Abstract #0906

Capturing brain spatial topography reconfiguration using ultra-high-order independent component analysis (ICA)

Armin Iraji¹, Zening Fu¹, Thomas P. DeRamus¹, Shile Qi¹, Harshvardhan Gazula¹, and Vince D. Calhoun^1,2,3

¹The Mind Research Network, Albuquerque, NM, United States, ²School of Medicine, Yale University, New Haven, CT, United States, ³Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States

The brain reorganizes its activity interactively at different temporal and spatial scales. These reconfigurations include variations within a region’s function, and changes in the spatial topography of functional organizations. Here, we present a novel approach utilizing the concept of functional hierarchy to capture between- and within-subject spatiotemporal variations. The approach uses ultra-high-order independent component analysis (ICA) (1000 components) to estimate functional units overcoming the limitation of current parcellation approaches and captures brain spatiotemporal reconfiguration at a finer spatial level. Our regularized cost function optimizes the selection of the best subsets of changes at each time-window and protects against spurious fluctuations.

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