Effects of Unilateral High Frequency Stimulation of the Subthalamic Nucleus on Risk-avoidant Behavior in a Partial 6-hydroxydopamine Model of Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established treatment for the motor symptoms of Parkinson's disease (PD). While PD is primarily characterized by motor symptoms such as tremor, rigidity, and bradykinesia, it also involves a range of non-motor symptoms, and anxiety is one of the most common. The relationship between PD and anxiety is complex and can be a result of both pathological neural changes and the psychological and emotional impacts of living with a chronic progressive condition. Managing anxiety in PD is critical for improving the patients' quality of life. However, patients undergoing STN DBS can occasionally experience increased anxiety. METHODS: This study investigates changes in risk-avoidant behavior following STN DBS in a pre-motor animal model of PD under chronic and acute unilateral high frequency stimulation. RESULTS: No significant changes in risk-avoidant behaviors were observed in rats who underwent STN DBS compared with sham stimulation controls. Chronic stimulation prevented sensitization in the elevated zero maze. CONCLUSIONS: These results suggest that unilateral stimulation of the STN may have minimal effects on risk-avoidant behaviors in PD. However, additional research is required to fully understand the mechanisms responsible for changes in anxiety during STN DBS for PD.

Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

The Human Tic Detector: An automatic approach to tic characterization using wearable sensors.

OBJECTIVE: Current rating scales for Tourette syndrome (TS) are limited by recollection bias or brief assessment periods. This proof-of-concept study aimed to develop a sensor-based paradigm to detect and classify tics. METHODS: We recorded both electromyogram and acceleration data from seventeen TS patients, either when voluntarily moving or experiencing tics and during the modified Rush Video Tic Rating Scale (mRVTRS). Spectral properties of voluntary and tic movements from the sensor that captured the dominant tic were calculated and used as features in a support vector machine (SVM) to detect and classify movements retrospectively. RESULTS: Across patients, the SVM had an accuracy, sensitivity, and specificity of 96.69 ± 4.84%, 98.24 ± 4.79%, and 96.03 ± 6.04%, respectively, when classifying movements in the test dataset. Furthermore, each patient's SVM was validated using data collected during the mRVTRS. Compared to the expert consensus, the tic detection accuracy was 85.63 ± 15.28% during the mRVTRS, while overall movement classification accuracy was 94.23 ± 5.97%. CONCLUSIONS: These results demonstrate that wearable sensors can capture physiological differences between tic and voluntary movements and are comparable to expert consensus. SIGNIFICANCE: Ultimately, wearables could individualize and improve care for people with TS, provide a robust and objective measure of tics, and allow data collection in real-world settings.

Wearable sensor-driven responsive deep brain stimulation for essential tremor.

BACKGROUND: Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET. OBJECTIVE: The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals. METHODS: Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states. RESULTS: Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS. CONCLUSIONS: The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.