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024 7 _ |a 10.1002/hbm.26481
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024 7 _ |a 1065-9471
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024 7 _ |a 1097-0193
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024 7 _ |a 10.34734/FZJ-2023-03536
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024 7 _ |a 37713540
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100 1 _ |a Silchenko, Alexander N.
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245 _ _ |a Impact of sample size and regression of tissue‐specific signals on effective connectivity within the core default mode network
260 _ _ |a New York, NY
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500 _ _ |a ACKNOWLEDGMENTSThis work was supported by the Forschungzentrum Jülich GmbH (Alexander Silchenko), Simon B. Eickhoff acknowledges funding by the European Union's Horizon 2020 Research and Innovation Program (grant agreements 945539 [HBP SGA3] and 826421 [VBC]), the Deutsche Forschungsgemeinschaft (DFG, SFB 1451 and IRTG 2150) and the National Institute of Health (R01 MH074457). Open Access funding enabled and organized by Projekt DEAL.
520 _ _ |a Interactions within brain networks are inherently directional, which are inaccessible to classical functional connectivity estimates from resting-state functional magnetic resonance imaging (fMRI) but can be detected using spectral dynamic causal modeling (DCM). The sample size and unavoidable presence of nuisance signals during fMRI measurement are the two important factors influencing the stability of group estimates of connectivity parameters. However, most recent studies exploring effective connectivity (EC) have been conducted with small sample sizes and minimally pre-processed datasets. We explore the impact of these two factors by analyzing clean resting-state fMRI data from 330 unrelated subjects from the Human Connectome Project database. We demonstrate that both the stability of the model selection procedures and the inference of connectivity parameters are highly dependent on the sample size. The minimum sample size required for stable DCM is approximately 50, which may explain the variability of the DCM results reported so far. We reveal a stable pattern of EC within the core default mode network computed for large sample sizes and demonstrate that the use of subject-specific thresholded whole-brain masks for tissue-specific signals regression enhances the detection of weak connections.
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700 1 _ |a Hoffstaedter, Felix
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700 1 _ |a Eickhoff, Simon B.
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773 _ _ |a 10.1002/hbm.26481
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