Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-04-15
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
Series
Human Brain Mapping
Abstract
Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting-state (RS) functional connectivity (FC) abnormalities found in this condition were shown to be variable across different MS phases, according to the severity of clinical manifestations. The article describes a system exploiting machine learning on RS FC matrices to discriminate different MS phenotypes and to identify relevant functional connections for MS stage characterization. To this end, the system exploits some mathematical properties of covariance-based RS FC representation, which can be described by a Riemannian manifold. The classification performance of the proposed framework was significantly above the chance level for all MS phenotypes. Moreover, the proposed system was successful in identifying relevant RS FC alterations contributing to an accurate phenotype classification.
Description
Funding Information: This study was partially supported by FISM with a research grant (FISM2018/R/5), and financed or co‐financed with the “5 per mille” public funding. Publisher Copyright: © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
Keywords
Connectomics, functional connectivity, geodesic clustering, multiple sclerosis, Riemannian manifold
Other note
Citation
Yamin , A , Valsasina , P , Tessadori , J , Filippi , M , Murino , V , Rocca , M A & Sona , D 2023 , ' Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence ' , Human Brain Mapping , vol. 44 , no. 6 , pp. 2294-2306 . https://doi.org/10.1002/hbm.26210