Myelin water imaging as a quantitative diagnostic tool for neurodegenerative diseases: a systematic review
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Aswathi Puliyakkara
,
Saikiran Pendem
,
Priyanka
Abstract
Introduction
Neurodegenerative diseases such as multiple Sclerosis (MS), Alzheimer’s disease (AD), and Parkinson’s disease (PD) share overlapping clinical and pathological features, complicating early diagnosis and management. Demyelination, a key pathological hallmark, underscores the importance of accurately assessing white matter (WM) integrity.
Content
Myelin water imaging (MWI), an advanced non-invasive MRI technique, quantifies the myelin water fraction (MWF) and offers high specificity for detecting myelin abnormalities. This systematic review explores the feasibility and diagnostic utility of MWI across MS, AD, and PD by analyzing 21 high-quality studies from major databases, following PRISMA guidelines.
Summary
MWI consistently revealed reduced MWF in MS patients across various WM regions, lesion types, and disease stages, including responsiveness to early treatment. In AD, MWF decline correlated with disease progression and apolipoprotein E4 (APOE4) genotype, supporting its potential in early diagnosis. Findings in PD were inconsistent, reflecting secondary or minimal myelin involvement in its pathology.
Outlook
MWI shows strong promise as a non-invasive imaging biomarker, particularly in MS and AD. Standardization of acquisition protocols, integration with multimodal imaging, and further longitudinal studies are essential to establish its clinical utility and support broader implementation in neurodegenerative disease diagnostics.
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Research ethics: This systematic review did not involve direct human participants or the use of patient data. All data was obtained from previously published studies, where ethical approval was granted. Therefore, no additional ethical approval was required.
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Informed consent: Not applicable.
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Author contributions: Aswathi P and Abhijith S contributed to the conceptualization, literature search and data collection, methodology, data analysis, and manuscript writing and drafting. Dr. Saikiran P provided supervision, methodology, and was involved in manuscript writing and drafting. Dr. Priyanka and Dr. Rajagopal Kadavigere was responsible for conceptualization, supervision, and manuscript revision. Thejas M S assisted with the literature search and data collection and contributed to manuscript revision. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: All supporting documents used for the studies will be supplied as supplementary files.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/dx-2025-0055).
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