BACKGROUND: The contribution of cholinergic degeneration to gait disturbance in Parkinson\'s disease (PD) is increasingly recognized, yet its relationship with dopaminergic-resistant gait parameters has been poorly investigated. We investigated the association between comprehensive gait parameters and cholinergic nucleus degeneration in PD.
METHODS: This cross-sectional study enrolled 84 PD patients and 69 controls. All subjects underwent brain structural magnetic resonance imaging to assess the gray matter density (GMD) and volume (GMV) of the cholinergic nuclei (Ch123/Ch4). Gait parameters under single-task (ST) and dual-task (DT) walking tests were acquired using sensor wearables in PD group. We compared cholinergic nucleus morphology and gait performance between groups and examined their association.
RESULTS: PD patients exhibited significantly decreased GMD and GMV of the left Ch4 compared to controls after reaching HY stage > 2. Significant correlations were observed between multiple gait parameters and bilateral Ch123/Ch4. After multiple testing correction, the Ch123/Ch4 degeneration was significantly associated with shorter stride length, lower gait velocity, longer stance phase, smaller ankle toe-off and heel-strike angles under both ST and DT condition. For PD patients with HY stage 1-2, there were no significant degeneration of Ch123/4, and only right side Ch123/Ch4 were corrected with the gait parameters. However, as the disease progressed to HY stage > 2, bilateral Ch123/Ch4 nuclei showed correlations with gait performance, with more extensive significant correlations were observed in the right side.
CONCLUSIONS: Our study demonstrated the progressive association between cholinergic nuclei degeneration and gait impairment across different stages of PD, and highlighting the potential lateralization of the cholinergic nuclei\'s impact on gait impairment. These findings offer insights for the design and implementation of future clinical trials investigating cholinergic treatments as a promising approach to address gait impairments in PD.

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.

Background and Aims: Previous research has focused on static functional connectivity in gait disorders caused by cerebral small vessel disease (CSVD), neglecting dynamic functional connections and network attribution. This study aims to investigate alterations in dynamic functional network connectivity (dFNC) and topological organization variance in CSVD-related gait disorders. Methods: A total of 85 patients with CSVD, including 41 patients with CSVD and gait disorders (CSVD-GD), 44 patients with CSVD and non-gait disorders (CSVD-NGD), and 32 healthy controls (HC), were enrolled in this study. Five networks composed of 10 independent components were selected using independent component analysis. Sliding time window and k-means clustering methods were used for dFNC analysis. The relationship between alterations in the dFNC properties and gait metrics was further assessed. Results: Three reproducible dFNC states were determined (State 1: sparsely connected, State 2: intermediate pattern, and State 3: strongly connected). CSVD-GD showed significantly higher fractional windows (FW) and mean dwell time (MDT) in State 1 compared with CSVD-NGD. Higher local efficiency variance was observed in the CSVD-GD group compared with HC, but no differences were found in the global efficiency comparison. Both the FW and MDT in State 1 were negatively correlated with gait speed and step length, and the relationship between MDT of State 1 and gait speed was mediated by overall cognition, information processing speed, and executive function. Conclusions: Our study uncovered abnormal dFNC indicators and variations in topological organization in CSVD-GD, offering potential early prediction indicators and freshening insights into the underlying pathogenesis of gait disturbances in CSVD.

UNASSIGNED: Gait disturbance is a vital characteristic of motor manifestation in α- synucleinopathies, especially Parkinson\'s disease. Subtle gait alterations are present in isolated rapid eye movement sleep behavior disorder (iRBD) patients before phenoconversion; it is yet unclear, if gait analysis may predict phenoconversion.
UNASSIGNED: To investigate subtle gait alterations and explore whether gait analysis using wearable sensors is associated with phenoconversion of iRBD to α-synucleinopathies.
UNASSIGNED: Thirty-one polysomnography-confirmed iRBD patients and 33 healthy controls (HCs) were enrolled at baseline. All participants walked for a minute while wearing 6 inertial sensors on bilateral wrists, ankles, and the trunk (sternal and lumbar region). Three conditions were tested: (i) normal walking, (ii) fast walking, and (iii) dual-task walking.
UNASSIGNED: Decreased arm range of motion and increased gait variation (stride length, stride time and stride velocity) discriminate converters from HCs at baseline. After an average of 5.40 years of follow-up, 10 patients converted to neurodegenerative diseases (converters). Cox regression analysis showed higher value of stride length asymmetry under normal walking condition to be associated with an early conversion of iRBD to α- synucleinopathies (adjusted HR 4.468, 95% CI 1.088- 18.349, p = 0.038).
UNASSIGNED: Stride length asymmetry is associated with progression to α- synucleinopathies in patients with iRBD. Gait analysis with wearable sensors may be useful for screening, monitoring, and risk stratification for disease-modifying therapy trials in patients with iRBD.

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.

Gait analysis can be utilized as an effective method for identifying Parkinson\'s disease (PD) [1]. However, research methods based on the time-domain gait feature analysis are influenced by population characteristics such as individual height, age, and weight, which unfavorably affect PD diagnostic decision-making.

BACKGROUND: Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson\'s disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD.
METHODS: We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments.
RESULTS: During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)).
CONCLUSIONS: Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.

OBJECTIVE: With the discovery of the potential role of gait and eye movement disorders in Parkinson\'s disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD.
METHODS: We enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages.
RESULTS: A total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients.
CONCLUSIONS: We demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.

BACKGROUND: Gait ability is often cited by stroke survivors. Robot-assisted gait training (RAGT) can help stroke patients with lower limb motor impairment regain motor coordination.
METHODS: PubMed, Cochrane Library, Embase were systematically searched until September 2023, to identify randomized controlled trials presenting: stroke survivors as participants; RAGT as intervention; conventional rehabilitation as a comparator; gait assessment, through scales or quantitative parameters, as outcome measures.
RESULTS: Twenty-seven publications involving 1167 patients met the inclusion criteria. Meta-analysis showed no significant differences in speed, cadence, spatial symmetry, and changes in joint mobility angles between the RAGT group and the control group. In addition, RAGT was associated with changes in affected side step length (SMD=0.02, 95% CI: 0.01, 0.03; P<0.0001), temporal symmetry (SMD=-0.38, 95% CI: -0.6, -0.16; P=0.0006], Six-Minute Walk Test (SMD=25.14, 95% CI: 10.19, 40.09; P=0.0010] and Functional Ambulation Categories (SMD=0.32, 95% CI: 0.01, 0.63; P=0.04). According to the PEDro scale, 19 (70.4%) studies were of high quality and eight were of moderate quality (29.6%).
CONCLUSIONS: Taken together, the review synthesis showed that RAGT might have a potential role in the recovery of walking dysfunction after stroke. However, its superiority over conventional rehabilitation requires further research. Additionally, it may provide unexpected benefits that the effects of RAGT with different types or treatment protocols were further compared.

BACKGROUND: Gait impairment has a major impact on quality of life in patients with Parkinson\'s disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies.
OBJECTIVE: We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait.
METHODS: We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues.
RESULTS: The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues.
CONCLUSIONS: We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.