UC Riverside

Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI (fMRI) studies, however findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. The Autism Brain Imaging Data Exchange (ABIDE) contains neuroimaging data from more than 17 international scanning sites and has become a useful tool in studying the brain-behavior relationships of ASD. However multi-site databases impose site effects that confound FC due to the use of different scanning hardware, models, and parameters. Although there are established methods to mitigate site effects, these strategies often result in reduced effect size in FC features known to be affected in diseased populations. In this work, we propose a site-wise de-meaning (SWD) strategy in multi-site FC analysis of fMRI and evaluate the performance against two common site effect mitigation methods (Generalized Linear Model and ComBat Harmonization). These methods were tested on two multi-site psychiatric consortium: ABIDE and Bipolar and Schizophrenia Network on Intermediate Phenotypes. Preservation of consistent FC alterations in patients were evaluated for each method through the calculation of effect size (Hedge’s g) between patients and controls. The SWD method demonstrated superior performance in preserving the effect size in FC features associated with neurodevelopmental and psychiatric disorders compared to the original data and commonly used methods. We then aim to identify the relationships between clinical symptoms and FC measures to help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. Canonical correlation analysis was performed on two-hundred and ten ASD subjects from ABIDE to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined three ASD subtypes based on distinct brain-behavior relationships. Overall, we reduce heterogeneity in multi-site fMRI databases and elucidate the heterogeneity of ASD clinical manifestations and connectomic profiles. The reduction of site effects and preservation of FC associated with disorders can lead to a better understanding of brain connectivity in diseased populations, and identification of distinct ASD subtypes may lead to better targeted therapies for individuals.