Hybrid dictionary learning-Ica approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity

Robyn L. Miller, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510633933
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: Feb 17 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Processing
CountryUnited States
CityHouston
Period2/17/202/20/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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