构建脑活动通用空间，揭示认知转换与个体差异的神经机制
A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences
- 基于时空联合建模，用雅可比矩阵量化脑动力学
- 在HCP数据上跨8种状态、963人实现fMRI信号预测相关性超0.9
- 适用于静息态、任务态及个体差异分析，适合神经科学与计算建模研究者
通过数据驱动构建动力系统通用空间已被证明有效；然而，人类脑活动的通用空间构建仍面临巨大挑战，尤其受不同认知状态和个体差异的影响。鉴于空间属性反映物理连接，时间属性反映功能活动，我们提出通用脑动力学（UBD）模型，构建专用于脑活动的通用空间，并利用模型导出的雅可比矩阵量化动态特性。关键的是，在人类连接组计划（HCP）中，UBD在8种状态、963名被试上实现了对功能性磁共振成像（fMRI）信号的高精度预测（皮尔逊相关系数 > 0.9）。通过在UBD中分析静息态fMRI，我们揭示了亚慢速波动（ISF）对脑活动的基础作用。此外，通过分析脑动力学的时间序列，我们提出了结构-功能耦合（SFC）的新视角。将UBD拓展至任务诱发状态，我们刻画了多种认知条件下的脑动力学，精细解析了认知转换的神经机制。针对个体差异，我们比较不同被试的脑动力学，识别其神经基础。结果表明，融合脑活动的空间与时间特性，可建立其演化过程的通用空间，支持在多变条件下对神经机制进行精确数值分析。
原文摘要 · Abstract (English)
Representing dynamical systems through data-driven universal spaces has proven effective; however, achieving this universality for human brain activity remains a significant challenge, further aggravated by diverse cognitive states and individual subjects. Recognizing that spatial properties reflect physical wiring while temporal properties reflect brain function, we develop Universal Brain Dynamics (UBD) to construct a universal space tailored to brain activity and quantify corresponding dynamics using a model-derived Jacobian matrix. Crucially, we validate UBD's universality by accurately predicting functional magnetic resonance imaging (fMRI) signals (Pearson's r > 0.9) across eight states and 963 subjects in the Human Connectome Project (HCP). Through evaluating resting-state fMRI represented within UBD, we gain insight into how infra-slow fluctuation (ISF) underpins brain activity. Furthermore, we reveal a new perspective on structure-function coupling (SFC) by analyzing the temporal sequence of brain dynamics. Extending UBD to task-evoked states, we derive brain dynamics across various cognitive conditions, elucidating the neural mechanisms driving cognitive transitions at a finer granularity. For individual differences, we compare brain dynamics across subjects to identify the neural underpinnings of these variations. Our findings suggest that synergistically integrating spatial and temporal properties of brain activity establishes a universal space for its unfolding, enabling the precise numerical analysis of underlying neural mechanisms across varying conditions.
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