Academic Journal
Exploring the Latent Structure of Behavior Using the Human Connectome Project’s Data
| Title: | Exploring the Latent Structure of Behavior Using the Human Connectome Project’s Data |
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| Authors: | Mikkel Schöttner, Thomas Bolton, Jagruti Patel, Anjali Tarun Nahálka, Sandra Viera, Patric Hagmann |
| Source: | Sci Rep Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023) Scientific reports, vol. 13, no. 1, pp. 713 |
| Publisher Information: | Center for Open Science, 2022. |
| Publication Year: | 2022 |
| Subject Terms: | 0301 basic medicine, Science, Brain, Magnetic Resonance Imaging, Article, 3. Good health, 03 medical and health sciences, Cognition, Mental Health, 0302 clinical medicine, Connectome, Medicine, Humans, Cluster Analysis, Connectome/methods, Brain/diagnostic imaging, Brain/physiology, Magnetic Resonance Imaging/methods |
| Description: | 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. |
| Document Type: | Article Conference object Other literature type |
| File Description: | application/pdf |
| ISSN: | 2045-2322 |
| DOI: | 10.31234/osf.io/3h987 |
| DOI: | 10.1038/s41598-022-27101-1 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/36639406 https://doaj.org/article/e7615da06b594ae2a394b656619ed972 https://serval.unil.ch/notice/serval:BIB_1E1541AD189D https://serval.unil.ch/resource/serval:BIB_1E1541AD189D.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_1E1541AD189D8 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....88ae2af7f0d5d30cec0b99ae581e87eb |
| Database: | OpenAIRE |
| ISSN: | 20452322 |
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| DOI: | 10.31234/osf.io/3h987 |