In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Gatica:2021:10.1089/brain.2020.0982,
author = {Gatica, M and Cofre, R and Mediano, PAM and Rosas, FE and Orio, P and Diez, I and Swinnen, SP and Cortes, JM},
doi = {10.1089/brain.2020.0982},
journal = {Brain Connectivity},
pages = {734--744},
title = {High-order interdependencies in the aging brain},
url = {http://dx.doi.org/10.1089/brain.2020.0982},
volume = {11},
year = {2021}
}
TY - JOUR
AB - Background: Brain interdependencies can be studied from either a structural/anatomical perspective (“structural connectivity”) or by considering statistical interdependencies (“functional connectivity” [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables.Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age.Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a “redundancy core” constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied.Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.Impact statementPast research has showcased multiple changes to the brain's structural and functional properties caused by aging. Here we expand prior work through recent advancements in multivariate information theory, which provide richer and more theoretically principled analyses than existing alternatives. We show that the
AU - Gatica,M
AU - Cofre,R
AU - Mediano,PAM
AU - Rosas,FE
AU - Orio,P
AU - Diez,I
AU - Swinnen,SP
AU - Cortes,JM
DO - 10.1089/brain.2020.0982
EP - 744
PY - 2021///
SN - 2158-0022
SP - 734
TI - High-order interdependencies in the aging brain
T2 - Brain Connectivity
UR - http://dx.doi.org/10.1089/brain.2020.0982
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000727693800006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR - https://www.liebertpub.com/doi/10.1089/brain.2020.0982
VL - 11
ER -
Data Science Imperial
William Penney Laboratory
天美传媒
South Kensington Campus
London SW7 2AZ
United Kingdom
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