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Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

BACKGROUND: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS: We introduce a new approach that analyzes time series trajectories to ident...

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Autores principales: Faghiri, Ashkan, Damaraju, Eswar, Belger, Aysenil, Ford, Judith M., Mathalon, Daniel, McEwen, Sarah, Mueller, Bryon, Pearlson, Godfrey, Preda, Adrian, Turner, Jessica A., Vaidya, Jatin G., Van Erp, Theodorus, Calhoun, Vince D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076753/
https://www.ncbi.nlm.nih.gov/pubmed/33927587
http://dx.doi.org/10.3389/fnins.2021.621716
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author Faghiri, Ashkan
Damaraju, Eswar
Belger, Aysenil
Ford, Judith M.
Mathalon, Daniel
McEwen, Sarah
Mueller, Bryon
Pearlson, Godfrey
Preda, Adrian
Turner, Jessica A.
Vaidya, Jatin G.
Van Erp, Theodorus
Calhoun, Vince D.
author_facet Faghiri, Ashkan
Damaraju, Eswar
Belger, Aysenil
Ford, Judith M.
Mathalon, Daniel
McEwen, Sarah
Mueller, Bryon
Pearlson, Godfrey
Preda, Adrian
Turner, Jessica A.
Vaidya, Jatin G.
Van Erp, Theodorus
Calhoun, Vince D.
author_sort Faghiri, Ashkan
collection PubMed
description BACKGROUND: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. RESULTS: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. CONCLUSION: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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spelling pubmed-80767532021-04-28 Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics Faghiri, Ashkan Damaraju, Eswar Belger, Aysenil Ford, Judith M. Mathalon, Daniel McEwen, Sarah Mueller, Bryon Pearlson, Godfrey Preda, Adrian Turner, Jessica A. Vaidya, Jatin G. Van Erp, Theodorus Calhoun, Vince D. Front Neurosci Neuroscience BACKGROUND: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. RESULTS: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. CONCLUSION: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods. Frontiers Media S.A. 2021-04-13 /pmc/articles/PMC8076753/ /pubmed/33927587 http://dx.doi.org/10.3389/fnins.2021.621716 Text en Copyright © 2021 Faghiri, Damaraju, Belger, Ford, Mathalon, McEwen, Mueller, Pearlson, Preda, Turner, Vaidya, Van Erp and Calhoun. https://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
Faghiri, Ashkan
Damaraju, Eswar
Belger, Aysenil
Ford, Judith M.
Mathalon, Daniel
McEwen, Sarah
Mueller, Bryon
Pearlson, Godfrey
Preda, Adrian
Turner, Jessica A.
Vaidya, Jatin G.
Van Erp, Theodorus
Calhoun, Vince D.
Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title_full Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title_fullStr Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title_full_unstemmed Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title_short Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
title_sort brain density clustering analysis: a new approach to brain functional dynamics
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076753/
https://www.ncbi.nlm.nih.gov/pubmed/33927587
http://dx.doi.org/10.3389/fnins.2021.621716
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