Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network.
One of the most fundamental features of the human brain is its ability to detect and attend to salient goal-relevant events in a flexible manner. The salience network (SN), anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this process through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. Here, we leverage the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and apply novel graph-theoretical techniques to investigate the dynamic spatiotemporal organization of the SN. We show that the large-scale brain dynamics of the SN are characterized by several distinctive and robust properties. First, the SN demonstrated the highest levels of flexibility in time-varying connectivity with other brain networks, including the frontoparietal network (FPN), the cingulate-opercular network (CON), and the ventral and dorsal attention networks (VAN and DAN). Second, dynamic functional interactions of the SN were among the most spatially varied in the brain. Third, SN nodes maintained a consistently high level of network centrality over time, indicating that this network is a hub for facilitating flexible cross-network interactions. Fourth, time-varying connectivity profiles of the SN were distinct from all other prefrontal control systems. Fifth, temporal flexibility of the SN uniquely predicted individual differences in cognitive flexibility. Importantly, each of these results was also observed in a second retest dataset, demonstrating the robustness of our findings. Our study provides fundamental new insights into the distinct dynamic functional architecture of the SN and demonstrates how this network is uniquely positioned to facilitate interactions with multiple functional systems and thereby support a wide range of cognitive processes in the human brain.
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
Tianwen
T
Chen
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
Weidong
W
Cai
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
Srikanth
S
Ryali
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
Kaustubh
K
Supekar
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.|Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.|Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, United States of America.
Vinod
V
Menon
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003071:Cognition / Q000502:physiology*; D005260:Female; D006801:Humans; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology*; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology*; D013997:Time Factors; D055815:Young Adult