{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T14:23:57Z","timestamp":1787149437946,"version":"build-2736575974"},"posted":{"date-parts":[[2025,10,14]]},"group-title":"Neuroscience","reference-count":63,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Scientific and technological innovation 2030 - the major project of the Brain Science and Brain-Inspired Intelligence Technology","award":["2021ZD0200500"],"award-info":[{"award-number":["2021ZD0200500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  This study proposes a spatiotemporal connectome-based framework to characterize the human brain\u2019s action network. Unlike temporal connectivity (TPC), this approach leverages full functional connectivity profiles derived from large-scale neural wave dynamics, termed spatial connectivity (SPC). We applied SPC to map the action network for the first time in a non-Western young adult cohort. Our results delineate the network\u2019s detailed functional architecture across the cerebral cortex, cerebellum, and subcortical nuclei. Compared to TPC, SPC is more robust to global signal regression when characterizing anticorrelation between the action and default networks. Critically, this intrinsic antagonism reflects a fundamental energy-saving balance in the brain\u2019s dynamical system. Moreover, SPC reveals that the salience\/parietal memory network resides at the interface of this antagonism, exhibiting weak but highly variable connectivity, a position that enables rapid state switching between external action and internal contemplation. These findings offer a mechanistic view of the brain\u2019s resting \u201cdark energy\u201d and suggest a blueprint for brain-inspired AI. Embedding a dual-system opponent architecture, balanced by a flexible switching hub, may foster adaptive, energy-efficient intelligence. All derived high-resolution SPC patterns and computational code are publicly shared to promote open science (\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/ccndc.scidb.cn\/en\">https:\/\/ccndc.scidb.cn\/en<\/jats:ext-link>\n                  ).\n                <\/jats:p>","DOI":"10.1101\/2025.10.12.681882","type":"posted-content","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T18:05:19Z","timestamp":1760465119000},"source":"Crossref","is-referenced-by-count":0,"title":["Spatial Connectivity Pattern of The Human Brain\u2019s Action Network"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4883-3971","authenticated-orcid":false,"given":"Xiu-Xia","family":"Xing","sequence":"first","affiliation":[{"name":"School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, 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