Early-stage Parkinson's Disease Patients Research Articles

Machine Learning for Early Detection of Cognitive Decline in Parkinson’s Disease Using Multimodal Biomarker and Clinical Data

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson’s Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

Increased dementia risk in patients with Parkinson's disease attributed to metabolic syndrome.

Metabolic syndrome (MetS) manifests resembling pathophysiological mechanisms with Parkinson's disease (PD). Current research on the overall population has emphasized MetS as a freestanding risk factor for cognition. This research aims to explore the impact of MetS on cognition in Parkinson's patients. We involved subjects identified as having early-stage PD patients. The MetS was diagnosed dependent on parameters overviewed in the National Cholesterol Education Program's Adult Treatment Panel III. The clinical severity and stages in patients with PD were dependent on the disease rating scales. The cognition was evaluated by the Turkısh version of the Montreal Cognitive Assessment Scale (MoCA-TR). The cases were categorized according to cognitive failure: mild cognitive impairment in PD (PD-MCI), and PD dementia (PDD). Metabolic syndrome was present in 39.6% of the participants. 22.0% of patients were in the normal cognition, 29.1% in the PD-MCI group, and 48.9% in the PD-D group. The cognitive scores in patients with MetS is considerably lower than MetS negative group. A statistically notable inverse association was detected between fasting blood glucose levels and the visual-spatial/executive functions, naming, language, and orientation scores. The multivariate logistic regression analysis showed individuals with MetS were found to have an 11.308 times higher risk of PD-D (odds ratio [OR]: 11,3, 95% confidence interval [CI]: 1.61-79.2 ). We discerned the occurrence of MetS in PD raises the possibility of advancing dementia. This suggests that considering MetS in this patient group could contribute to the effective management of dementia.

A prediction model for the walking and balance milestone in Parkinson's disease

BackgroundWalking and balance impairments, represented by freezing of gait and falls, are significant contributors to disability in advanced Parkinson's disease (PD) patients. However, the composite measure of the Walking and Balance Milestone (WBMS) has not been thoroughly investigated. MethodsThis study included 606 early-stage PD patients from the Parkinson's Progression Markers Initiative (PPMI) database, with a disease duration of less than 2 years and no WBMS at baseline. Patients were divided into a model development cohort (70 %) and a validation cohort (30 %) according to the enrollment site. Longitudinal follow-up data over a period of 12 years were analyzed. ResultsAmong all 606 patients, the estimated probability of being WBMS-free at the 5th and 10th year was 88 % and 60 %, respectively. Five clinical variables (Age, Symbol Digit Modalities Test (SDMT), postural instability and gait difficulty (PIGD) score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I (MDS-UPDRS-I) score, and REM Sleep Behavior Disorder (RBD) were used to construct the Cox predictive model. The C-index of the model was 0.75 in the development cohort and 0.76 in the validation cohort. By optimizing the PIGD and MDS-UPDRS-I variables, an easy-to-use model was achieved with comparable predictive performance. ConclusionA predictive model based on five baseline clinical measures (Age, SDMT, PIGD score, MDS-UPDRS-I score, RBD) could effectively estimate the risk of the WBMS in early PD patients. This model is valuable for prognostic counseling and clinical intervention trials for gait and balance impairment.

Gait analysis in the early stage of Parkinson's disease with a machine learning approach.

Gait disorder is a prominent motor symptom in Parkinson's disease (PD), objective and quantitative assessment of gait is essential for diagnosing and treating PD, particularly in its early stage. This study utilized a non-contact gait assessment system to investigate gait characteristics between individuals with PD and healthy controls, with a focus on early-stage PD. Additionally, we trained two machine learning models to differentiate early-stage PD patients from controls and to predict MDS-UPDRS III score. Early-stage PD patients demonstrated reduced stride length, decreased gait speed, slower stride and swing speeds, extended turning time, and reduced cadence compared to controls. Our model, after an integrated analysis of gait parameters, accurately identified early-stage PD patients. Moreover, the model indicated that gait parameters could predict the MDS-UPDRS III score using a machine learning regression approach. The non-contact gait assessment system facilitates the objective and quantitative evaluation of gait disorder in PD patients, effectively distinguishing those in the early stage from healthy individuals. The system holds significant potential for the early detection of PD. It also harnesses gait parameters for a reasoned prediction of the MDS-UPDRS III score, thereby quantifying disease severity. Overall, gait assessment is a valuable method for the early identification and ongoing monitoring of PD.

Cortical microstructural alterations in different stages of Parkinson's disease.

To explore the cortical microstructural alterations in Parkinson's disease (PD) at different stages.149 PD patients and 76 healthy controls were included. PD patients were divided into early stage PD (EPD) (Hoehn-Yahr stage ≤ 2) and moderate-to-late stage PD (MLPD) (Hoehn-Yahr stage ≥ 2.5) according to their Hoehn-Yahr stages. All participants underwent two-shell diffusion MRI and the images were fitted to Neurite Orientation Dispersion and Density Imaging (NODDI) model to obtain the neurite density index (NDI) and orientation dispersion index (ODI) to reflect the cortical microstructure. We used gray matter-based spatial statistics method to compare the voxel-wise cortical NODDI metrics between groups. Partial correlation was used to correlate the NODDI metrics and global composite outcome in PD patients.Compared with healthy controls, EPD patients showed lower ODI in widespread regions, covering bilateral frontal, temporal, parietal and occipital cortices, as well as regional lower NDI in bilateral cingulate and frontal lobes. Compared with healthy controls, MLPD patients showed lower ODI and NDI in more widespread regions. Compared with EPD patients, MLPD patients showed lower ODI in bilateral temporal, parietal and occipital cortices, where the ODI values were negatively correlated with global composite outcome in PD patients.PD patients showed widespread cortical microstructural degeneration, characterized by reduced neurite density and orientation dispersion, and the cortical neuritic microstructure exhibit progressive degeneration during the progression of PD.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson’s rehabilitation

BackgroundThe loss of gait automaticity is a key cause of motor deficits in Parkinson’s disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.MethodsThirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.ResultsThe turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen’s d = 0.933).ConclusionThe turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment.Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657

Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages

This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease.

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos

BackgroundEvaluation of disease severity in Parkinson's disease (PD) relies on motor symptoms quantification. However, during early-stage PD, these symptoms are subtle and difficult to quantify by experts, which might result in delayed diagnosis and suboptimal disease management. ObjectiveTo evaluate the use of videos and machine learning (ML) for automatic quantification of motor symptoms in early-stage PD. MethodsWe analyzed videos of three movement tasks—Finger Tapping, Hand Movement, and Leg Agility— from 26 aged-matched healthy controls and 31 early-stage PD patients. Utilizing ML algorithms for pose estimation we extracted kinematic features from these videos and trained three classification models based on left and right-side movements, and right/left symmetry. The models were trained to differentiate healthy controls from early-stage PD from videos. ResultsCombining left side, right side, and symmetry features resulted in a PD detection accuracy of 79 % from Finger Tap videos, 75 % from Hand Movement videos, 79 % from Leg Agility videos, and 86 % when combining the three tasks using a soft voting approach. In contrast, the classification accuracy varied between 40 % and 72 % when the movement side or symmetry were not considered. ConclusionsOur methodology effectively differentiated between early-stage PD and healthy controls using videos of standardized motor tasks by integrating kinematic analyses of left-side, right-side, and bilateral symmetry movements. These results demonstrate that ML can detect movement deficits in early-stage PD from videos. This technology is easy-to-use, highly scalable, and has the potential to improve the management and quantification of motor symptoms in early-stage PD.

The impact of combined aerobic and resistance exercise on the prognosis of early Parkinson's disease patients.

Although literature suggests that exercise can improve symptoms in Parkinson's Disease (PD) patients, research on the effects of aerobic exercise and resistance training (AE&RT) in early-stage PD remains limited. Understanding the synergistic effects of these exercise modalities can provide valuable insights for optimizing exercise interventions for PD patients, particularly in the early stages of the disease, where interventions may have the greatest impact on long-term functional outcomes. This study aimed to investigate the effects of a combined AE&RT program on motor function, postural stability, and cognitive processing speed in early stage PD patients. A total of 236 participants with early-stage PD were assigned to either the Aerobic Exercise Group (AE group) (n= 112) or the AE&RT Group (n= 124) inthis controlled randomized trial. The study employed a one-year supervised exercise program, with the AE Group participating in aerobic activities and the AE&RT Group engaging in combined AE&RT. Outcome measures included symptom improvement, motor function, postural stability, cognitive processing speed, peak oxygen consumption, quality of life evaluation, and the incidence of adverse events. The AE&RT Group demonstrated greater improvements in tremor, muscle rigidity, gait instability, sleep problems, and hyposmia compared to the AE Group. Additionally, the combined exercise group exhibited better cognitive processing speed, as well as enhanced motor function and postural stability. Peak oxygen consumption was significantly higher in the AE&RT Group. However, the quality of life evaluation indicated a statistically higher quality of life in the AE Group. There was no significant difference in the incidence of adverse events between the two groups. The findings suggest that the integration of AE&RT in early-stage PD patients leads to more comprehensive improvements in motor symptoms, cognitive function, postural stability, and cardiovascular fitness compared to aerobic exercise alone. These results have important implications for developing tailored exercise interventions to enhance the physical and cognitive well-being of individuals with early-stage PD.

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

BackgroundCircadian rhythm (CR) disturbance is intricately associated with Parkinson’s disease (PD). However, the involvement of CR-related mechanisms in the pathogenesis and progression of PD remains elusive.MethodsA total of 141 PD patients and 113 healthy participants completed CR-related clinical examinations in this study. To further investigate the CR-related mechanisms in PD, we obtained datasets (GSE7621, GSE20141, GSE20292) from the Gene Expression Omnibus database to identify differentially expressed genes between PD patients and healthy controls and further selected CR-related genes (CRRGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) followed by logistic algorithms were employed to identify the hub genes and construct a diagnostic model. The predictive performance was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses in the training set and external validation sets. Finally, RT‒qPCR and Western blotting were conducted to verify the expression of these hub genes in blood samples. In addition, Pearson correlation analysis was utilized to validate the association between expression of hub genes and circadian rhythm function.ResultsOur clinical observational study revealed that even early-stage PD patients exhibited a higher likelihood of experiencing sleep disturbances, nocturnal hypertension, reverse-dipper blood pressure, and reduced heart rate variability compared to healthy controls. Furthermore, 4 CR-related hub genes (AGTR1, CALR, BRM14, and XPA) were identified and subsequently incorporated as candidate biomarkers to construct a diagnostic model. The model showed satisfactory diagnostic performance in the training set (AUC = 0.941), an external validation set GSE20295 (AUC = 0.842), and our clinical centre set (AUC = 0.805). Additionally, the up-regulation of CALR, BRM14 and the down-regulation of AGTR1, XPA were associated with circadian rhythm disruption.ConclusionCR disturbance seems to occur in the early stage of PD. The diagnostic model based on CR-related genes demonstrated robust diagnostic efficacy, offering novel insights for future clinical diagnosis of PD and providing a foundation for further exploration into the role of CR-related mechanisms in the progression of PD.

Insular and limbic abnormal functional connectivity in early-stage Parkinson's disease patients with autonomic dysfunction.

Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-β-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.

Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI.

This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD). Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected. Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.

Association analyses of apolipoprotein E genotypes and cognitive performance in patients with Parkinson’s disease

BackgroundCognitive impairment is a common non-motor symptom of Parkinson’s disease (PD). The apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer’s disease (AD). However, the effect of APOEε4 on cognitive function of PD patients remains unclear. In this study, we aimed to understand whether and how carrying APOEε4 affects cognitive performance in patients with early-stage and advanced PD.MethodsA total of 119 Chinese early-stage PD patients were recruited. Movement Disorder Society Unified Parkinson’s Disease Rating Scale, Hamilton anxiety scale, Hamilton depression scale, non-motor symptoms scale, Mini-mental State Examination, Montreal Cognitive Assessment, and Fazekas scale were evaluated. APOE genotypes were determined by polymerase chain reactions and direct sequencing. Demographic and clinical information of 521 early-stage and 262 advanced PD patients were obtained from Parkinson’s Progression Marker Initiative (PPMI).ResultsNo significant difference in cognitive performance was found between ApoEε4 carriers and non-carriers in early-stage PD patients from our cohort and PPMI. The cerebrospinal fluid (CSF) Amyloid Beta 42 (Aβ42) level was significantly lower in ApoEε4 carrier than non-carriers in early-stage PD patients from PPMI. In advanced PD patients from PPMI, the BJLOT, HVLT retention and SDMT scores seem to be lower in ApoEε4 carriers without reach the statistical significance.ConclusionsAPOEε4 carriage does not affect the cognitive performance of early-stage PD patients. However, it may promote the decline of CSF Aβ42 level and the associated amyloidopathy, which is likely to further contribute to the cognitive dysfunction of PD patients in the advanced stage.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.

The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM). In our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model. The hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral "swallow tail" sign region was significant for classification. The integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention. The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice. Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability. The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08. The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Associations of Multimorbidity with Cerebrospinal Fluid Biomarkers for Neurodegenerative Disorders in Early Parkinson's Disease: A Crosssectional and Longitudinal Study.

The study aims to determine whether multimorbidity status is associated with cerebrospinal fluid (CSF) biomarkers for neurodegenerative disorders. A total of 827 patients were enrolled from the Parkinson's Progression Markers Initiative (PPMI) database, including 638 patients with early-stage Parkinson's disease (PD) and 189 healthy controls (HCs). Multimorbidity status was evaluated based on the count of long-term conditions (LTCs) and the multimorbidity pattern. Using linear regression models, cross-sectional and longitudinal analyses were conducted to assess the associations of multimorbidity status with CSF biomarkers for neurodegenerative disorders, including α-synuclein (αSyn), amyloid-β42 (Aβ42), total tau (t-tau), phosphorylated tau (p-tau), glial fibrillary acidic protein (GFAP), and neurofilament light chain protein (NfL). At baseline, the CSF t-tau (p = 0.010), p-tau (p = 0.034), and NfL (p = 0.049) levels showed significant differences across the three categories of LTC counts. In the longitudinal analysis, the presence of LTCs was associated with lower Aβ42 (β < -0.001, p = 0.020), and higher t-tau (β = 0.007, p = 0.026), GFAP (β = 0.013, p = 0.022) and NfL (β = 0.020, p = 0.012); Participants with tumor/musculoskeletal/mental disorders showed higher CSF levels of t-tau (β = 0.016, p = 0.011) and p-tau (β = 0.032, p = 0.044) than those without multimorbidity. Multimorbidity, especially severe multimorbidity and the pattern of mental/musculoskeletal/ tumor disorders, was associated with CSF biomarkers for neurodegenerative disorders in early-stage PD patients, suggesting that multimorbidity might play a crucial role in aggravating neuronal damage in neurodegenerative diseases.

The Dubousset Functional Test: a reliable and valid test in early stage Parkinson’s disease patients

IntroductionDubousset Functional Test (DFT) is an assessment test evaluating the functional capacity and dynamic balance. The study aimed to examine the reliability, validity, and responsiveness of the DFT in early stage Parkinson’s disease (PD) patients.MethodsThis was a cross-sectional study. Thirty-three early stage PD patients were recruited. The DFT was performed along with the Timed Up and Go (TUG) test, dual-task TUG, Functional Reach Test (FRT), 3-m backward walk test (3MBWT), Tinetti Performance-Oriented Mobility Assessment (POMA), and Berg Balance Scale (BBS).ResultsThe test–retest reliability of the subcomponents of the DFT was excellent. The ICCs were as follows: 0.952, 0.955, 0.917, and 0.919, respectively. The correlation with subcomponents of DFT and TUG, dual-task TUG, FRT, 3MBWT, BBS, and POMA was found to be statistically significant (p < 0.05). The standard measurement errors of the subcomponents of the DFT were 1.45, 1.39, 1.70, and 1.57, respectively. The minimal clinically important difference (MCID) of the subcomponents was 2.05, 1.97, 2.41, and 2.22, respectively.ConclusionThe DFT is a reliable, valid, and easy-to-administer tool in assessing the balance and physical function of early stage PD patients.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease.

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various β band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of β-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on β band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

Tai Chi improves non-motor symptoms of Parkinson's disease: One-year randomized controlled study with the investigation of mechanisms

BackgroundTai Chi was found to improve motor symptoms in Parkinson's disease (PD). Whether long-term Tai Chi training could improve non-motor symptoms (NMS) and the related mechanisms were unknown. ObjectiveTo investigate Tai Chi's impact on non-motor symptoms in PD and related mechanisms. Methods95 early-stage PD patients were recruited and randomly divided into Tai Chi (N = 32), brisk walking (N = 31), and no-exercise groups (N = 32). All subjects were evaluated at baseline, 6 months, and 12 months within one-year intervention. Non-motor symptoms (including cognition, sleep, autonomic symptoms, anxiety/depression, and quality of life) were investigated by rating scales. fMRI, plasma cytokines and metabolomics, and blood Huntingtin interaction protein 2 (HIP2) mRNA levels were detected to observe changes in brain networks and plasma biomarkers. ResultsSixty-six patients completed the study. Non-motor functions assessed by rating scales, e.g. PD cognitive rating scale (PDCRS) and Epworth Sleepiness scale (ESS), were significantly improved in the Tai Chi group than the control group. Besides, Tai Chi had advantages in improving NMS-Quest and ESS than brisk walking. Improved brain function was seen in the somatomotor network, correlating with improved PDCRS (p = 0.003, respectively). Downregulation of eotaxin and upregulation of BDNF demonstrated a positive correlation with improvement of PDCRS and PDCRS-frontal lobe scores (p ≤ 0.037). Improvement of energy and immune-related metabolomics (p ≤ 0.043), and elevation of HIP2 mRNA levels (p = 0.003) were also found associated with the improvement of PDCRS. ConclusionsTai Chi improved non-motor symptoms in PD, especially in cognition and sleep. Enhanced brain network function, downregulation of inflammation, and enhanced energy metabolism were observed after Tai Chi training.