Speaker
Description
arge-scale brain activity is increasingly described in terms of spatiotemporal modes that capture coordinated neural dynamics across space and time. Such modes can be derived from diverse approaches, including structure-based decompositions informed by cortical geometry or anatomical connectivity, as well as data-driven techniques such as PCA, complex PCA (CPCA), frequency-domain PCA (fdPCA), and Dynamic Mode Decomposition (DMD). Despite their widespread use, comparing modes obtained from different methods remains challenging because each framework emphasizes different mathematical properties.
Here, we propose a simple and method-agnostic framework that separates the origin of a mode from its characterization. We represent each mode as a complex-valued spatial pattern, allowing its amplitude and phase to be analyzed independently. From this representation, we define two complementary observables: one quantifying the spatial organization of the mode and another quantifying its temporal organization through phase relationships across brain regions. Together, these observables provide a common language for describing spatiotemporal brain modes and distinguishing standing-like, propagating, spatially smooth, and spatially disordered patterns, regardless of the method used to derive them.
We apply this framework to resting-state fMRI data from rodents, demonstrating that it enables a compact and interpretable description of the repertoire of brain modes while facilitating direct comparisons across decomposition methods. By focusing on fundamental spatiotemporal properties rather than the specific algorithm used to extract them, this approach offers a general framework for the systematic analysis and comparison of large-scale brain dynamics.
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