Connectomics in NeuroImaging

Abstract

Previous studies have identified disordered functional (from fMRI) and structural (from diffusion MRI) brain connectivities in Autism Spectrum Disorder (ASD). However, 'shape connections' between brain regions were rarely investigated in ASD – e.g., how morphological attributes of a specific brain region (e.g., sulcal depth) change in rela-tion to morphological attributes in other regions. In this paper, we use conventional T1-w MRI to define morphological connectivity networks, each quantifying shape similarity between different cortical regions for a specific cortical attribute at both low-order and high-order levels. For ASD identification, we present a connectomic manifold learning frame-work, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectomic features, to per-form dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against supervised and unsu-pervised state-of-the-art methods, while depicting the most discrimina-tive high-and low-order relationships between morphological regions in the left and right hemispheres.