High-order Connectomic Manifold Learning for Autistic Brain State Identification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10511)


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 relation 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 framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectomic features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against 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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.BASIRA Lab, CVIP Group, School of Science and Engineering, ComputingUniversity of DundeeDundeeUK
  2. 2.Department of Electrical EngineeringThe National Engineering School of Tunis (ENIT)TunisTunisia

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