Discovering Disrupted Connectivities in Neuroimaging Studies: A Meta Analytic Approach

Aberrant activities in the complex human brain network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and Autism Spectrum Disorder (ASD). Functional Magnetic Resonance Imaging (fMRI) has emerged as an important tool to delineate the neural networks affected by such diseases, particularly ASD. In seeking for earlier diagnosis, we aimed to find biomarkers through the analysis of resting state fMRI images. In this article, we present a holistic approach to detect disrupted connectivities in whole brain studies. Our meta analytic approach addresses multidimensional heterogeneities in the context of multiple ROIs borrowing strength from all sites. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange (ABIDE), which includes 361 subjects from eight medical centers. Our results are consistent with previous studies in Autism research. These interrupted regions are involved in language processing, social cognition, auditory effect on social communication which are associated with ASD. We believe that our findings have addressed the variations due to different hierarchies and thus lead to more reliable identification of therapeutic targets for intervention. Our disciplined study can be used for early detection of subjects who are at a higher risk of developing neurological disorders.

Keywords: Functional magnetic resonance imaging; Meta-analysis; Mixed-effects; False discovery rate; Hierarchical designs.

Abbreviations: ABIDE: Autism Brain Imaging Data Exchange; fMRI: Functional Magnetic Resonance Imaging; ASD: Autism Spectrum Disorder; TBI: Traumatic Brain Injury; ROI: Region of Interest; FDR: False Discovery Rate; INDI: International Neuroimaging Data-Sharing Initiative