Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.
Fabry disease
brain‐age
deep learning
neuroimaging biomarkers
quantitative imaging
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
revised:
23
12
2023
received:
11
07
2023
accepted:
07
01
2024
medline:
23
3
2024
pubmed:
23
3
2024
entrez:
23
3
2024
Statut:
ppublish
Résumé
While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e26599Informations de copyright
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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