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Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis...

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Autores principales: RaviPrakash, Harish, Anwar, Syed Muhammad, Biassou, Nadia M., Bagci, Ulas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933502/
https://www.ncbi.nlm.nih.gov/pubmed/33679310
http://dx.doi.org/10.3389/fnins.2021.629478
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author RaviPrakash, Harish
Anwar, Syed Muhammad
Biassou, Nadia M.
Bagci, Ulas
author_facet RaviPrakash, Harish
Anwar, Syed Muhammad
Biassou, Nadia M.
Bagci, Ulas
author_sort RaviPrakash, Harish
collection PubMed
description A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.
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spelling pubmed-79335022021-03-06 Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players RaviPrakash, Harish Anwar, Syed Muhammad Biassou, Nadia M. Bagci, Ulas Front Neurosci Neuroscience A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm. Frontiers Media S.A. 2021-02-19 /pmc/articles/PMC7933502/ /pubmed/33679310 http://dx.doi.org/10.3389/fnins.2021.629478 Text en Copyright © 2021 RaviPrakash, Anwar, Biassou and Bagci. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
RaviPrakash, Harish
Anwar, Syed Muhammad
Biassou, Nadia M.
Bagci, Ulas
Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title_full Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title_fullStr Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title_full_unstemmed Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title_short Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
title_sort morphometric and functional brain connectivity differentiates chess masters from amateur players
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933502/
https://www.ncbi.nlm.nih.gov/pubmed/33679310
http://dx.doi.org/10.3389/fnins.2021.629478
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