by their reduced SMN. These findings tied the presence of plastin 3 – alone, without SMN – to the recovery of lost movement.

The recovery was not complete. Fish without SMN in their cells still eventually died, so the addition of plastin 3 alone is not a therapeutic option. But further defining this protein’s role increases understanding of how spinal muscular atrophy develops.

“What all is lost when SMN is lost? That’s something we’re still struggling with,” said Christine Beattie, associate professor of neuroscience at Ohio State and lead author of the study.

“We think part of the motor neuron defects that are seen in spinal muscular atrophy are caused by this decrease in plastin 3 we get when SMN is lowered. And when we add plastin 3 back to motor neurons we can rescue defects that are seen when SMN is decreased, suggesting that a decrease in plastin 3 is contributing to some of the disease’s characteristics.” 

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April 11, 2012

A recent study finds that a new compound reverses many of the major symptoms associated with Fragile X syndrome (FXS), the most common form of inherited intellectual disability and a leading cause of autism. The paper, published by Cell Press in the April 12 issue of the journal Neuron, describes the exciting observation that the FXS correction can occur in adult mice, after the symptoms of the condition have already been established.

Fragile X patients suffer from a complex set of neuropsychiatric symptoms of varying severity which include anxiety, hyperactivity, learning and memory deficits, low IQ, social and communication deficits, and seizures. Previous research has suggested that inhibition of mGlu5, a subtype of receptor for the excitatory neurotransmitter glutamate, may be useful for ameliorating many of the major symptoms of the disease.

The new study, a collaboration between a group at F. Hoffmann-La Roche Ltd. in Switzerland, led by Dr. Lothar Lindemann, and a group at the Picower Institute for Learning at the Massachusetts Institute of Technology, led by Dr. Mark Bear, used a newly developed mGlu5 inhibitor called CTEP to examine whether pharmacologic inhibition of mGlu5 could reverse FXS symptoms.

The researchers used a mouse model of FXS and administered CTEP after the brain had matured. “We found that even when treatment with CTEP was started in adult mice, it reduced a wide range of FXS symptoms, including learning and memory deficits and auditory hypersensitivity, as well as morphological changes and signaling abnormalities characteristic of the disease,” reports Dr. Lindemann.

Although the CTEP drug itself is not being developed for humans, the findings have significance for human FXS. “The most important implications of our study are that many aspects of FXS are not caused by an irreversible disruption of brain development, and that correction of the altered glutamate signaling can provide widespread therapeutic benefit,” explains Dr. Bear.

The researchers agree that future work may shed light on treatment of FXS in humans. “It will be of great interest to see whether treatment of FXS in human patients can be addressed in a similar broad fashion and with a similar magnitude as was suggested by our preclinical data,” conclude Dr. Lindemann and Dr. Bear. “We anticipate that disturbed signaling can be corrected with other small molecule therapies targeting mGlu5 that are currently being used in human clinical trials.”

Provided by Cell Press

Source: medicalxpress.com

April 11, 2012

Scientists have discovered a mutation limited to brain tissue that causes hemimegalencephaly (HMG), a condition where one half of the brain is enlarged and dysfunctional, leading to intellectual disability and severe epilepsy. The research, published by Cell Press in the April 12 issue of Neuron, has broad significance as a potential model for other complex neuropsychiatric diseases that may also be caused by “brain-only” mutations.

Mutations can be inherited or occur spontaneously. Inherited mutations are present throughout all cells of the body, but some spontaneous mutations can occur during development and hence be limited to cells in some organs but not others. For some time it has been suspected that there might be neurological diseases that are caused by mutations limited to the brain, but this had not yet been definitively demonstrated as it is very difficult to study brain tissue.

"The striking asymmetry of the brain in individuals with HMG has long suggested that this disease may be caused by a spontaneous mutation restricted to one half of the brain and detectable by direct study of affected brain tissue," explains the study’s first author, Dr. Ann Poduri, from Children’s Hospital and Harvard Medical School.

Patients with HMG often have dozens of seizures per day, which so interferes with their cognitive development that doctors make the difficult decision to remove brain tissue in a desperate attempt to control the seizures. Fortunately, these operations are frequently successful in controlling seizures and allowing children to develop remarkably normally. Such operations provided brain tissue samples that were used by Dr. Poduri and her colleagues to identify mutations in the AKT3 gene in HMG brain tissue. Previous research has linked AKT3 with the control of brain size. The AKT3 mutations were restricted to the affected brain tissue, and were not evident in blood cells, suggesting that the mutation was spontaneous and not inherited.

"Our data suggest that spontaneous mutations resulting in abnormal activation of AKT3 contribute to overgrowth of one-half of the brain. The size and architecture of HMG may be determined in part by the stage at which the mutation occurs relative to the stage of brain development," concludes senior study author, Dr. Christopher Walsh from Children’s Hospital Boston, Howard Hughes Medical Institute, and Harvard Medical School. "It is also notable that, to our knowledge, this is the first disease attributed to mutations that are limited to brain tissue. There are other epilepsies and neuropsychiatric diseases that are associated with spontaneous mutations and are therefore also candidates for these sorts of ‘brain-only’ mutations."

The study was supported by the Howard Hughes Medical Institute, the National Institute of Neurological Diseases and Stroke, and the National Institute of Mental Health.

Provided by Cell Press

Source: medicalxpress.com

April 11, 2012

The discovery, using state-of-the-art informatics tools, increases the likelihood that it will be possible to predict much of the fundamental structure and function of the brain without having to measure every aspect of it. That in turn makes the Holy Grail of modelling the brain in silico — the goal of the proposed Human Brain Project — a more realistic, less Herculean, prospect.

“It is the door that opens to a world of predictive biology,” says Henry Markram. Credit: EPFL

"It is the door that opens to a world of predictive biology," says Henry Markram, the senior author on the study, which is published this week in PLoS ONE.

Within a cortical column, the basic processing unit of the mammalian brain, there are roughly 300 different neuronal types. These types are defined both by their anatomical structure and by their electrical properties, and their electrical properties are in turn defined by the combination of ion channels they present—the tiny pores in their cell membranes through which electrical current passes, which make communication between neurons possible.

Scientists would like to be able to predict, based on a minimal set of experimental data, which combination of ion channels a neuron presents. They know that genes are often expressed together, perhaps because two genes share a common promoter—the stretch of DNA that allows a gene to be transcribed and, ultimately, translated into a functioning protein—or because one gene modifies the activity of another. The expression of certain gene combinations is therefore informative about a neuron’s characteristics, and Georges Khazen and co-workers hypothesised that they could extract rules from gene expression patterns to predict those characteristics.

They took a dataset that Prof Markram and others had collected a few years ago, in which they recorded the expression of 26 genes encoding ion channels in different neuronal types from the rat brain. They also had data classifying those types according to a neuron’s morphology, its electrophysiological properties and its position within the six, anatomically distinct layers of the cortex. They found that, based on the classification data alone, they could predict those previously measured ion channel patterns with 78 per cent accuracy. And when they added in a subset of data about the ion channels to the classification data, as input to their data-mining programme, they were able to boost that accuracy to 87 per cent for the more commonly occurring neuronal types.

"This shows that it is possible to mine rules from a subset of data and use them to complete the dataset informatically," says one of the study’s authors, Felix Schürmann. "Using the methods we have developed, it may not be necessary to measure every single aspect of the behaviour you’re interested in." Once the rules have been validated in similar but independently collected datasets, for example, they could be used to predict the entire complement of ion channels presented by a given neuron, based simply on data about that neuron’s morphology, its electrical behaviour and a few key genes that it expresses.

Researchers could also use such rules to explore the roles of different genes in regulating transcription processes. And importantly, if rules exist for ion channels, they are also likely to exist for other aspects of brain organisation. For example, the researchers believe it will be possible to predict where synapses are likely to form in neuronal networks, based on information about the ratio of neuronal types in that network. Knowledge of such rules could therefore usher in a new era of predictive biology, and accelerate progress towards understanding and modelling the brain.

Provided by Ecole Polytechnique Federale de Lausanne

Source: medicalxpress.com

April 10, 2012

Scientists report that they have mapped the physical architecture of intelligence in the brain. Theirs is one of the largest and most comprehensive analyses so far of the brain structures vital to general intelligence and to specific aspects of intellectual functioning, such as verbal comprehension and working memory.

A new study found that specific structures, primarily on the left side of the brain, are vital to general intelligence and executive function (the ability to regulate and control behavior). Brain regions that are associated with general intelligence and executive function are shown in color, with red indicating common areas, orange indicating regions specific to general intelligence, and yellow indicating areas specific to executive function. Credit: Aron Barbey

Their study, published in Brain: A Journal of Neurology, is unique in that it enlisted an extraordinary pool of volunteer participants: 182 Vietnam veterans with highly localized brain damage from penetrating head injuries.

"It’s a significant challenge to find patients (for research) who have brain damage, and even further, it’s very hard to find patients who have focal brain damage," said University of Illinois neuroscience professor Aron Barbey, who led the study. Brain damage – from stroke, for example – often impairs multiple brain areas, he said, complicating the task of identifying the cognitive contributions of specific brain structures.

But the very focal brain injuries analyzed in the study allowed the researchers “to draw inferences about how specific brain structures are necessary for performance,” Barbey said. “By studying how damage to particular brain regions produces specific forms of cognitive impairment, we can map the architecture of the mind, identifying brain structures that are critically important for specific intellectual abilities.”

The researchers took CT scans of the participants’ brains and administered an extensive battery of cognitive tests. They pooled the CT data to produce a collective map of the cortex, which they divided into more than 3,000 three-dimensional units called voxels. By analyzing multiple patients with damage to a particular voxel or cluster of voxels and comparing their cognitive abilities with those of patients in whom the same structures were intact, the researchers were able to identify brain regions essential to specific cognitive functions, and those structures that contribute significantly to intelligence.

"We found that general intelligence depends on a remarkably circumscribed neural system," Barbey said. "Several brain regions, and the connections between them, were most important for general intelligence."

These structures are located primarily within the left prefrontal cortex (behind the forehead), left temporal cortex (behind the ear) and left parietal cortex (at the top rear of the head) and in “white matter association tracts” that connect them. (Watch a video about the findings.)

The researchers also found that brain regions for planning, self-control and other aspects of executive function overlap to a significant extent with regions vital to general intelligence.

The study provides new evidence that intelligence relies not on one brain region or even the brain as a whole, Barbey said, but involves specific brain areas working together in a coordinated fashion.

"In fact, the particular regions and connections we found support an emerging body of neuroscience evidence indicating that intelligence depends on the brain’s ability to integrate information from verbal, visual, spatial and executive processes," he said.

The findings will “open the door to further investigations into the biological basis of intelligence, exploring how the brain, genes, nutrition and the environment together interact to shape the development and continued evolution of the remarkable intellectual abilities that make us human,” Barbey said.

Provided by University of Illinois at Urbana-Champaign

Source: medicalxpress.com

April 9, 2012 by Stuart Mason Dambrot      

(Medical Xpress) — The link between dreaming and rapid eye movement (REM) sleep are well understood – but the fact that consciousness is reduced during nonrapid eye movement (NREM) sleep is not. Recently, scientists in the Cyclotron Research Centre at the University of Liège, in Liège, Belgium, and the Institut National de la Santé et de la Recherche Médicale at the Université Pierre et Marie Curie in Paris, and the Functional Neuroimaging Unit at the Montreal Geriatrics Institute, investigated NREM sleep with the hypothesis that this phenomenon is associated with increased modularity of the brain’s functional activity during these periods. Using functional clustering – which estimates how integration is hierarchically organized within and across the constituent parts of a system they found that while in NREM sleep, hierarchically-organized large-scale neural networks were disaggregated into smaller independent modules. The researchers concluded that this difference could reduce the ability of the brain to integrate information, thereby accounting for the decreased consciousness experienced during NREM sleep.

(A) The six networks extracted during wakefulness. (B) Levels of brain hierarchical integration. (C) Increases in functional clustering ratio in the brain and the six networks (all significant with a probability >0.95). Networks: dATT, dorsal attentional; DM, default mode; EC, executive control; MOT, sensorimotor; SAL, salience; VIS, visual. Copyright © PNAS, doi: 10.1073/pnas.111113310

Led by Pierre Maquet at the Cyclotron Research Centre and Habib Benali at the Institut National de la Santé et de la Recherche Médicale, the team faced a fundamental challenge in framing their research. Maquet first notes that there is currently no consensus on what consciousness really is, let alone how it arises.

“For many years,” he explains to Medical Xpress, “Giulio Tononi put forward the hypothesis that consciousness is related to the ability of the brain to integrate information. Our objective was simply to test this hypothesis, using novel tools allowing for the computation of information exchange within the brain and a set of EEG/fMRI data recorded in the same individuals during wakefulness and deep NREM sleep.” The latter state, he adds, is arguably the condition associated with the most reduce conscious content in normal human volunteers.

Maquet notes that the team used methods devised by Benali. “These allow us to measure the hierarchical organization of integration – i.e., information. The data itself,” he continues, “were acquired in Liège. Conducting simultaneous EEG/fMRI recordings in sleeping volunteers is not that easy.” Moreover, he notes, in practice, their findings are only one small step toward a better understanding of consciousness – and, for that matter, unconsciousness.

“The results were rather unexpected in that the amount of information exchanged in the brain actually increased during sleep. However, the patterns of exchange between regions were different than during wakefulness. Essentially, there was an increased information exchange within small clusters of mainly homologous brain areas whereas communication between clusters significantly decreased during sleep.” Thus, he points out, the data support their hypothesis.

The team has already defined the next steps in their research, says Maquet, who acknowledges that fMRI suffers from a rather sluggish signal. “The next step is to apply the methods to EEG, which has a much better time resolution.” He also states that it might it be possible to transition to in silico modeling, and that there are attempts in this direction in some laboratories.

A key advantage of the team’s approach was relying on functional clustering rather than so-called total integration in neural network analysis. “This is a big question,” states Maquet. “We don’t know what is the information exchanged within clusters, and I don’t see any technique that could currently answer this question in humans. More generally,” he adds, “it is thought that NREM sleep is regulated by the homeostasis of synaptic strength, and perhaps by neuronal energy metabolism.” These assumptions, he concludes, are being studied in animal models.

Source: medicalxpress.com

April 9, 2012

The part of the brain we use when engaging in egalitarian behavior may also be linked to a larger sense of morality, researchers have found. Their conclusions, which offer scientific support for Adam Smith’s theories of morality, are based on experimental research published in the latest issue of the Proceedings of the National Academy of Sciences.

The study, coming seven months after the start of the Occupy Wall Street Movement, which has been aimed at addressing income inequality, was conducted by researchers from: New York University’s Wilf Family Department of Politics; the University of Toronto; the University of California, San Diego; the University of California, Davis; and the University of Nebraska, Lincoln.

Previous scholarship has established that two areas of the brain are active when we behave in an egalitarian manner—the ventromedial prefrontal cortex (vmPFC) and the insular cortex, which are two neurological regions previously shown to be related to social preferences such as altruism, reciprocity, fairness, and aversion to inequality. Less clear, however, is how these parts of the brain may also be connected to egalitarian behavior in a group setting.

To explore this possibility, the researchers conducted an experiment in which individuals played a game to gauge brain activity in decision-making. In the “random income game” participants in a group are randomly assigned a level of income and the group is assigned to one of three income distributions. Subjects are shown the income of all members of their group, including their own, on a computer screen. Individuals are then asked if they wish to pay a cost in order to increase or decrease the incomes of group members. Subjects are told they may keep the money they don’t give away to the others shown on their screen, so there is a strong incentive not to part with any of the money already allocated to them. Nonetheless, the researchers found that the study’s subjects frequently sought to reallocate resources so the money was more equally distributed among the group members.

During this period, the researchers gauged the subjects’ neurological activity through functional magnetic resonance imaging (fMRI). As shown in previous studies, the researchers found significant activity in the brain’s vmPFC and insular cortex.

But to get at a more detailed understanding of neurological activity during these behaviors, they also examined whether activations in these areas were associated with two additional measures of egalitarian preferences elicited outside of the fMRI. As part of a survey, subjects were asked their level of agreement or disagreement to six questions, which included: “Our society should do whatever is necessary to make sure that everyone has an equal opportunity to succeed” and “This country would be better off if we worried less about how equal people are.” In addition, subjects completed a series of decision-making tasks asking them to split money with another anonymous person. The choices individuals make in this task are a measure of egalitarian behavior.

The researchers found that these two measures of egalitarian preferences were significantly associated with activations in the insular cortex, but not with the vmPFC.

This particular result is a potentially profound one as the insular cortex is also the part of the brain that processes the relationship of the individual with respect to her or his environment. In other words, egalitarian behavior may not exist in isolation, neurologically speaking, but, rather, be part of a larger process that stems from altruism and a sense of the larger social good.

Adam Smith, in The Theory of Moral Sentiments, expressed this perspective in his 18th-century essay.

"Adam Smith contended that moral sentiments like egalitarianism derived from a ‘fellow-feeling’ that would increase with our level of sympathy for others, predicting not merely aversion to inequity, but also our propensity to engage in egalitarian behaviors," the researchers wrote. "The evidence here supports such an interpretation—our results suggest that it is the brain mechanisms involved in experiencing the emotional and social states of self and others that appear to be driving egalitarian behaviors. This conclusion is consistent with a broader view of the insular cortex as a neural substrate that processes the relationship of the individual with respect to his or her environment."

Provided by New York University 

Source: medicalxpress.com