Upcoming Event: Oden Institute Seminar

Engineering and Understanding the Adaptive Brain: Waveform Optimization and Network Reserve

Joshua Chang, Dell Medical School

Abstract

The brain is a fascinating, dynamic, and adaptive system, capable of remarkable recovery in some instances, and yet resistant in others. Understanding and leveraging this adaptability is central to the future of neurotechnology and neuroscience. In this seminar, I will be presenting two complementary strands of my research aimed at both engineering and understanding brain network resilience. In the first part, I will explore the use of electroceuticals as a means of modulating neural systems whose full internal structures are unknown. I will show how we have used closed-loop algorithms to optimize stimulation waveform profiles for maximal therapeutic effect, opening the possibility of navigating neuronal dynamics in order to develop more selective stimulation. In the second part, I will introduce some of our emerging work on quantifying and characterizing cognitive reserve, the latent capacity of the brain to maintain function despite aging or pathology. I will discuss the use of dynamic brain network markers derived from high-density EEG and how graph-theoretic metrics combined with time-resolved connectivity measures can serve as potential biomarkers of reserve. I highlight how a deeper understanding of the neural instantiations and mechanisms underlying cognitive reserve could inform personalized neurostimulation approaches. Together, these two research strands point towards an integrated future where we can both quantify the brain's adaptive potential and tailor interventions to mitigate cognitive decline.

Biography

Joshua Chang, MD/PhD, is an Assistant Professor at Dell Medical School whose research bridges electrical engineering, computational neuroscience, and clinical neurology. He earned his BS and MEng in electrical engineering and computer science from the Massachusetts Institute of Technology, focusing on signal processing and artificial intelligence, with his master’s dissertation conducted at MIT Lincoln Laboratory on advanced radar tracking. Before medical training, he worked as a software engineer at the Broad Institute and at Harvard’s School of Public Health, developing computational tools for biomedical and health decision science. He completed his MD/PhD at the University of Massachusetts Medical School, where his doctoral work in quantitative health sciences and neurology, under Dr. David Paydarfar, developed adaptive control algorithms to optimize stimulation waveforms for implantable neurodevices. At Dell Medical School, his lab studies how to leverage artificial intelligence and physiologically-driven computational models to improve diagnostics, guide therapeutics, and enable personalized neurotechnologies.