Ultrafine Brain Intrinsic Connectivity Networks Template via Very-High-Order Independent Component Analysis of Large-Scale Resting-State Functional Magnetic Resonance Imaging Data

Shiva Mirzaeian^1*Kyle Jensen¹Ram Ballem¹Pablo Andrés Camazón²Jiayu Chen¹Vince D Calhoun¹Armin Iraji¹

• ¹Georgia State University Center for Translational Research in Neuroimaging and Data Science, Atlanta, United States
• ²Hospital General Universitario Gregorio Maranon, Madrid, Spain

Spatial group independent component analysis (sgr-ICA) is widely used in resting-state fMRI to identify intrinsic connectivity networks (ICNs). While lower-order decompositions reveal large-scale networks, higher-order models provide finer granularity but have been limited by small sample sizes. In this study, we applied sgr-ICA with 500 components to more than 100,000 subjects with rsfMRI to generate a robust fine-grained ICN template. Using this template, we examined whole brain functional network connectivity (FNC) in 502 individuals with schizophrenia and 640 typical controls and compared the findings with a lower order multiscale template. The 500-component template yielded a large set of reliable ICNs, particularly in the cerebellar and paralimbic regions, and revealed schizophrenia-related dysconnectivity patterns that were not detected at larger spatial scales. Specifically, we observed hypoconnectivity between the cerebellar and subcortical domains (basal ganglia and thalamus) and hyperconnectivity between the cerebellar domain and the visual, sensorimotor and higher cognitive domains. These results demonstrate that very high-order ICA can capture distinct fine-grained ICNs, improving the detection of disease-related connectivity differences and enriching current multiscale ICN templates. The derived ICNs can serve as a valuable reference for future studies and potentially enhance the clinical utility of rsfMRI in psychiatric research.