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Toward a statistical validation of brain signatures as robust measures of behavioral substrates

The “brain signature of cognition” concept has garnered interest as a data‐driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a met...

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Detalles Bibliográficos
Autores principales: Fletcher, Evan, Farias, Sarah, DeCarli, Charles, Gavett, Brandon, Widaman, Keith, De Leon, Fransia, Mungas, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171525/
https://www.ncbi.nlm.nih.gov/pubmed/36939069
http://dx.doi.org/10.1002/hbm.26265
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author Fletcher, Evan
Farias, Sarah
DeCarli, Charles
Gavett, Brandon
Widaman, Keith
De Leon, Fransia
Mungas, Dan
author_facet Fletcher, Evan
Farias, Sarah
DeCarli, Charles
Gavett, Brandon
Widaman, Keith
De Leon, Fransia
Mungas, Dan
author_sort Fletcher, Evan
collection PubMed
description The “brain signature of cognition” concept has garnered interest as a data‐driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high‐frequency regions as “consensus” signature masks. Using separate validation datasets, we evaluated replicability of cohort‐based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory‐based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.
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spelling pubmed-101715252023-05-11 Toward a statistical validation of brain signatures as robust measures of behavioral substrates Fletcher, Evan Farias, Sarah DeCarli, Charles Gavett, Brandon Widaman, Keith De Leon, Fransia Mungas, Dan Hum Brain Mapp Research Articles The “brain signature of cognition” concept has garnered interest as a data‐driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high‐frequency regions as “consensus” signature masks. Using separate validation datasets, we evaluated replicability of cohort‐based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory‐based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains. John Wiley & Sons, Inc. 2023-03-20 /pmc/articles/PMC10171525/ /pubmed/36939069 http://dx.doi.org/10.1002/hbm.26265 Text en © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Fletcher, Evan
Farias, Sarah
DeCarli, Charles
Gavett, Brandon
Widaman, Keith
De Leon, Fransia
Mungas, Dan
Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title_full Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title_fullStr Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title_full_unstemmed Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title_short Toward a statistical validation of brain signatures as robust measures of behavioral substrates
title_sort toward a statistical validation of brain signatures as robust measures of behavioral substrates
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171525/
https://www.ncbi.nlm.nih.gov/pubmed/36939069
http://dx.doi.org/10.1002/hbm.26265
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