TY  - JOUR
AU  - Zhang, Shufei
AU  - Jung, Kyesam
AU  - Langner, Robert
AU  - Florin, Esther
AU  - Eickhoff, Simon
AU  - Popovych, Oleksandr
TI  - Predicting response speed and age from task-evoked effective connectivity
JO  - Network neuroscience
VL  - .
SN  - 2472-1751
CY  - Cambridge, MA
PB  - The MIT Press
M1  - FZJ-2025-01650
SP  - 1-57
PY  - 2025
AB  - 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
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001489278300003
DO  - DOI:10.1162/netn_a_00447
UR  - https://juser.fz-juelich.de/record/1038813
ER  -