Article Figures & Data
Figures
- Figure 1 Data analysis flowchart
(A) WM lesion volumes derived from structural MRI data sets (T1W, T2W FLAIR) and (B) cortical atrophy rates (FreeSurfer processing) from the same patients were used as inputs for the pattern identification analysis. The weights were assigned based on the correlation between these 2 parameters. Parallel ICA was performed, and distinct covarying lesion patterns of cortical atrophy and WM lesions were identified. For each lesion pattern, the component loads of each patient related to cortical atrophy (brown line) and WM lesion volume (green line) were correlated. FLAIR = fluid-attenuated inversion recovery; ICA = independent component analysis; T1W = T1‐weighted; T2W = T2‐weighted; WM = white matter.
- Figure 2 Three significant covarying patterns of regional cortical atrophy and the corresponding WM lesion volume
(A) Cerebellar lesion pattern, (B) bihemispheric lesion pattern, and (C) left-lateralized lesion pattern. WM = white matter.
- Figure 3 Comparison of ROC curves showing the accuracy of covarying lesion patterns in predicting disability progression
The shadowed areas represent the 95% CIs. In the legend, the AUC and the 95% CIs are presented. The cerebellar pattern shows the best accuracy in predicting disability progression over time in comparison to the bihemispheric and left-lateralized patterns (*p < 0.05). AUC = area under the curve.
- Figure 4 Map of the cerebellar lesion distribution
Here, each particular voxel shows how this region contributes to the prediction analysis for disease progression in the study cohort (A) and in the replication cohort (B). Color bar indicates the component load of the cerebellar pattern. (C) shows the Buckner 7-network cerebellar nuclei atlas, and (D) represents the corresponding cortical connectivity map. ICA = independent component analysis.
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