A domain-general brain network underlying emotional and cognitive interference processing: evidence from coordinate-based and functional connectivity meta-analyses
The inability to control or inhibit emotional distractors characterizes a range of psychiatric disorders. Despite the use of a variety of task paradigms to determine the mechanisms underlying the control of emotional interference, a precise characterization of the brain regions and networks that support emotional interference processing remains elusive. Here, we performed coordinate-based and functional connectivity meta-analyses to determine the brain networks underlying emotional interference. Paradigms addressing interference processing in the cognitive or emotional domain were included in the meta-analyses, particularly the Stroop, Flanker, and Simon tasks. Our results revealed a consistent involvement of the bilateral dorsal anterior cingulate cortex, anterior insula, left inferior frontal gyrus, and superior parietal lobule during emotional interference. Follow-up conjunction analyses identified correspondence in these regions between emotional and cognitive interference processing. Finally, the patterns of functional connectivity of these regions were examined using resting-state functional connectivity and meta-analytic connectivity modeling. These regions were strongly connected as a distributed system, primarily mapping onto fronto-parietal control, ventral attention, and dorsal attention networks. Together, the present findings indicate that a domain-general neural system is engaged across multiple types of interference processing and that regulating emotional and cognitive interference depends on interactions between large-scale distributed brain networks.
KeywordsEmotional interference Cognitive control Activation likelihood estimation (ALE) Meta-analysis Meta-analytic connectivity modeling (MACM) Resting-state functional connectivity (RSFC) Large-scale network Functional decoding
This work was supported by the National Postdoctoral Program for Innovative Talents under Grant agreement no. BX201600019 (to C.F.), the China Postdoctoral Science Foundation under Grant agreement nos. 2017M610055 (to C.F.) and 2013M530401 (to T.C.), the National Natural Science Foundation of China under Grant agreement nos. 81401398 (to T.C.), 91632117 (to B.B.), and 31500920 (to C.F.), and the National Institute of Mental Health (R01-MH074457, to E.S.), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 7202070 (HBP SGA1, to E.S.).
Compliance with ethical standards
Conflict of interest
The authors are unaware of any conflicts of interest, financial or otherwise.
The study was approved by the Ethics Committee of Beijing Normal University.
Not applicable. This is a meta-analytic study.
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