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| 001 | 1038813 | ||
| 005 | 20250610131445.0 | ||
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| 100 | 1 | _ | |a Zhang, Shufei |0 P:(DE-Juel1)180326 |b 0 |
| 245 | _ | _ | |a Predicting response speed and age from task-evoked effective connectivity |
| 260 | _ | _ | |a Cambridge, MA |c 2025 |b The MIT Press |
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| 520 | _ | _ | |a 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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| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1038813/files/APC600625699.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1038813/files/Shufei%20Zhang_TaskDCM-MatAB_NetworkNeuroscience_R1_NN_Clean.docx |
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