Objective: Assess brain volume changes in patients with multiple system atrophy (MSA) over one year.

Background: Identifying disease progression biomarkers is crucial for advancing MSA treatment. Previous longitudinal MRI studies of brain atrophy in MSA have faced methodological challenges, hindering definitive conclusions about their utility to assess disease progression. Novel post-processing techniques, particularly deep-learning segmentation¹, can improve precision and potentially enhance MSA progression tracking.

Method: Seventeen participants meeting criteria for clinically probable MSA² were enrolled in the bioMUSE natural history study and had fluid biomarkers (α-synuclein SAA³ in CSF, and NfL in CSF and plasma⁴), 3T MRI and neurological exam at baseline, 6 and 12 months, including the UMSARS⁵ and NNIPPS⁶ clinical rating scales. Age-matched healthy controls (HC, n=19) and patients with Parkinson disease (PD, n=22) were also enrolled as controls and underwent a single MRI scan. AssemblyNet¹, an automated deep-learning technique, segmented 3D T1-weighted images, and regional volumes were normalized to total intracranial volume. Group differences were assessed with a least squares model with age and sex as covariates. Longitudinal volumetric changes were analyzed with a linear mixed-effect model. Associations between volumetric changes and disease progression were explored using Wald test, with statistical significance set at p-value<0.05.

Results: Fluid biomarkers identified MSA (n=10: 6 MSA-P, 4 MSA-C), PD-like (n=5), and α-synuclein negative (n=2) patients. At baseline, MSA patients had lower volumes in the cerebellar grey matter (CGM), cerebellar white matter (CWM), putamen (PT), globus pallidus (GP), and brainstem (BS) compared to both HC and PD controls. Longitudinally, PD-like patients showed no significant changes, while MSA-C and MSA-P exhibited decreased volumes in CGM, CWM, GP, and BS, with MSA-P also showing PT volume loss. Volumetric changes in these regions were negatively associated with changes in UMSARS and NNIPPS total scores.

Conclusion: Over the course of one year, MRI with deep-learning segmentation reveals substantial brain volume reduction in MSA patients, underscoring the critical role of structural MRI in both diagnosis and disease progression monitoring. Subcortical brain volume emerges as a potential biomarker for disease-modifying therapies.

References: 1. Coupé P, Mansencal B, Clément M, et al. AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation. Neuroimage. 2020;219:117026. doi:10.1016/J.NEUROIMAGE.2020.117026
2. Wenning GK, Stankovic I, Vignatelli L, et al. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Movement Disorders. 2022;37(6):1131-1148. doi:10.1002/MDS.29005
3. Concha-Marambio L, Shahnawaz M, Soto C. Detection of Misfolded α-Synuclein Aggregates in Cerebrospinal Fluid by the Protein Misfolding Cyclic Amplification Platform. Methods Mol Biol. 2019;1948:35-44. doi:10.1007/978-1-4939-9124-2_4
4. Singer W, Schmeichel AM, Sletten DM, et al. Neurofilament Light Chain in Spinal Fluid and Plasma in Multiple System Atrophy – A Prospective, Longitudinal Biomarker Study. Res Sq. Published online August 1, 2023. doi:10.21203/RS.3.RS-3201386/V1
5. Wenning GK, Tison F, Seppi K, et al. Development and validation of the Unified Multiple System Atrophy Rating Scale (UMSARS). Movement Disorders. 2004;19(12):1391-1402. doi:10.1002/MDS.20255
6. Bensimon G, Ludolph A, Agid Y, et al. Riluzole treatment, survival and diagnostic criteria in Parkinson plus disorders: The NNIPPS Study. Brain. 2009;132(1):156. doi:10.1093/BRAIN/AWN291

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MDS Abstracts - https://www.mdsabstracts.org/abstract/association-between-clinical-progression-in-multiple-system-atrophy-and-brain-volume-changes-evaluated-via-deep-learning-segmentation/