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Using delay differential equations to study mathematical models of Parkinson's disease and Huntington's disease is important to show how important it is for synchronization between basal ganglia loops to work together. We used the delay circuit RLC (resistor, inductor, capacitor) model to show how the direct pathway and the indirect pathway in the basal ganglia excite and inhibit the motor cortex, respectively. A term has been added to the mathematical model without time delay in the case of the hyperdirect pathway. It is proposed to add a non-linear term to adjust the synchronization. We studied Hopf bifurcation conditions for the proposed models. The desynchronization of response times between the direct pathway and the indirect pathway leads to different symptoms of Parkinson's disease. Tremor appears when the response time in the indirect pathway increases at rest. The simulation confirmed that tremor occurs and the motor cortex is in an inhibited state. The direct pathway can increase the time delay in the dopaminergic pathway, which significantly increases the activity of the motor cortex. The hyperdirect pathway regulates the activity of the motor cortex. The simulation showed bradykinesia occurs when we switch from one movement to another that is less exciting for the motor cortex. A decrease of GABA in the striatum or delayed excitation of the substantia nigra from the subthalamus may be a major cause of Parkinson's disease. An increase in the response time delay in one of the pathways results in the chaotic movement characteristic of Huntington's disease.

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Coronavirus disease 2019 (COVID-19) has been associated with a variety of neurological manifestations (i.e., anosmia, ageusia, myalgia, headache) and neurological syndromes (i.e., encephalopathy, ischemic and hemorrhagic stroke, myelitis, encephalitis) underlying different pathogenetic mechanisms. COVID-19 has also been associated with various movement disorders including acute or subacute parkinsonism. However, to date, only few cases of parkinsonism linked to COVID-19 have been reported, nevertheless raising the possibility of a post-viral parkinsonian syndrome. Furthermore, various studies in vitro and in animal models have highlighted a close relationship between SARS-CoV-2 virus and α-synuclein, leading to the hypothesis that COVID-19 could represent a factor favoring the long-term development of α-synucleopathies. In this chapter, we will discuss the pathophysiological mechanisms related to movement disorders' manifestations of COVID-19 focusing on the possible overlap between pathogenetic mechanisms of Parkinson's Disease and COVID-19.

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BACKGROUND: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment. OBJECTIVE: This review aims to evaluate existing studies on hand function evaluation using smartphones. METHODS: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions. RESULTS: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation. CONCLUSIONS: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.

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Parkinson's disease (PD) is typically a sporadic late-onset disorder, which has made it difficult to model in mice. Several transgenic mouse models bearing mutations in SNCA, which encodes alpha-Synuclein (α-Syn), have been made, but these lines do not express SNCA in a physiologically accurate spatiotemporal pattern, which limits the ability of the mice to recapitulate the features of human PD. Here, we generated knock-in mice bearing the G51D SNCA mutation. After establishing that their motor symptoms begin at 9 mo of age, we then sought earlier pathologies. We assessed the phosphorylation at Serine 129 of α-Syn in different tissues and detected phospho-α-Syn in the olfactory bulb and enteric nervous system at 3 mo of age. Olfactory deficit and impaired gut transit followed at 6 mo, preceding motor symptoms. The SncaG51D mice thus parallel the progression of human PD and will enable us to study PD pathogenesis and test future therapies.

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SOURCE CITATION: Meissner WG, Remy P, Giordana C, et al; LIXIPARK Study Group. Trial of lixisenatide in early Parkinson's disease. N Engl J Med. 2024;390:1176-1185. 38598572.

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PURPOSE: To describe the effects of subthalamic nucleus deep brain stimulation (STN-DBS) on the speech of Spanish-speaking Parkinson's disease (PD) patients during the first year of treatment. METHODS: The speech measures (SMs): maximum phonation time, acoustic voice measures, speech rate, speech intelligibility measures, and oral diadochokinesis rates of nine Colombian idiopathic PD patients (four females and five males; age = 63 ± 7 years; years of PD = 10 ± 7 years; UPDRS-III = 57 ± 6; H&Y = 2 ± 0.3) were studied in OFF and ON medication states before and every three months during the first year after STN-DBS surgery. Praat software and healthy native listeners' ratings were used for speech analysis. Statistical analysis tried to find significant differences in the SMs during follow-up (Friedman test) and between medication states (Wilcoxon paired test). Also, a pre-surgery variation interval (PSVI) of reference for every participant and SM was calculated to make an individual analysis of post-surgery variation. RESULTS: Non-significative post-surgery or medication state-related differences in the SMs were found. Nevertheless, individually, based on PSVIs, the SMs exhibited: no variation, inconsistent or consistent variation during post-surgery follow-up in different combinations, depending on the medication state. CONCLUSION: As a group, participants did not have a shared post-surgery pattern of change in any SM. Instead, based on PSVIs, the SMs varied differently in every participant, which suggests that in Spanish-speaking PD patients, the effects of STN-DBS on speech during the first year of treatment could be highly variable.

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BACKGROUND: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson's disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. AIM: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. METHOD: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. RESULTS: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. CONCLUSION: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments.

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State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study's objective was to evaluate concurrent validity and test-retest reliability for common clinical gait and balance tests in people with Parkinson's disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson's disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC(C,1) > 0.933) and test completion durations (ICC(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC(A,1) = 0.913) and moderate (turn 2, ICC(A,1) = 0.589). Regarding test-retest reliability, the within-system test-retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson's disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts.

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Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.

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Objective: Advancements in smartphone technology provide availability to evaluate movement in a more practical and feasible manner, improving clinicians' ability to diagnose and treat adults at risk for mobility loss. The purpose of this study was to evaluate the validity and reliability of a smartphone application to measure spatiotemporal outcomes during level (primary) and uphill/downhill (secondary) walking with and without an assistive device for older adults (OAs), Parkinson's Disease (PD) and cerebrovascular accident (CVA) populations. Methods: A total of 50 adults (OA = 20; PD = 15; CVA = 15) underwent gait analysis at self-selected gait speeds under 0-degree, 5-degree uphill and 5-degree downhill environments. The validity and reliability of the smartphone outcomes were compared to a motion-capture laboratory. Bland-Altman analysis was used to evaluate limits of agreement between the two systems. Intraclass correlation coefficients (ICCs) were used to determine absolute agreement, and Pearson correlation coefficients (r) were used to assess the strength of the association between the two systems. Results: For level walking, Bland-Altman analysis revealed relatively equal estimations of spatiotemporal outcomes between systems for OAs without an assistive device and slight to mild under- and overestimations of outcomes between systems for PD and CVA with and without an assistive device. Moderate to very high correlations between systems (without an assistive device: OA r-range, 0.72-0.99; PD r-range, 0.87-0.97; CVA r-range, 0.56-0.99; with an assistive device: PD r-range, 0.35-0.98; CVA r-range, 0.50-0.99) were also observed. Poor to excellent ICCs for reliability between systems (without an assistive device: OA ICC range, 0.71-0.99; PD ICC range, 0.73-0.97; CVA ICC range, 0.56-0.99; with an assistive device: PD ICC range, 0.22-0.98; CVA ICC range, 0.44-0.99) were observed across all outcomes. Conclusions: This smartphone application can be clinically useful in detecting most spatiotemporal outcomes in various walking environments for older and diseased adults at risk for mobility loss.

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Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremors and bradykinesia. PD's pathology involves the aggregation of α-synuclein and loss of dopaminergic neurons, leading to altered neural oscillations in the cortico-basal ganglia-thalamic network. Despite extensive research, the relationship between the motor symptoms of PD and transient changes in brain oscillations before and after motor tasks in different brain regions remain unclear. This study aimed to investigate neural oscillations in both healthy and PD model mice using local field potential (LFP) recordings from multiple brain regions during rest and locomotion. The histological evaluation confirmed the significant dopaminergic neuron loss in the injection side in 6-OHDA lesioned mice. Behavioral tests showed motor deficits in these mice, including impaired coordination and increased forelimb asymmetry. The LFP analysis revealed increased delta, theta, alpha, beta, and gamma band activity in 6-OHDA lesioned mice during movement, with significant increases in multiple brain regions, including the primary motor cortex (M1), caudate-putamen (CPu), subthalamic nucleus (STN), substantia nigra pars compacta (SNc), and pedunculopontine nucleus (PPN). Taken together, these results show that the motor symptoms of PD are accompanied by significant transient increases in brain oscillations, especially in the gamma band. This study provides potential biomarkers for early diagnosis and therapeutic evaluation by elucidating the relationship between specific neural oscillations and motor deficits in PD.

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OBJECTIVE: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit. METHODS AND PROCEDURES: We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization. RESULTS: The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively. CONCLUSION: The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement-A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.

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Understanding what matters to people with Parkinson's and their family is essential to derive relevant clinical outcome measures and guide clinical care. The purpose of this study was to explore what is important to people with Parkinson's disease vs. family over time. A qualitative content-analysis of online survey data collected by Parkinson's UK was conducted to identify types and frequencies of important symptoms and impacts of Parkinson's for people with the disease vs. family of people with Parkinson's. Independent T-tests were used to identify significance of between group differences for patients vs. family at < 2, 2-5, 6-10, 11-20, > 20-year durations. ANOVA was used to assess for within group differences by disease duration. We found that symptom priority changed significantly over time with longer disease duration. Tremor was reported less often later on, whereas mobility, dyskinesias, gait and speech/communication symptoms gained priority. In general, patients identified movement-related symptoms (e.g., walking, bradykinesia) as the most bothersome at all durations while family more strongly prioritized the physical and psychosocial impacts of disease (e.g., mobility, safety, interpersonal interactions, independence, and family impact). We conclude that important differences exist between family and patient perspectives of what matters and change over time with longer duration of disease.

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Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.

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INTRODUCTION: Parkinson's disease (PD) is a progressive neurological condition resulting from the degeneration of dopaminergic neurons in the substantia nigra. Impaired manual dexterity and cognitive impairment are common symptoms and are often associated with recurrent adverse events in this population. OBJECTIVE: To verify the association between cognitive performance and manual dexterity in people with PD. METHODS: This is a cross-sectional observational study, with 29 participants, who underwent cognitive and manual dexterity assessments, and the following tools were used: Trail Making Test, box and block test (BBT), Learning Test of Rey and Nine Hole Peg Test. Descriptive statistics for clinical and demographic data were performed using mean and standard deviation, and data normality was assessed using the Shapiro-Wilk test. Spearman's nonparametric test was used to determine the correlation between variables. RESULTS: Our findings revealed significant associations between cognitive performance and manual dexterity. The nine-hole peg test positively correlated with TMT-Part A and Part B, establishing a relationship between manual dexterity and cognitive functions such as attention and mental flexibility. On the other hand, BBT showed an inverse relationship with TMT-Part B, indicating that longer time on this task was associated with lower manual dexterity. CONCLUSION: Fine manual dexterity had a significant correlation with visual search skills and motor speed, while gross motor dexterity had a negative correlation with cognitive skills. No significant results were demonstrated regarding the interaction between manual dexterity and memory.

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Parkinson's disease (PD), a prevalent neurodegenerative condition, manifests predominantly through the degeneration of nigrostriatal dopaminergic (DA) pathways, culminating in a notable depletion of striatal dopamine. This pathophysiological process critically impairs the DA-mediated regulation of motor behaviors within the basal ganglia circuitry, particularly impacting various subtypes of striatal medium spiny neurons. Recent advancements in neuroscientific research have illuminated the pivotal role of D2-dopamine receptor expressing medium spiny neurons (D2-MSNs) plasticity in coordinating motor control in PD. Intriguingly, aerobic exercise emerges as a potent therapeutic intervention, capable of preventing or improving motor impairments. This ameliorative effect is mediated through the modulation of DA receptor activity and the consequent activation of downstream extracellular signal-regulated kinase (Erk) signaling pathway. This article meticulously reviewed the intricate regulatory mechanisms governing the structural and functional plasticity of striatal D2-MSNs in the context of PD. It particularly emphasized the transformative impact of aerobic exercise on motor deficits in PD, attributing this effect to the modulation of striatal D2-MSNs.

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Given the increasing trend of aging population in the world, neurodegenerative diseases (NDDs), a common type of diseases that mostly occur in the elderly, have attracted much more attention. It has been shown that tumor necrosis factor receptor-associated factor 6 (TRAF6) is involved in the regulation of neuroinflammation, an important pathological feature of NDDs, and affects the occurrence and development of NDDs. Most importantly, the regulatory effect of TRAF6 is related to its ubiquitination. Therefore, in the present paper, the molecular structure, biological function, and ubiquitination mechanism of TRAF6, and its relationship with some common NDDs, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis, were analyzed and summarized. The possible molecular mechanisms by which TRAF6 regulates the occurrence of NDDs were also elucidated, providing a theoretical basis for exploring the etiology and treatment of NDDs.

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Background: Measurement of freezing of gait (FOG) relies on the sensitivity and reliability of tasks to provoke FOG. It is currently unclear which tasks provide the best outcomes and how medication state plays into this. Objective: To establish the sensitivity and test-retest reliability of various FOG-provoking tasks for presence and severity of FOG, with (ON) and without (OFF) dopaminergic medication. Methods: FOG-presence and percentage time frozen (% TF) were derived from video annotations of a home-based FOG-provoking protocol performed in OFF and ON. This included: the four meter walk (4MW), Timed Up and Go (TUG) single (ST) and dual task (DT), 360° turns in ST and DT, a doorway condition, and a personalized condition. Sensitivity was tested at baseline in 63 definite freezers. Test-retest reliability was evaluated over 5 weeks in 26 freezers. Results: Sensitivity and test-retest reliability were highest for 360° turns and higher in OFF than ON. Test-retest intra-class correlation coefficients of % TF varied between 0.63-0.90 in OFF and 0.18-0.87 in ON, and minimal detectable changes (MDCs) were high. The optimal protocol included TUG ST, 360° turns ST, 360° turns DT and a doorway condition, provoking FOG in all freezers in OFF and 91.9% in ON and this could be done reliably in 95.8% (OFF) and 84.0% (ON) of the sample. Combining OFF and ON further improved outcomes. Conclusions: The highest sensitivity and reliability was achieved with a multi-trigger protocol performed in OFF + ON. However, the high MDCs for % TF underscore the need for further optimization of FOG measurement.

Freezing of gait is a very burdensome and episodic symptom in Parkinson's disease that is difficult to measure. Measurement of freezing is needed to determine whether someone has freezing and how severe this is, and relies on observation during a freezing-triggering protocol. However, it is unclear what protocol is sufficiently sensitive to trigger freezing in many freezers, and whether freezing can be triggered reliably at different timepoints. Here, we investigated 1) which tasks can trigger freezing-presence and freezing-severity sensitively and reliably, 2) how medication state influences this, and 3) what task combination was most reliable. Sixty-three patients with daily freezing performed several freezing-triggering tasks in their homes, both with (ON) and without (OFF) anti-Parkinsonian medication. In twenty-six patients, the measurement was repeated 5 weeks later to determine test-retest reliability. First, we found that performing 360° turns in place with a cognitive dual task was the most sensitive and reliable task to trigger FOG. Second, sensitivity and reliability were better in OFF than in ON. Third, the most reliable protocol included: the Timed-Up and Go, 360° turns in place with and without the dual task, and a doorway condition. This protocol triggered freezing in all patients in OFF and 91.9% in ON and did so reliably in 95.8% (OFF) and 84.0% (ON) of the sample. We recommend to measure freezing with this protocol in OFF + ON, which further improved reliability. However, the measurement error for freezing-severity was high, even for this optimal protocol, underscoring the need for further optimization of freezing measurement.

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INTRODUCTION: People with Parkinson's disease (PwPD) present motor alterations which can impact daily life tasks that require speed and/or accuracy of movement. OBJECTIVE: A sub analysis of NCT01439022, aiming to estimate the extent to which two different exercise training protocols (global and handwriting upper limb exercise training) impact reaction time, travel speed, and accuracy in PwPD. METHODS: Seventy PwPD, right-side dominant were randomised 1:1 into two six-month training protocol groups; 35 PwPD performed global exercise training and 35 performed specific training (handwriting upper limb exercise movements). Assessments of speed-accuracy and trade-off were carried out at baseline, after 3 and 6 months of training, and at a 12-month follow-up. The current study used data from a previous publication of a randomised controlled trial that included a 6-month self-managed community exercise programme for PwPD. For the present study we included only the participants who completed the Fitts' task during the baseline assessment. RESULTS: In the upper limb assessments, no main effects were found for the number of touches, but the exercise group showed a marginal increase over time on the left side. Error averages on the left side decreased significantly for the exercise group from baseline to 6 and 12 months. The exercise group also presented a lower Error CoV and the Reaction Time CoV increased on the right side. Significant findings for Fitts r on the left side indicated lower values for the exercise group, with improvements continuing at 12 months. CONCLUSION: We report the potential of global exercise interventions to facilitate improvements in reaction time and travel speed, as well as other motor control metrics, with lasting effects at 12 months, particularly on the non-dominant side.

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The motor impairments experienced by people with Parkinson's disease (PD) are exacerbated during memory-guided movements. Despite this, the effect of antiparkinson medication on memory-guided movements has not been elucidated. We evaluated the effect of antiparkinson medication on motor control during a memory-guided reaching task with short and long retention delays in participants with PD and compared performance to age-matched healthy control (HC) participants. Thirty-two participants with PD completed the motor section of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) and performed a memory-guided reaching task with two retention delays (0.5 s and 5 s) while on and off medication. Thirteen HC participants completed the MDS-UPDRS III and performed the memory-guided reaching task. In the task, medication increased movement velocity, decreased movement time, and decreased reaction time toward what was seen in the HC. However, movement amplitude and reaching error were unaffected by medication. Shorter retention delays increased movement velocity and amplitude, decreased movement time, and decreased error, but increased reaction times in the participants with PD and HC. Together, these results imply that antiparkinson medication is more effective at altering the neurophysiological mechanisms controlling movement velocity and reaction time compared with other aspects of movement control.

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