GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation.

Details

Serval ID
serval:BIB_5988DB04D91A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation.
Journal
Frontiers in neuroscience
Author(s)
Lu P.J., Barakovic M., Weigel M., Rahmanzadeh R., Galbusera R., Schiavi S., Daducci A., La Rosa F., Bach Cuadra M., Sandkühler R., Kuhle J., Kappos L., Cattin P., Granziera C.
ISSN
1662-4548 (Print)
ISSN-L
1662-453X
Publication state
Published
Issued date
2021
Peer-reviewed
Oui
Volume
15
Pages
647535
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironment of the brain tissue and the heterogeneity of MS lesions. In contrast, the damaged tissue can be characterized by mathematical models on multishell diffusion imaging data, which measure different compartmental water diffusion. In this work, we obtained 12 diffusion measures from eight diffusion models, and we applied a deep-learning attention-based convolutional neural network (CNN) (GAMER-MRI) to select the most discriminating measures in the classification of MS lesions and the perilesional tissue by attention weights. Furthermore, we provided clinical and biological validation of the chosen metrics-and of their most discriminative combinations-by correlating their respective mean values in MS patients with the corresponding Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL), which are measures of disability and neuroaxonal damage. Our results show that the neurite density index from neurite orientation and dispersion density imaging (NODDI), the measures of the intra-axonal and isotropic compartments from microstructural Bayesian approach, and the measure of the intra-axonal compartment from the spherical mean technique NODDI were the most discriminating (respective attention weights were 0.12, 0.12, 0.15, and 0.13). In addition, the combination of the neurite density index from NODDI and the measures for the intra-axonal and isotropic compartments from the microstructural Bayesian approach exhibited a stronger correlation with EDSS and sNfL than the individual measures. This work demonstrates that the proposed method might be useful to select the microstructural measures that are most discriminative of focal tissue damage and that may also be combined to a unique contrast to achieve stronger correlations to clinical disability and neuroaxonal damage.
Keywords
advanced quantitative diffusion MRI, clinically correlated measure selection, deep learning, multiple sclerosis, relative importance order
Pubmed
Web of science
Open Access
Yes
Create date
11/05/2021 9:57
Last modification date
30/05/2021 6:35
Usage data