Zhang Lu, Pini Lorenzo, Shulman Gordon L, Corbetta Maurizio
Department of Psychiatry, Affiliated Kangning Hospital of Ningbo University (Ningbo Kangning Hospital), Ningbo 315201, China.
Padova Neuroscience Center, University of Padova, Padova 35131, Italy.
Proc Natl Acad Sci U S A. 2025 Mar 18;122(11):e2415508122. doi: 10.1073/pnas.2415508122. Epub 2025 Mar 12.
Resting brain activity, in the absence of explicit tasks, appears as distributed spatiotemporal patterns that reflect structural connectivity and correlate with behavioral traits. However, its role in shaping behavior remains unclear. Recent evidence shows that resting-state spatial patterns not only align with task-evoked topographies but also encode distinct visual (e.g., lines, contours, faces, places) and motor (e.g., hand postures) features, suggesting mechanisms for long-term storage and predictive coding. While prior research focused on static, time-averaged task activations, we examine whether dynamic, time-varying motor states seen during active hand movements are also present at rest. Three distinct motor activation states, engaging the motor cortex alongside sensory and association areas, were identified. These states appeared both at rest and during task execution but underwent temporal reorganization from rest to task. Thus, resting-state dynamics serve as strong spatiotemporal priors for task-based activation. Critically, resting-state patterns more closely resembled those associated with frequent ecological hand movements than with an unfamiliar movement, indicating a structured repertoire of movement patterns that is replayed at rest and reorganized during action. This suggests that spontaneous neural activity provides priors for future movements and contributes to long-term memory storage, reinforcing the functional interplay between resting and task-driven brain activity.
在没有明确任务的情况下，静息脑活动表现为反映结构连通性并与行为特征相关的分布式时空模式。然而，其在塑造行为中的作用仍不清楚。最近的证据表明，静息状态空间模式不仅与任务诱发的地形图对齐，还编码不同的视觉（如线条、轮廓、面孔、地点）和运动（如手部姿势）特征，提示了长期存储和预测编码的机制。虽然先前的研究集中在静态、时间平均的任务激活上，但我们研究在主动手部运动期间看到的动态、随时间变化的运动状态在静息时是否也存在。确定了三种不同的运动激活状态，它们涉及运动皮层以及感觉和联合区域。这些状态在静息和任务执行期间均出现，但从静息到任务经历了时间重组。因此，静息状态动力学作为基于任务的激活的强大时空先验。至关重要的是，静息状态模式与频繁的生态手部运动相关的模式比与不熟悉的运动相关的模式更相似，表明存在一个结构化的运动模式库，在静息时回放并在行动期间重组。这表明自发神经活动为未来运动提供先验，并有助于长期记忆存储，加强了静息和任务驱动的脑活动之间的功能相互作用。
