Parkinson's Disease Subjects Research Articles

Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.

Dopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [11C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [123I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [11C]CFT PET images to [123I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements. A CycleGAN was trained on [11C]CFT PET (n = 602, 72%PD) and [123I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated. Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed. CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [11C]CFT PET to [123I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.

Muscle spindle receptors and their impact on Parkinson´s disease and Cerebral Palsy subjects.

In some neurological conditions, like Parkinson's disease (PD) and Cerebral Palsy (CP), as well as with ageing, muscle spindles have been mentioned as participating in the pathological response of observed muscles. The aim of this review has therefore been to examine what is known about muscle spindle receptors, their function and how they are involved in regulating precise muscle movement in relation to these two conditions. Data from acoustic myography (AMG) studies with healthy controls (HC), CP and PD subjects have been re-examined with a view to identifying possible effects of changes in muscle movement which could be related to muscle spindle receptor function. Studies of muscle spindles have shown that during shortening and lengthening contractions the fusimotor system is activated differently with different discharge frequencies and sensitivities. With increasing age comes a loss of precise proprioception, something that coincides with a change in the AMG E-score towards lower values, indicating a reduced level of coordination and efficiency of muscle use. With PD and CP there is likewise a documented decrease in proprioception, also showing lower E-values than age-matched HC subjects. We conclude that the decrease in proprioception observed in these subjects must be partly due to a change in the muscle spindle / C-centre feedback system.

An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.

The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting movement control. Detection of PD in early stages is of critical importance to prevent the progression of the disease and improve treatment processes. The aim of the current study is to develop a deep learning model that can perform accurate classification for early diagnosis of PD from MRI images. In this study, a densely connected feature fusion network with residual learning is designed to diagnose PD patients. The designed network consists of a serial dense block with skip connections and efficient attention mechanisms. In this architecture, squeeze-excitation (SE) blocks with ResNeXt (SE-ResNeXt block) modules are utilized to extract distinctive and high-level features. In the experiments, a publicly available T2-weighted MRI dataset is used, and an offline augmentation process is applied to limited data to increase the generalization ability and classification performance. The proposed method is evaluated and compared with current state-of-the-art deep learning methods. The obtained results show that the proposed model gives higher classification performance with an overall accuracy of 94.44%, precision of 91.67%, sensitivity of 91.67%, specificity of 95.83%, F1-score of 91.67%, and Matthew's correlation coefficient of 87.50% for the PD and healthy control subjects.

Biofluid Markers and Tissue Biopsies Analyses for the Prodromal and Earliest Phase of Parkinson's Disease.

The recent development of new methods to detect misfolded α-synuclein (αSyn) aggregates in biofluids and tissue biopsies in the earliest Parkinson's disease (PD) phases is dramatically challenging the biological definition of PD. The αSyn seed amplification methods in cerebrospinal fluid (CSF) showed high sensitivity and specificity for early diagnosis of PD and Lewy bodies disorders. Several studies in isolated REM sleep behavior disorders and other at-risk populations also demonstrated a high prevalence of CSF αSyn positivity and its potential value in predicting the phenoconversion to clinically manifested diseases. Growing evidence exists for αSyn aggregates in olfactory mucosa, skin, and other tissues in subjects with PD or at-risk subjects. DOPA decarboxylase and numerous other candidates have been additionally proposed for either diagnostic or prognostic purposes in earliest PD phases. The newly described αSyn detection in blood, through its quantification in neuronally-derived exosome vesicles, represents a technical challenge that could open a new scenario for the biological diagnosis of PD. Despite this growing evidence in the field, most of method of αSyn detection and markers still need to be validated in ongoing longitudinal studies through an accurate assessment of different prodromal disease subtypes and scenarios before being definitively implemented in clinical settings.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease.

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

A novel super-resolution microscopy platform for cutaneous alpha-synuclein detection in Parkinson's disease.

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

Clinicopathological Heterogeneity of Lewy Body Diseases: The Profound Influence of Comorbid Alzheimer's Disease.

In recent years, proposals have been advanced to redefine or reclassify Lewy body disorders by merging the long-established entities of Parkinson's disease (PD), Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB). These proposals reject the International DLB Consortium classification system that has evolved over three decades of consensus collaborations between neurologists, neuropsychologists and neuropathologists. While the Consortium's "one year rule" for separating PD and DLB has been criticized as arbitrary, it has been a pragmatic and effective tool for splitting the continuum between the two entities. In addition to the decades of literature supporting the non-homogeneity of PD and DLB, it has become increasingly apparent that Lewy body disorders may fundamentally differ in their etiology. Most PD subjects, as well as most clinically-presenting DLB subjects, might best be classified as having a "primary synucleinopathy" while most clinically-unidentified DLB subjects, who also have concurrent neuropathology-criteria AD (AD/DLB), as well as those with neuropathological AD and amygdala-predominant LBD insufficient for a DLB diagnosis, may best be classified as having a "secondary synucleinopathy. Importantly, the DLB Consortium recognized the importance of comorbid AD pathology by defining "Low", "Intermediate" and "High" subdivisions of DLB based on the relative brain stages of both Lewy body and AD pathology. If the one-year rule for separating PD from DLB, and for then dividing DLB into subtypes based on the presence and severity of comorbid AD pathology, is effective, then the divided groups should statistically differ in important ways. In this study we used the comprehensive clinicopathological database of the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) to empirically test this hypothesis. Furthermore, we used multivariable statistical models to test the hypothesis that comorbid AD neuropathology is a major predictor of the presence and severity of postmortem Lewy synucleinopathy. The results confirm the clinicopathological heterogeneity of Lewy body disorders as well as the profound influence of comorbid AD pathology.

Single-Cell Cortical Transcriptomics Reveals Common and Distinct Changes in Cell-Cell Communication in Alzheimer's and Parkinson's Disease.

Alzheimer's disease (AD) and Parkinson's disease (PD) cause significant neuronal loss and severely impair daily living. Despite different clinical manifestations, these disorders share common pathological molecular hallmarks, including mitochondrial dysfunction and synaptic degeneration. A detailed comparison of molecular changes at single-cell resolution in the cortex, as one of the main brain regions affected in both disorders, may reveal common susceptibility factors and disease mechanisms. We performed single-cell transcriptomic analyses of post-mortem cortical tissue from AD and PD subjects and controls to identify common and distinct disease-associated changes in individual genes, cellular pathways, molecular networks, and cell-cell communication events, and to investigate common mechanisms. The results revealed significant disease-specific, shared, and opposing gene expression changes, including cell type-specific signatures for both diseases. Hypoxia signaling and lipid metabolism emerged as significantly modulated cellular processes in both AD and PD, with contrasting expression alterations between the two diseases. Furthermore, both pathway and cell-cell communication analyses highlighted shared significant alterations involving the JAK-STAT signaling pathway, which has been implicated in the inflammatory response in several neurodegenerative disorders. Overall, the analyses revealed common and distinct alterations in gene signatures, pathway activities, and gene regulatory subnetworks in AD and PD. The results provide insights into coordinated changes in pathway activity and cell-cell communication that may guide future diagnostics and therapeutics.

Combining salivary α-synuclein seeding activity and miRNA-29a to distinguish Parkinson's disease and multiple system atrophy

IntroductionThe differential diagnosis of early Parkinson's disease (PD) by a single biomarker is still challenging due to its symptomatic overlap with other neurological diseases. Increasing evidences support the use of saliva biomarkers of neurodegeneration, including microRNAs and α-synuclein (α-syn) seeding activity, to diagnose patients with idiopathic PD and multiple system atrophy (MSA). Our previous study confirmed the salivary microRNA-29a-3p (miRNA-29a-3p) and α-syn seeding activity could differentiate PD and MSA from healthy control subjects (HCs) and patients with essential tremor (ET). MethodsWe set up α-syn real-time quaking induced conversion seed amplification assay (α-syn RT-QuIC SAA) in 203 participants from the Peking University First Hospital with PD (n = 101), MSA (n = 32), ET (n = 17) and healthy control subjects (HCs, n = 53). We also determined miRNA-29a-3p in saliva by real time quantitative PCR (RT-qPCR) and, in 155 participants (36HCs, 80PD, 22MSA, 17ET). ResultsSensitivity of RT-QuIC seed amplification assay (SAA) for PD was 70.30 %, for MSA was 56.25 % and specificity for healthy controls was 92.45 %. The expression level of saliva miRNA-29a-3p was significantly decreased in patients with PD (p < 0.001) and MSA (p < 0.0001), and allowed differentiation with HCs (PD vs. HCs, AUC 0.69; MSA vs. HCs, AUC 0.95). Sensitivity of salivary miRNA-29a-3p for PD and MSA were 70.00 % and 95.45 %, respectively, and specificity for PD and MSA were 77.23 % and 80.56 %, respectively. By combining the salivary α-syn RT-QuIC SAA with miRNA-29a-3p, sensitivity for PD vs. HCs increasing to 75.00 %, while sensitivity for MSA vs. HCs increasing to 90.00 %. Specificity was 91.67 % for PD and 88.89 % for MSA after combining assessment of salivary α-syn RT-QuIC SAA. Salivary α-syn RT-QuIC SAA yielded 100.00 % sensitivity and 79.21 % specificity for PD vs. ET, and 100.00 % sensitivity and 65.63 % specificity for MSA vs. ET. Salivary miRNA-29a-3p provied 88.24 % sensitivity and 48.75 % specificity for PD vs. ET and 86.36 % sensitivity and 88.24 % specificity for MSA vs. ET. The combined assessment of saliva markers provided a better diagnostic value for ET vs. synucleinopathies (ET vs. PD: 88.24 % sensitivity and 81.25 % specificity; ET vs. MSA: 94.12 % sensitivity and 90.00 % specificity) than RT-QuIC SAA alone, or miRNA-29a-3p alone. The combination of lag phase and miRNA-29a-3p could add higher specificity (85.71 %) which increased approximately 40 percent (specificity: miRNA-29a-3p 47.50 %, lag phase 48.98 %) for discriminating PD from MSA. However, the sensitivity of combining these two methods was 61.11 %, which was lower than lag phase alone (89.66 %) or miRNA-29a-3p alone (95.45 %). ConclusionsThis study confirmed that saliva, a non-invasive biofluid in synucleinopathies possessed potential diagnostic power between PD, MSA, ET and normal controls. We show the combined value of saliva miRNA-29a-3p and saliva α-syn RT-QuIC SAA in the diagnosis and differential diagnosis of Parkinsonism.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.

Modulating the biosynthesis and TLR4-interaction of lipopolysaccharide as an approach to counter gut dysbiosis and Parkinson's disease: Role of phyto-compounds

The prevalence of the world's second leading neurodegenerative disorder Parkinson's disease (PD) is well known while its pathogenesis is still a topical issue to explore. Clinical and experimental reports suggest the prevalence of disturbed gut microflora in PD subjects, with an abundance of especially Gram-negative bacteria. The endotoxin lipopolysaccharide (LPS) released from the outer cell layer of these bacteria interacts with the toll-like receptor 4 (TLR4) present on the macrophages and it stimulates the downstream inflammatory cascade in both the gut and brain. Recent research also suggests a positive correlation between LPS, alpha-synuclein, and TLR4 levels, which indicates the contribution of a parallel LPS-alpha-synuclein-TLR4 axis in stimulating inflammation and neurodegeneration in the gut and brain, establishing a body-first type of PD. However, owing to the novelty of this paradigm, further investigation is mandatory. Modulating LPS biosynthesis and LPS-TLR4 interaction can ameliorate gut dysbiosis and PD. Several synthetic LpxC (UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase; LPS-synthesizing enzyme) inhibitors and TLR4 antagonists are reported to show beneficial effects including neuroprotection in PD models, however, are not devoid of side effects. Plant-derived compounds have been long documented for their benefits as nutraceuticals and thus to search for effective, safer, and multitarget therapeutics, the present study focused on summarizing the evidence reporting the potential of phyto-compounds as LpxC inhibitors and TLR4 antagonists. Studies demonstrating the dual potential of phyto-compounds as the modulators of LpxC and TLR4 have not yet been reported. Also, very few preliminary studies have reported LpxC inhibition by phyto-compounds. Nevertheless, remarkable neuroprotection along with TLR4 antagonism has been shown by curcumin and juglanin in PD models. The present review thus provides a wide look at the research progressed to date in discovering phyto-compounds that can serve as LpxC inhibitors and TLR4 antagonists. The study further recommends the need for expanding the search for potential candidates that can render dual protection by inhibiting both the biosynthesis and TLR4 interaction of LPS. Such multitarget therapeutic intervention is believed to bring fruitful yields in countering gut dysbiosis, neuroinflammation, and dopaminergic neuron damage in PD patients through a single treatment paradigm.

Differential Responses to Low- and High-Frequency Subthalamic Nucleus Deep Brain Stimulation on Sensor-Measured Components of Bradykinesia in Parkinson's Disease.

The current approach to assessing bradykinesia in Parkinson's Disease relies on the Unified Parkinson's Disease Rating Scale (UPDRS), which is a numeric scale. Inertial sensors offer the ability to probe subcomponents of bradykinesia: motor speed, amplitude, and rhythm. Thus, we sought to investigate the differential effects of high-frequency compared to low-frequency subthalamic nucleus (STN) deep brain stimulation (DBS) on these quantified facets of bradykinesia. We recruited advanced Parkinson's Disease subjects with a chronic bilateral subthalamic nucleus (STN) DBS implantation to a single-blind stimulation trial where each combination of medication state (OFF/ON), electrode contacts, and stimulation frequency (60 Hz/180 Hz) was assessed. The Kinesia One sensor system was used to measure upper limb bradykinesia. For each stimulation trial, subjects performed extremity motor tasks. Sensor data were recorded continuously. We identified STN DBS parameters that were associated with improved upper extremity bradykinesia symptoms using a mixed linear regression model. We recruited 22 subjects (6 females) for this study. The 180 Hz STN DBS (compared to the 60 Hz STN DBS) and dopaminergic medications improved all subcomponents of upper extremity bradykinesia (motor speed, amplitude, and rhythm). For the motor rhythm subcomponent of bradykinesia, ventral contacts yielded improved symptom improvement compared to dorsal contacts. The differential impact of high- and low-frequency STN DBS on the symptoms of bradykinesia may advise programming for these patients but warrants further investigation. Wearable sensors represent a valuable addition to the armamentarium that furthers our ability to conduct objective, quantitative clinical assessments.

Sleep quality in patients with Parkinson’s disease in the Republic of Moldova – preliminary results

Background and objectives. The rising prevalence of sleep issues are a consequent societal problem, notably impacting individuals with Parkinson’s disease (PD). This study aims to examine the differences in sleep quality between diagnosed patients and their counterparts, while also highlighting its effects on their symptoms and quality of life. Materials and methods. This study enrolled 37 PD subjects. Sleep quality, established via the Pittsburgh Sleep Quality Index (PSQI), was compared through a case control approach against 49 control subjects. PD symptomatology assessment was ensured via: Hoehn &amp; Yahr (H&amp;Y); Movement Disorder Society-Unified Parkinson’s disease rating scale (MDS-UPDRS I-IV); Non-motor Symptom Scale (NMSS); Beck Depression Inventory (BDI); Montreal Cognitive Assessment (MoCA); Scale for Outcomes in Parkinson’s disease – Psychosocial Functioning (SCOPA-PS); 39-Item Parkinson’s Disease Questionnaire (PDQ-39). Results. Compared to their homologues, PD subjects were prone to worse subjective sleep quality (18.9% vs. 8.2%), sleep efficiency (13.5% vs. 4.1%), diurnal functionality (27% vs. 12.2%), sleep related breathing disorders (62.2% vs. 46.9%). The global PSQI positively correlates to H&amp;Y staging (rp= 0.443, p=0.008), UPDRS-III (rp= 0.369, p=0.029), UPDRS-IV (rp=0.412, p=0.011). PD subjects with PSQI &gt;5 registered higher UPDRS-III (p=0.091), BDI (p=0.928), SCOPA-PS (p=0.051). PSQI5 correlates to PDQ-39 (rp=0.423, p=0.010) and SCOPA-PS (rp= 0.462, p=0.004). Conclusions. The study proved a clear correlation between altered sleep patterns and the clinical presentation of PD delineating the worsening of motor along to non-motor symptoms. In addition, the quality of life along to the psychosocial functioning of PD subjects is at risk in those manifesting sleep disturbances. Correspondingly, a greater interest should be applied in the establishment of prophylactic measures.

Identification of metabolites reproducibly associated with Parkinson’s Disease via meta-analysis and computational modelling

Many studies have reported metabolomic analysis of different bio-specimens from Parkinson’s disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson’s disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.

Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson’s disease: a case series

IntroductionPeople with Parkinson’s Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects’ awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice.MethodsIn this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05).ResultsAfter training subjects improved gait speed (Pre-trn_No-Bf: 0.72(0.59,0.72) m/sec; Post-trn_Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn_No-Bf: 0.87(0.81,0.96) meters; Post-trn_Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects’ walking distance improved (Pre-trn_No-Bf: 97.5 (80.3,110.8) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn_No-Bf: 29.7(26.8,31.7) %; Post-trn_Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn_No-Bf: 108(103.8,116.7) step/min; Pre-trn_Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn_No-Bf: 112.5(97.5,124.5) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events.ConclusionThe BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments.Trial registrationThe study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy).

Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease.

Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments. This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication-OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation. A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance (p < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle. These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification.

Increased CSF DOPA Decarboxylase Correlates with Lower DaT-SPECT Binding: Analyses in Biopark and PPMI Cohorts.

Recent studies identified increased cerebrospinal fluid (CSF) DOPA decarboxylase (DDC) as a promising biomarker for parkinsonian disorders, suggesting a compensation to dying dopaminergic neurons. A correlation with 123I-FP-CIT-SPECT (DaT-SPECT) imaging could shed light on this link. The objective is to assess the relationship between CSF DDC levels and DaT-SPECT binding values. A total of 51 and 72 Parkinson's disease (PD) subjects with available DaT-SPECT and CSF DDC levels were selected from the PPMI and Biopark cohorts, respectively. DDC levels were analyzed using proximity extension assay and correlated with DaT-SPECT striatal binding ratios (SBR). All analyses were corrected for age and sex. CSF DDC levels in PD patients correlated negatively with DaT-SPECT SBR in both putamen and caudate nucleus. Additionally, SBR decreased with increased DDC levels over time in PD patients. CSF DDC levels negatively correlate with DaT-SPECT SBR in levodopa-treated PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.

Cognitive impairment in Parkinson's disease (PD) is associated with changes in the brain anatomical structures. The objective of this study, is to identify the atrophy patterns based on the severity of cognitive decline and evaluate the disease progression. In this study, gray matter alterations are analysed in 135 PD subjects under 3 cognitive domains (91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D)) by comparing them with 58 Healthy Control (HC) subjects. Voxel Based Morphometry (VBM) is used to segment the gray matter regions in magnetic resonance images and analyse the atrophy patterns statistically. Significant patterns of gray matter variations observed in the middle temporal and medial frontal region differentiate between HC and PD subject groups based on the severity of cognitive decline. Abnormalities in gray matter is substantiated through radiomic features extracted from the significant gray matter clusters. Significant radiomic features of the clusters are able to differentiate between the HC and PD-D subjects with an accuracy of 81.82%. Higher atrophy levels identified in PD-D subjects compared to NC-PD and PD-MCI group enables early diagnosis and treatment procedures. The combined and comprehensive analysis of gray matter alterations through VBM and radiomic features gives better assessment of cognitive impairment in PD.

Misfolded protein deposits in Parkinson’s disease and Parkinson’s disease-related cognitive impairment, a [11C]PBB3 study

Parkinson’s disease (PD) is associated with aggregation of misfolded α-synuclein and other proteins, including tau. We designed a cross-sectional study to quantify the brain binding of [11C]PBB3 (a ligand known to bind to misfolded tau and possibly α-synuclein) as a proxy of misfolded protein aggregation in Parkinson’s disease (PD) subjects with and without cognitive impairment and healthy controls (HC). In this cross-sectional study, nineteen cognitively normal PD subjects (CN-PD), thirteen cognitively impaired PD subjects (CI-PD) and ten HC underwent [11C]PBB3 PET. A subset of the PD subjects also underwent PET imaging with [11C](+)DTBZ to assess dopaminergic denervation and [11C]PBR28 to assess neuroinflammation. Compared to HC, PD subjects showed higher [11C]PBB3 binding in the posterior putamen but not the substantia nigra. There was no relationship across subjects between [11C]PBB3 and [11C]PBR28 binding in nigrostriatal regions. [11C]PBB3 binding was increased in the anterior cingulate in CI-PD compared to CN-PD and HC, and there was an inverse correlation between cognitive scores and [11C]PBB3 binding in this region across all PD subjects. Our results support a primary role of abnormal protein deposition localized to the posterior putamen in PD. This suggests that striatal axonal terminals are preferentially involved in the pathophysiology of PD. Furthermore, our findings suggest that anterior cingulate pathology might represent a significant in vivo marker of cognitive impairment in PD, in agreement with previous neuropathological studies.

Cardiac sympathetic denervation and anxiety in Parkinson disease

BackgroundAnxiety in Parkinson disease (PD) negatively impacts quality of life. While research predominantly focuses on central nervous system changes, some evidence suggests a connection between peripheral autonomic dysfunctions and PD-related anxiety. The role of the peripheral autonomic nervous system in this context may be overlooked. ObjectivesThis study explores the link between anxiety symptoms and cardiac sympathetic denervation in PD using 11C-meta-hydroxyephedrine ([11C]HED) PET cardiac imaging. MethodsWe studied 34 non-demented PD subjects, assessing anxiety levels through the Spielberg Anxiety State-Trait test trait section (STAI-T). Patients underwent comprehensive assessments along with [11C]HED cardiac and [11C]DTBZ brain PET. To identify subdimensions of STAI-T, we employed principal components analysis (PCA). We examined associations between the anxiety subdimensions and two measures of cardiac sympathetic denervation from [11C]HED PET. We utilized correlation and linear regression models for these analyses. ResultsPCA revealed two STAI-T results components: anxiety-depressive and pure anxiety subcomponents. Only pure anxiety significantly correlated with measures of cardiac sympathetic denervation (rhos −0.40, p = 0.018; 0.35, p = 0.043). Regression models confirmed a significant association, with cardiac sympathetic denervation explaining ∼20 % of pure anxiety variance, independent of sex, dopaminergic impairment, and anxiolytic treatments. DiscussionThis study provides preliminary evidence of peripheral autonomic nervous system abnormalities contributing to PD-related anxiety, suggesting dysregulation in peripheral autonomic functions influencing anxiety perception.