Using virtual reality to assess vestibulo-visual interaction in people with Parkinson's disease compared to healthy controls

The semicircular canal function is preserved with little impact on falls in patients with mild Parkinson's disease

INTRODUCTION

Postural instability is a cardinal symptom of Parkinson's disease (PD), which suggests the vestibular system may be affected in PD. This study aimed to determine whether vestibular dysfunction is associated with the risk of falls in PD.

METHODS

We prospectively recruited patients with de-novo PD at a tertiary medical center between December 2019 and March 2023. During initial assessment, each patient was queried about falls within the preceding year. All patients underwent evaluation of video head-impulse tests (video-HITs), motion analysis, mini-mental state examination (MMSE), and Montreal Cognitive Assessment (MOCA). We determined whether head impulse gain of the vestibulo-ocular reflex (VOR) was associated with clinical severity of PD or risk of falls.

RESULTS

Overall, 133 patients (mean age ± SD = 68 ± 10, 59 men) were recruited. The median Movement Disorder Society-Unified Parkinson's Disease Rating Scale motor part (MDS-UPDRS-III) was 23 (interquartile range = 16-31), and 81 patients (61 %) scored 2 or less on the Hoehn and Yahr scale. Fallers were older (p = 0.001), had longer disease duration (p = 0.001), slower gait velocity (p = 0.009), higher MDS-UPDRS-III (p < 0.001) and H&Y scale (p < 0.001), lower MMSE (p = 0.018) and MOCA scores (p = 0.001) than non-fallers. Multiple logistic regression showed that MDS-UPDRS-III had a positive association with falling (p = 0.004). Falling was not associated with VOR gain (p = 0.405). The VOR gain for each semicircular canal showed no correlation with the MDS-UPDRS-III or disease duration.

CONCLUSIONS

The semicircular canal function, as determined by video-HITs, is relatively spared and has little effect on the risk of falls in patients with mild-to-moderate PD.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Identification and description of telerehabilitation assessments for individuals with neurological conditions: A scoping review

Background

The clinical adoption of telerehabilitation accelerated rapidly over the last few years, creating opportunities for clinicians and researchers to explore the use of digital technologies and telerehabilitation in the assessment of deficits related to neurological conditions. The objectives of this scoping review were to identify outcome measures used to remotely assess the motor function and participation in people with neurological conditions and report, when available, the psychometric data of these remote outcome measures.

Methods

MEDLINE (Ovid), CINAHL, PubMed, PsychINFO, EMBASE, and Cochrane databases were searched between December 13, 2020, and January 4, 2021, for studies investigating the use of remote assessments to evaluate motor function and participation in people with neurological conditions. An updated search was completed on May 9, 2022, using the same databases and search terms. Two reviewers independently screened each title and abstract, followed by full-text screening. Data extraction was completed using a pre-piloted data extraction sheet where outcome measures were reported as per the International Classification of Functioning, Disability and Health.

Results

Fifty studies were included in this review. Eighteen studies targeted outcomes related to body structures and 32 targeted those related to activity limitation and participation restriction. Seventeen studies reported psychometric data; of these, most included reliability and validity data.

Conclusion

Clinical assessments of motor function of people living with neurological conditions can be completed in a telerehabilitation or remote context using validated and reliable remote assessment measures.

Motion sickness susceptibility and visually induced motion sickness as diagnostic signs in Parkinson's disease

Postural instability and loss of vestibular and somatosensory acuity can be part of the signs encountered in Parkinson's Disease (PD). Visual dependency is described in PD. These modifications of sensory input hierarchy are predictors of motion sickness (MS). The aim of this study was to assess MS susceptibility and effects of real induced MS in posture. 63 PD patients, whose medication levels (levodopa) reflected the pathology were evaluated, and 27 healthy controls, filled a MS questionnaire; 9 PD patients and 43 healthy controls were assessed by posturography using virtual reality. Drug amount predicted visual MS (p=0.01), but not real induced MS susceptibility. PD patients did not experience postural instability in virtual reality, contrary to healthy controls. Since PD patients do not seem to feel vestibular stimulated MS, they may not rely on vestibular and somatosensory inputs during the stimulation. However, they feel visually induced MS more with increased levodopa drug effect. Levodopa amount can increase visual dependency. The strongest MS predictors must be studied in PD to better understand the effect of visual stimulation and its absence in vestibular stimulation.

Recent developments in the understanding of the interactions between the vestibular system, memory, the hippocampus, and the striatum

Over the last two decades, evidence has accumulated to demonstrate that the vestibular system has extensive connections with areas of the brain related to spatial memory, such as the hippocampus, and also that it has significant interactions with areas associated with voluntary motor control, such as the striatum in the basal ganglia. In fact, these functions are far from separate and it is believed that interactions between the striatum and hippocampus are important for memory processing. The data relating to vestibular-hippocampal-striatal interactions have considerable implications for the understanding and treatment of Alzheimer's Disease and Parkinson's Disease, in addition to other neurological disorders. However, evidence is accumulating rapidly, and it is difficult to keep up with the latest developments in these and related areas. The aim of this review is to summarize and critically evaluate the relevant evidence that has been published over the last 2 years (i.e., since 2021), in order to identify emerging themes in this research area.

Resting state functional networks predict different aspects of postural control in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disorder causing postural control impairments. Postural control involves multiple domains, such as control of postural sway in stance, automatic postural responses (APRs) and anticipatory postural adjustments (APAs). We hypothesize that impairments in each postural domain is associated with resting-state functional connectivity (rsFC), accounted by predictive modeling and that cortical and cerebellar networks would predict postural control in people with PD (PwPD).

OBJECTIVE

To determine whether rsFC can predict three domains of postural control independently in PwPD and older adults (OA) based on predictive accuracy of models.

METHODS

The cohort consisted of 65 PwPD (67.7 +8.1 age) tested in their OFF-state and 42 OA (69.7 +8.2 age). Six body-worn, inertial sensors measured postural sway area while standing on foam, step length of APRs to a backward push-and-release perturbation, and magnitude of lateral APAs prior to voluntary gait initiation. Resting state-fMRI data was reported on 384 regions of interest that were grouped into 13 functional brain networks. Associations between rsFC and postural metrics were characterized using predictive modeling, with an independent training (n = 67) and validation (n = 40) dataset. Models were trained in the training sample and performance of the best model was validated in the independent test dataset.

RESULTS

rsFC of different brain networks predicted each domain of postural control in PD: Frontoparietal and Ventral Attention rsFC for APAs; Cerebellar-Subcortical and Visual rsFC and Auditory and Cerebellar-Subcortical rsFC for APRs; Ventral Attention and Ventral Multimodal rsFC for postural sway. In OA, CinguloOpercular and Somatomotor rsFC predicted APAs.

CONCLUSIONS

Our findings suggest that cortical networks predict postural control in PD and there is little overlap in brain network connectivities that predict different domains of postural control, given the rsFC methodology used. PwPD use different cortical networks for APAs compared to OA.

Assessment of Vestibular-Evoked Myogenic Potentials in Parkinson’s Disease: A Systematic Review and Meta-Analysis

(1) Background: The brainstem plays an essential role in the early stage of Parkinson’s disease (PD), but it is not widely tested in clinical examinations of PD. Vestibular-evoked myogenic potentials (VEMPs) are recognized as fundamental tools in the assessment of brainstem function. The aim of our meta-analysis was to assess the abnormal findings of VEMPs in patients with PD. (2) Methods: Up to 14 February 2022, PubMed, Embase, and Web of Science were searched to evaluate VEMPs in patients with PD in comparison with respective controls. The study protocol was registered at PROSPERO (CRD42022311103). (3) Results: A total of 15 studies were finally included in our meta-analysis. The absence rates of VEMPs in patients with PD were significantly higher than those of control groups (cVEMP: OR = 6.77; oVEMP: OR = 13.9; mVEMP: OR = 7.52). A delayed P13 latency, a decreased peak-to-peak amplitude, and an increased AAR of cVEMP, and a delayed oVEMP P15 latency were also found in patients with PD. (4) Conclusions: Our meta-analysis indicates abnormal VEMP findings in patients with PD, revealing the dysfunction of the brainstem in PD. VEMP tests, especially cVEMP tests, could be a helpful method for the early detection of PD.