@inproceedings{a9f19744bfe24fce9da8213868a35069,
title = "Hybrid dictionary learning-Ica approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity",
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.",
author = "Miller, {Robyn L.} and Calhoun, {Vince D.}",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Medical Imaging 2020: Image Processing ; Conference date: 17-02-2020 Through 20-02-2020",
year = "2020",
doi = "10.1117/12.2549368",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2020",
}