Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

Abstract

AbstractBackgroundWhile 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.MethodsMRI scans of FD patients and healthy controls (HC) 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 8 publicly available sources (N=2160; mean age=33y[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.ResultsWe studied 52 FD patients (40.6±12.6y; 28F) and 58 HC (38.4±13.4y; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error=4.01y, R2=0.90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1vs-0.1, p=0.01). Brain-PAD was associated with FASTEX score (B=0.10, p=0.02), brain parenchymal fraction (B=-153.50, p=0.001), white matter hyperintensities load (B=0.85, p=0.01), and tissue volume reduction throughout the brain.ConclusionsWe demonstrated that FD patients’ brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.Summary StatementUsing deep learning and the brain-age paradigm, we found that Fabry disease is associated with older-appearing brains. The gap between brain-predicted and chronological age correlates with multi-organ disease severity, offering a novel quantitative neuroimaging biomarker.Key PointsPatients with Fabry disease show significantly higher brain-predicted age difference values compared to healthy controls (estimated marginal means: 3.1 vs -0.1, p=0.01).Brain-predicted age difference correlates with multi-organ disease severity and is associated with brain parenchymal fraction, white matter hyperintensities load, and tissue volume throughout the brain.Brain-predicted age difference might represent a sensitive quantitative biomarker of brain involvement in Fabry disease, with potentially relevant implications for patient stratification and treatment response monitoring.