Neuromelanin-sensitive magnetic resonance imaging in the diagnosis and assessment of Parkinson's disease.

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized primarily by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc); noradrenergic neurons in the locus coeruleus (LC) are also frequently affected. Because clinical motor symptoms usually emerge only after substantial neurodegeneration has occurred, there is a compelling need for noninvasive imaging biomarkers capable of detecting early pathological changes. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) generates neuromelanin-related image contrast in the SNpc and LC and can indirectly reflect the structural integrity of these catecholaminergic nuclei. Current evidence suggests that NM-MRI may be useful for distinguishing patients with PD from healthy controls, supporting the differential diagnosis of selected atypical parkinsonian syndromes and essential tremor, assessing disease stage, monitoring longitudinal change, and evaluating high-risk individuals in the prodromal phase. This review provides an updated account of recent advances in the use of NM-MRI for diagnostic support, differential diagnosis, prodromal assessment, longitudinal monitoring, multimodal imaging, and artificial intelligence-based analysis, and discusses its clinical role relative to other imaging and fluid biomarkers. Combining NM-MRI with complementary imaging measures, such as quantitative susceptibility mapping and diffusion MRI, and with automated analytical methods may further enhance pathological characterization and quantitative assessment. However, the available evidence is derived largely from single-center or case-control studies with limited sample sizes. Substantial variability may arise from differences in scanners, magnetic field strengths, acquisition protocols, and analytical methods, while independent external validation and prospective multicenter longitudinal evidence remain limited. The biological basis of the NM-MRI signal has not been fully established, and artificial intelligence models developed for NM-MRI remain vulnerable to overfitting, limited interpretability, and limited cross-center generalizability. Accordingly, NM-MRI cannot currently serve as a stand-alone diagnostic tool for PD or replace diagnosis based on comprehensive clinical assessment. It is better regarded as a promising complementary imaging biomarker and research tool. Before NM-MRI can be widely used for routine clinical diagnosis, disease monitoring, or as a clinical trial outcome measure, acquisition and analytical methods must be further standardized, and its performance must be evaluated in large, prospective, multicenter longitudinal studies.