Brainstem neuromelanin and iron MRI reveals a precise signature for idiopathic and LRRK2 Parkinson's disease

Abstract

Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.

Fig. 1. Preprocessing, segmentation and quantification of MRI NM and Iron images.

3D automated atlas-based segmentation pipeline (3D-ABSP) workflow diagram for NM and iron quantification in the brainstem.

Fig. 2. Box and violin plots of the quantitative brainstem MRI measures.

Data is represented with a box plot (box edges: first 25th percentile quartile to third 75th percentile quartile; red line: median; black line: mean; whisker length: 1.5 times the interquartile range; red crosses: outlier data), a violin plot showing the histogram distribution, and individual scatter points of each subject’s quantitative measures. a SNc NM CR, b SNc NM nVol, c LC NM CR, d LC NM nVol, e SNc iron CR, f SNc iron nVol, g RN iron CR, and h RN iron nVol. Red lines represent the median and black lines represent the mean, whereas gray horizontal lines indicate statistically significant differences between groups (*p < 0.05, **p < 0.001, corrected for multiple testing).

Fig. 3. Topographical distribution of NM and iron nVol in the SNc.

Distribution of a NM nVol; and b iron nVol in the SNc according to PD subtypes, anatomical quadrants, and slices, from caudal (Q1) to dorsal (Q4).

Fig. 4. SNc NM and iron probabilistic maps.

a 2D axial slices of probabilistic maps of NM and iron obtained from healthy subjects and PD patients. b 3D renders of HC and PD for NM in SNc (left), iron in SNc (middle), and iron burden in SN (right). c Colorbar for normalized voxel NM/iron content. Average brainstem mask is shown in gray. The probabilistic map shows the voxels for which most subjects contain NM/iron (warm colors) and the voxels for which few subjects contain NM/iron (cold colors). To improve visual quality, all maps were spatially upsampled by a factor of four using bicubic interpolation.

Fig. 5. Probabilistic maps of the LC NM content in HC, iPD, and LRRK2-PD groups.

a 2D-slice axial visualization of the probabilistic maps, b 3D volumetric renders, and c Colorbar for the normalized NM content per voxel. Average brainstem mask is shown in gray. The probabilistic map shows the voxels for which most subjects contain NM (warm colors) and the voxels for which few subjects contain NM (cold colors). To improve visual quality, all maps were spatially upsampled by a factor of four using bicubic interpolation.

Fig. 6. ROC analyses for 3D-ASBP quantitative image variables’ diagnostic performance.

a Pairwise comparison for the differentiation of HC, iPD, and LRRK2-PD groups. b HC vs. PD group differentiation using individual quantitative parameters (left) or combined parameters through logistic regression models.