Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy.

Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (ntotal = 482 patients; nParkinson disease = 303; ndystonia = 64; ntremor = 39; ntreatment-resistant depression/anorexia nervosa = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426-443.

Focused ultrasound neuromodulation of the spleen activates an anti-inflammatory response in humans.

Focused ultrasound stimulation (FUS) activates mechanosensitive ion channels and is emerging as a method of noninvasive neuromodulation. In preclinical studies, FUS of the spleen (sFUS) activates an anti-inflammatory neural pathway which suppresses acute and chronic inflammation. However, the relevance of sFUS for regulating inflammatory responses in humans is unknown. Here, we used a modified diagnostic ultrasound imaging system to target the spleen of healthy human subjects with 3 min of continuously swept or stationary focused pulsed ultrasound, delivered at three different energy levels within allowable safety exposure limits. Potential anti-inflammatory effects of sFUS were assessed by measuring sFUS-elicited changes in endotoxin-induced tumor necrosis factor (TNF) production in whole blood samples from insonified subjects. We found that stimulation with either continuously swept or focused pulsed ultrasound has an anti-inflammatory effect: sFUS lowers TNF production for >2 h, with TNF returning to baseline by 24 h following sFUS. This response is independent of anatomical target (i.e., spleen hilum or parenchyma) or ultrasound energy level. No clinical, biochemical, or hematological parameters are adversely impacted. This is the first demonstration that sFUS suppresses the normal inflammatory response in humans, with potential implications for noninvasive bioelectronic therapy of inflammatory disorders.

Physical activity classification using time-frequency signatures of motion artifacts in multi-channel electrical impedance plethysmographs.

Physical activities are known to introduce motion artifacts in electrical impedance plethysmographic (EIP) sensors. Existing literature considers motion artifacts as a nuisance and generally discards the artifact containing portion of the sensor output. This paper examines the notion of exploiting motion artifacts for detecting the underlying physical activities which give rise to the artifacts in question. In particular, we investigate whether the artifact pattern associated with a physical activity is unique; and does it vary from one human-subject to another? Data was recorded from 19 adult human-subjects while conducting 5 distinct, artifact inducing, activities. A set of novel features based on the time-frequency signatures of the sensor outputs are then constructed. Our analysis demonstrates that these features enable high accuracy detection of the underlying physical activity. Using an SVM classifier we are able to differentiate between 5 distinct physical activities (coughing, reaching, walking, eating and rolling-on-bed) with an average accuracy of 85.46%. Classification is performed solely using features designed specifically to capture the time-frequency signatures of different physical activities. This enables us to measure both respiratory and motion information using only one type of sensor. This is in contrast to conventional approaches to physical activity monitoring; which rely on additional hardware such as accelerometers to capture activity information.

Modeling the magnetic disturbance of pulsatile blood flow in a static magnetic field.

Non-invasive continuous monitoring of blood flow may be particularly valuable for early detection of different anomalies such as hypovolemia and internal bleeding. Recent studies have demonstrated the potential clinical benefits of photo-plethysmography in detecting hypovolemia before the onset of cardiovascular decompensation. The magnetic sensing method bears advantages of size, weight, and cost along with less stringent body placement rules. In this work, a detailed three-dimensional mathematical model for the acquisition of the ventricular response using the disturbance created by magnetized blood undergoing a stationary permanent magnet is presented. The proposed model accounts for the different magnetic properties of the blood such as the relaxation time and the magnetic saturation. The blood flow is simulated by means of Navier-Stocks equations with pulsatile inlet pressure. The blood is assumed to be in the deoxygenated state and has a diamagnetic properties. Moreover, a moving mesh technique is implemented in the Finite-Element model to represent the idle and the moving states of the blood which provides the capability to model the magnetized blood as a moving magnet. The simulated magnetic field at different sensor locations is found to be in good agreement with experimental observations from the literature. The presented model can provide basis for understanding the magnetic modulated blood signal as well as the practical constraints that might be encountered in the design of such devices.