Fully automated estimation of fMRI-guided SPECT brain networks and their functional network connectivity in schizophrenia patients versus controls: A NeuroMark ICA approach

Single photon emission computerized tomography (SPECT) has emerged as a useful imaging modality that has been explored in the literature for the last 40 years. To date, little work has focused on studying functional network connectivity utilizing SPECT data. In this study, we present a fully automated, spatially constrained ICA (sc-ICA) approach to evaluate functional network connectivity profiles in SPECT data using the NeuroMark pipeline. We evaluate both the expression of brain networks along with the whole brain SPECT connectome to evaluate neuroimaging links to schizophrenia. SPECT images from 76 healthy controls and 137 schizophrenia patients were acquired from Amen Clinic sites along with diagnostic information. Each patient participated in two SPECT brain scans, acquired during rest and while performing a sustained attention task across twelve clinical imaging sites. Preprocessed SPECT data were analyzed via sc-ICA using 53 spatial priors derived from functional MRI data. 15 total components were found to show differences in various brain regions after correcting for multiple comparisons. FDR corrected resting SPECT results showed stronger covariation with CC-SC, CC-AUD, and DM-AUD networks. Many components showed reduced connectivity in patients. Additionally, relationships were associated with regressing loading parameters against age, sex, hearing voices and having disjointed thoughts. For the task data, connectivity between the cognitive control – default mode network was found in rest-task data after FDR correction. Task data covariation patterns largely remained the same as the rest data. In summary, we find large scale network disruptions consistent with prior schizophrenia fMRI studies.