Category Archives: Neuroscience

Neuroscience

Learning Is Based on Neurons’ Ability to Cooperate for Survival – Neuroscience News

Summary: Exploring systemwide intracellular metabolic cooperation as a mechanism for learning offers promise for a better understanding of how memory and learning occur in the brain.

Source: HSE

Exploring the predictive properties of neuronal metabolism can contribute to our understanding of how humans learn and remember.

This key finding from a consideration of molecular mechanisms of learning and memory conducted by scientists from Russia and the U.S. has beenpublishedinNeuroscience & Biobehavioral Reviews.

The emerging trend in neuroscience is to consider the work of neurons as anticipatory and future oriented, although this approach is not yet mainstream and features in just a few publications.

In a paper entitled Neuronal metabolism in learning and memory: The anticipatory activity perspective, Yuri I. Alexandrov, HSE Professor and Head of the V.B. Shvyrkov Laboratory of Psychophysiology at the Russian Academy of Sciences Institute of Psychology, and Mikhail V. Pletnikov, Professor of the Department of Physiology at the State University of New York, University at Buffalo, argue that neurons behave proactively because they strive to survivejust as all living organisms.

Neurons use microenvironmental metabolites as food, and neuronal impulse activity is aimed at obtaining these metabolites. Rather than responding to an incoming signal, neurons proactively trigger an influx of needed substances to the cell, such as neurotransmitters.

Yuri Alexandrov, Professor at HSESchool of Psychology said, When a specialized set of our neurons fire together, we act to obtain a behavioral outcome, while the neurons also obtain their own micro-outcome in the form of needed metabolites.

This process can be described as metabolic cooperation of cells, involving not only neurons but also glial, somatic, glandular, muscle and other cells throughout the body.

This principle of how cells work is central to learning, which essentially means creating systemwide groups of metabolically cooperating cells that drive human behavior.

The researchers note that for a long time, the stimulus-response paradigm was dominant in the study of molecular mechanisms of learning and memory; it was assumed that just as the entire human body responds to environmental stimuli, neurons respond to incoming impulses which cause excitation of certain parts of the neurons membrane. The neuron either fires or does not fire, depending on whether or not the excitation reaches a certain threshold.

Back in 1930s1970s, the Russian physiologist Peter Anokhin developed his theory of functional systems, including the concept of integrative activity of neurons, according to which a neurons excitation causes intraneuronal chemical processesrather than a summation of local excitations on the membrane. These chemical processes lead to a neuronal spike.

Building on Anokhins theory, his student Vyacheslav Shvyrkov and colleagues developed a systems-oriented approach to the study of neurons. However, Anokhins understanding of the sequence of events was traditional: excitation of a neuron comes first, followed by a response.

An important recent step in understanding how neurons work has been the idea that a neurons anticipatory activity, rather than an external impulse, is what comes first. The neuron does not respond to incoming excitation but proactively triggers an influx of activity, Alexandrov explains.

The authors argue that exploring systemwide intercellular metabolic cooperation as a learning mechanism could be a promising area of focus for further experimental research.

This approach, they believe, could lead to breakthroughs in studying the behavior of malignant cells and in developing new cancer treatments.

Malignancies consist of cells that metabolically cooperate not only with their immediate environment but also with other cells in the body. We plan to conduct experimental studies to explore tumor cell responses to diametrically opposed individual behaviors, such as striving towards a desirable event or avoiding an undesirable or dangerous one. This can give us insight into how various systemwide cellular integrations impact tumor cells survival.

As a result, we hope to propose an effective approach to influencing tumor cells through human behavior, Alexandrov concludes.

Author: Ksenia BregadzeSource: HSEContact: Ksenia Bregadze HSEImage: The image is in the public domain

Original Research: Closed access.Neuronal metabolism in learning and memory: The anticipatory activity perspective by Yuri Alexandrov et al. Neuroscience and Biobehavioral Review

Abstract

Neuronal metabolism in learning and memory: The anticipatory activity perspective

Current research on the molecular mechanisms oflearning and memoryis based on the stimulus-response paradigm, in which the neural circuits connecting environmental events with behavioral responses are strengthened.

By contrast, cognitive and systemsneuroscienceemphasize the intrinsic activity of the brain that integrates information, establishes anticipatory actions, executes adaptive actions, and assesses the outcome via regulatory feedback mechanisms.

We believe that the difference in the perspectives of systems and molecular studies is a major roadblock to further progress toward understanding the mechanisms of learning and memory.

Here, we briefly overview the current studies in molecular mechanisms of learning and memory and propose that studying the predictive properties of neuronal metabolism will significantly advance our knowledge of how intrinsic, predictive activity of neurons shapes a new learning event.

We further suggest that predictive metabolic changes in the brain may also take place in non-neuronal cells, including those of peripheral tissues.

Finally, we present a path forward toward more in-depth studies of the role of cell metabolism in learning and memory.

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Learning Is Based on Neurons' Ability to Cooperate for Survival - Neuroscience News

Neuroscience

An Appetite Map in the Brain – Neuroscience News

Summary: Researchers report the way the brain processes sensory input information depends on whether an animal lacks specific nutrients or is pregnant. The findings provide valuable new insight on the neural basis for behavior both within and beyond food choices.

Source: Champalimaud Center for the Unknown

Lets face it. As enticing as the idea of starting lunch with a chocolate cake might be, few would actually make that choice when it comes down to it. And yet, at the end of the meal, many would reach for that same cake without hesitation.

The cause behind this phenomenon is the bodys ever-changing internal states: by lunchtime, the body often needs protein, so the brain promotes that particular food choice. However, after the protein was ingested, carbs might be a nice extra for padding the bodys fat stores.

But internal states are rarely one-dimensional. An individual might be lacking several nutrients simultaneously (such as protein and salt), and also be pregnant, a state that carries its own host of needs. How does the brain sum up these parallel internal states to guide behavior?

A study published today (July 6th) inNatureprovides novel insight into this complex problem. We show that the way the brain processessensory inputdepends on whether animals lack specific nutrients or are pregnant, said the studys senior author Carlos Ribeiro, a principal investigator at the Champalimaud Foundation in Portugal.

Through this work, we identified a general principle by which internal states are integrated to shape brain function and decision-making. In addition, the new microscopy strategy we developed in this study may prove valuable for understanding the neural basis of behavior both within and beyond food choice.

Venturing into uncharted neural territory

To investigate how internal states shape behavior, Ribeiros team focused on a relatively poorly understood region of the fruit-fly brain called SEZ (the subesophageal zone). This region is thought to play a crucial role in food choice because it receives the majority of taste inputs and houses themotor neuronsthat control feeding. However, since this region mainly consists of densely tangled neural fibers, its anatomical sub-structure was not well-defined.

To understand how it operates, the team decided to create a functional atlas of the SEZ. In other words, they set out to identify the sub-structures that make up this region and attribute specific functions to each. To that end, Daniel Mnch, the studys lead author, first expressed a fluorescent activity reporter in all neurons in thefly brain. He then performed advanced 3D neuroimaging in four groups of flies, each representing distinct internal states.

We wanted to understand how two powerful protein-appetite modulatorsprotein deprivation and reproductive statusinteract in the brain. We, therefore, defined four experimental groups: fully-fed virgins, protein-deprived virgins, fully-fed mated flies, and protein-deprived mated flies. We recorded neural activity in the SEZ while the flies tasted sucrose, water and yeast (the flys natural protein source), Mnch explained.

An appetite map

The atlas the team created consists of 81 regions spanning the entire SEZ. These regions correspond to the majority of the SEZs previously described sensory and motor areas, and also include new, previously unidentified regions.

Our atlas captured some known regions. For example one shaped like a banana, which receives input from taste neurons that are located in the proboscis (the flys mouth), said Mnch.

We also discovered a winged-shaped area we named the Borboleta region (the Portuguese word for butterfly) in the back part of SEZ. This region later turned out to play a key role in driving protein appetite.

Beyond identifying new regions, the atlas also revealed the effect of internal state on neural activity, pinpointing the Borboleta region as a protein-appetite driver. Responses to water and sucrose hardly changed across the four groups. However, protein-rich food had a striking effect.

Protein-rich food evoked activity was strongly increased across large parts of the SEZ in protein-deprived animals. Mating however, mostly affected activity in the SEZs motor regions.

This was somewhat surprising, as mating and protein deprivation are both known to increase protein appetite, and so we didnt expect to find such different response patterns, Mnch said.

They also witnessed the synergistic effect that combined internal states have on neural activity. Mated, protein-deprived females had the highest activity in the SEZs motor regions, Mnch explained.

This means that even though this pair of co-existing internal states protein-deprivation and pregnancyare processed across distinct neural circuits, they end up converging at the same area to promote protein appetite.

Manipulating neurons to induce protein cravings

The team identified new regions in the SEZ and witnessed how different tastes and internal states influence neural activity in these regions. But how could they know whether these areas are actually involved in driving food preference?

Thats when we turned to our newly-discovered borboleta region, where protein taste evoked robust neural activity, said Mnch. We reasoned that if it is truly involved in this behavior, we could influence protein appetite by artificially activating neurons in this region.

The team aligned the atlas they created with another pre-existing atlas that maps the innervation patterns of groups of neurons. They then selected neurons in the borboleta region and activated them in fully-fed flies, who normally prefer sucrose over protein. This manipulation resulted in a marked increase in protein appetite.

We felt that we had come full circle: from observation to function, Mnch recalled.

First, we observed food preference in the four groups of flies, noting that protein-deprived and mated flies have a high preference forprotein. Then, we imagedneural activityin the SEZ, created the atlas, and identified new regions. Finally, we confirmed that one of these regions is involved in generating the behavior we had initially observed by manipulating its activity.

Overall, our approach allows for identifying and linking neurons to specific behaviors, relating to food choice and potentially others as well, Ribeiro added.

It would be difficult to implement our approach in any other system than in fruit-flies. The tools we have nowadays make the fruit fly an amazing experimental system that enables us to dissect how the brain functions. Importantly, the SEZ is similar to the vertebrate brainstem.

Our results, therefore, have broad implications for neuroscience. They may also inspire future studies aimed at bridging brain-wide activity mapping with functional circuit dissections. These are exciting times to be a neuroscientist, he concluded.

Author: Press OfficeSource: Champalimaud Center for the UnknownContact: Press Office Champalimaud Center for the UnknownImage: The image is credited to Ribeiro lab, Champalimaud Foundation

Original Research: Closed access.The neuronal logic of how internal states control food choice by Carlos Ribeiro et al. Nature

Abstract

The neuronal logic of how internal states control food choice

When deciding what to eat, animals evaluate sensory information about food quality alongside multiple ongoing internal state

How internal states interact to alter sensorimotor processing and shape decisions such as food choice remains poorly understood. Here we use pan-neuronal volumetric activity imaging in the brain ofDrosophilamelanogasterto investigate the neuronal basis of internal state-dependent nutrient appetites.

We created a functional atlas of the ventral fly brain and find that metabolic state shapes sensorimotor processing across large sections of the neuropil. By contrast, reproductive state acts locally to define how sensory information is translated into feeding motor output. These two states thus synergistically modulate protein-specific food intake and food choice.

Finally, using a novel computational strategy, we identify driver lines that label neurons innervating state-modulated brain regions and show that the newly identified borboleta region is sufficient to direct food choice towards protein-rich food.

We thus identify a generalizable principle by which distinct internal states are integrated to shape decision making and propose a strategy to uncover and functionally validate how internal states shape behaviour.

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An Appetite Map in the Brain - Neuroscience News

Neuroscience

What Causes the Brain’s Emotional Hub to Switch to Negative States? – Neuroscience News

Summary: Researchers explore how changes in the patterns of activity in the basolateral amygdala contribute to negative emotions such as fear, anxiety, and depression.

Source: Tufts University

Tucked into the temporal lobe, near the base of our brain, sits a small, almond-shaped region called the amygdala that processes our emotions.

Neuroscientists at Tufts University have been investigating the symphony of signals created within a subsection of this areathe basolateral amygdalato better understand how they contribute to negative feelings such as anxiety and fear.

This emotional processing hub plays a role in a lot of different behaviors, saidJamie Maguire, a Kenneth and JoAnn G. Wellner Professor in the neuroscience departmentatTufts University School of Medicineand a member of the neuroscience program faculty at theGraduate School of Biomedical Sciences(GSBS).

Were interested in how the network switches into these negative states, which is relevant to many different disorders, such as depression and post-traumatic stress disorder.

In a recent paperpublished in the journaleNeuro, Maguire and her colleagues found that alcohol can change the pattern of activity in the basolateral amygdala in a mouse model, essentially telling the brains orchestra to play a different tune. This is the first study to show that alcohol is capable of altering these patterns, often referred to as network states.

Their work opens the door to a better understanding of how the brain switches between different activity patterns associated with anxiety or other moods, which also may be relevant to alcohol dependence.

We know one of the reasons people drink is to relieve anxiety or stress, which are associated with this area of the brain, saidAlyssa DiLeo, who is first author on the paper and was a GSBS doctoral student in Maguires lab at the time of the study.

Uncovering how alcohol changes these network states may be the first step in understanding the transition from first drink to an alcohol use disorder.

The researchers found that alcohol can essentially shift a mouses brain to less of an anxious state and toward a more relaxed one. They were also able to identify specific receptors in the basolateral amygdala, known as delta subunit-containing GABA-A receptors, as an important part of the signaling network that causes this switch.

The effects were slightly different in male and female mice, Maguire said. Females seemed to need more alcohol than males to alter their network state, which might be related to the fact that female mice have fewer of the relevant receptors. Moreover, when the researchers deleted these receptors in male mice, the altered mice responded like their female counterparts.

That tells us that these receptors are playing a role in these sex differences and how alcohol affects the basolateral amygdala network, Maguire said.

A Fearful State of Mind

Earlier this year, Maguire and her team partnered with Tulane University cell and molecular biology professor Jeffrey Tasker and other researchers to pinpoint a different set of receptors in the basolateral amygdala that seem to be relevant to an animals fear response.

In a studypublished inNature Communications, the researchers used norepinephrine, a similar hormone to adrenaline, to stimulate the basolateral amygdala in mice and switch them into a fearful state.

Norepinephrine can interact with several neural receptors, but when the researchers deactivated one in particular, the 1A adrenoreceptor, the animals brains no longer went into the fearful mode.

If you block norepinephrines ability to communicate with cells through this receptor, then you lose norepinephrines ability to create a fear state, saidEric Teboul, a GSBS doctoral student in Maguires lab and lead author on the paper. Being able to create a binary behaviorfearful or not fearfulgives us insight into how the brain actually computes and does things.

By understanding the molecular interactions that switch the basolateral amygdala into and out of these negative network states, the researchers may find potential drug targets to help people treat mood disorders and addiction. A person suffering from post-traumatic stress disorder, for example, might be stuck in a fearful pattern of neural activity. Disrupting that pattern could help them recover.

Of course, it wont be as simple as switching these circuits on or off, Teboul said.

You dont want to just take out the fear; you dont want to take out the sadness; you dont want to take out the stress, because there are good reasons that we feel stressed and fearful of things, he said. We want to understand how this amygdala region computes things so that we can balance it at a normal level.

Author: Lisa LaPointSource: Tufts UniversityContact: Lisa LaPoint Tufts UniversityImage: The image is in the public domain

Original Research: Closed access.Sex differences in the alcohol-mediated modulation of BLA network states by Alyssa DiLeo et al. eNeuro

Abstract

Sex differences in the alcohol-mediated modulation of BLA network states

Alcohol use, reported by 85% of adults in the United States, is highly comorbid with mood disorders, like generalized anxiety disorder and major depression.

The basolateral amygdala (BLA) is an area of the brain that is heavily implicated in both mood disorders and alcohol use disorder. Importantly, modulation of BLA network/oscillatory states via parvalbumin-positive (PV) GABAergic interneurons has been shown to control the behavioral expression of fear and anxiety.

Further, PV interneurons express a high density of -subunit-containing GABAAreceptors (GABAARs), which are sensitive to low concentrations of alcohol.

Therefore, we hypothesized that the effects of alcohol may modulate BLA network states that have been associated with fear and anxiety behaviors via -GABAARs on PV interneurons in the BLA.

Given the impact of ovarian hormones on the expression of -GABAARs, we also examined the ability of alcohol to modulate local field potentials (LFPs) in the BLA from male and female C57BL/6J andGabrd-/-mice after acute and repeated exposure to alcohol.

Here, we demonstrate that acute and repeated alcohol can differentially modulate oscillatory states in male and female C57BL/6J mice, a process which involves -GABAARs.

This is the first study to demonstrate that alcohol is capable of altering network states implicated in both anxiety and alcohol use disorders.

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Neuroscience

Running and Dreaming Improve Left Brain-Right Brain Communication – Neuroscience News

Summary: Study uncovers how splines, a newly identified pattern of rhythmic communication between the right and left hemispheres of the brain, improve brain communication as a result of dreaming and running.

Source: University of Michigan

Youre out jogging and suddenly notice a low-hanging tree branch in your path. You quickly lower your head, narrowly avoiding the branch, and continue on the run without giving it another thought. But how did your brain help you so rapidly and precisely duck out of the way of the branch while running?

Researchers at the University of Michigan have now discovered a very fastbrainrhythm that helps your left brain and right brain communicate better as you run fasterand even when you dream.

The fast rhythm linking the left and right halves of the brain has a new name: splines, so-called because they visually resemble mechanical splines, the interlocking teeth on mechanical gears.

Omar Ahmed, assistant professor of psychology and lead author of a new study appearing inCell Reports, says that splines represent a pattern of rhythmic communication across the left and right brain that is different from other known brain rhythms.

Previously identified brain rhythms are akin to the left brain and right brain participating in synchronized swimming: The two halves of the brain try to do the same thing at the exact same time, he said.

Spline rhythms, on the other hand, are like the left and right brains playing a game of very fastand very precisepingpong. This back-and-forth game of neural pingpong represents a fundamentally different way for the left brain and right brain to talk to each other.

Study first author Megha Ghosh, doctoral student in psychology, says splines serve a key function in allowing the left and right brain to coordinate information.

These spline brain rhythms are faster than all other healthy, awakebrain rhythms, she said.

Splines also get stronger and even more precise when running faster. This is likely to help the left brain and right brain compute more cohesively and rapidly when an animal is moving faster and needs to make faster decisions.

Splines are also seen duringrapid eye movement, or REM, sleepwhen most dreams happen, Ahmed says.

Surprisingly, this back-and-forth communication is even stronger during dream-like sleep than it is when animals are awake and running, he said.

This means that splines play a critical role in coordinating information during sleep, perhaps helping to solidify awake experiences into enhanced long-term memories during this dream-like state.

The new findings focus on a part of the brain called theretrosplenial cortex. This region helps us figure out when to turn left vs. right, and is also important for memory and imagining the future. Importantly, it is also one of the first brain regions to become impaired in the early stages of Alzheimers disease.

We studied many different brain regions, and splines were consistently strongest in the retrosplenial cortex, Ahmed said.

Given that the retrosplenial cortex is altered very early in Alzheimers disease, this means that we may be able to use spline rhythms in people as an early biomarker for Alzheimers. We are currently investigating this possibility in preclinical models of neurodegenerative diseases.

Author: Press OfficeSource: University of MichiganContact: Press Office University of MichiganImage: The image is in the public domain

Original Research: Open access.Running speed and REM sleep control two distinct modes of rapid interhemispheric communication by Megha Ghosh et al. Cell Reports

Abstract

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication

Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory.

Here, using multisite recordings in both rats and mice, we show that even faster 140Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears.

Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds.

Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase.

Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

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Neuroscience

Depression in Fathers and Children Linked, Regardless of Genetic Relatedness – Neuroscience News

Summary: Paternal depression may contribute to adolescent depression and behavioral problems, regardless of whether or not the father and child are genetically related, researchers say.

Source: Penn State

Adolescent depression and behavior problems are on the rise and paternal depression may be contributing to this increase, regardless of whether the fathers and children are genetically related, according to new research from Penn State and Michigan State.

A lot of research focuses on depression within biologically related families, said Jenae Neiderhiser, Social Science Research Institute cofunded faculty member and distinguished professor of psychology and human development andfamily studiesat Penn State.

Now more information is becoming available for adoptive families and blended families.

The researchers looked at naturally occurring variations ingenetic relatednessbetween parents and theiradolescent childrenin the 720 families participating in the Nonshared Environment in Adolescent Development (NEAD) study, with over half of those families containing a child-rearing stepparent.

Mothers, fathers and children each answered questions to measure symptoms of depression, behaviors and parent-child conflict. The researchers then examined the association betweenpaternal depressionsymptoms and child behavioral symptoms in a series of models.

Neiderhiser and Alex Burt, professor of clinical science at Michigan State, along with their colleagues found paternal depression was associated withadolescent depressionand adolescentbehavior problemsregardless of whether the fathers and their children were genetically related.

The results pointed squarely to the environmental transmission of depression and behaviors between fathers and children, said Burt, who has been collaborating on projects with Neiderhiser since the early 2000s.

Additionally, we continued to see these associations in a subset of blended families in which the father was biologically related to one participating child but not to the other, which was an important confirmation of our results.

We also found that much of this effect appeared to be a function of parent-child conflict. These kinds of findings add to the evidence that parentchild conflict plays a role as an environmental predictor of adolescent behaviors.

According to Neiderhiser, while the results were expected, they also thought the effects onchildrens behavior and depression would be greater in parent-child pairs who were genetically related.

It would be great to do more studies on step and blended families, she said. They tend to be an underutilized natural experiment we could learn more from to help us disentangle the impacts of environmental factors and genetics on families.

Author: Press OfficeSource: Penn StateContact: Press Office Penn StateImage: The image is in the public domain

Original Research: Closed access.Illuminating the origins of the intergenerational transmission of psychopathology with a novel genetically informed design by S. Alexandra Burt et al. Development & Psychopathology

Abstract

Illuminating the origins of the intergenerational transmission of psychopathology with a novel genetically informed design

Although it is well known that parental depression is transmitted within families across generations, the etiology of this transmission remains unclear.

Our goal was to develop a novel study design capable of explicitly examining the etiologic sources of intergenerational transmission.

We specifically leveraged naturally-occurring variations in genetic relatedness between parents and their adolescent children in the 720 families participating in the Nonshared Environment in Adolescent Development (NEAD) study, 58.5% of which included a rearing stepparent (nearly always a stepfather).

Results pointed squarely to the environmental transmission of psychopathology between fathers and children.

Paternal depression was associated with adolescent depression and adolescent behavior problems (i.e., antisocial behavior, headstrong behavior, and attention problems) regardless of whether or not fathers and their children were genetically related.

Moreover, these associations persisted to a subset of blended families in which the father was biologically related to one participating child but not to the other, and appeared to be mediated via fatherchild conflict.

Such findings are not only fully consistent with the environmental transmission of psychopathology across generations, but also add to extant evidence that parentchild conflict is a robust and at least partially environmental predictor of adolescent psychopathology.

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Neuroscience

Using GPUs to Discover Human Brain Connectivity – Neuroscience News

Summary: Researchers developed a new GPU-based machine learning algorithm to help predict the connectivity of networks within the brain.

Source: IISC

A new GPU-based machine learning algorithm developed by researchers at the Indian Institute of Science (IISc) can help scientists better understand and predict connectivity between different regions of the brain.

The algorithm, called Regularized, Accelerated, Linear Fascicle Evaluation, or ReAl-LiFE, can rapidly analyse the enormous amounts of data generated from diffusion Magnetic Resonance Imaging (dMRI) scans of the human brain.

Using ReAL-LiFE, the team was able to evaluate dMRI data over 150 times faster than existing state-of-the-art algorithms.

Tasks that previously took hours to days can be completed within seconds to minutes, says Devarajan Sridharan, Associate Professor at the Centre for Neuroscience (CNS), IISc, and corresponding author of the study published in the journalNature Computational Science.

Millions of neurons fire in the brain every second, generating electrical pulses that travel across neuronal networks from one point in the brain to another through connecting cables or axons. These connections are essential for computations that the brain performs.

Understanding brain connectivity is critical for uncovering brain-behaviour relationships at scale, says Varsha Sreenivasan, PhD student at CNS and first author of the study.

However, conventional approaches to study brain connectivity typically use animal models, and are invasive.dMRI scans, on the other hand, provide a non-invasive method to study brain connectivity in humans.

The cables (axons) that connect different areas of the brain are its information highways. Because bundles of axons are shaped like tubes, water molecules move through them, along their length, in a directed manner. dMRI allows scientists to track this movement, in order to create a comprehensive map of the network of fibres across the brain, called a connectome.

Unfortunately, it is not straightforward to pinpoint these connectomes. The data obtained from the scans only provide the net flow of water molecules at each point in the brain.

Imagine that the water molecules are cars. The obtained information is the direction and speed of the vehicles at each point in space and time with no information about the roads. Our task is similar to inferring the networks of roads by observing these traffic patterns, explains Sridharan.

To identify these networks accurately, conventional algorithms closely match the predicted dMRI signal from the inferred connectome with the observed dMRI signal. Scientists had previously developed an algorithm called LiFE (Linear Fascicle Evaluation) to carry out this optimisation, but one of its challenges was that it worked on traditional Central Processing Units (CPUs), which made the computation time-consuming.

In the new study, Sridharans team tweaked their algorithm to cut down the computational effort involved in several ways, including removing redundant connections, thereby improving upon LiFEs performance significantly.

To speed up the algorithm further, the team also redesigned it to work on specialised electronic chips the kind found in high-end gaming computers called Graphics Processing Units (GPUs), which helped them analyse data at speeds 100-150 times faster than previous approaches.

This improved algorithm, ReAl-LiFE, was also able to predict how a human test subject would behave or carry out a specific task. In other words, using the connection strengths estimated by the algorithm for each individual, the team was able to explain variations in behavioural and cognitive test scores across a group of 200 participants.

Such analysis can have medical applications too. Data processing on large scales is becoming increasingly necessary for big-data neuroscience applications, especially for understanding healthy brain function and brain pathology, says Sreenivasan.

For example, using the obtained connectomes, the team hopes to be able to identify early signs of aging or deterioration of brain function before they manifest behaviourally in Alzheimers patients.

In another study, we found that a previous version of ReAL-LiFE could do better than other competing algorithms for distinguishing patients with Alzheimers disease from healthy controls, says Sridharan.

He adds that their GPU-based implementation is very general, and can be used to tackle optimization problems in many other fields as well.

Author: Office of CommunicationsSource: IISCContact: Office of Communications IISCImage: The image is credited to Varsha Sreenivasan and Devarajan Sridharan

Original Research: Open access.GPU-accelerated connectome discovery at scale by Devarajan Sridharan et al. Nature Computational Science

Abstract

GPU-accelerated connectome discovery at scale

Diffusion magnetic resonance imaging and tractography enable the estimation of anatomical connectivity in the human brain, in vivo. Yet, without ground-truth validation, different tractography algorithms can yield widely varying connectivity estimates. Although streamline pruning techniques mitigate this challenge, slow compute times preclude their use in big-data applications.

We present Regularized, Accelerated, Linear Fascicle Evaluation (ReAl-LiFE), a GPU-based implementation of a state-of-the-art streamline pruning algorithm (LiFE), which achieves >100 speedups over previous CPU-based implementations.

Leveraging these speedups, we overcome key limitations with LiFEs algorithm to generate sparser and more accurate connectomes. We showcase ReAl-LiFEs ability to estimate connections with superlative testretest reliability, while outperforming competing approaches.

Moreover, we predicted inter-individual variations in multiple cognitive scores with ReAl-LiFE connectome features. We propose ReAl-LiFE as a timely tool, surpassing the state of the art, for accurate discovery of individualized brain connectomes at scale.

Finally, our GPU-accelerated implementation of a popular non-negative least-squares optimization algorithm is widely applicable to many real-world problems.

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Neuroscience

Researchers discover brain pathway that helps to explain light’s effect on mood – Brown University

By assessing the functional MR images taken during the exercise, the researchers identified 26 human brain regions where activity either decreased or increased in accordance with light-intensity. This luxotonic-related activation occurred across the cerebral cortex, in diverse subcortical structures, and in the cerebellum, encompassing regions with functions related to visual image formation, motor control, cognition and emotion.

They found that light suppressed activity in the prefrontal cortex in proportion to the light intensity. The light-evoked responses in the prefrontal cortex and their alteration by prior light exposure resembled the responses of the intrinsically photosensitive retinal ganglion cells.

Its well-known that changes in ambient lighting that do not necessarily have anything to do with form or object vision influence various basic functions, such as circadian rhythms, visual-reflexes, mood and likely cognitive processing, Sanes said. However, it had remained unclear how these light-intensity signals reached the relevant areas of the human brain.

In this study, the researchers showed that the prefrontal regions of the human brain have light-sensitive signals, and that these signals are similar to intrinsically photosensitive retinal ganglion cells which together, Sanes said, may explain the effects of light intensity on complex emotional and cognitive behaviors.

The findings from our study offer a functional link between light exposure and prefrontal cortex-mediated cognitive and affective responses, Sanes said.

One next logical question to ask, Sanes said, concerns how light affects these same brain pathways and regions in people with mood disorders like seasonal affective disorder or major depressive disorders.

How does that compare to a control group of healthy people not diagnosed with these disorders? he asked. Does light activate the same regions, and if so, are these regions more or less sensitive to light activation? What is the magnitude of difference in the effect? This is an area of ongoing investigation, he said, adding that the answers could inform the development of therapeutic treatments for mood disorders.

Michael Worden from Browns Department of Neuroscience and Carney Institute for Brain Science also contributed to this research, as did researchers from the Hebrew University of Jerusalem.

The research was funded by the National Institutes of Health (R01EY12793, P20GM103645, S10OD025181), an Alcon Research Institute Award, Brown Universitys Division of Biology and Medicine, the National Institute of Psychobiology of Israel, and a Banting Postdoctoral Fellowship of Canada.

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Researchers discover brain pathway that helps to explain light's effect on mood - Brown University

Neuroscience

Adolescents More Vulnerable to Cannabis Addiction but Not Other Mental Health Risks – Neuroscience News

Summary: Study reports adolescents are three times more likely to develop cannabis use disorder than adults, but may not be at more risk of developing other mental health disorders associated with cannabis use.

Source: UCL

Adolescents are over three times more vulnerable to developing a cannabis addiction than adults, but may not be at increased risk of other mental health problems related to the drug, finds a new study led by UCL and Kings College London researchers.

The study, published today in theJournal of Psychopharmacology, found that adolescents who used cannabis were no more likely to have higher levels of subclinical depression or anxiety than adults who use cannabis, nor were they more vulnerable than adult users to the associations with psychotic-like symptoms.

These findings build on a separate study by the same team, published recently inPsychopharmacologythat found adolescents were not more vulnerable to associations between chronic cannabis use and cognitive impairment.

Lead author Dr Will Lawn (UCL Clinical Psychopharmacology Unit andInstitute of Psychiatry, Psychology and Neuroscience at Kings College London) said: There is a lot of concern about how the developing teenage brain might be more vulnerable to the long-term effects of cannabis, but we did not find evidence to support this general claim.

Cannabis addiction is a real issue that teenagers should be aware of, as they appear to be much more vulnerable to it than adults.

On the other hand, the impact that cannabis use has during adolescence on cognitive performance or on depression and anxiety may be weaker than hypothesised.

But we also replicated previous work that if someone becomes addicted to cannabis, that may increase the severity of subclinical mental health symptoms. Given adolescents are also at a greater risk of experiencing difficulties with mental health than adults, they should be proactively discouraged from regular cannabis use.

The findings in both papers come from the CannTeen study, funded by the Medical Research Council, which is comparing the effects of regular cannabis use among adolescents and adults, while also comparing to age-matched controls (non-users of cannabis), a completely novel design.

The study involved 274 participants, including 76 adolescents (aged 16 and 17) who used cannabis one to seven days per week, alongside similar numbers of adult (aged 26-29) users, and teenage and adult control (comparison) participants, who all answered questions about their cannabis use over the last 12 weeks and responded to questionnaires commonly used to assess symptoms of mental ill health.

The cannabis users in the study, on average, used it four times per week. The adolescent and adult users were also carefully matched on gender, ethnicity, and type and strength of cannabis.

The researchers found that adolescent cannabis users were three and a half times as likely to develop severe cannabis use disorder (addiction) than adult users, a finding which is in line with previous evidence using different study designs.

Cannabis use disorder is defined by symptoms such as, among others: cravings; cannabis use contributing to failures in school or work; heightened tolerance; withdrawal; interpersonal problems caused by or exacerbated by cannabis use; or intending to cut back without success.

The researchers found that 50% of the teenage cannabis users studied have six or more cannabis use disorder symptoms, qualifying as severe cannabis use disorder.

Among people of any age, previous studies have found that roughly 9-22% of people who try the drug develop cannabis use disorder, and that risk is higher for people who tried it at a younger age. The increased risk of cannabis addiction during adolescence has now been robustly replicated.

The researchers say that adolescents might be more vulnerable to cannabis addiction because of factors such as increased disruption to relationships with parents and teachers, a hyper-plastic (malleable) brain and developing endocannabinoid system (the part of the nervous system that THC in cannabis acts upon), and an evolving sense of identity and shifting social life.

Adolescent users were more likely than adult users or adolescent non-users to develop psychotic-like symptoms, but the analysis revealed that this is becausealladolescents, andallcannabis users, are more likely to newly develop psychotic-like symptoms, rather than cannabis affecting the teenagers differently to adults.

In other words, there was no adolescent vulnerability, as the increased risk of psychotic-like symptoms was an additive effect (of the two already known risk factors for psychotic-like symptoms, cannabis use and adolescent age), rather than an interaction between age and cannabis use.

The researchers say this fits in with prior evidence that cannabis use may increase the likelihood of developing a psychotic disorder such as schizophrenia, but they warn their study did not investigate the risk of clinical psychosis or schizophrenia.

The researchers found that neither teenage nor adult cannabis users were more likely to develop depressive or anxiety symptoms than non-users. Only the adolescents that have severe cannabis use disorder had worse mental health symptoms, but the researchers caution that the small sample size for this group limits their confidence in this finding.

The separate study published inPsychopharmacologyfound that cannabis users were no more likely to have impaired working memory or impulsivity. Cannabis users were more likely to have poor verbal memory (remembering things said to you); this effect was the same in adults and teenagers, so again there was no adolescent vulnerability.

However, the researchers caution that cannabis use could impact school performance during a key developmental stage of life.

The researchers caution that these findings were cross-sectional (only looking at one time point), and that longitudinal analyses of how their participants changed over time are ongoing.

Senior author Professor Val Curran (UCL Clinical Psychopharmacology Unit, UCL Psychology & Language Sciences) said: Our findings suggest that schools should be teaching pupils more about the risk of addiction to cannabis,which has been neglected in drugs education.

Becoming addicted to cannabis is a serious problem in itself, but it can also increase the likelihood of other mental health problems. Teenagers should therefore be informed of their greater risk of addiction.

Author: Chris LaneSource: UCLContact: Chris Lane UCLImage: The image is in the public domain

Original Research: Open access.The CannTeen Study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls by Will Lawn et al. Journal of Psychopharmacology

Abstract

The CannTeen Study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls

Adolescence is characterised by psychological and neural development. Cannabis harms may be accentuated during adolescence. We hypothesised that adolescents would be more vulnerable to the associations between cannabis use and mental health and addiction problems than adults.

As part of the CannTeen study, we conducted a cross-sectional analysis. There were 274 participants: split into groups of adolescent users (n=76; 1617years old) and controls (n=63), and adult users (n=71; 2629years old) and controls (n=64). Among users, cannabis use frequency ranged from 1 to 7days/week, while controls had 010 lifetime exposures to cannabis. Adolescent and adult cannabis users were matched on cannabis use frequency (mean=4 days/week). We measured Diagnostic and Statistical Manual (DSM-5) Cannabis Use Disorder (CUD), Beck Depression Inventory, Beck Anxiety Inventory and Psychotomimetic States Inventory-adapted.

After adjustment for covariates, adolescent users were more likely to have severe CUD than adult users (odd ratio=3.474, 95% confidence interval (CI)=1.5018.036). Users reported greater psychotic-like symptoms than controls (b=6.004, 95% CI=1.21110.796) and adolescents reported greater psychotic-like symptoms than adults (b=5.509, 95% CI=1.0709.947). User-group was not associated with depression or anxiety. No significant interactions between age-group and user-group were identified. Exploratory analyses suggested that cannabis users with severe CUD had greater depression and anxiety levels than cannabis users without severe CUD.

Adolescent cannabis users are more likely than adult cannabis users to have severe CUD. Adolescent cannabis users have greater psychotic-like symptoms than adult cannabis users and adolescent controls, through an additive effect. There was no evidence of an amplified vulnerability to cannabis-related increases in subclinical depression, anxiety or psychotic-like symptoms in adolescence. However, poorer mental health was associated with the presence of severe CUD.

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Adolescents More Vulnerable to Cannabis Addiction but Not Other Mental Health Risks - Neuroscience News

Neuroscience

A Skill Called "O": People Vary a Lot in How Well They Recognize, Match or Categorize the Things They See – Neuroscience News

Summary: A newly identified skill, dubbed O is a generalized ability that may help you to succeed at tasks that demand perceptual decisions.

Source: The Conversation

Like snowflakes, no two people are exactly the same. Youre probably used to the idea that people differ substantially in personality and in cognitive abilities skills like problem-solving or remembering information.

In contrast, theresa widely held intuitionthat people vary far less in their ability to recognize, match or categorize objects. Many everyday tasks, hobbies and even critical jobs like interpreting satellite imagery, matching fingerprints or diagnosing medical conditions rely on these perceptual skills.

The common expectation is that smart and motivated people who receive the appropriate training should eventually be able to excel at occupations that require hundreds of perceptual decisions every day.

Wearepsychologists who measure how people compare on challenging perceptual tasks. Our research has found that this intuition that everyone has the same capacity for perceptual skills is not supported by the evidence.

Its not a problem if you choose to spend every weekend bird-watching without ever getting very good at it you may still get some fresh air and have fun. But when perceptual decisions influence safety, health or legal outcomes, theres a case for seeking people who can achieve the best possible performance. Our research suggests some people are just better than others at learning to discriminate things perceptually, whatever the objects may be.

Classic psychological studiesat the turn of the 20th century discovered that performance across a range of cognitive tasks designed to test memory, math and verbal skills is correlated. In real life, this means someone who is great at sudoku is also likely to be good at memorizing their shopping list. This finding led to the modern notion of general intelligence, describing a collection of faculties that together predict a wide range of outcomes, fromincometohealth and longevity.

In a similar way, our studies reveal that those who are thebest at bird recognition may also excel at plane identification, and they may also be the best at learning to spot tumors inchest X-rays. In other research, the same ability predicted better performance inreading musical notationorrecognizing images of prepared food.

Of course, people vary in their experience with birds or medical images. The more familiar you are with them, thebetter you are at recognizing them. Experience and training have an important role in how people make decisions based on visual information. But does everyone start on the same footing when they begin training?

We were interested in whether everyone starts at about the same baseline of perceptual talent. To investigate this question, we measured peoples abilities with artificial objects they had never seen, to prevent any advantage due to different levels of experience.

Inone large study, we assessed 246 people for 13 hours each, testing them on several tasks with six categories of computer-generated artificial objects. We asked people to remember and recognize objects, to match them, or to make judgments about some of their parts.

Our results across tasks like these repeatedly reveal that people vary as much in perceptual abilities as they do in cognitive skills. Usingstatistical methodshistorically applied to intelligence and personality tests, we found that over 89% of the differences between people in their performance with these different tasks and categories could be explained by a general ability. We called this ability o for object recognition and in honor of the g factor, which stands for similar statistical evidence for general intelligence.

Infollow-up studies, weve found that o applies in the same way to artificial and real objects, and that people with high o are better at computing summary statistics for groups of objects (such as estimating the average of several objects) and also better atrecognizing objects by touch. You can compare yourself to others inthis short demo.

Since it is so general, is o just another name for general intelligence? We dont think so.

In one study, we found thatneither IQ nor SAT scores predict recognitionof novel objects.In other work, we found that o was distinct from g, but also from the personality trait of conscientiousness. This means that book smarts may not be enough to excel in domains that rely heavily on perceptual abilities.

We tested this idea by measuring how good people with or without expertise in radiology were at detecting lung nodules in chest X-rays. Those with the highest o were better at this task, even after controlling for intelligence and experience in radiology.

This finding demonstrates the added value of measuring o. Even when medical students are selected to be smart and provided with training, it may not guarantee the highest levels of performance in specializations that rely on perceptual skills.

Many doors open when you demonstrate that youre cognitively talented, which seems only fair. But it is fair only to the extent that general intelligence is the best way or even a sufficient way to predict success in a given domain. Many have raised warnings that intelligence testing can lead to inequities in hiring or career placement tied to race, gender or socioeconomic status.

Over the years, many thinkers have downplayed innate talents to emphasize environmental influences. They argued that success can be shaped through years ofdeliberate practice, programs to change onesattitudes about learning, or evenhours of playing video games.

But the evidence in favor of the influence of innate talents remains strong, and denying them or overpromising on the efficacy of environmental factorsmay sometimes be harmful. People can waste time and resources that could be better invested, and may run the risk of experiencing stigma if their efforts do not succeed because of factors they cannot control.

One answer to this problem is to learn more about talents beyond those related to intelligence and then to make better use of them. Classical notions of intelligence may be just one factor of many that determine overall ability. An increased focus on perceptual abilities, specifically those that are general, could help reduce inequities. For instance, while differences in experience can drivesex differences in the recognition of objects in some familiar categories, weve foundno such differences in the general ability o.

Author: Isabel Gauthier and Jason ChowSource: The ConversationContact: Isabel Gauthier and Jason Chow The ConversationImage: The image is credited to Isabel Gauthier

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A Skill Called "O": People Vary a Lot in How Well They Recognize, Match or Categorize the Things They See - Neuroscience News