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Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Humanos , Inteligência Artificial , MarchaRESUMO
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Humanos , Doença de Parkinson/diagnóstico , Transtornos Neurológicos da Marcha/diagnóstico , Qualidade de Vida , Acelerometria/métodos , Marcha/fisiologia , AlgoritmosRESUMO
BACKGROUND: Freezing of gait (FOG) is a disabling motor symptom occurring mainly in the advanced stage of Parkinson's disease (PD). METHODS: This review outlines the clinical manifestation of FOG and its relationship with levodopa treatment, the differential diagnosis with respect to other neurodegenerative and secondary forms and the available treatment. RESULTS: We report the proposed models explaining the FOG phenomenon and summarize the available knowledge on FOG etiology's potential genetic contribution. A complete understanding of the mechanisms underlying FOG in PD is essential to find the best therapy. Different treatment options exist but are still not entirely successful, and often a combination of approaches is needed. CONCLUSIONS: Further studies focusing on the potential genetic role in FOG may increase the knowledge on the FOG etiology and pathophysiology, allowing further individualized treatment strategies for this very disabling phenomenon.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Causalidade , Marcha , Transtornos Neurológicos da Marcha/tratamento farmacológico , Transtornos Neurológicos da Marcha/terapia , Humanos , Levodopa/uso terapêutico , Doença de Parkinson/complicações , Doença de Parkinson/diagnóstico , Doença de Parkinson/genéticaRESUMO
BACKGROUND: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson's disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. METHODS: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. RESULTS: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). CONCLUSIONS: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Marcha , Humanos , Doença de Parkinson/diagnóstico , Equilíbrio Postural , Estudos de Tempo e MovimentoRESUMO
Diverse but complementary methodologies are required to uncover the complex determinants and pathophysiology of freezing of gait. To develop future therapeutic avenues, we need a deeper understanding of the disseminated functional-anatomic network and its temporally associated dynamic processes. In this targeted review, we will summarize the latest advances across multiple methodological domains including clinical phenomenology, neurogenetics, multimodal neuroimaging, neurophysiology, and neuromodulation. We found that (i) locomotor network vulnerability is established by structural damage, e.g. from neurodegeneration possibly as result from genetic variability, or to variable degree from brain lesions. This leads to an enhanced network susceptibility, where (ii) modulators can both increase or decrease the threshold to express freezing of gait. Consequent to a threshold decrease, (iii) neuronal integration failure of a multilevel brain network will occur and affect one or numerous nodes and projections of the multilevel network. Finally, (iv) an ultimate pathway might encounter failure of effective motor output and give rise to freezing of gait as clinical endpoint. In conclusion, we derive key questions from this review that challenge this pathophysiological view. We suggest that future research on these questions should lead to improved pathophysiological insight and enhanced therapeutic strategies.
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Encéfalo/fisiopatologia , Transtornos Neurológicos da Marcha/fisiopatologia , Doença de Parkinson/fisiopatologia , Apolipoproteína E4/genética , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Citocromo P-450 CYP2D6/genética , Neuroimagem Funcional , Transtornos Neurológicos da Marcha/diagnóstico por imagem , Transtornos Neurológicos da Marcha/genética , Glucosilceramidase/genética , Humanos , Serina-Treonina Proteína Quinase-2 com Repetições Ricas em Leucina/genética , Imageamento por Ressonância Magnética , Mutação , Vias Neurais/fisiopatologia , Doença de Parkinson/diagnóstico por imagem , Doença de Parkinson/genética , Tomografia por Emissão de Pósitrons , Receptores de Dopamina D2/genética , Tomografia Computadorizada de Emissão de Fóton ÚnicoRESUMO
Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. METHODS: A cohort of 11 Parkinson's disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. RESULTS: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. CONCLUSIONS: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm's effectiveness.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Dispositivos Eletrônicos Vestíveis , Acelerometria , Idoso , Feminino , Marcha , Transtornos Neurológicos da Marcha/diagnóstico , Transtornos Neurológicos da Marcha/etiologia , Humanos , Aprendizado de Máquina , Masculino , Doença de Parkinson/complicações , Doença de Parkinson/diagnóstico , Doença de Parkinson/tratamento farmacológico , Equilíbrio Postural , Estudos de Tempo e MovimentoRESUMO
Parkinson's disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients' quality of life, and even death. Existing FOG assessments by doctors are based on a patient's diaries and experts' manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom's characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called "Kin-FOG". The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient's home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.
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Transtornos Neurológicos da Marcha/terapia , Movimento/fisiologia , Doença de Parkinson/terapia , Caminhada/fisiologia , Adulto , Algoritmos , Teorema de Bayes , Fenômenos Biomecânicos , Feminino , Transtornos Neurológicos da Marcha/fisiopatologia , Humanos , Masculino , Doença de Parkinson/fisiopatologia , Qualidade de Vida , Processamento de Sinais Assistido por ComputadorRESUMO
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.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Equilíbrio Postural , Humanos , Doença de Parkinson/fisiopatologia , Doença de Parkinson/complicações , Masculino , Feminino , Transtornos Neurológicos da Marcha/fisiopatologia , Transtornos Neurológicos da Marcha/etiologia , Idoso , Equilíbrio Postural/fisiologia , Pessoa de Meia-Idade , Cognição/fisiologia , Atenção/fisiologia , Sistema Límbico/fisiopatologiaRESUMO
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.
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Background: The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way. Objective: This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD. Methods: Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance. Results: A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection. Conclusion: These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.
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OBJECTIVE: An executive dysfunction is supposed to contribute to freezing of gait (FoG) in Parkinson's disease. We aimed to investigate at a behavioral and cortical levels whether an attentional load (particularly, a conflicting situation) can specifically impact preparation and execution phases of step initiation in parkinsonian patients with FoG. METHODS: Fifteen patients with FoG, 16 without and 15 controls performed an adapted version of the Attention Network Test, with step initiation as response instead of the standard manual keypress. Kinetic and kinematic features of gait initiation as well as high-resolution electroencephalography were recorded during the task. RESULTS: Patients with FoG presented an impaired executive control. Step execution time was longer in parkinsonian patients. However, the executive control effect on step execution time was not different between all groups. Compared to patients, controls showed a shorter step initiation-locked alpha desynchronization, and an earlier, more intense and shorter beta desynchronization over the sensorimotor cortex. Even though controls were faster, the induced alpha and beta activity associated with the effect of executive control didn't differ between patients and controls. CONCLUSIONS: Tasks of conflict resolution lead to a comparable alteration of step initiation and its underlying brain activity in all groups. Links between executive control, gait initiation and FoG seem more complex than expected. SIGNIFICANCE: This study questions the cognitive hypothesis in the pathophysiology of freezing of gait. Executive dysfunction is associated with FoG but is not the main causal mechanism since the interaction between attention and motor preparation didn't provoke FoG.
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Transtornos Neurológicos da Marcha , Doença de Parkinson , Humanos , Função Executiva/fisiologia , Transtornos Neurológicos da Marcha/etiologia , Cognição , Marcha/fisiologiaRESUMO
Background: Freezing of gait (FOG) is a common disabling motor disturbance in Parkinson's disease (PD). Our study aimed to probe the topological organizations of structural and functional brain networks and their coupling in FOG. Methods: In this cross-sectional retrospective study, a total of 30 PD patients with FOG (PD-FOG), 40 patients without FOG, and 25 healthy controls (HCs) underwent clinical assessments and magnetic resonance imaging (MRI) scanning. Large-scale structural and functional brain networks were constructed. Subsequently, global and nodal graph theoretical properties and functional-structural coupling were investigated. Finally, correlations between the altered brain topological properties and freezing severity were analyzed in PD-FOG. Results: For structural networks, at the global level, PD-FOG exhibited increased normalized characteristic path length (P=0.040, Bonferroni-corrected) and decreased global efficiency (P=0.005, Bonferroni-corrected) compared with controls, and showed reduced global (P=0.001, Bonferroni-corrected) and local (P=0.032, Bonferroni-corrected) efficiency relative to patients without FOG. At the nodal level, nodal efficiency of structural networks was reduced in PD-FOG compared with PD patients without FOG, located in the left supplementary motor area (SMA), gyrus rectus, and middle cingulate cortex (MCC) (all P<0.05, Bonferroni-corrected). Notably, altered global and nodal properties of structural networks were significantly correlated with Freezing of Gait Questionnaire scores [all P<0.05, false discovery rate (FDR)-corrected]. However, only an increase in local efficiency (P=0.003, Bonferroni-corrected) of functional networks was identified in PD-FOG compared with those without FOG. No significant structural-functional coupling was detected among the 3 groups. Conclusions: This study demonstrates the extensively impaired structural and relatively reserved functional network topological organizations in PD-FOG. Our results also provide evidence that the pathogenesis of PD-FOG is primarily attributable to network vulnerability established by crucial structural damage, especially in the left SMA, gyrus rectus, and MCC.
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Substantia nigra pars compacta (SNc) and locus coeruleus (LC) are neuromelanin-rich nuclei implicated in diverse cognitive and motor processes in normal brain function and disease. However, their roles in aging and neurodegenerative disease mechanisms have remained unclear due to a lack of tools to study them in vivo. Preclinical and post-mortem human investigations indicate that the relationship between tissue neuromelanin content and neurodegeneration is complex. Neuromelanin exhibits both neuroprotective and cytotoxic characteristics, and tissue neuromelanin content varies across the lifespan, exhibiting an inverted U-shaped relationship with age. Neuromelanin-sensitive MRI (NM-MRI) is an emerging modality that allows measurement of neuromelanin-associated contrast in SNc and LC in humans. NM-MRI robustly detects disease effects in these structures in neurodegenerative conditions, including Parkinson's disease (PD). Previous NM-MRI studies of PD have largely focused on detecting disease group effects, but few studies have reported NM-MRI correlations with phenotype. Because neuromelanin dynamics are complex, we hypothesize that they are best interpreted in the context of both disease stage and aging, with neuromelanin loss correlating with symptoms most clearly in advanced stages where neuromelanin loss and neurodegeneration are coupled. We tested this hypothesis using NM-MRI to measure SNc and LC volumes in healthy older adult control individuals and in PD patients with and without freezing of gait (FOG), a severe and disabling PD symptom. We assessed for group differences and correlations between NM-MRI measures and aging, cognition and motor deficits. SNc volume was significantly decreased in PD with FOG compared to controls. SNc volume correlated significantly with motor symptoms and cognitive measures in PD with FOG, but not in PD without FOG. SNc volume correlated significantly with aging in PD. When PD patients were stratified by disease duration, SNc volume correlated with aging, cognition, and motor deficits only in PD with disease duration >5 years. We conclude that in severe or advanced PD, identified by either FOG or disease duration >5 years, the observed correlations between SNc volume and aging, cognition, and motor function may reflect the coupling of neuromelanin loss with neurodegeneration and the associated emergence of a linear relationship between NM-MRI measures and phenotype.
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Background: Quantitative susceptibility mapping (QSM) is a novel imaging method for detecting iron content in the brain. The study aimed determine whether the iron deposition in the brains of people with Parkinson's disease (PD) is correlated with freezing of gait (FOG). Methods: We retrospectively collected the data of 24 patients with PD from the Movement Disorders Program and 36 healthy controls (HCs) from January 2021 to December 2021. Clinical assessments included mental intelligence scales, Parkinson rating scales, motor-related scales, and clinical gait assessments. All exercise scales and gait assessments were performed in the "ON" and "OFF" states. Magnetic resonance imaging (MRI) data were collected using 3-dimensional fast low-angle shot sequences. We chose the bilateral red nucleus, substantia nigra, thalamus, putamen, caudate nucleus, and globus pallidus as regions of interest for QSM analysis. Results: The iron deposition in the substantia nigra of the PD group was significantly higher than that of the HC group (P<0.01). In the PD group, the iron deposition in the substantia nigra of patients with FOG was significantly higher than that in patients without FOG (P=0.04). The iron deposition in the substantia nigra was positively correlated with the New Freezing of Gait Questionnaire (P=0.03). The scores for depression and anxiety of the PD group were significantly higher than those of the HC group, while the Berg balance scale score was significantly lower (P<0.01). Conclusions: The iron deposition in the substantia nigra of patients with PD is increased compared with that of controls and is associated with FOG. QSM can be used to detect brain iron deposition in patients with PD, which would help to explore the mechanism of abnormal neurobiological activity in FOG.
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Freezing of Gait (FoG) is one of the most critical debilitating motor symptoms of advanced Parkinson's disease (PD) with a higher rate of occurrence in aged people. PD affects the cardinal motor functioning and leads to non-motor symptoms, including cognitive and neurobehavioral abnormalities, autonomic dysfunctions and sleep disorders. Since its pathogenesis is complex and unclear yet, this paper targets the studies done on the pathophysiology and epidemiology of FoG in PD. Gait disorder and cardinal features vary from festination (involuntary hurrying in walking) to freezing of gait (breakdown of repetitive movement of steps despite the intention to walk) in patients. Hence, it is difficult to assess the FoG in clinical trials. Therefore, the current research emphasizes wearable sensor-based systems over pharmacology and surgical methods.â¢This paper presents a technological review of various techniques used for the assessment of FoG with a comprehensive comparison.â¢Researchers are aiming at the development of wireless sensor-based assistive devices to (a) predict the FoG episode in a different environment, (b) acquire the long-term data for real-time analysis, and (c) cue the FoG patients.â¢We summarize the work done till now and future research directions needed for a suitable cueing mechanism to overcome FoG.
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Parkinson's disease (PD) is generally associated with abnormally increased beta band oscillations in the cortico-basal ganglia loop during walking. PD patients with freezing of gait (FOG) exhibit a more distinct, prolonged narrow band of beta oscillations that are locked to the initiation of movement at â¼18 Hz. Upon initiation of cycling movements, this oscillation has been reported to be weaker and rather brief in duration. Due to the suppression of the overall beta band power during cycling and its continuous nature of the movement, cycling is considered to be less demanding for cortical networks compared to walking, including reduced need for sensorimotor processing, and thus unimpaired continuous cycling motion. Furthermore, cycling has been considered one of the most efficient non-pharmacological therapies with an influence on the subthalamic nucleus (STN) beta rhythms implicative of the deep brain stimulation effects. In the current review, we provide an overview of the currently available studies and discuss the underlying mechanism of preserved cycling ability in relation to the FOG in PD patients. The mechanisms are presented in detail using a graphical scheme comparing cortical oscillations during walking and cycling in PD.
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[This corrects the article DOI: 10.3389/fneur.2020.571086.].
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Freezing of gait (FOG) is a particularly debilitating symptom of Parkinson's disease (PD) and is often refractory to treatment. A striking feature of FOG is that external sensory cues can be used to overcome freezing and improve gait. Local field potentials (LFPs) recorded from the subthalamic nucleus (STN) and globus pallidus (GP) show that beta-band power modulates with gait phase. In the STN, beta-band oscillations are modulated by external cues, but it is unknown if this relationship holds in the globus pallidus (GP). Here we report LFP data recorded from the left GP, using a Medtronic PC + S device, in a 68-year-old man with PD and FOG during treadmill walking. A "stepping stone" task was used during which stepping was cued using visual targets of constant color or targets that unpredictably changed color, requiring a step length adjustment. Gait performance was quantified using measures of treadmill ground reaction forces and center of pressure and body kinematics from video monitoring. Beta-band power (12-30 Hz) and number of freezing episodes were measured. Cues which unpredictably changed color improved FOG more than conventional cues and were associated with greater modulation of beta-band power in phase with gait. This preliminary finding suggests that cueing-induced improvement of FOG may relate to beta-band modulation.
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Background: Pharmacotherapy is the first-line treatment option for Parkinson's disease, and levodopa is considered the most effective drug for managing motor symptoms. However, side effects such as motor fluctuation and dyskinesia have been associated with levodopa treatment. For these conditions, alternative therapies, including invasive and non-invasive medical devices, may be helpful. This review sheds light on current progress in the development of devices to alleviate motor symptoms in Parkinson's disease. Methods: We first conducted a narrative literature review to obtain an overview of current invasive and non-invasive medical devices and thereafter performed a systematic review of recent randomized controlled trials (RCTs) of these devices. Results: Our review revealed different characteristics of each device and their effectiveness for motor symptoms. Although invasive medical devices are usually highly effective, surgical procedures can be burdensome for patients and have serious side effects. In contrast, non-pharmacological/non-surgical devices have fewer complications. RCTs of non-invasive devices, especially non-invasive brain stimulation and mechanical peripheral stimulation devices, have proven effectiveness on motor symptoms. Nearly no non-invasive devices have yet received Food and Drug Administration certification or a CE mark. Conclusion: Invasive and non-invasive medical devices have unique characteristics, and several RCTs have been conducted for each device. Invasive devices are more effective, while non-invasive devices are less effective and have lower hurdles and risks. It is important to understand the characteristics of each device and capitalize on these.
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Freezing of gait can cause reduced independence and quality of life for many with Parkinson's disease. Episodes frequently occur at points of transition such as navigating a doorway. Therapeutic interventions, i.e., drugs and exercise, do not always successfully mitigate episodes. There are several different, but not exclusive causes for freezing of gait. People with freezing of gait are able to navigate dynamic situations like stairways by utilizing a different attentional strategy to over-ground walking, but may freeze when passing through a doorway. The question is, is it possible to employ a special attentional strategy to prevent freezing at this point? Motor imagery allows for learning motor skills in absolute safety and has been widely employed in a variety of populations, including other neuro-compromised groups. Motor imagery is not studied in a homologous manner in people with Parkinson's Disease, leading to conflicting results, but may have the potential to establish a different attentional strategy which allows a subject to mitigate freezing of gait episodes. This paper will identify and discuss the questions that still need to be answered in order to consider this approach i.e., can this population access motor imagery, can motor imagery alter the attentional strategy employed when moving through doorways, what is the best motor imagery approach for people with Parkinson's Disease and freezing of gait, and what dosage is most effective, while briefly outlining future research considerations.