Resting‐state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting‐state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (
- Award ID(s):
- 1926829
- NSF-PAR ID:
- 10304411
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Neural Engineering
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1741-2560
- Page Range / eLocation ID:
- Article No. 026004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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