Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis
Lozano Bagén, Toni (Universitat Autònoma de Barcelona. Departament d'Informàtica)
Martinez-Heras, Eloy (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Pontillo, Giuseppe (University of Naples Federico II)
Solana, Elisabeth (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Vivó Pascual, Francesc (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Petracca, Maria (Sapienza University of Rome. Department of Human Neurosciences)
Calvi, Alberto (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Garrido Romero, Sandra (Universitat Oberta de Catalunya)
Solé-Ribalta, Albert (Universitat Oberta de Catalunya)
Llufriu, Sara (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Prados Carrasco, Ferran (Universitat Oberta de Catalunya)
Casas Roma, Jordi (Universitat Autònoma de Barcelona. Departament d'Informàtica)

Data:	2025
Resum:	Brain networks, or graphs, derived from magnetic resonance imaging (MRI) offer a powerful framework for representing the structural, morphological, and functional organization of the brain. Graph-theoretical metrics have been widely employed to characterize properties such as efficiency, integration, and communication within these networks. More recently, topological data analysis techniques, such as persistent homology and Betti curves, have emerged as complementary approaches for capturing higher-order network patterns. In this study, we present a comparative analysis of these feature-generation methodologies in the context of neurodegenerative disease. Specifically, we evaluate the effectiveness of Betti curves and graph-theoretical metrics in extracting features for distinguishing people with multiple sclerosis (PwMS) from healthy volunteers (HV). Features are derived from structural connectivity, morphological gray matter, and resting-state functional networks, using both single layer and multilayer graph architectures. Our experiments, conducted on a cohort of PwMS and HV, demonstrate that features extracted using Betti curves generally outperform those based on graph-theoretical metrics. Furthermore, we show that multimodal data in terms of feature concatenation and multilayer graph architectures provide a more comprehensive representation of alterations in complex brain mechanisms associated with MS, leading to improved classification performance. These findings highlight the potential of topological features and multimodal integration for enhancing the understanding and diagnosis of neurodegenerative disorders. PI15/00587, PI18/01030 and PI21/01189.
Ajuts:	Ministerio de Economía y Competitividad PI15/00587 Instituto de Salud Carlos III PI18/01030 Instituto de Salud Carlos III PI21/01189 Agencia Estatal de Investigación PID2021-128966NB-I00PID2024-157778OB-I00
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	MRI ; Brain networks ; Graph theory ; Persistent homology ; Multiple sclerosis ; Machine learning
Publicat a:	Health Information Science and Systems, Vol. 13 (October 2025) , art. 68, ISSN 2047-2501

DOI: 10.1007/s13755-025-00386-y
PMID: 41122298

18 p, 3.0 MB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats