Thomas O. Mera

Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson's disease.

Parkinsons disease (PD) is a neurodegenerative disorder characterized by motor symptoms including tremor and bradykinesia (slowness of movement). Drug treatment, although capable of controlling these symptoms over a number of years, becomes less effective as the disease progresses and leads to motor complications such as drug-induced dyskinesia (involuntary abnormal movements). Deep brain stimulation (DBS) provides an alternative means of controlling motor symptoms in these patients, and while DBS has been effective in improving motor symptoms, these improvements are largely based on accurate placement of the lead and the ability of medical personnel to adequately program the DBS device following implantation. While guidelines exist for DBS programming, selection of stimulation parameters and patient outcome is greatly dependent on subjective clinical assessments and the experience of the medical personnel performing the programming. The aim of this project was to assess the feasibility of using a quantitative and objective approach to programming. Two subjects underwent standard procedures for DBS programming while wearing a small, compact motion sensor. Kinematic data were collected from subjects as they completed motor tasks to evaluate DBS efficacy. Quantitative variables characterizing tremor and bradykinesia were related to stimulation parameters. Results indicated different stimulation settings might be required for optimal improvement of different motor symptoms. A standardized method of programming DBS parameters utilizing motion analysis may provide an objective method of assessment that the programmer can use to better identify stimulation parameters to achieve optimal improvement across multiple motor symptoms.

Automated motion sensor quantification of gait and lower extremity bradykinesia

The objective was to develop and evaluate algorithms for quantifying gait and lower extremity bradykinesia in patients with Parkinsons disease using kinematic data recorded on a heel-worn motion sensor unit. Subjects were evaluated by three movement disorder neurologists on four domains taken from the Movement Disorders Society Unified Parkinsons Disease Rating Scale while wearing the motion sensor unit. Multiple linear regression models were developed based on the recorded kinematic data and clinician scores and produced outputs highly correlated to clinician scores with an average correlation coefficient of 0.86. The newly developed models have been integrated into a home-based system for monitoring Parkinsons disease motor symptoms.

Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease.

BACKGROUND Chronic use of medication for treating Parkinsons disease (PD) can give rise to peak-dose dyskinesia. Adjustments in medication often sacrifice control of motor symptoms, and thus balancing this trade-off poses a significant challenge for disease management. OBJECTIVE To determine whether a wrist-worn motion sensor unit could be used to ascertain global dyskinesia severity over a levodopa dose cycle and to develop a severity scoring algorithm highly correlated with clinician ratings. METHODS Fifteen individuals with PD were instrumented with a wrist-worn motion sensor unit, and data were collected with arms in resting and extended positions once every hour for three hours after taking a levodopa dose. Two neurologists blinded to treatment status viewed subject videos and rated global and upper extremity dyskinesia severity based on the modified Abnormal Involuntary Movement Scale (mAIMS). Linear regression models were developed using kinematic features extracted from motion sensor data and extremity, global, or combined (average of extremity and global) mAIMS scores. RESULTS Dyskinesia occurring during a levodopa dose cycle was successfully measured using a wrist-worn sensor. The logarithm of the power spectrum area between 0.3-3 Hz and the combined clinician scores resulted in the best model performance, with a correlation coefficient between clinician and model scores of 0.81 and root mean square error of 0.55, both averaged across the arms resting and extended postures. CONCLUSIONS One sensor unit worn on either hand can effectively predict global dyskinesia severity during the arms resting or extended positions.

Motion sensor dyskinesia assessment during activities of daily living.

BACKGROUND Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinsons disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement. OBJECTIVE To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living. METHODS Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores. RESULTS Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used. CONCLUSIONS A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.

Quantitative analysis of gait and balance response to deep brain stimulation in Parkinson's disease

Gait and balance disturbances in Parkinsons disease (PD) can be debilitating and may lead to increased fall risk. Deep brain stimulation (DBS) is a treatment option once therapeutic benefits from medication are limited due to motor fluctuations and dyskinesia. Optimizing DBS parameters for gait and balance can be significantly more challenging than for other PD motor symptoms. Furthermore, inter-rater reliability of the standard clinical PD assessment scale, Unified Parkinsons Disease Rating Scale (UPDRS), may introduce bias and washout important features of gait and balance that may respond differently to PD therapies. Study objectives were to evaluate clinician UPDRS gait and balance scoring inter-rater reliability, UPDRS sensitivity to different aspects of gait and balance, and how kinematic features extracted from motion sensor data respond to stimulation. Forty-two subjects diagnosed with PD were recruited with varying degrees of gait and balance impairment. All subjects had been prescribed dopaminergic medication, and 20 subjects had previously undergone DBS surgery. Subjects performed seven items of the gait and balance subset of the UPDRS while wearing motion sensors on the sternum and each heel and thigh. Inter-rater reliability varied by UPDRS item. Correlation coefficients between at least one kinematic feature and corresponding UPDRS scores were greater than 0.75 for six of the seven items. Kinematic features improved (p<0.05) from DBS-OFF to DBS-ON for three UPDRS items. Despite achieving high correlations with the UPDRS, evaluating individual kinematic features may help address inter-rater reliability issues and rater bias associated with focusing on different aspects of a motor task.

Motion Sensor Strategies for Automated Optimization of Deep Brain Stimulation in Parkinson’s disease

BACKGROUND Deep brain stimulation (DBS) is a well-established treatment for Parkinsons disease (PD). Optimization of DBS settings can be a challenge due to the number of variables that must be considered, including presence of multiple motor signs, side effects, and battery life. METHODS Nine PD subjects visited the clinic for programming at approximately 1, 2, and 4 months post-surgery. During each session, various stimulation settings were assessed and subjects performed motor tasks while wearing a motion sensor to quantify tremor and bradykinesia. At the end of each session, a clinician determined final stimulation settings using standard practices. Sensor-based ratings of motor symptom severities collected during programming were then used to develop two automated programming algorithms--one to optimize symptom benefit and another to optimize battery life. Therapeutic benefit was compared between the final clinician-determined DBS settings and those calculated by the automated algorithm. RESULTS Settings determined using the symptom optimization algorithm would have reduced motor symptoms by an additional 13 percentage points when compared to clinician settings, typically at the expense of increased stimulation amplitude. By adding a battery life constraint, the algorithm would have been able to decrease stimulation amplitude by an average of 50% while maintaining the level of therapeutic benefit observed using clinician settings for a subset of programming sessions. CONCLUSIONS Objective assessment in DBS programming can identify settings that improve symptoms or obtain similar benefit as clinicians with improvement in battery life. Both options have the potential to improve post-operative patient outcomes.

Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology

The objective was to capture levodopa-induced dyskinesia (LID) in patients with Parkinsons disease (PD) using body-worn motion sensors. Dopaminergic treatment in PD can induce abnormal involuntary movements, including choreatic dyskinesia (brief, rapid, irregular movements). Adjustments in medication to reduce LID often sacrifice control of motor symptoms, and balancing this tradeoff poses a significant challenge for management of advanced PD. Fifteen PD subjects with known LID were recruited and instructed to perform two stationary motor tasks while wearing a compact wireless motion sensor unit positioned on each hand over the course of a levodopa dose cycle. Videos of subjects performing the motor tasks were later scored by expert clinicians to assess global dyskinesia using the modified Abnormal Involuntary Rating Scale (m-AIMS). Kinematic features were extracted from motion data in different frequency bands (1-3Hz and 3-8Hz) to quantify LID severity and to distinguish between LID and PD tremor. Receiver operator characteristic analysis was used to determine thresholds for individual features to detect the presence of LID. A sensitivity of 0.73 and specificity of 1.00 were achieved. A neural network was also trained to output dyskinesia severity on a 0 to 4 scale, similar to the m-AIMS. The model generalized well to new data (coefficient of determination= 0.85 and mean squared error= 0.3). This study demonstrated that hand-worn motion sensors can be used to assess global dyskinesia severity independent of PD tremor over the levodopa dose cycle.

Objective Quantification of Arm Rigidity in MPTP-treated Primates

Rigidity is a cardinal symptom of Parkinsons disease and is frequently used as an outcome measure in clinical and non-human primate studies examining the effects of medication or surgical intervention. A limitation of current rigidity assessment methods is that they are inherently subjective. To better understand the physiological mechanisms of rigidity and how various therapeutic approaches work, a more objective and quantitative method is needed. In this study, an automated arm rigidity testing (ART) system was developed to objectively quantify rigidity while the primates limb was moved between two user-specified angles. Recordings of normal force versus elbow-angle were categorized according to area and slope. These quantitative measures of rigidity were investigated in three rhesus macaque monkeys treated with 1-methyl 4-phenyl 1,2,3,6-tetrahydropyridine and compared with clinical assessment methods. The ART system incorporates electromyographical recordings that can detect and differentiate active from actual resistance. The ART system detected significant changes in rigidity measures following administration of apomorphine or deep brain stimulation of the globus pallidus internus. The most sensitive measures were total area, extension slope, and flexion slope. The ART system provides precise and reliable measures of rigidity that are objective and quantitative.

Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors

Objective: Fluctuations in response to levodopa in Parkinsons disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. Methods: Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. Results: Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). Conclusion: A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. Significance: This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.