Wang Maxwell, G'Sell Max, Castellano James F, Richardson R Mark, Ghuman Avniel
Carnegie Mellon University.
University of Pittsburgh.
Res Sq. 2024 Jan 15:rs.3.rs-2752903. doi: 10.21203/rs.3.rs-2752903/v1.
Many important neurocognitive states, such as performing natural activities and fluctuations of arousal, shift over minutes-to-hours in the real-world. We harnessed 3-12 days of continuous multi-electrode intracranial recordings in twenty humans during natural behavior (socializing, using digital devices, sleeping, etc.) to study real-world neurodynamics. Applying deep learning with dynamical systems approaches revealed that brain networks formed consistent stable states that predicted behavior and physiology. Changes in behavior were associated with bursts of rapid neural fluctuations where brain networks chaotically explored many configurations before settling into new states. These trajectories traversed an hourglass-shaped structure anchored around a set of networks that slowly tracked levels of outward awareness related to wake-sleep stages, and a central attractor corresponding to default mode network activation. These findings indicate ways our brains use rapid, chaotic transitions that coalesce into neurocognitive states slowly fluctuating around a stabilizing central equilibrium to balance flexibility and stability during real-world behavior.
许多重要的神经认知状态,如进行自然活动和唤醒波动,在现实世界中会在数分钟到数小时内发生变化。我们利用20名人类在自然行为(社交、使用数字设备、睡眠等)期间进行的3至12天连续多电极颅内记录,来研究现实世界中的神经动力学。将深度学习与动力系统方法相结合的研究表明,大脑网络形成了一致的稳定状态,这些状态能够预测行为和生理状况。行为变化与快速神经波动的爆发有关,在此期间,大脑网络在进入新状态之前会混乱地探索多种配置。这些轨迹穿过一个沙漏形结构,该结构围绕着一组网络,这些网络缓慢跟踪与清醒-睡眠阶段相关的外在意识水平,以及一个与默认模式网络激活相对应的中央吸引子。这些发现揭示了我们的大脑如何利用快速、混沌的转变,这些转变汇聚成围绕稳定的中央平衡缓慢波动的神经认知状态,从而在现实世界行为中平衡灵活性和稳定性。