Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integra
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ORIGINAL RESEARCH
Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration Nada Chaari 1 & Hatice Camgöz Akdağ 2 & Islem Rekik 1,3 Accepted: 21 September 2020 # The Author(s) 2020
Abstract The estimation of a connectional brain template (CBT) integrating a population of brain networks while capturing shared and differential connectional patterns across individuals remains unexplored in gender fingerprinting. This paper presents the first study to estimate gender-specific CBTs using multi-view cortical morphological networks (CMNs) estimated from conventional T1weighted magnetic resonance imaging (MRI). Specifically, each CMN view is derived from a specific cortical attribute (e.g. thickness), encoded in a network quantifying the dissimilarity in morphology between pairs of cortical brain regions. To this aim, we propose Multi-View Clustering and Fusion Network (MVCF-Net), a novel multi-view network fusion method, which can jointly identify consistent and differential clusters of multi-view datasets in order to capture simultaneously similar and distinct connectional traits of samples. Our MVCF-Net method estimates a representative and well-centered CBTs for male and female populations, independently, to eventually identify their fingerprinting regions of interest (ROIs) in four main steps. First, we perform multi-view network clustering model based on manifold optimization which groups CMNs into shared and differential clusters while preserving their alignment across views. Second, for each view, we linearly fuse CMNs belonging to each cluster, producing local CBTs. Third, for each cluster, we non-linearly integrate the local CBTs across views, producing a cluster-specific CBT. Finally, by linearly fusing the cluster-specific centers we estimate a final CBT of the input population. MVCF-Net produced the most centered and representative CBTs for male and female populations and identified the most discriminative ROIs marking gender differences. The most two gender-discriminative ROIs involved the lateral occipital cortex and pars opercularis in the left hemisphere and the middle temporal gyrus and lingual gyrus in the right hemisphere. Keywords Cortical morphological networks . Gender differences . Connectional brain template estimation . Multi-view clustering . Population-driven connectome . Brain connectome atlas learning
Introduction Several human neuroimaging studies have been conducted to analyze brain connectivity between regions with respect to gender differences providing fundamental insights into the organization and integration of brain networks in male and female populations (Ingalhalikar et al. 2014; Jiang et al. 2019). In particular, brain * Islem Rekik [email protected] 1
BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey
2
Faculty of Management, Istanbul Technical University, Istanbul, Turkey
3
Computing, School of Science and Engineering, University of Dundee, Dundee, UK
connectiv
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