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Proceedings Paper

Hybrid dictionary learning-ICA approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity
Author(s): Robyn L. Miler; Vince D. Calhoun
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Paper Abstract

The study of brain network connectivity as a time‐varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in fMRI data. We introduce a constellation of interrelated data‐driven methods that hierarchically employ multichannel 1D sparse convolutional dictionary learning (SCDL) and independent component analysis (ICA) for extracting multiscale time‐varying representations of functional network connectivity (FNC). This work also relies upon a novel wavelet‐based method for computing dynamically varying FNC (dFNC) at each timepoint in the scan, yielding a much more resolved picture of evolving connectivity than currently popular sliding‐window approaches. The methods are validated in application to a large multisite fMRI study of schizophrenia where they expose properties of time‐varying connectivity in schizophrenia patients vs. controls that are surprising based on long‐accepted theories of the disorder.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131V (10 March 2020); doi: 10.1117/12.2549368
Show Author Affiliations
Robyn L. Miler, Georgia State Univ. (United States)
Georgia Institute of Technology (United States)
Emory Univ. (United States)
Vince D. Calhoun, Georgia State Univ. (United States)
Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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