Essential tremor-Parkinson's disease syndrome: clinical characteristics and subtypes using cluster analysis.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are common movement disorders. ET-PD syndrome is characterized by the occurrence of PD in patients with a previous history of ET, which may be an independent phenotype distinct from PD. This study aims to identify clinical characteristics and subtypes in ET-PD. METHODS: A total of 93 newly diagnosed ET-PD patients and 93 newly diagnosed PD patients matched for age, sex, education, and disease duration of PD were selected using propensity score matching analysis. The K-means cluster analysis was performed for 11 variables derived from the ET-PD group, and cluster profiles were established through statistical analysis of demographic and clinical variables. RESULTS: The ET-PD group consisted of a high number of patients with a family history of ET exhibiting evident tremor with milder hypokinesia and postural instability symptoms, as compared to the PD group. Through the cluster analysis, two clusters of ET-PD patients were identified. The ET-PD cluster 1 ( n  = 34) had a shorter ET duration before PD onset, lower number of patients with a family history of ET, higher unified PD rating scale instability scores, higher non-motor symptoms scores (non-motor symptoms scale D1 scores, Hamilton depression scale scores, Hamilton anxiety scale scores, and PD sleep scale-2 scores), and higher Chinese version of the PD questionnaire-39 scores relative to the ET-PD cluster 2 ( n  = 59). CONCLUSION: ET-PD patients had significantly different characteristics for motor symptoms as compared to PD patients, and may be distinctly divided into two clinical subtypes, namely, the ET-PD complex type and the ET-PD simple type.

New therapeutic approaches to Parkinson's disease targeting GBA, LRRK2 and Parkin.

Parkinson's disease (PD) is defined as a complex disorder with multifactorial pathogenesis, yet a more accurate definition could be that PD is not a single entity, but rather a mixture of different diseases with similar phenotypes. Attempts to classify subtypes of PD have been made based on clinical phenotypes or biomarkers. However, the most practical approach, at least for a portion of the patients, could be to classify patients based on genes involved in PD. GBA and LRRK2 mutations are the most common genetic causes or risk factors of PD, and PRKN is the most common cause of autosomal recessive form of PD. Patients carrying variants in GBA, LRRK2 or PRKN differ in some of their clinical characteristics, pathology and biochemical parameters. Thus, these three PD-associated genes are of special interest for drug development. Existing therapeutic approaches in PD are strictly symptomatic, as numerous clinical trials aimed at modifying PD progression or providing neuroprotection have failed over the last few decades. The lack of precision medicine approach in most of these trials could be one of the reasons why they were not successful. In the current review we discuss novel therapeutic approaches targeting GBA, LRRK2 and PRKN and discuss different aspects related to these genes and clinical trials.

Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this purpose, a novel model-based multi-partition clustering method was applied on data from an international, multi-center, cross-sectional study of 402 Parkinson's disease patients. Both motor and non-motor symptoms were considered. As a result, eight sets of related symptoms were identified. Each of them provided a different way to group patients: impulse control issues, overall non-motor symptoms, presence of dyskinesias and pyschosis, fatigue, axial symptoms and motor fluctuations, autonomic dysfunction, depression, and excessive sweating. Each of these groups could be seen as a subtype of the disease. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment. Independent confirmation of these results could have implications for the clinical management of Parkinson's disease patients.

Determinants of quality of life in Parkinson's disease: a perspective of novel clinical subtypes.

OBJECTIVE: New subtyping classification systems of Parkinson's disease (PD) have been proposed for phenotyping patients into three different subtypes: mild motor-predominant (PD-MMP), intermediate (PD-IM) and diffuse malignant (PD-DM). The quality of life (QoL) underlying the novel PD clinical subtypes is unknown. This study aimed explore the feasibility of the classification in Chinese PD patients and to investigate the potential heterogeneous determinants of QoL among the three subtypes. METHODS: 298 PD patients were enrolled, including 129 PD-MMP patients, 121 PD-IM patients and 48 PD-DM patients. All patients completed the QoL assessment, clinical evaluations and neuropsychological tests. Univariate linear analysis and multiple stepwise regression analysis were performed to identify determinants of QoL. RESULTS: Compared to PD-MMP patients, PD-IM and PD-DM patients had more impaired QoL. The Geriatric Depression Rating Scale (GDS) score, Non-Motor Symptoms Questionnaire (NMSQ) score, Unified Parkinson's Disease Rating Scale part III (UPDRS-III) score and Epworth Sleepiness Score (ESS) were independent contributors to QoL in PD-MMP patients. The GDS score, ESS and sniffin' sticks screening 12 test score were independent contributors to QoL in PD-IM patients. The GDS score and Mini Mental State Examination score were independent contributors to QoL in PD-DM patients. INTERPRETATION: The new novel subtyping classification is feasible for Chinese PD patients. Although depression was the most crucial determinant for QoL in PD-MMP, PD-IM and PD-DM patients, the other contributors of QoL in the three subtypes were heterogeneous. These findings may prompt clinicians to target specific factors for improving QoL depending on PD subtypes.

Does Subthalamic Deep Brain Stimulation Impact Asymmetry and Dyscoordination of Gait in Parkinson's Disease?

Background. Subthalamic deep brain stimulation (STN-DBS) is an effective treatment for selected Parkinson's disease (PD) patients. Gait characteristics are often altered after surgery, but quantitative therapeutic effects are poorly described. Objective. The goal of this study was to systematically investigate modifications in asymmetry and dyscoordination of gait 6 months postoperatively in patients with PD and compare the outcomes with preoperative baseline and to asymptomatic controls without PD. Methods. A convenience sample of thirty-two patients with PD (19 with postural instability and gait disorder (PIGD) type and 13 with tremor dominant disease) and 51 asymptomatic controls participated. Parkinson patients were tested prior to the surgery in both OFF and ON medication states, and 6-months postoperatively in the ON stimulation condition. Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) I to IV and medication were compared to preoperative conditions. Asymmetry ratios, phase coordination index, and walking speed were assessed. Results. MDS-UPDRS I to IV at 6 months improved significantly, and levodopa equivalent daily dosages significantly decreased. STN-DBS increased step time asymmetry (hedges' g effect sizes [95% confidence interval] between pre- and post-surgery: .27 [-.13, .73]) and phase coordination index (.29 [-.08, .67]). These effects were higher in the PIGD subgroup than the tremor dominant (step time asymmetry: .38 [-.06, .90] vs .09 [-.83, 1.0] and phase coordination index: .39 [-.04, .84] vs .13 [-.76, .96]). Conclusions. This study provides objective evidence of how STN-DBS increases asymmetry and dyscoordination of gait in patients with PD and suggests motor subtypes-associated differences in the treatment response.

Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI.

OBJECTIVE: This study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Ninety-six PD patients, which included thirty-nine postural instability and gait difficulty (PIGD) subtype and fifty-seven tremor-dominant (TD) subtype, were enrolled and allocated to training and validation datasets with a ratio of 7:3. A total of five types of index, consisting of mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and functional connectivity (FC), were extracted. The features were then selected using a two-sample t-test, the least absolute shrinkage and selection operator (LASSO), and Spearman's rank correlation coefficient. Finally, support vector machine (SVM) models based on the separate index and multilevel indices were built, and the performance of models was assessed via the area under the receiver operating characteristic curve (AUC). Feature importance was evaluated using Shapley additive explanation (SHAP) values. RESULTS: The optimal SVM model was obtained based on multilevel rs-fMRI indices, with an AUC of 0.934 in the training dataset and an AUC of 0.917 in the validation dataset. The AUCs of the models based on the separate index were ranged from 0.783 to 0.858 for the training dataset and from 0.713 to 0.792 for the validation dataset. SHAP analysis revealed that functional activity and connectivity in frontal lobe and cerebellum were important features for differentiating PD subtypes. CONCLUSIONS: Our findings demonstrated multilevel rs-fMRI indices could provide more comprehensive information on brain functionalteration. Furthermore, the machine learning method based on multilevel rs-fMRI indices might be served as an alternative approach for automatically classifying clinical subtypes in PD at the individual level.

Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes.

BACKGROUND: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. OBJECTIVE: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. METHODS: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. RESULTS: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition + CS), and bvFTD versus AD (71.7%, social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. CONCLUSION: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.

Distinct progression patterns across Parkinson disease clinical subtypes.

OBJECTIVE: To examine specific symptom progression patterns and possible disease staging in Parkinson disease clinical subtypes. METHODS: We recently identified Parkinson disease clinical subtypes based on comprehensive behavioral evaluations, "Motor Only," "Psychiatric & Motor," and "Cognitive & Motor," which differed in dementia and mortality rates. Parkinson disease participants ("Motor Only": n = 61, "Psychiatric & Motor": n = 17, "Cognitive & Motor": n = 70) and controls (n = 55) completed longitudinal, comprehensive motor, cognitive, and psychiatric evaluations (average follow-up = 4.6 years). Hierarchical linear modeling examined group differences in symptom progression. A three-way interaction among time, group, and symptom duration (or baseline age, separately) was incorporated to examine disease stages. RESULTS: All three subtypes increased in motor dysfunction compared to controls. The "Motor Only" subtype did not show significant cognitive or psychiatric changes compared to the other two subtypes. The "Cognitive & Motor" subtype's cognitive dysfunction at baseline further declined compared to the other two subtypes, while also increasing in psychiatric symptoms. The "Psychiatric & Motor" subtype's elevated psychiatric symptoms at baseline remained steady or improved over time, with mild, steady decline in cognition. The pattern of behavioral changes and analyses for disease staging yielded no evidence for sequential disease stages. INTERPRETATION: Parkinson disease clinical subtypes progress in clear, temporally distinct patterns from one another, particularly in cognitive and psychiatric features. This highlights the importance of comprehensive clinical examinations as the order of symptom presentation impacts clinical prognosis.

Premotor antidepressants use differs according to Parkinson's disease subtype: A cohort study.

BACKGROUND: Depression is more frequently associated with akinetic-rigid/postural instability gait difficulty subtypes of Parkinson's disease than with tremor-dominant subtype. OBJECTIVES: The aim of the study is to investigate the frequency of exposure to antidepressant drugs, as proxy of depression, before motor onset according to Parkinson's disease subtypes. METHOD: Based on a historical cohort design, the exposure to antidepressant drugs before Parkinson's disease motor onset was obtained from the drug prescription database and assessed in the resident population of the Local Healthcare Trust of Bologna (443,117 subjects older than 35 years). Diagnosis of Parkinson's disease and subtype (tremor dominant, non-tremor dominant) at onset were recorded by neurologists and obtained from the "ParkLink Bologna" record linkage system. Exposure to antidepressants was compared both to the general population and between the two subtypes. RESULTS: From 2006 to 2018, 198 patients had a tremor dominant subtype at onset whereas 450 did not. Comparison with the general population for antidepressant exposure showed an adjusted hazard ratio of 0.86 (95% CI 0.44-1.70) for the tremor dominant subtype and 1.66 (1.16-2.39) for the non-tremor dominant subtype. Comparison of non-tremor dominant with tremor dominant subtypes showed an adjusted odds ratio of 1.86 (1.05-3.95) for antidepressant exposure. CONCLUSIONS: In our study, non-tremor dominant Parkinson's disease at onset was significantly associated with exposure to antidepressants in comparison to the general population and in comparison with the tremor dominant subtype. These results support the hypothesis of different biological substrates for different Parkinson's disease subtypes even before motor onset.

The inconsistency and instability of Parkinson's disease motor subtypes.

INTRODUCTION: Tremor-dominant (TD), indeterminate/mixed (ID/M) and postural instability gait difficulty/akinetic-rigid (PIGD/AR) are commonly used subtypes to categorize Parkinson's disease (PD) patients based on their most prominent motor signs. Three different algorithms to determine these motor subtypes are used. Here, we examined if PD subtypes are consistent among algorithms and if subtype stability over time depends on the applied algorithm. METHODS: Using a large longitudinal PD database, we applied 3 published algorithms of PD motor subtype classification in two sets of analyses: 1) cross-sectional analysis in 1185 patients, determining the prevalence of subtypes in 5-year intervals of disease duration; 2) longitudinal analysis of 178 patients, comparing subtypes of individual patients at baseline (within 5 years of diagnosis) and at follow-up ≥ 5 years after baseline. RESULTS: Cross-sectionally, prevalence of subtypes varied widely among the 3 algorithms: 5-32% TD, 9-31% ID/M, and 59-75% PIGD/AR. For all 3 algorithms, cross-sectional analysis showed a marked decline of TD prevalence with disease duration and a corresponding increase in PIGD/AR prevalence, driven by increasing gait/balance scores over time. Longitudinally, only 15-36% of baseline TD patients were still categorized as TD at 6.2 ± 1.0 years of follow-up. In 15-39% of baseline TD patients, the subtype changed to ID/M, and 46-50% changed to PIGD/AR. This shift was observed using all 3 algorithms. CONCLUSION: PD motor subtypes determined by different established algorithms are inconsistent and unstable over time. Lack of subtype fidelity should be considered when interpreting biomarker-subtype correlation and highlights the need for better definition of PD subtypes.

Plasma NfL, clinical subtypes and motor progression in Parkinson's disease.

INTRODUCTION: neurofilament light chain (NfL) levels have been proposed as reliable biomarkers of neurodegeneration in Parkinson's disease (PD) but the relationship between plasma NfL, clinical subtypes of PD and motor progression is still debated. METHODS: plasma NfL concentration was measured in 45 healthy controls and consecutive 92 PD patients who underwent an extensive motor and non-motor assessment at baseline and after 2 years of follow-up. PD malignant phenotype was defined as the combination of at least two out of cognitive impairment, orthostatic hypotension and REM sleep behavior disorder. PD patients were divided according to the age-adjusted cut-offs of plasma NfL levels into high and normal NfL (H-NfL and N-NfL, respectively). A multivariable linear regression model was used to assess the value of plasma NfL as predictor of 2-years progression in PD. RESULTS: NfL was higher in PD patients than in controls (p = 0.037). H-NfL (n = 16) group exhibited more severe motor and non-motor symptoms, higher prevalence of malignant phenotype and worse motor progression (MDS-UPDRS-III 11.3 vs 0.7 points, p = 0.003) compared to N-NfL group (n = 76). In linear regression analyses plasma NfL emerged as the best predictor of 2-year motor progression compared to age, sex, disease duration, baseline motor/non-motor variables. CONCLUSION: increased plasma NfL concentration is associated with malignant PD phenotype and faster motor progression. These findings support the role of NfL assessment as a useful measure for stratifying patients with different baseline slopes of decline in future clinical trials of putative disease-modifying treatments.

A new quantitative index in the diagnosis of Parkinson syndrome by dopamine transporter single-photon emission computed tomography.

OBJECTIVE: Dopamine transporter single-photon emission computed tomography (DAT SPECT) has been widely used to diagnose Parkinson syndrome. Using the standardized uptake value (SUV) of DAT SPECT, we propose "functional dopamine transporter volume (f-DTV)" as a new quantitative index to evaluate the three-dimensional volume of functional dopamine transporters and assess its diagnostic ability in differentiating dopaminergic neurodegenerative diseases (dNDD) from non-dNDD. METHODS: Seventy-nine patients were enrolled (42 dNDD, 37 non-dNDD; 38 men; age 24-88 years). We analyzed seven quantitative indices. The specific binding ratio (SBR) was calculated using a program specialized for DAT SPECT (SBR_Bolt). The SUVmax, SUVpeak, and SUVmean were calculated using a quantification program for bone SPECT. SBR_SUV was calculated by dividing striatal SUVmean by the average of background SUVmean. The cutoff value of the active dopamine transporter level was examined using three methods (threshold of 40% of SUVmax, SUV 2, and SUV 3) to calculate the active dopamine transporter volume (ADV). The f-DTV was calculated by multiplying ADV and SUVmean. We assessed the correlations between SBR_Bolt and SBR_SUV, and compared the mean value of each index between the dNDD and non-dNDD groups. The abilities of SBR_Bolt, SBR_SUV, SUVmax, SUVpeak, SUVmean, ADV, and f-DTV in differentiating dNDD from non-dNDD were determined by the area under the receiver operating curve (AUC) generated by the receiver operating characteristics analysis. RESULTS: The SBR_Bolt and SBR_SUV highly correlated with each other (r = 0.71). The cutoff value of the active dopamine transporter level was determined as SUV 3. All seven quantitative indices showed lower values in the dNDD group than in the non-dNDD group, and the difference between the two groups was statistically significant (p < 0.05). Sensitivity, specificity, and AUC of f-DTV were slightly lower than those of SBR_Bolt (71%, 79%, and 0.81, respectively, for f-DTV, and 81%, 84%, 0.88, respectively, for SBR_Bolt). The difference in AUC between f-DTV and SBR_Bolt was not statistically significant. CONCLUSIONS: This study demonstrates the utility of f-DTV as a novel quantitative index for evaluating the three-dimensional volume of functional dopamine transporters, and that f-DTV has almost the same diagnostic ability to differentiate dNDD from non-dNDD using DAT SPECT.

Is Cortical Activation During Walking Different Between Parkinson's Disease Motor Subtypes?

Parkinson's disease (PD) is often classified into tremor dominant (TD) and postural instability gait disorder (PIGD) subtypes. Degeneration of subcortical/cortical pathways is different between PD subtypes, which leads to differences in motor behavior. However, the influence of PD subtype on cortical activity during walking remains poorly understood. Therefore, we aimed to investigate the influence of PD motor subtypes on cortical activity during unobstructed walking and obstacle avoidance. Seventeen PIGD and 19 TD patients performed unobstructed walking and obstacle avoidance conditions. Brain activity was measured using a mobile functional near-infrared spectroscopy-electroencephalography (EEG) systems, and gait parameters were analyzed using an electronic carpet. Concentrations of oxygenated hemoglobin (HbO2) of the prefrontal cortex (PFC) and EEG absolute power from alpha, beta, and gamma bands in FCz, Cz, CPz, and Oz channels were calculated. These EEG channels correspond to supplementary motor area, primary motor cortex, posterior parietal cortex, and visual cortex, respectively. Postural instability gait disorder patients presented higher PFC activity than TD patients, regardless of the walking condition. Tremor dominant patients presented reduced beta power in the Cz channel during obstacle avoidance compared to unobstructed walking. Both TD and PIGD patients decreased alpha and beta power in the FCz and CPz channels. In conclusion, PIGD patients need to recruit additional cognitive resources from the PFC for walking. Both TD and PIGD patients presented changes in the activation of brain areas related to motor/sensorimotor areas in order to maintain balance control during obstacle avoidance, being that TD patients presented further changes in the motor area (Cz channel) to avoid obstacles.

Statistically Defined Parkinson's Disease Executive and Memory Cognitive Phenotypes: Demographic, Behavioral, and Structural Neuroimaging Comparisons.

BACKGROUND: Some individuals with Parkinson's disease (PD) experience working memory and inhibitory difficulties, others learning and memory difficulties, while some only minimal to no cognitive deficits for many years. OBJECTIVE: To statistically derive PD executive and memory phenotypes, and compare PD phenotypes on disease and demographic variables, vascular risk factors, and specific neuroimaging variables with known associations to executive and memory function relative to non-PD peers. METHODS: Non-demented individuals with PD (n = 116) and non-PD peers (n = 62) were recruited to complete neuropsychology measures, blood draw, and structural magnetic resonance imaging. Tests representing the cognitive domains of interest (4 executive function, 3 memory) were included in a k-means cluster analysis comprised of the PD participants. Resulting clusters were compared demographic and disease-related variables, vascular risk markers, gray/white regions of interest, and white matter connectivity between known regions involved in executive and memory functions (dorsolateral prefrontal cortices to caudate nuclei; entorhinal cortices to hippocampi). RESULTS: Clusters showed: 1) PD Executive, n = 25; 2) PD Memory, n = 35; 3) PD Cognitively Well; n = 56. Even after disease variable corrections, PD Executive had less subcortical gray matter, white matter, and fewer bilateral dorsolateral-prefrontal cortex to caudate nucleus connections; PD Memory showed bilaterally reduced entorhinal-hippocampal connections. PD Cognitively Well showed only reduced putamen volume and right entorhinal cortex to hippocampi connections relative to non-PD peers. Groups did not statistically differ on cortical integrity measures or cerebrovascular disease markers. CONCLUSION: PD cognitive phenotypes showed different structural gray and white matter patterns. We discuss data relative to phenotype demographics, cognitive patterns, and structural brain profiles.

Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. OBJECTIVE: We aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. METHODS: We included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. RESULTS: We identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. CONCLUSION: Multimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.

Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms.

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer's (AD) and Parkinson's Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

An investigation of thalamic nuclei volumes and the intrinsic thalamic structural network based on motor subtype in drug naïve patients with Parkinson's disease.

INTRODUCTION: This study aimed to investigate the alterations in thalamic nuclei volumes and the intrinsic thalamic structural network in patients with de novo Parkinson's disease (PD) based on their predominant symptoms. METHODS: We enrolled 65 patients with de novo PD (44 patients with tremor-dominant [TD] subtype and 21 patients with postural instability and gait disturbance [PIGD] subtype) and 20 healthy controls. All subjects underwent three-dimensional T1-weighted magnetic resonance imaging. The thalamic nuclei were segmented using the FreeSurfer program. RESULTS: We obtained volumetric differences in the thalamic nuclei of each subtype of PD in comparison of healthy control. Volumes of the right and left suprageniculate nuclei were significantly increased, whereas that of the left parafascicular nucleus was decreased in patients with the TD subtype. Volumes of the right and left suprageniculate nuclei and right ventromedial nucleus were significantly increased, whereas those of the right and left parafascicular nuclei volumes were decreased in patients with the PIGD subtype. The measures of the intrinsic thalamic global network were not different between patients with TD PD and healthy controls. However, in patients with the PIGD subtype, the global and local efficiencies were significantly increased compared to healthy controls. Moreover, although there were no differences in thalamic volume and intrinsic thalamic global network between patients with the TD and PIGD variants, we identified significant differences in the intrinsic thalamic local network between the two groups. CONCLUSIONS: Alterations in thalamic nuclei volumes and the intrinsic thalamic network in patients with PD differed based on their predominant symptoms. These findings might be related to the underlying pathogenesis and suggest that PD is a heterogeneous syndrome.

A Dual Centre Study of Pain in Parkinson's Disease and Its Relationship with Other Non-Motor Symptoms.

BACKGROUND: Pain is a disabling and often underestimated non-motor symptom (NMS) detrimentally affecting the quality of life of patients with Parkinson's disease (PD). OBJECTIVE: Here, we conducted a cross-sectional, observational international study on 167 patients with idiopathic PD in order to analyze the potential relationship between pain and other NMS. METHODS: Subjects were assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) part III, Hoehn and Yahr (H&Y) stage, King's Parkinson's Disease Pain Scale (KPPS), Brief Pain Inventory (BPI), Non-Motor Symptoms Scale (NMSS), and Beck Depression Inventory (BDI). Spearman's rank correlation coefficient, multiple regression and multiple index-based clustering algorithms were used for data analysis. RESULTS: The prevalence of pain was 88.6%, was not correlated with age, motor severity (UPDRS part III) or disease duration, whereas a weak correlation with female gender and H&Y stage >2.5 was found. Multiple NMS correlated significantly with pain. Specifically, sleep disturbance had the strongest correlation with pain, followed by depression, gastrointestinal and cardiovascular disturbances. Further analyses showed that sleep and cardiovascular disturbance were independently associated with pain, and that these symptoms clustered together in a subset of PD patients. The relationship between pain, sleep and dysautonomia persisted independently from dopamine replacement therapy. CONCLUSION: Our study suggests that sleep disruption and cardiovascular disturbance are associated with pain in PD, and possibly identifies a specific subtype within PD patients with pain. Our data also indicate that sleep disruption, pain and dysautonomia may have a common pathophysiology, possibly involving non-dopaminergic pathways.

Patients with Parkinson's Disease Show Alteration in their Visuospatial Abilities and in their Egocentric and Allocentric Spatial Orientation Measured by Card Placing Tests.

BACKGROUND: Visuospatial skills are impaired in Parkinson's disease (PD). Other related skills exist, such as spatial orientation have been poorly studied. The egocentric (based on internal cues) and allocentric frameworks (based on external cues) are used in daily spatial orientation. Depending on PD onset, the allocentric framework may have a higher level of impairment in tremor-dominant and the egocentric one in akinetic-rigid. OBJECTIVE: To evaluate spatial orientation and visuospatial functions in PD patients and controls, and to assess whether their performance is related to disease duration and the PD subtype (tremor-dominant and akinetic-rigid). METHODS: We evaluated egocentric and allocentric spatial orientation (Egocentric and Allocentric Spatial Memory Tasks) and visuospatial abilities, span and working memory in 59 PD patients and 51 healthy controls. RESULTS: Visuospatial skills, visuospatial span, and egocentric and allocentric orientation are affected in PD. Visuospatial skills and allocentric orientation undergo deterioration during the first 5 years of the disease progression, while egocentric orientation and visuospatial span do so at later stages (9-11 years). The akinetic-rigid subtype presents worse results in all the spatial abilities that were measured when compared to controls, and worse scores in visuospatial working memory, visuospatial abilities and allocentric orientation when compared to the tremor-dominant group. The tremor-dominant group performed worse than controls in egocentric and allocentric orientation. CONCLUSION: PD patients show deficits in their visuospatial abilities and in their egocentric and allocentric spatial orientation compared to controls, specifically in akinetic-rigid PD. Only spatial orientation are affected in tremor-dominant PD patients. Allocentric orientation is affected earlier in the progression of the disease.

[Abnormalities of white matter differentiate the Parkinson variant of multiple system atrophy from Parkinson's disease].

Objective: To identify objective markers between the Parkinson variant of multiple system atrophy (MSA-P) and Parkinson's disease (PD). Methods: Retrospective analysis was performed on 10 patients with MSA-P, 15 patients with PD, and 15 healthy control group during the period from August 2016 to February 2019 in Baoshan Branch of Shanghai First People's Hospital.We combined the novel tract based spatial statistics (TBSS) and region of interest (ROI) analyses for the first time to investigate three groups with diffusion tensor imaging. By TBSS, we performed pairwise comparisons of mean diffusivity and fractional anisotropy (FA) maps. The clusters with significant differences between MSA-P and PD were used as ROIs for further analyses. Results: FA values in the left anterior thalamic radiation(ATR) (ROI values were 0.371(0.287-0.535), 0.472(0.390-0.594), 0.473(0.388-0.555); P values were 0.008, 0.008) and left superior longitudinal fasciculus (SLF)(ROI values were 0.397(0.291-0.469), 0.456(0.338-0.560), 0.473(0.427-0.530); P values were 0.013,<0.001) were significantly decreased in MSA-P compared with PD or controls, and significantly correlated with clinical data((r =-0.807, P =0.005),(r =-0.455, P =0.022)). Conclusion: Our findings indicate the abnormalities of left ATR and left SLF as specific biomarkers for differential diagnosis.