Functional Role of the Cerebellum in Parkinson Disease: A PET Study

Cerebellar involvement in Parkinson's disease: Pathophysiology and neuroimaging

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disease characterized by various motor and non-motor symptoms. The complexity of its symptoms suggests that PD is a heterogeneous neurological disorder. Its pathological changes are not limited to the substantia nigra-striatal system, but gradually extending to other regions including the cerebellum. The cerebellum is connected to a wide range of central nervous system regions that form essential neural circuits affected by PD. In addition, altered dopaminergic activity and α-synuclein pathology are found in the cerebellum, further suggesting its role in the PD progression. Furthermore, an increasing evidence obtained from imaging studies has demonstrated that cerebellar structure, functional connectivity, and neural metabolism are altered in PD when compared to healthy controls, as well as among different PD subtypes. This review provides a comprehensive summary of the cerebellar pathophysiology and results from neuroimaging studies related to both motor and non-motor symptoms of PD, highlighting the potential significance of cerebellar assessment in PD diagnosis, differential diagnosis, and disease monitoring.

The characteristic and biomarker value of transcranial sonography in cerebellar ataxia

OBJECTIVE

Transcranial sonography (TCS) is a noninvasive neuroimaging technique, visualizing deep brain structures and the ventricular system. Although widely employed in diagnosing various movement disorders, such as Parkinson's disease and dystonia, by detecting disease-specific abnormalities, the specific characteristics of the TCS in cerebellar ataxia remain inconclusive. We aimed to assess the potential value of TCS in patients with cerebellar ataxias for disease diagnosis and severity assessment.

METHODS

TCS on patients with genetic and acquired cerebellar ataxia, including 94 with spinocerebellar ataxias (SCAs) containing 10 asymptomatic carriers, 95 with cerebellar subtype of multiple system atrophy (MSA-C), and 100 healthy controls (HC), was conducted. Assessments included third ventricle width, substantia nigra (SN) and lentiform nucleus (LN) echogenicity, along with comprehensive clinical evaluations and genetic testing.

RESULTS

The study revealed significant TCS abnormalities in patients with cerebellar ataxia, such as enlarged third ventricle widths and elevated rates of hyperechogenic SN and LN. TCS showed high accuracy in distinguishing patients with SCA or MSA-C from HC, with an AUC of 0.870 and 0.931, respectively. TCS abnormalities aided in identifying asymptomatic SCA carriers, effectively differentiating them from HC, with an AUC of 0.725. Furthermore, third ventricle width was significantly correlated with SARA and ICARS scores in patients with SCA3 and SCOPA-AUT scores in patients with MSA-C. The SN area and SARA or ICARS scores in patients with SCA3 were also positively correlated.

INTERPRETATION

Our findings illustrate remarkable TCS abnormalities in patients with cerebellar ataxia, serving as potential biomarkers for clinical diagnosis and progression assessment.

Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease

INTRODUCTION

Tandem gait performance reportedly predicts fall risk in people with Parkinson's disease (PwPD) and help distinguish PwPD from atypical parkinsonism. In a cross-sectional study, we previously showed that tandem gait step-width widens with increasing Hoehn and Yahr (H&Y) staging scores. In this longitudinal study, we aimed to determine if progression in tandem gait deficits is dependent on disease severity in PwPD.

METHODS

Participants underwent an instrumented tandem gait measurement every 6 months for at least 2 years. The mean and variability of 4 tandem gait parameters were calculated at each visit: step-width, step-length, step-time, and step-velocity. The change in these parameters over time for 3 H&Y groups (stage 1, 2 and 2.5+) compared to aging controls was determined using a random coefficients regression model. The annual percent change in tandem gait parameters was correlated with initial disease features using Kendall's τB.

RESULTS

66 participants were analyzed (46 PD, 20 controls). Mean step-width increased over time in an H&Y stage-dependent manner, with H&Y 2 and H&Y 2.5+ experiencing increases of 6% and 10% per year (p = 0.001 and 0.024 respectively). Annual percent-change in step-width was correlated with initial motor Unified Parkinson's Disease Rating Scale (UPDRS) scores (Kendall's τB = 0.229), total UPDRS scores (τB = 0.249), H&Y scores (τB = 0.266) and inversely correlated with Montreal Cognitive Assessment (MoCA) scores (τB = -0.209; ps ≤ 0.019).

CONCLUSION

Tandem gait step-width widens over time more rapidly in more severely affected PD patients. These results suggest that tandem gait should be routinely clinically evaluated and considered in the management of imbalance in PwPD.

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.

Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study

Background

Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices.

Methods

Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine.

Results

Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus.

Conclusions

Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.

Amelioration of Parkinsonian tremor evoked by DBS: which role play cerebello-(sub)thalamic fiber tracts?

BACKGROUND

Current pathophysiological models of Parkinson's disease (PD) assume a malfunctioning network being adjusted by the DBS signal. As various authors showed a main involvement of the cerebellum within this network, cerebello-cerebral fiber tracts are gaining special interest regarding the mediation of DBS effects.

OBJECTIVES

The crossing and non-decussating fibers of the dentato-rubro-thalamic tract (c-DRTT/nd-DRTT) and the subthalamo-ponto-cerebellar tract (SPCT) are thought to build up an integrated network enabling a bidimensional communication between the cerebellum and the basal ganglia. The aim of this study was to investigate the influence of these tracts on clinical control of Parkinsonian tremor evoked by DBS.

METHODS

We analyzed 120 electrode contacts from a cohort of 14 patients with tremor-dominant or equivalence-type PD having received bilateral STN-DBS. Probabilistic tractography was performed to depict the c-DRTT, nd-DRTT, and SPCT. Distance maps were calculated for the tracts and correlated to clinical tremor control for each electrode pole.

RESULTS

A significant difference between "effective" and "less-effective" contacts was only found for the c-DRTT (p = 0.039), but not for the SPCT, nor the nd-DRTT. In logistic and linear regressions, significant results were also found for the c-DRTT only (pmodel logistic = 0.035, ptract logistic = 0,044; plinear = 0.027).

CONCLUSIONS

We found a significant correlation between the distance of the DBS electrode pole to the c-DRTT and the clinical efficacy regarding tremor reduction. The c-DRTT might therefore play a major role in the mechanisms of alleviation of Parkinsonian tremor and could eventually serve as a possible DBS target for tremor-dominant PD in future.

A review of the neurotransmitter system associated with cognitive function of the cerebellum in Parkinson's disease

The dichotomized brain system is a concept that was generalized from the 'dual syndrome hypothesis' to explain the heterogeneity of cognitive impairment, in which anterior and posterior brain systems are independent but partially overlap. The dopaminergic system acts on the anterior brain and is responsible for executive function, working memory, and planning. In contrast, the cholinergic system acts on the posterior brain and is responsible for semantic fluency and visuospatial function. Evidence from dopaminergic/cholinergic imaging or functional neuroimaging has shed significant insight relating to the involvement of the cerebellum in the cognitive process of patients with Parkinson's disease. Previous research has reported evidence that the cerebellum receives both dopaminergic and cholinergic projections. However, whether these two neurotransmitter systems are associated with cognitive function has yet to be fully elucidated. Furthermore, the precise role of the cerebellum in patients with Parkinson's disease and cognitive impairment remains unclear. Therefore, in this review, we summarize the cerebellar dopaminergic and cholinergic projections and their relationships with cognition, as reported by previous studies, and investigated the role of the cerebellum in patients with Parkinson's disease and cognitive impairment, as determined by functional neuroimaging. Our findings will help us to understand the role of the cerebellum in the mechanisms underlying cognitive impairment in Parkinson's disease.

Aging, Neurodegenerative Disorders, and Cerebellum

An important part of the central nervous system (CNS), the cerebellum is involved in motor control, learning, reflex adaptation, and cognition. Diminished cerebellar function results in the motor and cognitive impairment observed in patients with neurodegenerative disorders such as Alzheimer's disease (AD), vascular dementia (VD), Parkinson's disease (PD), Huntington's disease (HD), spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS), Friedreich's ataxia (FRDA), and multiple sclerosis (MS), and even during the normal aging process. In most neurodegenerative disorders, impairment mainly occurs as a result of morphological changes over time, although during the early stages of some disorders such as AD, the cerebellum also serves a compensatory function. Biological aging is accompanied by changes in cerebellar circuits, which are predominantly involved in motor control. Despite decades of research, the functional contributions of the cerebellum and the underlying molecular mechanisms in aging and neurodegenerative disorders remain largely unknown. Therefore, this review will highlight the molecular and cellular events in the cerebellum that are disrupted during the process of aging and the development of neurodegenerative disorders. We believe that deeper insights into the pathophysiological mechanisms of the cerebellum during aging and the development of neurodegenerative disorders will be essential for the design of new effective strategies for neuroprotection and the alleviation of some neurodegenerative disorders.

Linking the cerebellum to Parkinson disease: an update

Parkinson disease (PD) is characterized by heterogeneous motor and non-motor symptoms, resulting from neurodegeneration involving various parts of the central nervous system. Although PD pathology predominantly involves the nigral-striatal system, growing evidence suggests that pathological changes extend beyond the basal ganglia into other parts of the brain, including the cerebellum. In addition to a primary involvement in motor control, the cerebellum is now known to also have an important role in cognitive, sleep and affective processes. Over the past decade, an accumulating body of research has provided clinical, pathological, neurophysiological, structural and functional neuroimaging findings that clearly establish a link between the cerebellum and PD. This Review presents an overview and update on the involvement of the cerebellum in the clinical features and pathogenesis of PD, which could provide a novel framework for a better understanding the heterogeneity of the disease.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices

BACKGROUND

Cognitive dysfunction is the most common non-motor symptom in Parkinson's disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups.

METHODS

We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman's rank correlation coefficient (LDHs) and Kendall's coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values.

RESULTS

The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features.

CONCLUSIONS

More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

Abnormal cerebellum connectivity patterns related to motor subtypes of Parkinson's disease

Cerebellar dysfunction may substantially contribute to the clinical symptoms of Parkinson's disease (PD). The role of cerebellar subregions in tremors and gait disturbances in PD remains unknown. To investigate alterations in cerebellar subregion volumes and functional connectivity (FC), as well as FC between the dentate nucleus (DN) and ventral lateral posterior nucleus (VLp) of the thalamus, which are potentially involved in different PD motor subtypes. We conducted morphometric and resting-state functional connectivity analyses in various cerebellar subregions in 22 tremor-dominant (TD)-PD and 35 postural instability gait difficulty dominant (PIGD)-PD patients and 38 sex- and age-matched healthy controls (HCs). The volume and FC alterations in various cerebellar subregions and the neural correlates of these changes with the clinical severity scores were investigated. The PIGD-PD group showed greater FC between the right motor cerebellum (CBMm) and left postcentral gyrus than the HC group, and a higher FC was associated with less severe PIGD symptoms. In contrast, the TD-PD group had decreased FC between the right DN and left VLp compared with the PIGD-PD and HC groups, and lower FC was associated with worse TD symptoms. Furthermore, the PIGD-PD group had higher FC between the left DN and left inferior temporal gyrus than the TD-PD group. Morphometric analysis revealed that the TD-PD group showed a significantly higher volume of left CBMm than the HC group. Our findings point to differential alteration patterns in cerebellar subregions and offer a new perspective on the pathophysiology of motor subtypes of PD.

Free water imaging as a novel biomarker in Wilson's disease: A cross-sectional study

BACKGROUND

The bi-tensor free water imaging may provide more specific information in detecting microstructural brain tissue alterations than conventional single tensor diffusion tensor imaging. The study aimed to investigate microstructural changes in deep gray matter (DGM) nuclei of Wilson's disease (WD) using a bi-tensor free water imaging and whether the findings correlate with the neurological impairment in WD patients.

METHODS

The study included 29 WD patients and 25 controls. Free water and free water corrected fractional anisotropy (FA_T) in DGM nuclei of WD patients were calculated. The correlations of free water and FA_T with the Unified WD Rating Scale (UWDRS) neurological subscale of WD patients were performed.

RESULTS

Free water and FA_T values were significantly increased in multiple DGM nuclei of neurological WD patients compared to controls. WD patients with normal appearing on conventional MRI also had significantly higher free water and FA_T values in multiple DGM nuclei than controls. Positive correlations were noted between the UWDRS neurological subscores and free water values of the putamen and pontine tegmentum as well as FA_T values of the dentate nucleus, red nucleus, and globus pallidus. In addition, the measured free water and FA_T values of specific structures also showed a positive correlation with specific clinical symptoms in neurological WD patients, such as dysarthria, parkinsonian signs, tremor, dystonia, and ataxia.

CONCLUSIONS

Free water imaging detects microstructural changes in both normal and abnormal appearing DGM nuclei of WD patients. Free water imaging indices were correlated with the severity of neurological impairment in WD patients.

Common and unique dysconnectivity profiles of dorsal and median raphe in Parkinson's disease

The serotonergic (5-HT) system, which undergoes degeneration in Parkinson's disease (PD), is involved in the pathogenesis of motor and nonmotor symptoms. The dorsal raphe (DR) and median raphe (MR) nuclei are the main source of 5-HT neurons, however, brain connectivity changes in these two nuclei have not been delineated in PD. Here we used resting-state fMRI (rs-fMRI) to characterize functional connectivity profiles of DR and MR and further examine the associations between dysconnectivity of raphe nuclei and clinical phenotypes of PD. We found that DR and MR commonly hypo-connected with the sensorimotor, temporal, and occipital cortex, limbic system, left thalamus, putamen, and cerebellum in PD. DR had unique decreased connectivity with the bilateral prefrontal and cingulate cortices, while MR had lower connectivity with the pons. Moreover, reduced connectivity of DR correlated with depression, drowsiness, and anxiety, whereas dysconnectivity of MR correlated with depression, cognitive deficits, sleep disturbances, and pain. Our findings highlight the complex roles of raphe nuclei in motor and nonmotor symptoms, providing novel insights into the neurophysiological mechanisms underlying pathogenesis of PD.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.