Brain Mapping brain

New Open-Source App for Precise Brain Mapping

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Summary: A newly developed, open-source app that utilizes AI technology allows researchers to precisely map the structure of the hippocampus.

Source: University of Western Ontario

The hippocampus is a small, complex, folded brain structure that holds clues to several brain disorders. It is also one of the most difficult-to-map regions of the brain.

After developing a successful technique to digitally unfold the hippocampus, researchers at the Western Institute for Neuroscience have now built a new app using artificial intelligence (AI) to precisely map the structure.

As part of a team led by Schulich School of Medicine & Dentistry professor Ali Khan, former Ph.D. student Jordan DeKraker has developed an open-source app, HippUnfold, which uses state-of-the-art AI to digitally unfold the hard-to-reach areas of the hippocampus.

A new paper, published in eLife, describes the web-based tool and the ways it provides a new precision approach for mapping out the hippocampus.

“The hippocampus is a part of the brain that helps us remember, and is also one of the first brain structures to be affected in neurodegenerative disorders such as Alzheimer’s disease.  It has been challenging to detect subtle abnormalities in the hippocampus with imaging because it is small and folded in layers,” said Khan, who is the Canada Research Chair in Computational Neuroimaging and director of the Khan Computational Imaging Lab at Robarts Research Institute.

This shows an image of the hippocampus
HippUnfold uses artificial intelligence to unfold the hard-to-reach areas of the hippocampus (highlighted).

“With this tool, researchers and clinicians can extract a wide range of accurate and precise measurements of the hippocampus using magnetic resonance images (MRI),” added Khan, who supervised DeKraker’s work.

The researchers are excited about the wide range of applications that are possible using this tool and the potential for significant clinical impact.

“We have been using this tool in our lab, but now we have structured it in a way that anyone, anywhere in the world, can download it on any system and use it,” said DeKraker. “Currently, HippUnfold is being used in labs in Canada, U.S., U.K. and Germany, among several others,” said DeKraker.

Ongoing work in these labs focuses on topics including epilepsy, Alzheimer’s disease, and major depressive disorder, which all have major impacts on the hippocampus that are otherwise hard to measure precisely. The new app could potentially be used in the future to detect these disorders earlier, or to help inform treatment plans.

Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals.

Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology.

This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale.

HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution.

In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

Mapping Brain Stem’s Control of Eating Could Lead to Better Treatments for Obesity

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Summary: Researchers investigate neural pathways that meet in the brainstem which help control feeding behaviors.

Source: University of Michigan

Every meal you sit down to makes an impression, with foods filed away as something delicious to be sought out again, or to be avoided in disgust if we associate the flavor with gut malaise.

How this decision is made turns out to be so fundamental to our well-being—determining what foods to seek and avoid—that the signals are coordinated within the most primitive parts of our brains, the brain stem or hindbrain. This brain region also helps us decide when we are “full” and should stop eating.

To date, scientists interested in how and why people gain weight and the diseases that can result from overeating and obesity have focused on a part of the brain called the hypothalamus, following discoveries of two intertwined systems that play important roles in controlling energy balance, the leptin and melanocortin systems.

A paper in the journal Nature Metabolism looks outside this brain region and reviews the various brain pathways that meet in the brain stem to control feeding behavior, using a technique that offers an unbiased look at the neurons involved.

“Everything the hypothalamus does ends up converging in the brainstem. The brain stem is super important in the control of feeding because it takes all sorts of information from your gut, including whether the stomach is distended and whether nutrients have been ingested, and integrates this with information from the hypothalamus about nutritional needs before passing it all on to the rhythmic pattern generators that control food intake,” said Martin Myers, Jr., M.D., Ph.D., professor of Internal medicine and Molecular & Integrative Physiology and director of the Elizabeth Weiser Caswell Diabetes Institute.

The recent review builds on recent findings in mice from the Myers lab that revealed the existence of two different food intake-suppressing brain stem circuits- one that causes nausea and disgust, and one that does not, as well as from collaborations with colleague Tune Pers, Ph.D., of the University of Copenhagen. Pers and his group used single cell mapping of brain cells within the dorsal vagal complex, a region in the brain stem that mediates a host of unconscious processes, including feelings of satiety (or sickness) after eating.

This shows the outline of a fork and spoon
He notes that many of these cell populations are targets for new and effective anti-obesity drugs— for example, a class of drugs for diabetes called GLP1 receptor agonists that can lower blood sugar and help you eat less.

The new review paper, from first author Wenwen Cheng, Ph.D., Myers, Pers, and their colleagues, integrates these findings with other recent discoveries to build a new model of brainstem neuronal circuits and how they control food intake and nausea.

“Taking all of this information together allows us to predict which set of neurons control this or that function,” said Myers.

He notes that many of these cell populations are targets for new and effective anti-obesity drugs— for example, a class of drugs for diabetes called GLP1 receptor agonists that can lower blood sugar and help you eat less.

“There is a population of GLP1 neurons in the brain stem which, if you turn them on, will stop food intake but cause violent illness, but there may be another population of neurons that stops eating without making you feel badly.”

Having a detailed map of these neurons and understanding the effects of modifying these cell targets, Myers explains, can assist in making drugs with fewer negative side effects.

Abstract

Hindbrain circuits in the control of eating behaviour and energy balance

Body weight and adiposity represent biologically controlled parameters that are influenced by a combination of genetic, developmental and environmental variables.

Although the hypothalamus plays a crucial role in matching caloric intake with energy expenditure to achieve a stable body weight, it is now recognized that neuronal circuits in the hindbrain not only serve to produce nausea and to terminate feeding in response to food consumption or during pathological states, but also contribute to the long-term control of body weight.

Additionally, recent work has identified hindbrain neurons that are capable of suppressing food intake without producing aversive responses like those associated with nausea. Here we review recent advances in our understanding of the hindbrain neurons that control feeding, particularly those located in the area postrema and the nucleus tractus solitarius.

We frame this information in the context of new atlases of hindbrain neuronal populations and develop a model of the hindbrain circuits that control food intake and energy balance, suggesting important areas for additional research.

A New Statistical Method for Improved Brain Mapping

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Summary: Researchers propose a new, more robust statistical method for mapping the brain.

Source: Paris Brain Institute

Brain mapping consists in finding the brain regions associated with different traits, such as diseases, cognitive functions, or behaviours, and is a major field of research in neuroscience. This approach is based on statistical models and is subject to numerous biases.

To try to counter them, researchers from the ARAMIS team, a joint team between the Paris Brain institute and Inria, and their collaborators at the University of Queensland (Australia) and Westlake University (China), propose a new statistical model for brain mapping.

The results are published in the Journal of Medical Imaging.

Mapping the brain

Mapping the brain is a challenge that mobilizes many neuroscience researchers around the world. The goal of this approach is to identify the brain regions associated with different traits, such as diseases, cognitive scores, or behaviors.

This type of study is also known as “Brain-wide association study” and rely on an exhaustive screening of brain regions to identify those associated with the trait of interest.

“The difficulty is that we are looking for a needle in a haystack, except that we don’t know how many needles there are, or in our case, how many brain regions there are to find,” explains Baptiste Couvy-Duchesne (Inria), first author of the study.

Meeting the challenges of signal redundancy

A first challenge lies in the number of brain measurements available per individual, which can quickly reach one million or more.

In addition, brain regions are correlated with each other. Some regions are highly connected and associated with many others, like nodes in a network.

Others, however, are more isolated, either because they are independent of other brain regions or because they contribute to very specific cognitive trait or brain function. 

“If a brain region associated with our trait of interest is part of a highly connected network, the analysis will tend to detect the whole network, because the signal propagates within regions that are correlated with each other,” continues the researcher,

“This signal, which may seem very strong at first sight, is in fact redundant. How then can we find the region or regions that really contribute to the trait of interest within the network?”

To solve this problem, the researchers are proposing new statistical methods that are suited to the high dimensional image as well as for modelling the complex correlation structure within the brain.

Simulations to develop new statistical methods

In order to test the developed statistical methods, the researchers need very controlled data.

“We cannot compare methods directly on real traits or diseases, since we do not know what we are supposed to find,” explains Baptiste Couvy-Duchesne, “one method could find 10 regions associated with a trait, another 20, although we cannot tell which one is giving the correct answer.” 

The key to this solving problem is to use simulations. Researchers use real brain images, but study fake diseases or fake scores, which they have constructed to be associated with dozens or hundreds of predefined brain regions.

This way, they are able to check whether the statistical methods detect the expected regions, but also whether they detect others (‘false positives’). 

A more robust method and open questions

Once their method had been calibrated through these simulations (which revealed that the proposed approach was more accurate than the existing ones) the researchers used real traits as validation.

This shows a brain and a target
A first challenge lies in the number of brain measurements available per individual, which can quickly reach one million or more. Image is in the public domain

“Our new method finds fewer regions on average because it manages to remove some of the redundant associations. The next step is to apply it to study Alzheimer’s disease,” concludes the researcher.

A central result of the study it to demonstrate how pervasive are the redundant associations, using the current statistical methods. Thus, many associations identified to date may not be robust of directly pertinent for the trait studied.

In addition, several factors that are difficult to control can affect the quality of MRIs, such as head movements or the type of machines used, which can exacerbate the problem and lead to false associations.

Beyond the development of more refined analysis methods, the issue of data quality and homogeneity remains crucial.

About this brain mapping research news

Abstract

A parsimonious model for mass-univariate vertex-wise analysis

Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses.

Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies.

Results: We showed that when performed on a large sample (N  =  8662, UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate >0.6). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes.

Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.

A New Statistical Method for Improved Brain Mapping

Posted by

0


Summary: Researchers propose a new, more robust statistical method for mapping the brain.

Source: Paris Brain Institute

Brain mapping consists in finding the brain regions associated with different traits, such as diseases, cognitive functions, or behaviours, and is a major field of research in neuroscience. This approach is based on statistical models and is subject to numerous biases.

To try to counter them, researchers from the ARAMIS team, a joint team between the Paris Brain institute and Inria, and their collaborators at the University of Queensland (Australia) and Westlake University (China), propose a new statistical model for brain mapping.

The results are published in the Journal of Medical Imaging.

Mapping the brain

Mapping the brain is a challenge that mobilizes many neuroscience researchers around the world. The goal of this approach is to identify the brain regions associated with different traits, such as diseases, cognitive scores, or behaviors.

This type of study is also known as “Brain-wide association study” and rely on an exhaustive screening of brain regions to identify those associated with the trait of interest.

“The difficulty is that we are looking for a needle in a haystack, except that we don’t know how many needles there are, or in our case, how many brain regions there are to find,” explains Baptiste Couvy-Duchesne (Inria), first author of the study.

Meeting the challenges of signal redundancy

A first challenge lies in the number of brain measurements available per individual, which can quickly reach one million or more.

In addition, brain regions are correlated with each other. Some regions are highly connected and associated with many others, like nodes in a network.

Others, however, are more isolated, either because they are independent of other brain regions or because they contribute to very specific cognitive trait or brain function. 

“If a brain region associated with our trait of interest is part of a highly connected network, the analysis will tend to detect the whole network, because the signal propagates within regions that are correlated with each other,” continues the researcher,

“This signal, which may seem very strong at first sight, is in fact redundant. How then can we find the region or regions that really contribute to the trait of interest within the network?”

To solve this problem, the researchers are proposing new statistical methods that are suited to the high dimensional image as well as for modelling the complex correlation structure within the brain.

Simulations to develop new statistical methods

In order to test the developed statistical methods, the researchers need very controlled data.

“We cannot compare methods directly on real traits or diseases, since we do not know what we are supposed to find,” explains Baptiste Couvy-Duchesne, “one method could find 10 regions associated with a trait, another 20, although we cannot tell which one is giving the correct answer.” 

The key to this solving problem is to use simulations. Researchers use real brain images, but study fake diseases or fake scores, which they have constructed to be associated with dozens or hundreds of predefined brain regions.

This way, they are able to check whether the statistical methods detect the expected regions, but also whether they detect others (‘false positives’). 

A more robust method and open questions

Once their method had been calibrated through these simulations (which revealed that the proposed approach was more accurate than the existing ones) the researchers used real traits as validation.

This shows a brain and a target
A first challenge lies in the number of brain measurements available per individual, which can quickly reach one million or more. Image is in the public domain

“Our new method finds fewer regions on average because it manages to remove some of the redundant associations. The next step is to apply it to study Alzheimer’s disease,” concludes the researcher.

A central result of the study it to demonstrate how pervasive are the redundant associations, using the current statistical methods. Thus, many associations identified to date may not be robust of directly pertinent for the trait studied.

In addition, several factors that are difficult to control can affect the quality of MRIs, such as head movements or the type of machines used, which can exacerbate the problem and lead to false associations.

Beyond the development of more refined analysis methods, the issue of data quality and homogeneity remains crucial.