Efficacy of exercise interventions combined with Selegiline in ameliorating freezing of gait in Parkinson's disease patients.

OBJECTIVE: To evaluate the efficacy of exercise interventions combined with Selegiline in ameliorating freezing of gait (FOG) in Parkinson's disease (PD) patients. METHODS: A total of 60 PD patients with FOG treated in the First People's Hospital of Fuyang District from January 2020 to January 2023 were retrospectively collected and analyzed. Patients were divided into a control group (n = 28, treated with Selegiline alone) and an observation group (n = 32, treated with Selegiline and exercise interventions). Gait parameters, FOG indices, motor and balance functions, Berg Balance, psychological status, and quality of life were compared between the groups pre- and post-treatment. RESULTS: After treatment, the observation group exhibited longer step length, higher step speed, and lower step frequency (P = 0.000, 0.003, 0.001, respectively), with enhanced balance as indicated by lower Timed Up and Go Test and higher Berg Balance Scale scores than the control group (P = 0.000, 0.000, respectively). The Beck Depression Inventory and Beck Anxiety Inventory scores were notably lower in the observation group than those in the control group (P = 0.000, 0.004, respectively). Additionally, the observation group showed better quality of life across several dimensions of the Parkinson's Disease Quality of Life Questionnaire, including mobility, activities of daily living, emotional well-being, stigma, social support, cognition, communication, and bodily discomfort (P = 0.000, 0.000, 0.000, 0.000, 0.017, 0.000, 0.000, 0.000, respectively) than the control group. CONCLUSION: The combination of exercise interventions and Selegiline effectively rectifies the gait parameters, enhances the balance function, alleviates psychological distress, and improves the overall quality of life in PD patients experiencing FOG.

Which Gait Tasks Produce Reliable Outcome Measures of Freezing of Gait in Parkinson's Disease?

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.

Detection of freezing of gait in Parkinson's disease from foot-pressure sensing insoles using a temporal convolutional neural network.

Backgrounds: Freezing of gait (FoG) is a common and debilitating symptom of Parkinson's disease (PD) that can lead to falls and reduced quality of life. Wearable sensors have been used to detect FoG, but current methods have limitations in accuracy and practicality. In this paper, we aimed to develop a deep learning model using pressure sensor data from wearable insoles to accurately detect FoG in PD patients. Methods: We recruited 14 PD patients and collected data from multiple trials of a standardized walking test using the Pedar insole system. We proposed temporal convolutional neural network (TCNN) and applied rigorous data filtering and selective participant inclusion criteria to ensure the integrity of the dataset. We mapped the sensor data to a structured matrix and normalized it for input into our TCNN. We used a train-test split to evaluate the performance of the model. Results: We found that TCNN model achieved the highest accuracy, precision, sensitivity, specificity, and F1 score for FoG detection compared to other models. The TCNN model also showed good performance in detecting FoG episodes, even in various types of sensor noise situations. Conclusions: We demonstrated the potential of using wearable pressure sensors and machine learning models for FoG detection in PD patients. The TCNN model showed promising results and could be used in future studies to develop a real-time FoG detection system to improve PD patients' safety and quality of life. Additionally, our noise impact analysis identifies critical sensor locations, suggesting potential for reducing sensor numbers.

The plurality of mechanisms of gait initiation disorders in Parkinson's disease: Relationships with motor, cognitive and limbic functions.

BACKGROUND: Deficient postural adaptation and freezing lead to gait initiation abnormalities in Parkinson's disease. Gait initiation is characterized by longer motor preparation, which is a marker of increased risk of falling, and by abnormal postural adjustments. Better understanding the nature of these motor preparation disturbances will enable us to adapt rehabilitation and reduce falls. RESEARCH QUESTION: Our objective was to describe the different components (in the motor, cognitive and limbic domains) of gait initiation parameters in Parkinson's disease. METHODS: Forty-four patients with Parkinson's disease performed repeated step initiations under high attentional load with decision-making. The proportions of multiple anticipatory postural adjustments and anticipatory postural adjustment errors, markers of abnormal motor preparation, were measured. A logistic regression analysis studied the relationships between step initiation perturbations and the demographic, motor, cognitive, and neuropsychiatric characteristics of the patients. RESULTS: Multiple anticipatory postural adjustments and anticipatory postural adjustments errors lengthened step execution time. Motor severity explained the multiple anticipatory postural adjustments, suggesting a pathological role. Attentional performance explained anticipatory postural adjustments errors. Demographic and neuropsychiatric characteristics didn't contribute significantly to the abnormal anticipatory postural adjustments. SIGNIFICANCE: Motor disability contributes to the delay in step execution in Parkinson's disease through multiple anticipatory postural adjustments, highlighting the need to target motor preparation improvement in rehabilitation.

Vibrotactile Foot Device for Freezing of Gait in Parkinson's Disease: A Pilot Study.

BACKGROUND: Vibrotactile stimulation has been studied in its efficacy of reducing freezing of gait (FOG) in patients with Parkinson's disease (PD). However, the results are still controversial. We evaluated the efficacy of a newly developed vibrotactile foot device on freezing severity and gait measures in PD patients with FOG. OBJECTIVE: To evaluate the efficacy of vibrotactile foot device on PD patients with FOG. METHODS: Thirty-three PD patients with FOG were examined during their "off" medication state. The efficacy of the vibrotactile foot device was evaluated using a gait protocol comprising walking trials with vibrotactile stimulation "off" and "on." Walking trials were videotaped for the offline rating by two movement disorder specialists. The Opal inertial sensor unit (128 Hz; Mobility Lab; APDM Inc., Portland, OR, USA) was used for quantitative gait analysis. RESULTS: The results demonstrated 33.1% reduction in number of FOG episodes (P < 0.001) and 32.6% reduction of freezing episodes (P < 0.001). Quantitative gait analysis showed a significant increase in step length (P = 0.033). A moderate negative correlation was observed between the change of percent time frozen and age (r = -0.415, P = 0.016). 73% of participants reported minimal to substantial improvement in walking with this vibrating stimulation delivered by the vibrotactile foot device. CONCLUSIONS: The vibrotactile foot device is an efficient device that could significantly reduce freezing severity and provide gait regulation to patients with PD experiencing frequent freezing. It could potentially be used in the home environment for improving the quality of life.

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors. Hence, we propose a novel multi-channel time-series neural network (MCT-Net) approach to merge multi-channel gait features into a comprehensive prediction framework, alerting patients to FOG symptoms in advance. Owing to the causal distributed convolution, MCT-Net is a real-time method available to give optimal prediction earlier and implemented in remote devices. Moreover, intra-channel and inter-channel transformers of MCT-Net extract and integrate different sensor position features into a unified deep learning model. Compared with four other state-of-the-art FOG prediction baselines, the proposed MCT-Net obtains 96.21% in accuracy and 80.46% in F1-score on average 2 s before FOG occurrence, demonstrating the superiority of MCT-Net.

Investigating spatiotemporal and kinematic gait parameters in individuals with Parkinson's disease with a history of freezing of gait and exploring the effects of dopaminergic therapy on freezing of gait subtypes.

Introduction: Freezing of Gait (FOG) is a prevalent and debilitating symptom in idiopathic Parkinson's disease (PD). This study evaluated spatiotemporal and kinematic gait parameters in individuals with PD with a history of FOG and explored the effects of dopaminergic therapy on FOG subtypes. Methods: One hundred and nine individuals with PD underwent clinical assessments and quantitative biomechanical measures during walking cycles before and after dopaminergic therapy. Individuals with FOG were classified into levodopa-responsive and levodopa-unresponsive groups. Results: Individuals with FOG displayed longer disease duration and higher Unified Parkinson's Disease Rating Scale (UPDRS) II, III, IV scores, and total scores and levodopa equivalent dose, than those without FOG (all p < 0.0001). Following propensity score matching of 15 pairs based on UPDRS total score and disease duration during the off-medication state, the analysis comparing the FOG and non-FOG groups revealed no significant differences in spatiotemporal and kinematic parameters. In 39 cases of FOG, dopaminergic therapy improved gait performance in individuals with PD, enhancing spatiotemporal parameters (speed, stride length, step length, step variability) and kinematic parameters (shoulder and elbow flexion/extension range of motion (ROM), pelvic rotation, and hip abduction/adduction ROM) regardless of FOG responsiveness to dopaminergic therapy. A significant difference in trunk sway ROM (p = 0.029) remained before and after dopaminergic therapy, even after adjusting for disease duration and clinical severity. Discussion: Dopaminergic therapy had varying effects on PD with FOG, improving several spatiotemporal and kinematic gait parameters but being less effective in levodopa-unresponsive cases. Quantitative biomechanical measures offer detailed insights into gait performance, aiding personalized fall risk assessment and guiding individualized rehabilitation programs.

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

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Cortical modulations before lower limb motor blocks are associated with freezing of gait in Parkinson's disease: an EEG source localization study.

BACKGROUND: Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) characterized by paroxysmal episodes in which patients are unable to step forward. A research priority is identifying cortical changes before freezing in PD-FOG. METHODS: We tested 19 patients with PD who had been assessed for FOG (n=14 with FOG and 5 without FOG). While seated, patients stepped bilaterally on pedals to progress forward through a virtual hallway while 64-channel EEG was recorded. We assessed cortical activities before and during lower limb motor blocks (LLMB), defined as a break in rhythmic pedaling, and stops, defined as movement cessation following an auditory stop cue. This task was selected because LLMB correlates with FOG severity in PD and allows recording of high-quality EEG. Patients were tested after overnight withdrawal from dopaminergic medications ("off" state) and in the "on" medications state. EEG source activities were evaluated using individual MRI and standardized low resolution brain electromagnetic tomography (sLORETA). Functional connectivity was evaluated by phase lag index between seeds and pre-defined cortical regions of interest. RESULTS: EEG source activities for LLMB vs. cued stops localized to right posterior parietal area (Brodmann area 39), lateral premotor area (Brodmann area 6), and inferior frontal gyrus (Brodmann area 47). In these areas, PD-FOG (n=14) increased alpha rhythms (8-12 Hz) before LLMB vs. typical stepping, whereas PD without FOG (n=5) decreased alpha power. Alpha rhythms were linearly correlated with LLMB severity, and the relationship became an inverted U-shape when assessing alpha rhythms as a function of percent time in LLMB in the "off" medication state. Right inferior frontal gyrus and supplementary motor area connectivity was observed before LLMB in the beta band (13-30 Hz). This same pattern of connectivity was seen before stops. Dopaminergic medication improved FOG and led to less alpha synchronization and increased functional connections between frontal and parietal areas. CONCLUSIONS: Right inferior parietofrontal structures are implicated in PD-FOG. The predominant changes were in the alpha rhythm, which increased before LLMB and with LLMB severity. Similar connectivity was observed for LLMB and stops between the right inferior frontal gyrus and supplementary motor area, suggesting that FOG may be a form of "unintended stopping." These findings may inform approaches to neurorehabilitation of PD-FOG.

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention. METHOD: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG. RESULTS: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained. CONCLUSION: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

A Pragmatic Review on Spinal Cord Stimulation Therapy for Parkinson's Disease Gait Related Disorders: Gaps and Controversies.

BACKGROUND: Parkinson's Disease (PD) is a progressive neurological disorder that results in potentially debilitating mobility deficits. Recently, spinal cord stimulation (SCS) has been proposed as a novel therapy for PD gait disorders. The highest levels of evidence remain limited for SCS. OBJECTIVES: In this systematic review and narrative synthesis, the literature was searched using combinations of key phrases indicating spinal cord stimulation and PD. METHODS: We included pre-clinical studies and all published clinical trials, case reports, conference abstracts as well as protocols for ongoing clinical trials. Additionally, we included trials of SCS applied to atypical parkinsonism. RESULTS: A total of 45 human studies and trials met the inclusion criteria. Based on the narrative synthesis, a number of knowledge gaps and future avenues of potential research were identified. This review demonstrated that evidence for SCS is currently not sufficient to recommend it as an evidence-based therapy for PD related gait disorders. There remain challenges and significant barriers to widespread implementation, including issues regarding patient selection, effective outcome selection, stimulation location and mode, and in programming parameter optimization. Results of early randomized controlled trials are currently pending. SCS is prone to placebo, lessebo and nocebo as well as blinding effects which may impact interpretation of outcomes, particularly when studies are underpowered. CONCLUSION: Therapies such as SCS may build on current evidence and be shown to improve specific gait features in PD. Early negative trials should be interpreted with caution, as more evidence will be required to develop effective methodologies in order to drive clinical outcomes.

The effects of laser cane cues on the freezing of gait of Parkinson's disease patients: can increasing the laser light beam width play a role?

INTRODUCTION: Freezing of gait can be seen in a significant number of people with Parkinson's disease. Disappointingly, the classic standard treatment of Parkinson's disease with dopamine replacement has not shown promising results in improving the freezing of gait. Hence the approach have shifted towards using non-invasive methods to address this problem. OBJECTIVES: To assess the effect of laser cane as a visual cue on the freezing of gait of people with Parkinson's disease and further determine the effect of laser light beam width and color on the freezing of gait. METHODS: 7 known Parkinson's Disease patients were enrolled in this study, all of whom had at least one episode of freezing at at least one clinical visit. These patients underwent gait analysis in 4 stages: walking without a cane, walking with a thin red light laser cane, a thick red light laser cane, and a green light laser cane. RESULTS: Using laser canes effectively improved nearly all parameters of walking, including right and left stride length, step length, the velocity of movement, and rotation time, compared to walking without a stick. Using different colors of laser cane didn't make any significant difference in improving the freezing of gait of our patients. Nevertheless, increasing the laser light beam width significantly improved almost all walking parameters. CONCLUSION: This is the first study assessing the effect of laser light beam width on freezing of gait in Parkinson's disease patients and shows promising results in regards to increasing the thickness of laser lights in order to improve walking parameters in Parkinson's disease patients more effectively. Furthermore, this is the second study to evaluate the effect of laser light color, contradicting the previous results by showing no significant correlation between the color of laser light and improvements in walking parameters.

Effects of Continuous Dopaminergic Stimulation on Parkinson's Disease Gait: A Longitudinal Prospective Study with Levodopa Intestinal Gel Infusion.

Background: Gait issues, including reduced speed, stride length and freezing of gait (FoG), are disabling in advanced phases of Parkinson's disease (PD), and their treatment is challenging. Levodopa/carbidopa intestinal gel (LCIG) can improve these symptoms in PD patients with suboptimal control of motor fluctuations, but it is unclear if continuous dopaminergic stimulation can further improve gait issues, independently from reducing Off-time. Objective: To analyze before (T0) and after 3 (T1) and 6 (T2) months of LCIG initiation: a) the objective improvement of gait and balance; b) the improvement of FoG severity; c) the improvement of motor complications and their correlation with changes in gait parameters and FoG severity. Methods: This prospective, longitudinal 6-months study analyzed quantitative gait parameters using wearable inertial sensors, FoG with the New Freezing of Gait Questionnaire (NFoG-Q), and motor complications, as per the MDS-UPDRS part IV scores. Results: Gait speed and stride length increased and duration of Timed up and Go and of sit-to-stand transition was significantly reduced comparing T0 with T2, but not between T0-T1. NFoG-Q score decreased significantly from 19.3±4.6 (T0) to 11.8±7.9 (T1) and 8.4±7.6 (T2) (T1-T0 p = 0.018; T2-T0 p < 0.001). Improvement of MDS-UPDRS-IV (T0-T2, p = 0.002, T0-T1 p = 0.024) was not correlated with improvement of gait parameters and NFoG-Q from T0 to T2. LEDD did not change significantly after LCIG initiation. Conclusion: Continuous dopaminergic stimulation provided by LCIG infusion progressively ameliorates gait and alleviates FoG in PD patients over time, independently from improvement of motor fluctuations and without increase of daily dosage of dopaminergic therapy.

Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life.

BACKGROUND: Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. METHODS: Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. RESULTS: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. CONCLUSIONS: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.

Comparison of externally and internally guided dance movement to address mobility, cognition, and psychosocial function in people with Parkinson's disease and freezing of gait: a case series.

Objective: The aim of this study is to explore the impact of internally guided (IG) versus externally guided (EG) adapted tango (AT) dance training (i.e., dancing the IG "Leader" role or the EG "Follower" role), on motor and non-motor functions in individuals with Parkinson's disease and freezing of gait (PD-FOG). The "Leader" role, a proxy for IG movements, conveys direction, timing, and amplitude of steps with tactile cues. The "Follower" role, a proxy for EG movements, detects and responds to the leader's tactile cues. Case description: Six participants were randomly assigned to the IG ("Leader") or EG ("Follower") roles for 20, 90-min AT lessons over 12 weeks. Participants were assessed for PD-specific and non-PD-specific functions before and twice after the end of the 12-week intervention, at 1-week and 1-month post-intervention. Results: EG participants improved and/or maintained performance on more outcomes across all domains than IG participants. Five participants improved in PD motor symptoms, dynamic gait, global cognitive function, and the FOG Questionnaire immediately or 1 month after intervention. All participants expressed positive attitudes toward the intervention, including improvements in walking, balance, and endurance. Conclusion: AT training in the follower role may benefit individuals with PD-FOG to a greater extent compared to the leader role. Impact: This case series study could inform additional research with the goal of enhancing physical therapy or music-based therapy approaches for addressing PD-FOG.

Validity, reliability, and measurement error of the Japanese version of the Freezing of Gait Questionnaire for patients with Parkinson's disease.

BACKGROUND: The Freezing of Gait Questionnaire has been translated into several languages. However, it has not been translated into Japanese and its measurement error remains unclear.Objectives: This study aimed to translate the Freezing of Gait Questionnaire into Japanese, investigate its validity and reliability, and calculate its measurement errors. METHODS: Thirty-five patients with Parkinson's disease participated in the study. The Freezing of Gait Questionnaire was translated into Japanese using a forward - backward translation method. Convergent validity was assessed using the Freezing of Gait Questionnaire and Unified Parkinson's Disease Rating Scale Part II(item 14-freezing). The content validity index was calculated using the Freezing of Gait Questionnaire score using correlation coefficients. Internal consistency was measured using Cronbach's alpha. The test - retest reliability was evaluated using the intraclass correlation coefficient(1,1). The Bland - Altman analysis was performed to detect the limits of agreement. RESULTS: The mean Freezing of Gait Questionnaire score was 9.1 (5.0) points. Convergent validity was 0.655 and content validity index was 0.958. Cronbach's alpha was 0.958, intraclass correlation coefficient(1,1) was 0.951, and the limits of agreement ranged from - 4.9 to3.2 points. CONCLUSIONS: The Japanese version of the Freezing of Gait Questionnaire is a valid and useful tool to evaluate patients with Parkinson's disease.

Iron and neuromelanin imaging in basal ganglia circuitry in Parkinson's disease with freezing of gait.

OBJECTIVE: This study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI). METHODS: Twenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NMRC-SNpc) and volume (NMvolume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage. RESULTS: The PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NMvolume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NMRC-SNpc. CONCLUSIONS: The PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NMvolume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients.

On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study.

Goal: Parkinson's disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. By avoiding this we aim at improving usability, robustness, and detection delay. Methods: We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. As references, motion phases and FoG episodes have been video-annotated. Results: The analysis confirms adequate gait phase and FoG detection performance. The mobility assistant detected foot motions with a rate above 94 % and classified them with an accuracy of 84 % into normal or FoG-affected. The FoG detection delay is mainly defined by the foot-motion duration, which is below the delay in existing sliding-window approaches. Conclusions: Direct computing on the sensor and cueing devices ensures robust detection of FoG-affected motions for on demand cueing synchronized with the gait. The proposed solution can be easily adopted to other sensor and cueing modalities.

Effect of dopamine on limbic network connectivity at rest in Parkinson's disease patients with freezing of gait.

Background: Freezing of gait (FOG) in Parkinson's disease (PD) has a poorly understood pathophysiology, which hinders treatment development. Recent work showed a dysfunctional fronto-striato-limbic circuitry at rest in PD freezers compared to non-freezers in the dopamine "OFF" state. While other studies found that dopaminergic replacement therapy alters functional brain organization in PD, the specific effect of dopamine medication on fronto-striato-limbic functional connectivity in freezers remains unclear. Objective: To evaluate how dopamine therapy alters resting state functional connectivity (rsFC) of the fronto-striato-limbic circuitry in PD freezers, and whether the degree of connectivity change is related to freezing severity and anxiety. Methods: Twenty-three PD FOG patients underwent MRI at rest (rsfMRI) in their clinically defined "OFF" and "ON" dopaminergic medication states. A seed-to-seed based analysis was performed between a priori defined limbic circuitry ROIs. Functional connectivity was compared between OFF and ON states. A secondary correlation analyses evaluated the relationship between Hospital Anxiety and Depression Scale (HADS)-Anxiety) and FOG Questionnaire with changes in rsFC from OFF to ON. Results: PD freezers' OFF compared to ON showed increased functional coupling between the right hippocampus and right caudate nucleus, and between the left putamen and left posterior parietal cortex (PPC). A negative association was found between HADS-Anxiety and the rsFC change from OFF to ON between the left amygdala and left prefrontal cortex, and left putamen and left PPC. Conclusion: These findings suggest that dopaminergic medication partially modulates the frontoparietal-limbic-striatal circuitry in PD freezers, and that the influence of medication on the amygdala, may be related to clinical anxiety in freezer.