Journal Article

FZJ-2025-01650

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Predicting response speed and age from task-evoked effective connectivity

Zhang, S.FZJ* ; Jung, K.FZJ* ; Langner, R.FZJ* ; Florin, E. ; Eickhoff, S.FZJ* ; Popovych, O. (Corresponding author)FZJ*

2025
The MIT Press Cambridge, MA

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Please use a persistent id in citations: doi:10.1162/netn_a_00447  doi:10.34734/FZJ-2025-01650

Abstract: Recent neuroimaging studies demonstrated that task-evoked functional connectivity (FC) may better predict individual traits than resting-state FC. However, the prediction properties of task-evoked effective connectivity (EC) remain unexplored. We investigated this by predicting individual reaction time (RT) performance in the stimulus-response compatibility task and age, using intrinsic EC (I-EC, calculated at baseline) and task-modulated EC (M-EC, induced by experimental conditions) with dynamic causal modeling (DCM) across various data-processing conditions, including different general linear model (GLM) designs, Bayesian model reduction, and different cross-validation schemes and prediction models. We report evident differences in predicting RT and age between I-EC and M-EC, as well as between event-related and block-based GLM and DCM designs. M-EC outperformed both I-EC and task-evoked FC in RT prediction, while all types of connectivity performed similarly for age. Event-related GLM and DCM designs performed better than block-based designs. Our findings suggest that task-evoked I-EC and M-EC may capture different phenotypic attributes, with performance influenced by data processing and modeling choices, particularly the GLM-DCM design. This evaluation of methods for behavior prediction from brain EC may contribute to a meta-scientific understanding of how data processing and modeling frameworks influence neuroimaging-based predictions, offering insights for improving their robustness and efficacy.Keywords: task fMRI, dynamic causal modeling, analytic flexibility, machine learning, brain-based prediction, stimulus-response compatibility, functional connectivity

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)
2. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)

Appears in the scientific report 2025

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Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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