The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.

BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation. METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests. RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933). CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.

Better understanding fall risk: AI-based computer vision for contextual gait assessment.

Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively.

Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.

With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (PD) and finds that walking recording empowered by deep learning is a valid digital biomarker for early-recognizing PD patients. Specifically, the inertial sensor data is preprocessed, including normalization, scaling, and rotation, and then the processed data is fed into the proposed deep NeuroEnhanceNet. Finally, determine the individual prediction score using the PD-prone strategy and generate the detection results. The proposed deep NeuroEnhanceNet, specifically designed for inertial sensor data, can focus on both the long-term data characteristics within a single channel and the inter-channel correlations. Our method obtains a low false negative rate of 0.053 for the early detection of PD. We further analyze and compare the effectiveness of digital biomarkers captured from the walking and resting processes for early detection of PD. All the code for this work is available at: https://github.com/heyiyia/NeuroEnhanceNet.

Decoding visual evoked potential latency: revealing neurological connections in Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by diverse motor and non-motor symptoms. Visual evoked potentials (VEPs) provide valuable insights into the neurological changes in PD. This study examines VEP latency to explore potential connections between visual processing and PD progression, focusing on whether inter-eye latency differences are influenced by disease severity and symptomatology. A cross-sectional observational study was conducted with 59 PD patients at the Neurology I Clinic, Cluj-Napoca County Emergency Clinical Hospital, from October 2019 to October 2021. Patients underwent neurological and psychological evaluations, including VEP testing with a reversal pattern technique. P100 wave latency was assessed for both eyes, and associations with clinical indicators like Hoehn and Yahr stages, UPDRS scores, and non-motor symptoms were analyzed. VEP latencies for the right and left eyes were 108.7 ± 10.6 ms and 108.4 ± 9.7 ms, respectively, with no significant inter-eye differences (P = 0.8). UPDRS item 4 scores correlated significantly with both latencies (P = 0.003 for the left eye and P <0.001 for the right). Latency differences between eyes were shorter in patients with symmetrical parkinsonism compared to those with unilateral predominance. Age correlated weakly with P100 latency, and a weak correlation was found between anhedonia scores and right-eye latency. VEP latency is sensitive to PD motor severity, with shorter inter-eye latency differences in symmetrical parkinsonism, suggesting balanced dopaminergic dysfunction. VEP latency differences offer insights into neurophysiological changes in PD, reflecting dopaminergic dysfunction and its impact on visual processing. These findings support the potential of VEPs as diagnostic and prognostic tools in PD assessment.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

Association between cognitive performance and manual dexterity in patients with Parkinson's disease.

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

Improved Mathematical Models of Parkinson's Disease with Hopf Bifurcation and Huntington's Disease with Chaos.

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