Exploring the latent structure of behavior using the Human Connectome Project's data.
Détails
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_1E1541AD189D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Exploring the latent structure of behavior using the Human Connectome Project's data.
Périodique
Scientific reports
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Statut éditorial
Publié
Date de publication
13/01/2023
Peer-reviewed
Oui
Volume
13
Numéro
1
Pages
713
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
Publication Status: epublish
Publication Status: epublish
Résumé
How behavior arises from brain physiology has been one central topic of investigation in neuroscience. Considering the recent interest in predicting behavior from brain imaging using open datasets, there is the need for a principled approach to the categorization of behavioral variables. However, this is not trivial, as the definitions of psychological constructs and their relationships-their ontology-are not always clear. Here, we propose to use exploratory factor analysis (EFA) as a data-driven approach to find robust and interpretable domains of behavior in the Human Connectome Project (HCP) dataset. Additionally, we explore the clustering of behavioral variables using consensus clustering. We find that four and five factors offer the best description of the data, a result corroborated by the consensus clustering. In the four-factor solution, factors for Mental Health, Cognition, Processing Speed, and Substance Use arise. With five factors, Mental Health splits into Well-Being and Internalizing. Clustering results show a similar pattern, with clusters for Cognition, Processing Speed, Positive Affect, Negative Affect, and Substance Use. The factor structure is replicated in an independent dataset using confirmatory factor analysis (CFA). We discuss how the content of the factors fits with previous conceptualizations of general behavioral domains.
Mots-clé
Humans, Connectome/methods, Brain/diagnostic imaging, Brain/physiology, Cognition, Cluster Analysis, Mental Health, Magnetic Resonance Imaging/methods
Pubmed
Web of science
Open Access
Oui
Création de la notice
23/01/2023 11:56
Dernière modification de la notice
23/01/2024 8:21