Common neural network for different functions: an investigation of proactive and reactive inhibition

Successful behavioral inhibition involves both proactive and reactive inhibition. We created 70 dynamic causal models (DCMs) representing the alternative hypotheses of modulatory effects from proactive and reactive inhibition in the IFG-SMA-STN-M1 network. Bayesian model selection (BMS) showed that...

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Bibliographic Details
Published in生体医工学 Vol. Annual57; no. Abstract; p. S28_1
Main Authors 張, 帆, 岩木, 直
Format Journal Article
LanguageJapanese
Published 公益社団法人 日本生体医工学会 2019
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Summary:Successful behavioral inhibition involves both proactive and reactive inhibition. We created 70 dynamic causal models (DCMs) representing the alternative hypotheses of modulatory effects from proactive and reactive inhibition in the IFG-SMA-STN-M1 network. Bayesian model selection (BMS) showed that causal connectivity from the IFG to the SMA was modulated by both proactive and reactive inhibition. We then compared 13 DCMs representing the alternative hypotheses of proactive modulation, and BMS revealed that the effective connectivity from the caudate to the IFG is modulated only in the proactive inhibition condition but not in the reactive inhibition. Together, our results demonstrate that a longer pathway (DLPFC-caudate-IFG-SMA-STN-M1) playing a modulatory role in proactive inhibitory control, and a shorter pathway (IFG-SMA-STN-M1) involved in reactive inhibition. These results provide causal evidence for the roles of indirect and hyperdirect pathways in mediating proactive and reactive inhibitory control.
ISSN:1347-443X
1881-4379
DOI:10.11239/jsmbe.Annual57.S28_1