Cerebellar and cerebral white matter changes in Parkinson's disease with resting tremor

Temporal evolution of microstructural integrity in cerebellar peduncles in Parkinson's disease: Stage-specific patterns and dopaminergic correlates

BACKGROUND

Previous research revealed differences in cerebellar white matter integrity by disease stages, indicating a compensatory role in Parkinson's disease (PD). However, the temporal evolution of cerebellar white matter microstructure in patients with PD (PwPD) remains unclear.

OBJECTIVE

To unravel temporal evolution of cerebellar white matter and its dopaminergic correlates in PD.

METHODS

We recruited 124 PwPD from the PPMI study. The participants were divided into two subsets: Subset 1 (n = 41) had three MRI scans (baseline, 2 years, and 4 years), and Subset 2 (n = 106) had at least two MRI scans at baseline, 1 year, and/or 2 years. Free water-corrected diffusion metrics were used to measure the microstructural integrity in cerebellar peduncles (CP), the main white matter tracts connecting to and from the cerebellum. The ACAPULCO processing pipeline was used to assess cerebellar lobules volumes. Linear mixed-effect models were used to study longitudinal changes. We also examined the relationships between microstructural integrity in CP, striatal dopamine transporter specific binding ratio (SBR), and clinical symptoms.

RESULTS

Microstructural changes in CP showed a non-linear pattern in PwPD. Free water-corrected fractional anisotropy (FAt) increased in the first two years but declined from 2 to 4 years, while free water-corrected mean diffusivity exhibited the opposite trend. The initial increased FAt in CP correlated with cerebellar regional volume atrophy, striatal dopaminergic SBR decline, and worsening clinical symptoms, but this correlation varied across disease stages.

CONCLUSIONS

Our findings suggest a non-linear evolution of microstructural integrity in CP throughout the course of PD, indicating the adaptive structural reorganization of the cerebellum simultaneously with progressive striatal dopaminergic degeneration in PD.

Comparison of diffusion tensor imaging (DTI) tissue characterization parameters in white matter tracts of patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD)

OBJECTIVE

To investigate the microstructural properties of T2 lesion and normal-appearing white matter (NAWM) in 20 white matter tracts between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and correlations between the tissue damage and clinical variables.

METHODS

The white matter (WM) compartment of the brain was segmented for 56 healthy controls (HC), 48 patients with MS, and 38 patients with NMOSD, and for the patients further subdivided into T2 lesion and NAWM. Subsequently, the diffusion tensor imaging (DTI) tissue characterization parameters of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared for 20 principal white matter tracts. The correlation between tissue damage and clinical variables was also investigated.

RESULTS

The higher T2 lesion volumes of 14 fibers were shown in MS compared to NMOSD. MS showed more microstructure damage in 13 fibers of T2 lesion, but similar microstructure in seven fibers compared to NMOSD. MS and NMOSD had microstructure damage of NAWM in 20 fibers compared to WM in HC, with more damage in 20 fibers in MS compared to NMOSD. MS patients showed higher correlation between the microstructure of T2 lesion areas and NAWM. The T2 lesion microstructure damage was correlated with duration and impaired cognition in MS.

CONCLUSIONS

Patients with MS and NMOSD show different patterns of microstructural damage in T2 lesion and NAWM areas. The prolonged disease course of MS may aggravate the microstructural damage, and the degree of microstructural damage is further related to cognitive impairment.

CLINICAL RELEVANCE STATEMENT

Microstructure differences between T2 lesion areas and normal-appearing white matter help distinguish multiple sclerosis and neuromyelitis optica spectrum disorder. In multiple sclerosis, lesions rather than normal-appearing white matter should be a concern, because the degree of lesion severity correlated both with normal-appearing white matter damage and cognitive impairment.

KEY POINTS

• Multiple sclerosis and neuromyelitis optica spectrum disorder have different damage patterns in T2 lesion and normal-appearing white matter areas. • The microstructure damage of normal-appearing white matter is correlated with the microstructure of T2 lesion in multiple sclerosis and neuromyelitis optica spectrum disorder. • The microstructure damage of T2 lesion in multiple sclerosis is correlated with duration and cognitive impairment.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.