Local excitations of functional eigenmodes characterize spatiotemporally coordinated neural dynamics in the human brain

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Abstract
Functional magnetic resonance imaging (fMRI) has been widely employed for brain function mapping by localizing neural activities evoked by conditional stimuli, with temporal variations in fMRI signals modeled as superpositions of linear hemodynamic responses to these stimuli. However, this standard model overlooks the brain’s nonlinear responses to external stimuli and inherent spatial organizations of brain activities. The dynamics of many natural systems can be well described by their eigenmodes, which are the fundamental resonant patterns determined by their internal structures. Here, we explore the potential of characterizing brain dynamics in a framework that integrates temporal and spatial profiles of brain activities using an eigenmode paradigm. Specifically, spatiotemporally coordinated neural dynamics are represented as local excitations of brain eigenmodes derived from functional connectomes that reflect the brain’s axonal connection structures. We found that eigenmode-represented signals can reliably characterize subtle spatiotemporal trajectories of brain dynamics. Through this representation, we further reveal the widespread existence of non-linear brain responses, which potentially bias the results of conventional analyses. To map brain functions with non-linearity explicitly considered, we exploited the similarity in eigenmode-represented signals measured under repeated stimuli to identify evoked brain responses. Our findings demonstrate that cognitive tasks elicit more extensive engagements of brain networks than those detected using linear models, unveiling previously underappreciated neural architectures underlying specific brain functions. Moreover, our results of function mapping exhibit significantly enhanced individual-level repeatability and accelerated convergence to group consensus, enabling investigations into personalized brain function and potentially advancing applications of fMRI that target individual brain variations.

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