Early-stage Parkinson's Disease Patients Research Articles

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

ObjectivesThe 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).MethodsIn 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.ResultsThe 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.ConclusionThe 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.

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

Dubousset 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. This 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). The 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. The 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.

A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells

Parkinson´s disease (PD) is a common neurodegenerative movement disorder and leucine-rich repeat kinase 2 (LRRK2) is a promising therapeutic target for disease intervention. However, the ability to stratify patients who will benefit from such treatment modalities based on shared etiology is critical for the success of disease-modifying therapies. Ciliary and centrosomal alterations are commonly associated with pathogenic LRRK2 kinase activity and can be detected in many cell types. We previously found centrosomal deficits in immortalized lymphocytes from G2019S-LRRK2 PD patients. Here, to investigate whether such deficits may serve as a potential blood biomarker for PD which is susceptible to LRKK2 inhibitor treatment, we characterized patient-derived cells from distinct PD cohorts. We report centrosomal alterations in peripheral cells from a subset of early-stage idiopathic PD patients which is mitigated by LRRK2 kinase inhibition, supporting a role for aberrant LRRK2 activity in idiopathic PD. Centrosomal defects are detected in R1441G-LRRK2 and G2019S-LRRK2 PD patients and in non-manifesting LRRK2 mutation carriers, indicating that they accumulate prior to a clinical PD diagnosis. They are present in immortalized cells as well as in primary lymphocytes from peripheral blood. These findings indicate that analysis of centrosomal defects as a blood-based patient stratification biomarker may help nominate idiopathic PD patients who will benefit from LRRK2-related therapeutics.

The association of CSF biomarkers and cognitive decline with choroid plexus volume in early Parkinson's disease

ObjectiveThis study aims to determine the link between choroid plexus (CP) volume and cognitive decline in patients with early-stage Parkinson's disease (PD) and to test whether pathological proteins in the cerebrospinal fluid (CSF) are involved in the modulation of any detrimental effects from CP volume. MethodsData on 95 early-stage PD patients with 5 years of follow-up were collected from the Parkinson's Progression Marker Initiative cohort. The patients were separated into three groups based on tertiles of baseline CP volume. We then used a linear mixed model for longitudinal analysis and conducted path analysis to investigate mediating effects. ResultsAt baseline, the patients in both the upper and middle tertile group were older and had lower concentrations of CSF Aβ1-42 than those in the lowest tertile group. Longitudinal analysis showed that the upper tertile group suffered from a more rapid cognitive decline in the Symbol Digit Modalities test, Hopkins Verbal Learning Test (HVLT)-retention, and HVLT delayed recalled score. Furthermore, path analysis showed that the pathological effects of CP volume on the 5-year decline in memory might be partly mediated by the CSF Aβ1-42/αsyn ratio. ConclusionCP enlargement could be an independent risk factor for decreased cognition in patients with early-stage PD, and this risk may be mediated by CSF pathological proteins.

Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones.

Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.

Autophagy and neuroprotection in astrocytes exposed to 6-hydroxydopamine is negatively regulated by NQO2: relevance to Parkinson’s disease

Dopaminergic degeneration is a central feature of Parkinson’s disease (PD), but glial dysfunction may accelerate or trigger neuronal death. In fact, astrocytes play a key role in the maintenance of the blood–brain barrier and detoxification. 6-hydroxydopamine (6OHDA) is used to induce PD in rodent models due to its specific toxicity to dopaminergic neurons, but its effect on astrocytes has been poorly investigated. Here, we show that 6OHDA dose-dependently impairs autophagy in human U373 cells and primary murine astrocytes in the absence of cell death. LC3II downregulation was observed 6 to 48 h after treatment. Interestingly, 6OHDA enhanced NRH:quinone oxidoreductase 2 (NQO2) expression and activity in U373 cells, even if 6OHDA turned out not to be its substrate. Autophagic flux was restored by inhibition of NQO2 with S29434, which correlated with a partial reduction in oxidative stress in response to 6OHDA in human and murine astrocytes. NQO2 inhibition also increased the neuroprotective capability of U373 cells, since S29434 protected dopaminergic SHSY5Y cells from 6OHDA-induced cell death when cocultured with astrocytes. The toxic effects of 6OHDA on autophagy were attenuated by silencing NQO2 in human cells and primary astrocytes from NQO2−/− mice. Finally, the analysis of Gene Expression Omnibus datasets showed elevated NQO2 gene expression in the blood cells of early-stage PD patients. These data support a toxifying function of NQO2 in dopaminergic degeneration via negative regulation of autophagy and neuroprotection in astrocytes, suggesting a potential pharmacological target in PD.

Reorganization of intrinsic functional connectivity in early-stage Parkinson's disease patients with probable REM sleep behavior disorder

Reorganization of intrinsic functional connectivity in early-stage Parkinson's disease patients with probable REM sleep behavior disorder

Autonomic symptoms in early-stage Parkinson’s patients and their relationship with cognition and disease parameters

Aims: Autonomic dysfunction is a prevalent feature throughout various stages of the disease and can significantly exacerbate the overall impact of the condition. Moreover, it is linked to accelerated disease advances and diminished vitality rates in individuals with Parkinson’s disease (PD). The main goal of this study is to evaluate the prevalence of autonomic symptoms and cognitive findings and investigate their associations with disease-related factors in early-stage PD patients.&#x0D; Methods: A total of 49 individuals diagnosed with PD were enrolled in this study. Disease severity was assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS), and the disease stage was determined through the modified Hoehn &amp; Yahr Rating Scale (mHYRS). By the mH&amp;Y scale, only individuals in the early stages (≤2.5) of the disease were included in this investigation. The evaluation of autonomic symptoms in PD was conducted using the Scales for Outcomes in Parkinson’s Disease for Autonomic Symptoms (SCOPA-AUT) scale. The cognitive functions of the patients were assessed utilizing the Turkish version of the Montreal Cognitive Assessment Scale (MOCA-TR).&#x0D; Results: The study included 44% (n=22) females and 56% (n=27) males as participants. The average age was 61.5±10.1 years. The mean SCOPA-AUT score was 18.9±9.36, with the most prevalent autonomic symptoms related to the gastrointestinal system. A positive correlation was shown with autonomic symptoms and disease stages (p=0.024, r=0.322). However, no significant relationship was found between autonomic symptoms, other disease parameters, and cognition. We observed a notable inverse correlation between the disease stage and cognitive status (p=0.003, r=-0.417).&#x0D; Conclusion: Our study concluded that autonomic dysfunctions manifest from the early stages of Parkinson’s disease and can intensify as the disease progresses. Identifying and addressing these dysfunctions at an early stage would play a pivotal role in lessening the overall impact of the disease.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates.

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD). In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms. We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach. PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC. Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

Motor progression phenotypes in early-stage Parkinson’s Disease: A clinical prediction model and the role of glymphatic system imaging biomarkers

BackgroundSubstantial heterogeneity of motor symptoms in Parkinson’s disease (PD) poses a challenge to disease prediction. ObjectivesThe aim of this study was to construct a nomogram model that can distinguish different longitudinal trajectories of motor symptom changes in early-stage PD patients. MethodsData on 90 patients with 5-years of follow-up were collected from the Parkinson’s Progression Marker Initiative (PPMI) cohort. We used a latent class mixed modeling (LCMM) to identify distinct progression patterns of motor symptoms, and backward stepwise logistic regression with baseline information was conducted to identify the potential predictors for motor trajectory and to develop a nomogram. The performance of the nomogram model was then evaluated using the optimism-corrected C-index for internal validation, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for discrimination, the calibration curve for predictive accuracy, and decision curve analysis (DCA) for its clinical value. ResultsWe identified two trajectories for motor progression patterns. The first, Class 1 (Motor deteriorated group), was characterized by sustained, continuously worsening motor symptoms, and the second, Class 2 (Motor stable group), had stable motor symptoms throughout the follow-up period. The best combination of 7 baseline variables was identified and assembled into the nomogram: Scopa-AUT [odds ratio (OR), 1.11; p = 0.091], Letter number sequencing (LNS) (OR, 0.76; p = 0.068), the asymmetry index of putamen (OR, 0.95; p = 0.034), mean caudate uptake (OR, 0.14; p = 0.086), CSF pTau/α-synuclein (OR, 0.00; p = 0.011), CSF tTau/Aβ (OR, 25434806; p = 0.025), and the index for diffusion tensor image analysis along the perivascular space (ALPS-index) (OR, 0.02; p = 0.030). The nomogram achieved good discrimination, with an original AUC of 0.901 (95% CI, 0.813–0.989), and the bias-corrected concordance index (C-index) with 1,000 bootstraps was 0.834. The calibration curve and DCA also suggested both the high accuracy and clinical usefulness of the nomogram, respectively. ConclusionsThis study proposes an effective nomogram to predict different motor progression patterns in early-stage PD. Furthermore, the imaging biomarker indicating glymphatic function could be an independent predictive factor for PD motor progression.

Clinical and neurochemical correlates of the APOE genotype in early-stage Parkinson’s disease

Emerging evidence indicates that apolipoprotein E (APOE) genotype may influence Parkinson's disease (PD) course, although clinical and neurochemical correlates have not been completely established. This study aimed to determine the associations of APOE genotypes (ε4 vs. non-ε4) with cerebrospinal fluid (CSF) neurodegeneration biomarkers and clinical parameters in early-stage PD patients. One hundred and seventy-five PD patients and 89 non-neurodegenerative controls grouped in APOE-ε4 carriers (28 PD; 12 controls) and non-APOE-ε4 carriers (147 PD; 78 controls) were enrolled. CSF levels of amyloid-β-42, amyloid-β-40, total and 181-phosphorylated tau, and clinical scores were compared among groups adjusting for main covariates. APOE genotypes prevalence was similar in PD and controls. PD APOE-ε4 carriers had lower amyloid-β-42 CSF levels than PD non-APOE-ε4 carriers and controls, independently from age. PD APOE-ε4 carriers also had higher total and “item 5” (attention and memory) non-motor symptoms scale scores than PD non-APOE-ε4 carriers, independently from confounding factors. APOE-ε4 genotype might thus account for a more vulnerable PD subtype characterized by prominent amyloidopathy and a greater burden of non-motor symptoms in the early disease stages. Data AvailabilityData are available upon reasonable request.

Collaborative plasma biomarkers for Parkinson's disease development and progression: a cross-sectional and longitudinal study.

Relying on a single biomarker for early diagnosis of Parkinson's Disease (PD) may not yield accurate results. We aimed to assess the combined diagnostic value of multiple biomarkers, including plasma CCL2, plasma CXCL12, and plasma neuronal exosomal α-synuclein (α-syn) for early-stage PD diagnosis and their predictive value in PD progression. This study included both cross-sectional and longitudinal designs. The CCL2, CXCL12, and neuronal exosomal α-syn levels were analyzed in 50 healthy controls (HCs) and 50 early-stage PD patients. Then, a prospective follow-up of 30 early-stage PD patients was performed. In early-stage PD, we observed a significant increase in CCL2, CXCL12, and plasma neuronal exosomal α-syn compared to HCs (P < 0.05). Utilizing a combined diagnostic approach of CCL2, CXCL12and α-syn significantly improved the area under the curve (AUC= 0.89, P < 0.001). Spearman correlation analysis revealed that CCL2 levels were correlated with PD clinical stage and autonomic symptoms (P < 0.05). CXCL12 levels were associated with non-motor symptoms (P < 0.05). Plasma neuronal exosome α-syn levels were connected to the clinical stage, motor symptoms, and non-motor symptoms in early-stage PD (P < 0.01). In the longitudinal cohort, the Cox regression analysis showed that high CCL2 levels were associated with motor progression after a mean follow-up of 24 months. Our study suggested that the combined measurement of plasma CCL2, CXCL12, and neuronal exosomal α-syn can improve early-stage PD diagnosis, and CCL2 may serve as a prognostic marker for PD progression.

Antiparkinsonian medication masks motor signal progression in de novo patients

Patients not yet receiving medication provide insight to drug-naïve early physiology of Parkinson's Disease (PD). Wearable sensors can measure changes in motor features before and after introduction of antiparkinsonian medication. We aimed to identify features of upper limb bradykinesia, postural stability, and gait that measurably progress in de novo PD patients prior to the start of medication, and determine whether these features remain sensitive to progression in the period after commencement of antiparkinsonian medication.Upper limb motion was measured using an inertial sensor worn on a finger, while postural stability and gait were recorded using an array of six wearable sensors. Patients were tested over nine visits at three monthly intervals. The timepoint of start of medication was noted.Three upper limb bradykinetic features (finger tapping speed, pronation supination speed, and pronation supination amplitude) and three gait features (gait speed, arm range of motion, duration of stance phase) were found to progress in unmedicated early-stage PD patients. In all features, progression was masked after the start of medication.Commencing antiparkinsonian medication is known to lead to masking of progression signals in clinical measures in de novo PD patients. In this study, we show that this effect is also observed with digital measures of bradykinetic and gait motor features.

Cortical and Subcortical Changes in MEG Activity Reflect Parkinson’s Progression over a Period of 7 Years

In this study of early functional changes in Parkinson’s disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process.

Cerebrospinal fluid alpha-synuclein, amyloid beta, total tau, and phosphorylated tau in tremor-dominant Parkinson's disease.

Protein misfolding within specific brain regions is a common characteristic of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease (PD). Therefore, a common term often used for these disorders is "proteinopathy". Currently, there has been increasing attention toward the overlap of pathogenesis between proteinopathies. We aimed to explore the cross-sectional and longitudinal level of the CSF α-synuclein (α-syn), amyloid βeta (Aβ1-42), total tau (t-tau), and phosphorylated tau (p-tau) in PD subjects with tremor dominant (TD) and a non-tremor dominant (nonTD) subtype from the Parkinson Progression Markers Initiative (PPMI). We enrolled 411 early-stage PD patients and 187 healthy controls (HCs) from the PPMI. We compared the level of CSF biomarkers at four time points including baseline, 6months, 1year, and 2years. To investigate longitudinal changes in CSF proteins within each group, we used linear mixed models. The level of CSF biomarkers was significantly lower in PD patients compared to HCs at any visit. Moreover, there was no statistically significant difference in the level of CSF α-syn, Aβ1-42, t-tau, and p-tau between PD-TD and PD-nonTD. Longitudinal analysis showed significant CSF α-syn reduction after one year from baseline in PD-TD patients (P = 0.047). Also, there was a significant reduction in the level of CSF Aβ1-42 after two years in PD-nonTD patients but not HCs and PD-TD (P = 0.033). Our results indicate that different patterns in longitudinal changes of CSF biomarkers could be due to different pathophysiological mechanisms involved in each PD motor subtype.

Pinpointing the precise stimulation targets for brain rehabilitation in early-stage Parkinson’s disease

BackgroundTranscranial magnetic stimulation (TMS) is increasingly used as a promising non-pharmacological treatment for Parkinson’s disease (PD). Scalp-to-cortex distance (SCD), as a key technical parameter of TMS, plays a critical role in determining the locations of treatment targets and corresponding dosage. Due to the discrepancies in TMS protocols, the optimal targets and head models have yet to be established in PD patients.ObjectiveTo investigate the SCDs of the most popular used targets in left dorsolateral prefrontal cortex (DLPFC) and quantify its impact on the TMS-induced electric fields (E-fields) in early-stage PD patients.MethodsStructural magnetic resonance imaging scans from PD patients (n = 47) and normal controls (n = 36) were drawn from the NEUROCON and Tao Wu datasets. SCD of left DLPFC was measured by Euclidean Distance in TMS Navigation system. The intensity and focality of SCD-dependent E-fields were examined and quantified using Finite Element Method.ResultsEarly-stage PD patients showed an increased SCDs, higher variances in the SCDs and SCD-dependent E-fields across the seven targets of left DLPFC than normal controls. The stimulation targets located on gyral crown had more focal and homogeneous E-fields. The SCD of left DLPFC had a better performance in differentiating early-stage PD patients than global cognition and other brain measures.ConclusionSCD and SCD-dependent E-fields could determine the optimal TMS treatment targets and may also be used as a novel marker to differentiate early-stage PD patients. Our findings have important implications for developing optimal TMS protocols and personalized dosimetry in real-world clinical practice.

A Systematic Review of the Usage of Rotigotine During Early and Advanced Stage Parkinson's.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that occurs in old age due to a decrease in dopamine, which causes nerve cell destruction. This disease is difficult to diagnose since its symptoms are similar to those of the aging process. Those with PD have impaired motor control and function, dyskinesia, and tremors. To treat PD, drugs that enhance the amount of dopamine given to the brain are administered to alleviate symptoms. This inquiry examines the prescription of rotigotine to achieve this objective. The primary objective of thisreview is to examine the usage of rotigotine in both the late and early stages of PD. The statistical model utilized in the review found that there was not a significant difference in the dosage of rotigotine prescribed to late and early-stage PD patients, however, there were some confounding variables that may have skewed this result; therefore, further research is necessary to validate or nullify this hypothesis.