Occurrence Of Freezing Of Gait Research Articles

A novel variational mode decomposition based convolutional neural network for the identification of freezing of gait intervals for patients with Parkinson's disease

Freezing of gait (FoG) is a debilitating and serious motor system complication of Parkinson's disease (PD) that may expose patients to frequent falls and life-threating injuries. Several artificial and machine learning methods have been proposed for the prediction of FoG based upon a limited time-duration of sensory data, However, most of the related work has been insufficiently trained and tested on smaller datasets compromising the generalizability of the models. Further, the proposed models provided a prediction at a lower rate (e.g., every 7.8 s). In response to the above shortcomings, we propose a novel variational mode decomposition (VMD) based deep learning that is capable of efficiently inferring the occurrence of FoG at a higher time-resolution (i.e., every sampling period of 7.8 ms) and with a subject-independent accuracy up to 98.8 % outperforming the state-of-the-art architectures and the standard LSTM models. The proposed model will enable the prompt detection of FoG episodes and support PD sufferers reducing the likelihood of falls.

Vestibular-evoked myogenic potential abnormalities in Parkinson's disease with freezing of gait.

Vestibular dysfunction is closely associated with the pathophysiology of Parkinson's disease (PD) accompanied by freezing of gait (FOG); however, evidence supporting this clinical association is lacking. Vestibular-evoked myogenic potentials (VEMPs) have been widely acknowledged as a crucial electrophysiological parameter in the clinical evaluation of vestibular function. The present study investigated the possible correlation of FOG occurrence with VEMP observations in patients diagnosed with PD. Altogether, 95 idiopathic PD patients were recruited into the present cross-sectional study. All patients underwent motor and non-motor assessments using serial scales. In addition, the electrophysiological vestibular evaluation was conducted, which included cervical (cVEMP) and ocular VEMP (oVEMP) assessments. Furthermore, the correlations of bilateral c/oVEMP absence with clinical phenotypes, especially FOG, among the PD patients were analyzed. Among the 95 patients with PD, 44 (46.3%) had bilateral oVEMP absence and 23 (24.2%) had bilateral cVEMP absence, respectively. The proportions of patients with bilateral oVEMP absence (77.8% vs 30.9%, p = 0.004) and bilateral cVEMP absence (44.4% vs 19.5%, p = 0.035) were higher in the patient group exhibiting FOG than in the group without FOG. Following the adjustment of confounding variables, bilateral oVEMP absence (OR = 8.544, p = 0.007), rather than bilateral cVEMP absence, was shown to independently predict FOG occurrence in patients with PD. The close correlation between bilateral oVEMP absence and FOG in PD patients sheds new light on the possible role of central vestibular/upper brainstem dysfunction in FOG development in patients with PD.

Comparison of sleep characteristics between Parkinson's disease with and without freezing of gait: A systematic review

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Among the motor complaints, freezing of gait (FOG) is a common and disabling phenomenon that episodically hinders patients' ability to produce efficient steps. Concurrently, sleep disorders are prevalent in PD and significantly impact the quality of life of affected individuals. Numerous studies have suggested a bidirectional relationship between FOG and sleep disorders. Therefore, our objective was to systematically review the literature and compare sleep outcomes in PD patients with FOG (PD + FOG) and those without FOG (PD-FOG). By conducting a comprehensive search of the PubMed and Web of Science databases, we identified 20 eligible studies for inclusion in our analysis. Our review revealed that compared to PD-FOG, PD + FOG patients exhibited more severe symptoms of rapid eye movement sleep behavior disorder in nine studies, increased daytime sleepiness in eight studies, decreased sleep quality in four studies, and more frequent and severe sleep disturbances in four studies. These findings indicate that PD + FOG patients generally experience worse sleep quality, higher levels of daytime sleepiness, and more disruptive sleep disturbances compared to those without FOG (PD-FOG). The association between sleep disturbances and FOG highlights the importance of evaluating and monitoring these symptoms in PD patients and open the possibility for future studies to assess the impact of managing sleep disturbances on the severity and occurrence of FOG, and vice versa.

Predicting freezing of gait in patients with Parkinson's disease by combination of Manually-Selected and deep learning features

Freezing of gait (FoG) is a serious gait disorder commonly seen in patients with advanced Parkinson's disease (PD). It is of great interest to pre-judge the occurrence of FoG before its actual onset, thus making it possible to prevent the occurrence of gait freezing by providing suitable external cues. Most of the previous FoG prediction approaches focused on using manually selected gait features for FoG prediction. However, the extraction of these gait features heavily depends on expert knowledge and may be insufficient to well represent different gait classes, resulting in a poor generalization of the prediction models based on these features. In contrast, the deep learning model can learn discriminative features in a latent high-dimensional space to cover inter-patient variations, and has the potential to provide more excellent prediction performance. In this study, the manually selected features are embedded into a deep neural network, the ResNeXt, for better learning the discriminative class-specific gait features. Our experiments are conducted on the public Daphnet dataset, where gait recordings of ten PD patients are collected and eight patients demonstrate FoG epochs. In our experiments, different segment lengths and different pre-FoG durations were explored, and the best performance was obtained when the pre-FoG duration and the segment length is set to be 5 s and 1 s, respectively. The best prediction accuracy is 95.40% with an MF1 score of 0.89 and a Kappa coefficient of 0.87. The comparison with other state-of-the-arts indicates that the proposed approach can provide competitive performance for FoG prediction.

Exploring the levodopa-paradox of freezing of gait in dopaminergic medication-naïve Parkinson’s disease populations

The relationship between dopaminergic treatment and freezing of gait (FOG) in Parkinson’s disease (PD) is complex: levodopa is the most effective symptomatic treatment for FOG, but long-term pulsatile levodopa treatment has also been linked to an increase in the occurrence of FOG. This concept, however, continues to be debated. Here, we compared the occurrence of FOG between a levodopa-naive PD cohort and a levodopa-treated cohort. Forty-nine treatment-naive patients and 150 levodopa-treated patients were included. The time since first motor symptoms was at least 5 years. Disease severity was assessed using the MDS-UPDRS part III. Occurrence of FOG was assessed subjectively (new freezing-of-gait-questionnaire) and objectively (rapid turns test and Timed Up-and-Go test). The presence of FOG was compared between the levodopa-treated and levodopa-naive groups using a chi-square test of homogeneity. We also performed a binomial Firth logistic regression with disease duration, disease severity, country of inclusion, location of measurement, and executive function as covariates. Subjective FOG was more common in the levodopa-treated cohort (n = 41, 27%) compared to the levodopa-naive cohort (n = 2, 4%, p < 0.001). The association between FOG and levodopa treatment remained after adjustment for covariates (OR = 6.04, 95%Cl [1.60, 33.44], p = 0.006). Objectively verified FOG was more common in the levodopa-treated cohort (n = 21, 14%) compared to the levodopa-naive cohort (n = 1, 2%, p = 0.02). We found an association between long-term pulsatile levodopa treatment and an increased occurrence of FOG. Future studies should further explore the role of nonphysiological stimulation of dopamine receptors in generating FOG, as a basis for possible prevention studies.

A Multimodal Model-Fusion Approach for Improved Prediction of Freezing of Gait in Parkinson’s Disease

Freezing of gait (FoG) is a widely observed movement disorder in Parkinson’s Disease patients (PD). Its prediction is crucial for effectively giving the cue to avoid FoG occurrence. However, present methods of prediction of FoG are inaccurate for large but practical prediction horizons (PH)s. Therefore, this work presents a comprehensive analysis of the electroencephalography (EEG) and inertial measurement units (IMU)s to predict FoG advance in time. An ensemble model consisting of two neural networks, EEGFoGNet and IMUFoGNet, was developed and tested at different PHs and ensemble weights. Moreover, the model is tested for two practical scenarios: clinical or research applications and personal uses. For clinical or research applications, stratified 5-Folds cross-validation was used. For personal uses, a transfer learning technique was used for learning user-specific FoG-related features. The model obtained the best accuracy of 92.1% at 1 second’s PH and the least accuracy of 86.2% at 5 seconds’ PH. The presented results are encouraging and show the proposed model’s clinical applicability. This study will also help practitioners in comparing the efficacy of different cueing methods.

Comparative analysis of freezing of gait in distinct Parkinsonism types by diffusion tensor imaging method and cognitive profiles.

Freezing of gait (FOG) is an episodic gait pattern that is common in advanced Parkinson's disease (PD) and other atypical parkinsonism syndromes. Recently, disturbances in the pedunculopontine nucleus (PPN) and its connections have been suggested to play a critical role in the development of FOG. In this study, we aimed to demonstrate possible disturbances in PPN and its connections by performing the diffusion tensor imaging (DTI) technique. We included 18 patients of PD with FOG [PD-FOG], 13 patients of PD without FOG [PD-nFOG] and 12 healthy subjects as well as a group of patients with progressive supranuclear palsy (PSP), an atypical parkinsonism syndrome which is very often complicated with FOG [6 PSP-FOG, 5 PSP-nFOG]. To determine the specific cognitive parameters that can be related to FOG, deliberate neurophysiological evaluations of all the individuals were performed. The comparative analyses and correlation analyses were performed to reveal the neurophysiological and DTI correlates of FOG in either group. We have found disturbances in values reflecting microstructural integrity of the bilateral superior frontal gyrus (SFG), bilateral fastigial nucleus (FN), left pre-supplementary motor area (SMA) in the PD-FOG group relative to the PD-nFOG group. The analysis of the PSP group also demonstrated disturbance in left pre-SMA values in the PSP-FOG group likewise, while negative correlations were determined between right STN, left PPN values and FOG scores. In neurophysiological assessments, lower performances for visuospatial functions were demonstrated in FOG ( +) individuals for either patient group. The disturbances in the visuospatial abilities may be a critical step for the occurrence of FOG. Together with the results of DTI analyses, it might be suggested that impairment in the connectivity of disturbed frontal areas with disordered basal ganglia, maybe the key factor for the occurrence of FOG in the PD group, whereas left PPN which is a nondopaminergic nucleus may play a more prominent role in the process of FOG in PSP. Moreover, our results support the relationship between right STN, and FOG as mentioned before, as well as introduce the importance of FN as a new structure that may be involved in FOG pathogenesis.

Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson’s disease

Freezing of gait (FOG) greatly impacts the daily life of patients with Parkinson’s disease (PD). However, predictors of FOG in early PD are limited. Moreover, recent neuroimaging evidence of cerebral morphological alterations in PD is heterogeneous. We aimed to develop a model that could predict the occurrence of FOG using machine learning, collaborating with clinical, laboratory, and cerebral structural imaging information of early drug-naïve PD and investigate alterations in cerebral morphology in early PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson’s Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a 5-year follow-up period. We found that models trained with structural morphological features showed fair to good performance (accuracy range, 0.67–0.73). Performance improved when clinical and laboratory data was added (accuracy range, 0.71–0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69–0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the structural morphological features that were mainly distributed in the left cerebrum. Moreover, the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Overall, we found that T1WI morphometric markers helped predict future FOG occurrence in patients with early drug-naïve PD at the individual level. The OLF exhibits predominantly cortical expansion in early PD.

Predicting the Onset of Freezing of Gait Using EEG Dynamics

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.

Spine kinematics in patients with Parkinson's disease with and without Freezing of Gait

Despite the importance of spine and upper limb movements in walking, most of previous studies on human locomotion focused exclusively on lower limbs [1]. Patients with Parkinson’s disease (PD) are characterized by trunk postural alterations and reduced limb movements [2,3]. It is still unclear to what extent postural alterations in PD concur to, or counteract, poor balance [3]. Specifically, their contribution to the occurrence of freezing of gait (FoG), an episodic failure of forward stepping exposing patients to high risk of falls, is yet to be fully elucidated. We aimed at characterizing spine kinematics in PD patients with and without FoG during walking and standing. We investigated linear gait in a cohort of 14 patients with PD. They were grouped into patients with (PDF, N=6, Unified Parkinson Disease Rating Scale part III [UPDRS-III]: 35(23-49)) and without FoG (PDNF, N=7, UPDRS-III: 31(12-48)), based on their clinical history. All patients performed the experimental protocol after overnight suspension of all dopaminergic drugs (meds-off). They were instructed to perform at least three trials of barefoot walking at self-selected speed on a walkway of 8 meters. A session of quiet standing of about 60 s ended the experiment. Kinematics was monitored by means of a full body marker protocol [4]. Three markers placed on the seventh cervical vertebrae (C7), the maximum kyphosis, and a virtual marker reconstructed in correspondence of the coccyx served as reference points for the calculation of the lordosis and kyphosis spine angles. Specifically, the coccyx was reconstructed in the pelvis reference system based on previous radiography studies [5]. The distance between the C7 and the middle point between the posterior-superior iliac spines (MID-PSIS) was used to estimate the rachis length. Height of the C7 and MID-PSIS were also computed. Trunk inclination was defined as the angle between the vertical axis of the laboratory and the line connecting C7 and MID-PSIS. The shoulder-pelvis angle was computed as the angle between the line connecting the two acromion and the line connecting the anterior superior iliac spines and projected to the frontal and horizontal planes of the pelvis reference system. For the walking sessions, all variables were computed and averaged across gait cycles at steady-state velocity. For the standing sessions, variables were averaged over time. A Kruskal–Wallis test identified differences between groups for each condition (i.e., walking and standing). A Wilcoxon matched-pair test was used to compare standing and walking conditions inside each group. Threshold for statistical significance was set at 0.05. No differences between groups were found in the spine variables during standing and walking. PDF showed higher values of the shoulder-pelvis angles in the frontal plane only during the standing condition. During walking, the average height of the MID-PSIS decreased with respect to standing in both groups, while C7 height decreased selectively in the PDNF group, despite a trend was present also in the PDF group. Only PDNF patients showed increased trunk inclination during walking with respect to standing. Our analyses did not show a clear difference in the spine kinematics during gait and standing between PDF and PDNF patients. We speculate that trunk postural alterations may not be at the basis of the occurrence of FoG, but rather be compensatory (ineffective) attempts to overcome this symptom. Considering the small sample size employed in our study, further analyses on larger cohorts are warranted to confirm these preliminary results.

Association between levels of high-sensitivity C-reactive protein in plasma and freezing of gait in Parkinson's disease.

This study explored the potential relationship between levels of high-sensitivity C-reactive protein (hs-CRP) in plasma and freezing of gait (FOG) in Parkinson's disease (PD) in China. A total of 72 healthy subjects, 62 PD patients with FOG, and 83 PD patients without FOG from our center were enrolled in this prospective study. Patients with FOG showed significantly higher hs-CRP levels than controls, but patients without FOG did not. Binary logistic regression analysis identified levels of hs-CRP in plasma to be an independent risk factor for FOG among the patients in our cohort (OR 6.371, 95% CI 2.589-15.678, p < 0.001). In fact, a cut-off level of 0.935mg/L distinguished patients with or without FOG [area under the ROC curve (AUC) = 0.908, sensitivity 87.1%, specificity 89.2%]. Our study suggests that high levels of hs-CRP in plasma are associated with the occurrence of FOG in PD. The pooled data combined with a previous study carried out in Spain also indicate a positive association between plasma hs-CRP levels and FOG in PD. However, more research is still needed to verify the plasma hs-CRP as a potential biomarker of FOG.

The correlation between genetic factors and freezing of gait in patients with Parkinson's disease

BackgroundClinical-related risk factors to freezing of gait (FOG) in Parkinson's disease (PD) have been identified. Still, the influence of genetic variations on the FOG occurrence has been poorly studied thus far. AimWe aimed to evaluate the association of six selected polymorphisms of DRD2, ANKK1, and COMT genes with the FOG occurrence and explore the influence of ANNK1/DRD2 haplotypes on the onset of FOG in the group of PD patients. MethodPD patients (n = 234), treated with levodopa for at least two years, were genotyped for the rs4680 in COMT, rs6277, rs1076560, and rs2283265 in DRD2, and rs1800497 and rs2734849 polymorphisms in ANKK1 genes. FOG was evaluated by posing a direct question. In addition, a comprehensive set of clinical scales was applied to all patients. ResultsFOG occurred in 132 (56.4%) PD patients in our cohort. Freezers were younger at PD onset, had longer disease duration, used higher levodopa daily doses and dopaminergic agents, and had higher motor and non-motor scales scores than non-freezers. FOG was more frequent among AA rs4680 COMT carriers than AG and GG rs4680 COMT carriers. Independent predictors of FOG were: disease duration of more than ten years, levodopa daily dose higher than 500 mg/day, motor status, and COMT AA genotype. AGGAA and GGAAA haplotypes were revealed as protective and vulnerability factors for FOG occurrence. ConclusionIn addition to previously identified disease- and therapy-related risk factors, our results suggested a possible contribution of dopamine-related genes to the FOG occurrence.

Hyposmia may predict development of freezing of gait in Parkinson's disease.

To explore the effect of olfactory dysfunction on treatment of motor manifestations in Parkinson's disease (PD). The current longitudinal retrospective cohort study consecutively recruited 108 de novo PD patients. Of whom 29 were normosmia and 79 were hyposmia, respectively, which was determined by the Korean Version of Sniffin' Sticks Test II at the time of diagnosis. All the participants underwent serial clinical examinations including Unified Parkinson's Disease Rating Scale (UPDRS), Mini-Mental State Examination, and Montreal Cognitive Assessment. The normosmic group demonstrated a significantly greater reduction of the UPDRS III score (30.3 ± 5.9 to 21.9 ± 5.1) than that of the hyposmic group (34.5 ± 9.3 to 28.5 ± 8.1) from baseline to 1-year later (p, 0.003; Bonferroni correction for p < 0.0045). Of subdomains in UPDRS III, the axial domain revealed a remarkable decrease in the normosmic group. Further, the hyposmic group exhibited a higher development rate of freezing of gait (FOG) compared to the normosmic group (29/79 (36.7%) vs 2/29 (6.9%); p, 0.002) during 33.9 ± 7.7 months of the mean follow-up period. A Cox proportional hazards model demonstrated the hyposmia to be a significant risk factor for the future development of FOG (HR, 4.23; 95% CI 1.180-17.801; p, 0.05). Our data demonstrated the olfactory dysfunction to be a significant risk factor for the development of the FOG in PD. Hyposmic PD patients should be paid more careful attention to the occurrence of FOG in the clinical practice.

A Multimodal Imaging Approach Demonstrates Reduced Midbrain Functional Network Connectivity Is Associated With Freezing of Gait in Parkinson's Disease.

Background: The pathophysiological mechanisms underlying freezing of gait (FOG) are poorly defined. MRI studies in FOG showed a distinct pattern of cortical atrophy and decreased functional connectivity (FC) within motor and cognitive networks. Furthermore, reduced rs-FC within midbrain, frontal, and temporal areas has been also described. This study investigated the patterns of whole-brain FC alterations within midbrain inter-connected regions in PD-FOG patients, and whether these patterns are linked to midbrain structural damage using a multi-modal imaging approach, combing structural and functional imaging techniques.Methods: Thirty three PD patients (16 PD-FOG, 17 PD noFOG), and 21 sex- and age-matched healthy controls (HCs) were prospectively enrolled. All subjects underwent MRI scan at 1.5T, whereas only PD patients underwent clinical and cognitive assessment. Grey matter (GM) integrity was measured using voxel-based morphometry (VBM). VBM findings served as basis to localize midbrain damage, and were further used as a seed region for investigating whole-brain FC alterations using rs-fMRI.Results: In rs-fMRI, patients with PD and FOG demonstrated significant decrease of midbrain-cortical FC levels in the R PCG, right postcentral, and supramarginal gyri compared to controls and the middle cingulate compared to noFOG group. Based on the regression analysis, MOCA, UPDRS-III total score, and FOG severity scores were associated with FC levels in several frontal, parietal and temporal regions.Discussion: The present results suggest that midbrain structural damage as well as decreased FC within the brainstem functional network might contribute to FOG occurrence in PD patients.

Predicting State Transition in Freezing of Gait via Acceleration Measurements for Controlled Cueing in Parkinson’s Disease

Freezing of gait (FoG) leads to imbalance and falls in Parkinson’s disease. Cues potentially prevent FoG occurrence and unfreeze the patient from FoG. Although FoG detection and its prediction have been intensively studied, its termination is mostly neglected. Continuous cueing after the termination of freezing is annoying and has potential side effects in the long term. In this article, for the first time, we attempt to develop a machine learning approach for the prediction of start and termination of freezing, which can potentially provide automated controlled cueing in Parkinson’s individuals. We hypothesize certain attributes that correspond to the transition from walking to freezing and vice versa. To this end, we propose unique labeling of classes to predict freezing termination as follows: no FoG, pre-FoG (immediate state preceding FoG), FoG, and pre of post FoG (state just before unfreezing). Daphnet dataset, freely available online, was utilized to develop customized attributes measured from accelerometers with the data recorded from ten participants. The high-dimensional attributes were reduced using a principal component (PC) analysis before being fed to the k-nearest neighbor (kNN) classifier for prediction. With 45 PCs, we achieved average (SD) precision, sensitivity, specificity, f1 score, and accuracy of 95.55% (4.60%), 94.97% (4.86%), 99.19% (0.85%), 95.25% (4.72%), and 98.92% (1.56%), respectively, across the four classes while utilizing the attributes from 60 data points ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 0.93$ </tex-math></inline-formula> s) prior to a given instant. Specifically, for the newly introduced labeling—pre of post FoG, we observed average (SD) precision, sensitivity, specificity, and f1 score of 92.73% (10.15%), 91.50% (10.34%), 99.83% (0.32%), and 92.10% (10.25%), respectively, with 60 previous data points and 45 PCs. These results show the potential of the present approach in the timely deactivation of cues in real time to avoid any side effects of cueing. Enhanced clinical benefit for Parkinson’s patients is the major contribution of this study.

The Prevalence and Determinants of Freezing of Gait Among Iranian Patients with Parkinson’s Disease

Background & Objective: Freezing of gait (FOG) is accompanied by various complications, reduced quality of life, and increased risk of mortality and morbidity in patients with Parkinson’s disease. The incidence of this disabling phenomenon can be predicted in at least one-third of patients with Parkinson’s disease. Therefore, the current study aimed to investigate the prevalence of FOG among Iranian patients with Parkinson’s disease and to determine the predictors of this phenomenon in these patients. Materials & Methods: This cross-sectional study was performed on 135 patients with Parkinson’s disease, referred to Rasoul Akram Hospital, Tehran, Iran. The subjects were selected using the convenience sampling method. FOG was confirmed via clinical examination by two fellows of the movement disorder fellowship program. Variables were evaluated using bivariate and multivariate logistic regression analyses, receiver operating characteristics (ROC) curve analysis, and measurement of the area under the ROC curve (AUC). Results: Out of 135 patients with Parkinson’s disease, 36 (26.7) had FOG. Based on the results, FOG had a significantly higher prevalence in older patients, compared to younger patients. According to the results, FOG had no significant relationship with other baseline variables, including gender, disease duration, and disease manifestations. According to the multivariate logistic regression model, advanced age (OR: 0.915; P=0.031) and longer disease duration (OR: 0.992; P=0.019) predicted the incidence of FOG in the presence of underlying factors. Conclusion: The prevalence of FOG among patients with Parkinson’s disease was estimated at 26.7, which is within the global range. The results revealed that age and duration of disease were two factors, which could predict the occurrence of FOG with high sensitivity and specificity. © 2020.

Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors.

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease.

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the gait. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state-of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG.

Changes in Resting-State Functional Connectivity Related to Freezing of Gait in Parkinson's Disease

Freezing of gait (FOG) is a common motor symptom in Parkinson's disease (PD) thought to arise from the dysfunctional cortico-basal ganglia-thalamic circuity. The purpose of this study was to assess the changes in brain resting-state functional connectivity (rs-FC) of subcortical structures comprising the cortico-basal ganglia-thalamic circuity in individuals with PD with and without FOG. Resting-state functional magnetic resonance imaging was acquired in 27 individuals with idiopathic PD (14 with FOG and 13 without FOG). A seed-to-voxel analysis was performed with the seeds in the bilateral basal ganglia nuclei, thalamus, and pedunculopontine nucleus. Between-group differences in rs-FC revealed that the bilateral thalamus and globus pallidus external were significantly more connected with visual areas in PD with FOG compared to PD without FOG. In addition, PD with FOG had increased connectivity between the left putamen and retrosplenial cortex as well as with the cerebellum. Our findings suggest an increased connectivity at rest of subcortical and cortical regions involved in sensory and visuospatial processing that may be compensating for sensorimotor deficits in FOG. This increased connectivity may contribute to the hypothesized overload in the cortico-basal ganglia-thalamic circuity processing capacity, which may ultimately result in FOG occurrence.

Aberrant functional connectivity in patients with Parkinson's disease and freezing of gait: a within- and between-network analysis.

Freezing of gait (FOG) is a disabling motor symptom that affects patients with Parkinson's disease (PD). MRI-based evidence suggest that multiple brain structures are involved in the occurrence of FOG. We investigated the integrity of the neuronal networks in PD patients with FOG (PD-FOG), considering both within-network resting-state functional connectivity (rsFC) and between-network rsFC. Thirty-one PD patients (15 PD-FOG and 16 PD-nFOG) and 16 healthy subjects (HS) underwent a rsfMRI study. The data was analysed by using FSL Melodic and FSLNets software to study within- and between-network rsFC. PD-FOG displayed a higher within-network rsFC that involved a greater number of resting-state networks (RSNs) than PD-nFOG. rsFC in the basal ganglia network significantly correlated with the Timed Up and Go test. Moreover, when compared with HS, PD-FOG displayed reduced rsFC between the right fronto-parietal and executive-control RSNs, which significantly correlated with FOG severity. This study demonstrates that FOG is associated with an impaired interplay and communication between the RSNs that underpin attentive and executive abilities, especially in the right hemisphere.