Comparative survey of multigraph integration methods for holistic brain connectivity mapping

Nada Chaari, Hatice Camgöz Akdağ, Islem Rekik*

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıİnceleme makalesibilirkişi

9 Atıf (Scopus)

Özet

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

Orijinal dilİngilizce
Makale numarası102741
DergiMedical Image Analysis
Hacim85
DOI'lar
Yayın durumuYayınlandı - Nis 2023

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© 2023

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