DySCo: A general framework for dynamic functional connectivity
Leverage (statistics)
Dynamic functional connectivity
DOI:
10.1371/journal.pcbi.1012795
Publication Date:
2025-03-07T18:29:53Z
AUTHORS (7)
ABSTRACT
A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across areas change over time. However, the main approaches have been developed and applied mostly empirically, lacking common theoretical framework clear view on interpretation results derived matrices. Moreover, community has not using most efficient algorithms compute process matrices efficiently, which prevented showing its full potential with datasets and/or real-time applications. In paper, we introduce Symmetric Matrix (DySCo), associated repository. DySCo presents commonly used measures language implements them computationally way. This allows study activity at different spatio-temporal scales, down voxel level. provides single to: (1) Use as tool capture interaction patterns data form easily translatable imaging modalities. (2) Provide comprehensive set quantify properties evolution time: amount connectivity, similarity between matrices, their informational complexity. By combining it possible perform analysis. (3) Leverage Temporal Covariance EVD algorithm (TCEVD) store eigenvectors values then also EVD. Developing eigenvector space orders magnitude faster more memory than naïve space, without loss information. The methodology here validated both synthetic dataset rest/N-back task experimental fMRI Human Connectome Project dataset. We show all proposed are sensitive changes configurations consistent time subjects. To illustrate computational efficiency toolbox, performed level, demanding but afforded by TCEVD.
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