Neuroimaging advances in Parkinson's disease with freezing of gait: A systematic review

Neural Correlates of Freezing of Gait in Parkinson's Disease: An Electrophysiology Mini-Review

Freezing of gait (FoG) is a disabling symptom characterized as a brief inability to step or by short steps, which occurs when initiating gait or while turning, affecting over half the population with advanced Parkinson's disease (PD). Several non-competing hypotheses have been proposed to explain the pathophysiology and mechanism behind FoG. Yet, due to the complexity of FoG and the lack of a complete understanding of its mechanism, no clear consensus has been reached on the best treatment options. Moreover, most studies that aim to explore neural biomarkers of FoG have been limited to semi-static or imagined paradigms. One of the biggest unmet needs in the field is the identification of reliable biomarkers that can be construed from real walking scenarios to guide better treatments and validate medical and therapeutic interventions. Advances in neural electrophysiology exploration, including EEG and DBS, will allow for pathophysiology research on more real-to-life scenarios for better FoG biomarker identification and validation. The major aim of this review is to highlight the most up-to-date studies that explain the mechanisms underlying FoG through electrophysiology explorations. The latest methodological approaches used in the neurophysiological study of FoG are summarized, and potential future research directions are discussed.

Impaired Topographical Organization of Functional Brain Networks in Parkinson's Disease Patients With Freezing of Gait

Objective: This study aimed to explore alterations in the topological properties of the functional brain network in primary Parkinson’s disease (PD) patients with freezing of gait (PD-FOG).

Methods: Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 23 PD-FOG patients, 33 PD patients without FOG (PD-nFOG), and 24 healthy control (HC) participants. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 90 brain regions, and topological properties were analyzed by using graph theory approaches. The network-based statistics (NBS) method was used to determine the suprathreshold connected edges (P < 0.05; threshold T = 2.725), and statistical significance was estimated by using the non-parametric permutation method (5,000 permutations). Statistically significant topological properties were further evaluated for their relationship with clinical neurological scales.

Results: The topological properties of the functional brain network in PD-FOG and PD-nFOG showed no abnormalities at the global level. However, compared with HCs, PD-FOG patients showed decreased nodal local efficiency in several brain regions, including the bilateral striatum, frontoparietal areas, visual cortex, and bilateral superior temporal gyrus, increased nodal local efficiency in the left gyrus rectus. When compared with PD-nFOG patients and HCs, PD-FOG showed increased betweenness centrality in the left hippocampus. Moreover, compared to HCs, both PD-FOG and PD-nFOG patients displayed reduced network connections by using the NBS method, mainly involving the sensorimotor cortex (SM), visual network (VN), default mode network (DMN), auditory network (AN), dorsal attention network (DAN), subcortical regions, and limbic network (LIM). The local node efficiency of the right temporal pole: superior temporal gyrus in PD-FOG patients was positively correlated with the Freezing of Gait Questionnaire (FOGQ) scores.

Conclusions: This study demonstrates the disrupted regional topological organization in PD-FOG patients, especially associated with damage to the subcortical regions and multiple cortical regions. Our results provide insights into the dysfunctional mechanisms of the relevant networks and indicate potential neuroimaging biomarkers of PD-FOG.

Aberrant Advanced Cognitive and Attention-Related Brain Networks in Parkinson's Disease with Freezing of Gait

Background

Freezing of gait (FOG) is a disabling gait disorder influencing patients with Parkinson's disease (PD). Accumulating evidence suggests that FOG is related to the functional alterations within brain networks. We investigated the changes in brain resting-state functional connectivity (FC) in patients with PD with FOG (FOG+) and without FOG (FOG-).

Methods

Resting-state functional magnetic resonance imaging (RS-fMRI) data were collected from 55 PD patients (25 FOG+ and 30 FOG-) and 26 matched healthy controls (HC). Differences in intranetwork connectivity between FOG+, FOG-, and HC individuals were explored using independent component analysis (ICA).

Results

Seven resting-state networks (RSNs) with abnormalities, including motor, executive, and cognitive-related networks, were found in PD patients compared to HC. Compared to FOG- patients, FOG+ patients had increased FC in advanced cognitive and attention-related networks. In addition, the FC values of the auditory network and default mode network were positively correlated with the Gait and Falls Questionnaire (GFQ) and Freezing of Gait Questionnaire (FOGQ) scores in FOG+ patients.

Conclusions

Our findings suggest that the neural basis of PD is associated with impairments of multiple functional networks. Notably, alterations of advanced cognitive and attention-related networks rather than motor networks may be related to the mechanism of FOG.

Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity

Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F 1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.

Clinical and Oculomotor Correlates With Freezing of Gait in a Chinese Cohort of Parkinson's Disease Patients

Accumulating evidence suggests that freezing of gait (FOG) is a unique gait disturbance in Parkinson’s disease (PD), and its pathophysiology is not fully elucidated. The present study aims to investigate the clinical and oculomotor associations with FOG in Chinese PD patients. From Jan 2017 to Dec 2019, a total of 210 PD patients were consecutively registered for FOG evaluation based on item-3 of the Freezing of Gait Questionnaire (FOGQ). We explored the demographic, motor, and non-motor symptom differences in FOG positive (PD+FOG, n = 45) vs. negative (PD-FOG, n = 165) group. In addition, 40 PD patients and 37 healthy controls (HC) also underwent oculomotor test via videonystagmography (VNG). Visually guided saccade (VGS) latency, saccade accuracy and gain in smooth pursuit eye movement (SPEM) at three frequencies of horizontal axis were compared among PD+FOG (n = 20), PD-FOG (n = 20), and HC (n = 37). Compared with PD-FOG, PD+FOG had longer disease duration, more severe motor symptoms, lower cognitive scores, more severe depressive and autonomic impairments, as well as higher daily levodopa equivalent dosage. FOG occurred more frequently in patients with wearing-off. VNG subgroup analysis demonstrated that PD+FOG had prolonged saccade latency and decreased saccade accuracy relative to PD-FOG or HC. SPEM gain at 0.1 and 0.2 Hz was also decreased in PD+FOG compared with HC. Furthermore, prolonged saccade latency was correlated with higher FOGQ scores in PD patients. Our results verify that PD with FOG patients suffer from more severe motor and non-motor symptoms, indicating more extensive neurodegeneration. Prolonged saccade latency could be a practical oculomotor parameter both for identification and progression of FOG in PD.

Muscle Synergies in Parkinson's Disease

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.