A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences

TL;DR

This paper introduces Universal Brain Dynamics (UBD), a data-driven framework that constructs a universal space to accurately model and analyze human brain activity across diverse states and individuals, revealing neural mechanisms of cognition.

Contribution

The study develops UBD to unify spatial and temporal brain properties, enabling precise modeling of brain dynamics and individual differences across cognitive states.

Findings

UBD predicts fMRI signals with Pearson's r > 0.9 across 8 states and 963 subjects.

Insights into infra-slow fluctuations underpin brain activity.

New perspective on structure-function coupling through temporal analysis.

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.