No abstract

Neurosurgical operations are long and intensive medical procedures, during which the surgeon must constantly have an unobscured view of the brain in order to be able to properly operate, and thus must use a variety of tools to clear obstructions (like blood and fluid) from the operating area. Currently, cotton balls are the most versatile and effective option to accomplish this as they absorb fluids, are soft enough to safely manipulate the brain, act as a barrier between other tools and the brain, and function as a spacer to keep anatomies of the brain open and visible during the operation. While cotton balls allow neurosurgeons to effectively improve visibility of the operating area, they may also be accidentally left in the brain upon completion of the surgery. This can lead to a wide range of post-operative risks including dangerous immune responses, additional medical care or surgical operations, and even death. This project seeks to develop a unique medical device that utilizes ultrasound technology in order to minimize cotton retention after neurosurgical procedures in order to reduce undesired post-operative risks, and maximize visibility.

<p> Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson’s Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, <em>p </em>< 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic. </p>

<p> Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson’s Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, <em>p </em>< 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic. </p>

Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson's Disease (PD).Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with videobased clinical scores of symptom severity (Spearman rank test, p < 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic.