AUTHOR=Cengiz Kubra , Rekik Islem 

TITLE=Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis

JOURNAL=Frontiers in Neurology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1391950

DOI=10.3389/fneur.2024.1391950

ISSN=1664-2295

ABSTRACT=Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can alter the cortex morphology at different levels: (i) a low-order level where cortical regions are examined individually, (ii) a high-order level where the relationship between two cortical regions is considered, and (iii) a multi-view high-order level where the relationship between regions is examined across multiple brain views. In this study, we propose to use the emerging multi-view cortical morphological networks (CMN), derived from T1-w magnetic resonance imaging (MRI), to profile autistic and typical brains and pursue new ways of fingerprinting 'cortical morphology' at the intersection of 'network neuroscience'. Each CMN view models the pairwise morphological dissimilarity at the connection level using a specific cortical attribute (e.g. thickness). Specifically, we set out to identify the inherently most representative morphological connectivities shared across different views of the cortex in both autistic and normal control (NC) populations using tensor component analysis. We thus discover the connectional profiles of both populations shared across different CMNs of left and right hemispheres, respectively. The first most representative morphological connectivity in the right hemisphere involved (the bank of the superior temporal sulcus↔the entorhinal cortex (EC)) in both ASD and NC subjects. While the first most representative morphological connectivity linked (EC↔rostal middle frontal gyrus) in the ASD left hemisphere, (EC↔insula cortex) profiled the NC left hemisphere.