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Article

Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

Communications biology - 26 Jun 2024

Omidvarnia Amir, Sasse Leonard, Larabi Daouia I, Raimondo Federico, Hoffstaedter Felix, Kasper Jan, Dukart Jürgen, Petersen Marvin, Cheng Bastian, Thomalla Götz, Eickhoff Simon B, Patil Kaustubh R

Abstract excerpt

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional...

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Identifiers

PMID: 38926486
DOI: 10.1038/s42003-024-06438-5
PMCID: PMC11208538

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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