Dataset Information

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction.

ABSTRACT: Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in PD, were calculated from 1-second, non-overlapping epochs of the raw positional and pressure data captured during task completion. These metrics were used to generate a classifier using a support vector machine (SVM) model to produce a unifying, scalar "motor error score" (MES). The data generated from these patients with PD were compared to same-day standard clinical assessments. Additionally, these data were compared to analogous data generated from a separate group of 12 patients with essential tremor (ET) to assess the task's specificity for different movement disorders. Finally, an SVM model was generated for each of the eight patients with PD and with DBS to differentiate between their motor dysfunction in the "DBS On" and "DBS Off" stimulation states. Results: The eight metrics calculated from the raw positional and force data captured during task completion were non-redundant. MES generated by the SVM analysis protocol showed a strong correlation with MDS-UPDRS-III scores assigned by movement disorder specialists. Analysis of the relative contributions of each of the eight metrics showed a significant difference between the motor dysfunction of PD and ET. Much of this difference was attributable to the homogenous, tremor-dominant phenotype of ET motor dysfunction. Finally, in individual patients with PD with DBS, task performance and subsequent SVM classification effectively differentiated between the "DBS On" and "DBS Off" stimulation states. Conclusion: This tablet-based task and analysis protocol correlated strongly with expert clinical assessments of PD motor dysfunction. Additionally, the task showed specificity for PD when compared to ET, another common movement disorder. This specificity was driven by the relative heterogeneity of motor dysfunction of PD compared to ET. Finally, the task was able to distinguish between the "DBS On" and "DBS Off" states within single patients with PD. This task provides temporally-precise and specific information about motor dysfunction in at least two movement disorders that could feasibly correlate to neural activity.

SUBMITTER: Sanderson JB

PROVIDER: S-EPMC7530842 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction.

Sanderson John B JB Yu James H JH Liu David D DD Amaya Daniel D Lauro Peter M PM D'Abreu Anelyssa A Akbar Umer U Lee Shane S Asaad Wael F WF

Frontiers in neurology 20200918

Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total ...[more]

PMID: 33071924

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Project description:ObjectiveIdentifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson's disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. We sought to determine the extent to which motor error in PD across patients can be decoded on a rapid timescale using spectral features of neural activity.ApproachHere, we recorded neural activity from the subthalamic nucleus (STN) of subjects with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment that synthesized heterogenous PD motor manifestations to generate a scalar measure of motor dysfunction at short timescales. We then leveraged natural motor performance variations as a 'ground truth' to identify corresponding neurophysiological biomarkers.Main resultsSupport vector machines using multi-spectral decoding of neural signals from the STN succeeded in tracking the degree of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic 'β' oscillations, contributed to this decoding, and multi-spectral models consistently outperformed those generated using more isolated frequency bands. While generalized decoding models derived across subjects were able to estimate motor impairment, patient-specific models typically performed better.SignificanceThese results demonstrate that quantitative information about short-timescale PD motor dysfunction is available in STN neural activity, distributed across various patient-specific spectral components, such that an individualized approach will be critical to fully harness this information for optimal disease tracking and closed-loop neuromodulation.

Project description:BackgroundTo quantify pharmacological effects on tremor in patients with essential tremor (ET) or Parkinson's Disease (PD), laboratory-grade accelerometers have previously been used. Over the last years, consumer products such as smartphones and smartwatches have been increasingly applied to measure tremor in an easy way. However, it is unknown how the technical performance of these consumer product accelerometers (CPAs) compares to laboratory-grade accelerometers (LGA). This study was performed to compare the technical performance of CPAs with LGA to measure tremor in patients with Parkinson's Disease (PD) and essential tremor (ET).MethodsIn ten patients with PD and ten with ET, tremor peak frequency and corresponding amplitude were measured with 7 different CPAs (Apple iPhone 7, Apple iPod Touch 5, Apple watch 2, Huawei Nexus 6P, Huawei watch, mbientlabMetaWear (MW) watch, mbientlab MW clip) and compared to a LGA (Biometrics ACL300) in resting and extended arm position.ResultsBoth in PD and ET patients, the peak frequency of CPAs did not significantly differ from the LGA in terms of limits of agreement. For the amplitude at peak frequency, only the iPhone and MW watch performed comparable to the LGA in ET patients, while in PD patients all methods performed comparable except for the iPod Touch and Huawei Nexus. Amplitude was higher when measured with distally-located CPAs (Clip, iPhone, iPod) compared with proximally-located CPAs (all watches). The variability between subjects was higher than within subjects for frequency (25.1% vs. 13.4%) and amplitude measurement (331% vs. 53.6%). Resting arm position resulted in lower intra-individual variability for frequency and amplitude (13.4 and 53.5%) compared to extended arm position (17.8 and 58.1%).ConclusionsPeak frequencies of tremor could be measured with all tested CPAs, with similar performance as LGA. The amplitude measurements appeared to be driven by anatomical location of the device and can therefore not be compared. Our results show that the tested consumer products can be used for tremography, allowing at-home measurements, in particular in studies with a cross-over or intra-individual comparison design using the resting arm position.Trial registrationThis trial was registered in the Dutch Competent Authority (CCMO) database with number NL60672.058.17 on May 30th 2017.

Dataset Information

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction.

Publications

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction.

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