Differentiating Patients with Parkinson’s Disease from Normal Controls Using Gray Matter in the Cerebellum

Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements

Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.

Understanding the role of cerebellum in early Parkinson's disease: a structural and functional MRI study

Increasing evidence suggests that the cerebellum may have a role in the pathophysiology of Parkinson's disease (PD). Hence, the scope of this study was to investigate whether there are structural and functional alterations of the cerebellum and whether they correlate with motor and non-motor symptoms in early PD patients. Seventy-six patients with early PD and thirty-one age and sex-matched healthy subjects (HS) were enrolled and underwent a 3 T magnetic resonance imaging (MRI) protocol. The following MRI analyses were performed: (1) volumes of 5 cerebellar regions of interest (sensorimotor and cognitive cerebellum, dentate, interposed, and fastigial nuclei); (2) microstructural integrity of the cerebellar white matter connections (inferior, middle, and superior cerebellar peduncles); (3) functional connectivity at rest of the 5 regions of interest already described in point 1 with the rest of brain. Compared to controls, early PD patients showed a significant decrease in gray matter volume of the dentate, interposed and fastigial nuclei, bilaterally. They also showed abnormal, bilateral white matter microstructural integrity in all 3 cerebellar peduncles. Functional connectivity of the 5 cerebellar regions of interest with several areas in the midbrain, basal ganglia and cerebral cortex was altered. Finally, there was a positive correlation between abnormal functional connectivity of the fastigial nucleus with the volume of the nucleus itself and a negative correlation with axial symptoms severity. Our results showed that structural and functional alterations of the cerebellum are present in PD patients and these changes contribute to the pathophysiology of PD in the early phase.

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.

Voxel-based morphometry of grey matter structures in Parkinson's Disease with wearing-off

Our study aimed to investigate the grey matter (GM) changes using voxel-based morphometry (VBM) in Parkinson's disease (PD) patients with wearing-off (WO). 3D-T1-weighted imaging was performed on 48 PD patients without wearing-off (PD-nWO), 39 PD patients with wearing-off (PD-WO) and 47 age and sex-matched healthy controls (HCs). 3D structural images were analyzed by VBM procedure with Statistical Parametric Mapping (SPM12) to detect grey matter volume. Widespread areas of grey matter changes were found in patients among three groups (in bilateral frontal, temporal lobes, lingual gyrus, inferior occipital gyrus, right precuneus, right superior parietal gyrus and right cerebellum). Grey matter reductions were found in frontal lobe (right middle frontal gyrus, superior frontal gyrus and precentral gyrus), right parietal lobe (precuneus, superior parietal gyrus, postcentral gyrus), right temporal lobe (superior temporal gyrus, middle temporal gyrus), bilateral lingual gyrus and inferior occipital gyrus in PD-WO group compared with the PD-nWO group. Our results suggesting that wearing-off may be associated with grey matter atrophy in the cortical areas. These findings may aid in a better understanding of the brain degeneration process in PD with wearing-off.

Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study

BACKGROUND

Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated.

OBJECTIVE

To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group.

METHODS

Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated.

RESULTS

Overall, people with PD had a regionally smaller posterior lobe (dmax  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17).

CONCLUSIONS

We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Cerebellar alterations in Parkinson's disease with postural instability and gait disorders

BACKGROUND

Few studies interrogated the involvement of cerebellum in modulating gait in Parkinson's disease (PD) patients with postural instability and gait disorders (PD-PIGD). This study aimed at assessing cerebellar atrophy and activity alterations during functional MRI (fMRI) gait-simulating motor- and dual-tasks in PD-PIGD.

METHODS

Twenty-one PD-PIGD and 23 healthy controls underwent clinical assessment, structural MRI, and fMRI including a motor-task (foot anti-phase movements) and a dual-task (foot anti-phase movements while counting backwards by threes). Grey matter cerebellar volumes were assessed using SUIT atlas. FMRI activations were extracted from each cerebellar lobule, and we correlated cerebellar and basal ganglia activity.

RESULTS

PD-PIGD patients had reduced volumes of cerebellar motor and non-motor areas relative to controls. During fMRI motor-task, patients showed greater activation of cognitive cerebellar areas (VI and Crus I-II) vs controls. During fMRI dual-task, PD-PIGD patients showed increased activity of cognitive areas (Crus II) and reduced activity of motor areas (I-IV). Cerebellar structural alterations correlated with increased fMRI activity of cerebellar cognitive areas and with lower executive-attentive performance. The increased activity of Crus I during the motor-task correlated with a better motor performance in PD-PIGD. Moreover, the increased activity of cerebellum correlated with a reduced activity of putamen.

CONCLUSIONS

In PD-PIGD, the increased activity of non-motor cerebellar areas during gait-simulating tasks may be a consequence of grey matter atrophy or an attempt to compensate the functional failure of cerebellar motor areas and basal ganglia. Cerebellar MRI metrics are useful to characterize brain correlates of motor and dual-task abilities in PD-PIGD patients.

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA

BACKGROUND

Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly.

PURPOSE

Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from PD via these biomarkers to validate their classification performance.

METHODS

This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and PD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/PD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and PD have been applied to classify ET from PD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment.

RESULTS

According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and PD and achieved superior classification performance (PCA + SVM, accuracy = 100%).

CONCLUSIONS

This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and PD. Future studies using a large data set are needed to confirm the potential clinical application of machine learning techniques to discern between PD and ET.

Gray matter volume of cerebellum associated with idiopathic normal pressure hydrocephalus: A cross-sectional analysis

The cause of idiopathic normal pressure hydrocephalus's (iNPH) clinical symptoms remains unclear. The cerebral cortex is the center of the brain and provides a structural basis for complex perception and motor function. This study aimed to explore the relationship between changes in cerebral cortex volume and clinical symptoms in patients with iNPH. This study included 21 iNPH patients and 20 normal aging (NA) controls. Voxel-based morphometry statistical results showed that, compared with NA, the gray matter volumes of patients with iNPH in the bilateral temporal lobe, bilateral hippocampus, bilateral thalamus, bilateral insula, left amygdala, right lenticular nucleus, right putamen, and cerebellum decreased, while the volumes of gray matter in the bilateral paracentral lobules, precuneus, bilateral supplementary motor area, medial side of the left cerebral hemisphere, and median cingulate and paracingulate gyri increased. Correlation analysis among the volumes of white matter and gray matter in the cerebrum and cerebellum and the iNPH grading scale (iNPHGS) revealed that the volume of white matter was negatively correlated with the iNPHGS (P < 0.05), while the gray matter volumes of cerebellar area 6 and area 8 were negatively correlated with the clinical symptoms of iNPH (P < 0.05). The volume of gray matter in the cerebellar vermis was negatively correlated with gait, and the gray matter volume of cerebellar area 6 was negatively correlated with cognition. Our findings suggest that the cerebellum also plays an important role in the pathogenesis of iNPH, potentially highlighting new research avenues for iNPH.

Action and emotion perception in Parkinson's disease: A neuroimaging meta-analysis

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The neural substrates for action and emotion perception deficits in PD are still unclear.

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We addressed this issue via coordinate-based meta-analyses of previous fMRI data.

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PD patients exhibit decreased response in the basal ganglia.

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PD patients exhibit a trend toward decreased response in the parietal areas.

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PD patients exhibit a trend toward increased activation in the posterior cerebellum.

Patients with Parkinson disease (PD) may show impairments in the social perception. Whether these deficits have been consistently reported, it remains to be clarified which brain alterations subtend them. To this aim, we conducted a neuroimaging meta-analysis to compare the brain activity during social perception in patients with PD versus healthy controls. Our results show that PD patients exhibit a significantly decreased response in the basal ganglia (putamen and pallidum) and a trend toward decreased activity in the mirror system, particularly in the left parietal cortex (inferior parietal lobule and intraparietal sulcus). This reduced activation may be tied to a disruption of cognitive resonance mechanisms and may thus constitute the basis of impaired others’ representations underlying action and emotion perception. We also found increased activation in the posterior cerebellum in PD, although only in a within-group analysis and not in comparison with healthy controls. This cerebellar activation may reflect compensatory mechanisms, an aspect that deserves further investigation. We discuss the clinical implications of our findings for the development of novel social skill training programs for PD patients.

Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features

Purpose

This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.

Methods

Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.

Results

After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.

Conclusion

The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease

Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Automatic classification of idiopathic Parkinson's disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry

Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [¹¹C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [¹¹C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson's disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.

Pathological changes in the cerebellum of patients with multiple system atrophy and Parkinson's disease-a stereological study

Multiple system atrophy (MSA) and Parkinson's disease (PD) are synucleinopathies characterized by aggregation of α-synuclein in brain cells. Recent studies have shown that morphological changes in terms of cerebral nerve cell loss and increase in glia cell numbers, the degree of brain atrophy and molecular and epidemiological findings are more severe in MSA than PD. In the present study, we performed a stereological comparison of cerebellar volumes, granule and Purkinje cells in 13 patients diagnosed with MSA [8 MSA-P (striatonigral subtype) and 5 MSA-C (olivopontocerebellar subtype)], 12 PD patients, and 15 age-matched control subjects. Only brains from MSA-C patients showed a reduction in the total number of Purkinje cells (anterior lobe) whereas both MSA-P and MSA-C patients had reduced Purkinje cell volumes (perikaryons and nuclei volume). The cerebellum of both diseases showed a reduction in the white matter volume compared to controls. The number of granule cells was unaffected in both diseases. Analyses of cell type-specific mRNA expression supported our structural data. This study of the cerebellum is in line with previous findings in the cerebrum and demonstrates that the degree of morphological changes is more pronounced in MSA-C than MSA-P and PD. Further, our results support an explicit involvement of cerebellar Purkinje cells and white matter connectivity in MSA-C > MSA-P and points to the potential importance of white matter alterations in PD pathology.

Cerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings

Background: Parkinson's disease (PD) results in both motor and non-motor symptoms. Traditionally, the underlying mechanism of PD has been linked to neurodegeneration of the basal ganglia. Yet it does not adequately account for the non-motor symptoms of the disease, suggesting that other brain regions may be involved. One such region is the cerebellum, which is known to be involved, together with the basal ganglia, in both motor and non-motor functions. Many studies have found the cerebellum to be hyperactive in PD patients, a finding that is seldom discussed in detail, and warrants further examination. The current study thus aims to examine quantitively the current literature on the cerebellar involvement in both motor and non-motor functioning in PD. Methods: A meta-analysis of functional neuroimaging literature was conducted with Seed-based D mapping. Only the studies testing functional activation in response to motor and non-motor paradigms in PD and healthy controls (HC) were included in the meta-analysis. Separate analyses were conducted by including only studies with non-motor paradigms, as well as meta-regressions with UPDRS III scores and disease duration. Results: A total of 57 studies with both motor and non-motor paradigms fulfilled our inclusion criteria and were included in the meta-analysis, which revealed hyperactivity in Crus I-II and vermal III in PD patients compared to HC. An analysis including only studies with cognitive paradigms revealed a cluster of increased activity in PD patients encompassing lobule VIIB and VIII. Another meta-analysis including the only 20 studies that employed motor paradigms did not reveal any significant group differences. However, a descriptive analysis of these studies revealed that 60% of them reported cerebellar hyperactivations in PD and included motor paradigm with significant cognitive task demands, as opposed to 40% presenting the opposite pattern and using mainly force grip tasks. The meta-regression with UPDRS III scores found a negative association between motor scores and activation in lobule VI and vermal VII-VIII. No correlation was found with disease duration. Discussion: The present findings suggest that one of the main cerebellar implications in PD is linked to cognitive functioning. The negative association between UPDRS scores and activation in regions implicated in motor functioning indicate that there is less involvement of these areas as the disease severity increases. In contrast, the lack of correlation with disease duration seems to indicate that the cerebellar activity may be a compensatory mechanism to the dysfunctional basal ganglia, where certain sub-regions of the cerebellum are employed to cope with motor demands. Yet future longitudinal studies are needed to fully address this possibility.

Cerebello-striatal interaction mediates effects of subthalamic nucleus deep brain stimulation in Parkinson's disease

BACKGROUND

In Parkinson's disease (PD), dopamine replacement therapy (DRT) enhances the effective connectivity of the prefrontal cortex (PFC) and supplementary motor area (SMA). The clinical effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) go beyond DRT effects including highly beneficial tremor suppression.

OBJECTIVES

Here, we aimed to determine DBS-related changes of a motor network using resting state fMRI in PD patients with chronic STN DBS.

METHODS

In a repeated-measurement design, 26 medicated PD patients (60.9 years (SD 8.9)) were investigated using resting state fMRI while bipolar STN stimulation was (i) active or (ii) switched off, and dynamic causal modelling was subsequently performed.

RESULTS

DBS improved the MDS-UPDRS-III score by 26.4% (DBS ON/Med ON vs. DBS OFF/Med ON). Active stimulation resulted in an increased effective connectivity from cerebellum to putamen (p = 0.00118). In addition, there was a stronger coupling from PFC to cerebellum (p = 0.021), as well as from cerebellum to SMA (p = 0.043) on an uncorrected level. Coupling strength from PFC to cerebellum correlated with the DBS-related change of the resting tremor subscore (r = 0.54, p = 0.031). Self-connections increased as a function of DBS in the right PFC, PMC, SMA, M1, thalamus and left cerebellum.

CONCLUSIONS

DBS-related improvement of Parkinsonian signs appears to be driven by an interaction between the cerebellum and the putamen. Resting tremor suppression may be related to an enhanced prefronto-cerebellar network. Activation of the mesial premotor loop (PFC-SMA) as seen in DRT may thus be secondary due to the primary modulation of cerebellar networks.

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.

Structural MRI in Idiopathic Parkinson's Disease

Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.

Cerebellar atrophy in different subtypes of Parkinson's disease

BACKGROUND

To investigate, using Magnetic Resonance Imaging (MRI) and voxel-based morphometry (VBM), morphometric changes of cerebellum in Parkinson's disease with different motor and affective subtypes.

METHODS

Fifty-four patients with idiopathic Parkinson's disease (PD) were classified into tremor-predominant-PD (PDT) (n = 37) and akinetic/rigidity-predominant-PD (PDAR) (n = 17). Moreover, PD groups were divided into four affective subtypes, including depressive but not anxious PD (dPD, n = 5), anxious but not depressive PD (aPD, n = 8), comorbid depressive and anxious PD (coPD, n = 8), and PD patients without depressive or anxious symptoms (nPD, n = 33). They were additionally compared at a group level with thirty-nine normal controls (NCs). An analysis of covariance followed by post hoc tests was performed to examine the alterations of cerebellar grey matter volume (GMV) in different groups of PD.

RESULTS

Compared with NCs, PD showed grey matter (GM) atrophy in the right Crus II, pyramis, culmen, the right lobules IV, and V, and the left lobule VI. PDT, PDAR and NCs did not differ in the volume of the cerebellum. Relative to nPD group, dPD group exhibited GMV reduction in the left Crus I, while aPD group showed GMV reduction in the tonsil and the right lobule VIII. The GM atrophy was also found in the coPD group compared to NCs, including the tonsil, the left lobule VIII, the right lobule VI, the left Crus I, and vermis IV, and V. There was a significant negative correlation between the Hamilton Rating Scale for Depression (HAMD) score and the right lobule IX volume, and a significant negative correlation between the Hamilton Rating Scale for Anxiety (HAMA) score and the right lobule VIII volume.

CONCLUSIONS

These findings suggest that cerebellar changes are involved in PD. It also supports a possible role of the cerebellum in the depressive and anxious symptoms in PD.

Longitudinal Alterations of Local Spontaneous Brain Activity in Parkinson's Disease

We used resting-state fMRI to evaluate longitudinal alterations in local spontaneous brain activity in Parkinson's disease (PD) over a 2-year period. Data were acquired from 23 PD patients at baseline and follow-up, and 27 age- and sex-matched normal controls. Regional homogeneity (ReHo) and voxel-based-morphometry (VBM) were used to identify differences in local spontaneous brain activity and grey matter volume. With disease progression, we observed a progressive decrease in ReHo in the sensorimotor cortex, default-mode network, and left cerebellum, but increased ReHo in the supplementary motor area, bilateral temporal gyrus, and hippocampus. Moreover, there was a significant positive correlation between the rates of ReHo change in the left cerebellum and the rates of change in the Unified Parkinson's Disease Rating Scale-III scores. VBM revealed no significant differences in the grey matter volume among the three sets of acquisitions. We conclude that ReHo may be a suitable non-invasive marker of progression in PD.