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Salience network orchestrates large-scale brain dynamics during target detection: Insights from an EEG dFNC study
Chanlin Yi1, Junpu Wang1, Tingru Luo1
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Abstract:
Efficient detection of behaviorally relevant stimuli is essential for adaptive cognition and has potential cognitive and neurotechnological applications. However, how large-scale brain networks dynamically coordinate this process remains poorly understood. Here, we leveraged electroencephalogram (EEG)-based dynamic functional network connectivity (dFNC) to investigate large-scale network organization during target and standard conditions in a visual oddball paradigm. Across distinct post-stimulus windows, condition-dependent changes occurred within a "filtering-integration-regulation" three-stage framework. Early (150-250 ms) differences (standard > target) were observed mainly in the Visual Network (VN), Sensory/Somatomotor Network (SMN), and Salience Network (SN), reflecting rapid sensory discrimination and pre-attentive tagging of frequent and predictable non-target events. The mid (350-450 ms) and late (450-550 ms) windows involved widespread connectivity increases for targets (target > standard) across the memory retrieval network (MRN), default mode network (DMN), dorsal attention network (DAN), frontoparietal task control network (FPCN), SN, VN, and cingulo-opercular network (CON). The central hubs shifted from SN in the mid stage to FPCN in the late stage, reflecting a transition from integrative processing to regulatory control. Recursive feature elimination (RFE) analysis identified early and late SN, late DAN, and mid-to-late DMN as the most critical networks for distinguishing target from standard stimuli and uncovered a functional dissociation centered on SN, with its engagement strength alone predicting P300 amplitude, while its timing best predicted P300 latency, alongside contributions from FPCN and DAN dynamics. These findings highlight the central role of SN-centered network dynamics in target detection, offering a large-scale perspective on the neural mechanisms of processing behaviorally relevant stimuli.
