Quantitative brain regional relaxometry metrics for early diagnosis of Parkinson's disease.

Research overview

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BackgroundThe early identification of Parkinson's disease (PD) prior to the emergence of motor symptoms is paramount for effective treatment and mitigation of disease progression. Moreover, early predictions and assessments of disease progression in certain patients are critical for timely clinical interventions.PurposeThis study investigates the neuroanatomical alterations in various brain regions during the initial stages and progression of PD, to explore the potential of quantitative regional metrics as candidate imaging markers for early diagnosis and disease progression.Materials and methodsWe enrolled 31 PD patients and 25 healthy controls (HCs), categorizing PD patients into early-stage Parkinson's (ESP) (n = 22) and advanced-stage Parkinson's (ASP)(n = 9) based on the Hoehn and Yahr grading. The study employed 3D T1BRAVO and synthetic MRI for data acquisition, followed by voxel-based morphometry (VBM) and extraction of T1, T2, and proton density (PrD) values. Comparative analysis of brain volume and regional relaxation metrics was performed among the groups. A classification model based on regions showing significant group differences was evaluated using internal cross-validation.ResultsSignificant variations were identified in specific brain regions when comparing the ESP group with HCs, particularly in the right Calcarine_T1GM and left Cuneus_T1GM regions. Additionally, notable differences were discerned between the ESP group and the ASP group, specifically in the left Putamen_T1GM, left ParaHippocampal_T1WM, Precentral_T2WM, left ParaHippocampal_T2WM, Anterior Cingulate Cortex (ACC)_T2WM, and left Putamen_PDGM regions. Scatter plot analysis revealed a strong correlation between these brain regions (with the exception of left ParaHippocampal_T2WM and left Putamen_PDGM) and both the Hoehn and Yahr (H&Y) and Movement Disorder Society (MDS) scores. Under internal cross-validation, T1-based gray-matter regional metrics demonstrated the most stable discriminative performance among the evaluated modalities. Cross-validated classification performance was moderate, particularly for distinguishing ESP from ASP, indicating limited but potentially informative progression-related signals.ConclusionSynthetic MRI-derived regional relaxometry reveals stage-related brain alterations in PD. T1-based gray-matter metrics show relatively robust performance under internal validation and may serve as candidate imaging markers associated with early disease-related changes and progression in Parkinson's disease. However, all classification results should be regarded as exploratory and warrant further validation in larger, independent cohorts.

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