ECE Departmental Seminar
Functional Feedback Methods for Closed-Loop Ultrasound Neuromodulation
Soumyajit Mandal
Brookhaven National Laboratory
Friday, 11/18/22, 1:30pm
Light Engineering 250
Abstract: Focused ultrasound (FUS) is a versatile therapeutic modality that offers a non-invasive way to deposit targeted acoustic energy deep in the body, resulting in complex interactions with tissues and organs that occur through both thermal and non-thermal mechanisms. A particularly interesting example is neuromodulation, in which ultrasound results in noninvasive and spatially/temporally precise changes in the firing rates and patterns of peripheral nerves, likely due to a combination of both tissue heating and mechanical activation of ion channels. Promising clinical applications for ultrasound neuromodulation include treatment of overactive bladder syndrome (OAB), which is prevalent in ~23% of the U.S. population, and chronic pain. These applications require chronic and patient-specific treatment to obtain long-term health benefits, which in turn suggests the use of wearable and autonomous FUS devices. However, realizing such devices faces several fundamental challenges, including i) safe and precise delivery of FUS therapy to a specific body target (in this case, a nerve) in the presence of positioning errors and internal tissue/organ motion; and ii) determining optimal values for the neuromodulation parameters (frequency, repetition rate, waveform, power level), which are expected to be both patient-specific (due to biological heterogeneity) and time-dependent. This talk will describe our recent work on developing a closed-loop learning-based approach to wearable FUS neuromodulation that addresses these challenges.
The talk will present wearable body-conformal ultrasound array designs for closed-loop neuromodulation that integrate two linear sub-arrays within a single lightweight device: the first for neuromodulation, and the second for B-mode imaging to identify the target nerve. Such arrays are integrated with a functional feedback mechanism, related to physiological changes in the organ or tissue of interest, that uses real-time machine learning for target detection and automated optimization of modulation parameters at the targeted position. The proposed closed-loop neuromodulation concept has been demonstrated on a benchtop prototype by using an active tissue phantom that includes embedded models of the posterior tibial nerve, nearby blood vessels, and innervated skeletal muscle.
Bio: Soumyajit Mandal received the B.Tech. degree in Electronics & Electrical Communications Engineering from the Indian Institute of Technology, Kharagpur, West Bengal, India, in 2002, and the S.M. and Ph.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 2004 and 2009, respectively.
He was a Research Scientist with Schlumberger-Doll Research, Cambridge (2010 - 2014), an Assistant Professor with the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH (2014 – 2019), and an Associate Professor with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL (2019 – 2021). He is currently a Research Staff Member in the Instrumentation Division at Brookhaven National Laboratory, Upton, NY. He has over 175 publications in peer-reviewed journals and conferences and has been awarded 26 patents. His research interests include analog and biological computation, magnetic resonance sensors, low-power analog and RF circuits, and precision instrumentation for various biomedical and sensor interface applications. He was a recipient of the President of India Gold Medal in 2002, the MIT Microsystems Technology Laboratories (MTL) Doctoral Dissertation Award in 2009, the T. Keith Glennan Fellowship in 2016, and the IIT Kharagpur Young Alumni Achiever Award in 2018.