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 (
Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject correspondence limits the clinical utility of rsfMRI and its application to single‐subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi‐spatial‐scale canonical intrinsic connectivity network (ICN) templates via the use of multi‐model‐order independent component analysis (ICA). We also study the feasibility of estimating subject‐specific ICNs via spatially constrained ICA. The results show that the subject‐level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large‐scale ICNs require less data to achieve specific levels of (within‐ and between‐subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject‐level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within‐subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
more » « less- PAR ID:
- 10467636
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Human Brain Mapping
- Volume:
- 44
- Issue:
- 17
- ISSN:
- 1065-9471
- Format(s):
- Medium: X Size: p. 5729-5748
- Size(s):
- p. 5729-5748
- Sponsoring Org:
- National Science Foundation
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