Spatially constrained ICA enables robust detection of schizophrenia from very short resting-state fMRI data

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 novel 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 individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluate the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 minute) reference time course were sufficiently approximated with just 3-3.5 minutes of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 minutes (r = 0.86). The results from the shorter clinical data were consistent with the results from the longer simulated data, reliably estimating both individual- and group-level metrics from the full-length (30 minute) reference with just 3-4 minutes of data (r = 0.85 - 0.88). Furthermore, we found a model trained on 2 minutes of data retained 97-98% classification accuracy relative to that of the full-length reference model. Our results suggest that clinical rsfMRI scans, when decomposed with scICA, could potentially be shortened to just 2-4 minutes without significant loss of individual rsFNC information or classification performance of longer scan lengths.