🤖 AI Summary
This study addresses the challenge of constructing a universal representational space for brain activity that generalizes across diverse cognitive states and individuals to elucidate mechanisms of cognitive transitions and individual variability. To this end, we propose the Universal Brain Dynamics (UBD) framework, which integrates spatial connectivity and temporal dynamics from fMRI data and leverages the Jacobian matrix derived from dynamical systems theory to quantify neural dynamics. Applying this approach to data from 963 participants in the Human Connectome Project across eight cognitive states, we achieve high-accuracy prediction of fMRI signals (Pearson correlation > 0.9). This work establishes, for the first time, a unified cross-state representation at scale, clarifies the dynamical basis of structure–function coupling, and highlights the critical role of ultra-low-frequency fluctuations in organizing brain activity.
📝 Abstract
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.