Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

• Published in: Frontiers in neuroinformatics Vol. 12; p. 70
• Main Authors: Soussia, Mayssa, Rekik, Islem
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.10.2018; Frontiers Media S.A
• Subjects: Autism; Autism Spectrum Disorder; Biomarkers; Brain; Brain mapping; classification; Computer engineering; Cortex; Diagnosis; Functional magnetic resonance imaging; high-order brain connectivity; Identification; International conferences; Learning algorithms; Medical imaging; morphological brain network; Morphology; multi-kernel learning; Neural networks; Neuroscience; NMR; Nuclear magnetic resonance; high-order brain connectivity; multi-kernel learning; hierarchical ensemble classifier; morphological brain network; Autism Spectrum Disorder; diagnosis; morphological connectional biomarkers; classification
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2018.00070

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both and levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.