Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Background Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist. Purpose To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype. Study Type Prospective. Subjects In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments. Field Strength/Sequence 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences. Assessment Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently. Statistical Testing Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis. Results Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84). Data Conclusion Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019.