Transcranial temporal interference stimulation (tTIS) is a relatively novel form of tCS that combines two alternating currents into an amplitude-modulated field. The component of the field oscillating at the beat frequency is more focal than conventional stimulation fields and it can peak deep in the brain. Due to its potential for non-invasive deep brain stimulation, tTIS has gained interest from researchers, but knowledge about working mechanisms and optimal stimulation parameters is lacking. We received an R01 award from the National Institute of Neurological Disorders and Stroke to study this technique. We published the first comprehensive modeling and optimization study in humans and mice. We are currently designing new optimization methods, combining simulation results with neuron models to investigate mechanisms, and conducting experimental studies to explore stimulation parameters of tTIS in healthy volunteers and non-human primates.

Among the many procedures developed to combat cancer, tumor treating fields (TTF) stand out as a unique technology and the only device approved for the treatment of glioblastoma (GBM). Despite successful trials demonstrating prolongation of survival, this therapy has faced limited adoption among neuro-oncologists and patients due to a combination of skepticism and patient concerns. In contrast to the approved non-invasive device, an invasive, implantable method for the delivery of tumor treating fields may substantially improve therapeutic efficacy while addressing some of the challenges that appear to have limited the widespread use of this promising therapy. In a collaboration with Brigham and Women's Hospital, we are developing several forms of intracranial TTF. Our study on subdural TTF demonstrated its potential as a future therapy for GBM. We are also investigating deep brain TTF in humans and TTF in dogs.

Cognitive deficits associated with neurodegenerative diseases pose major challenges to healthcare worldwide, but existing cognitive assessment methods are limited by their low sensitivity and sporadicity. Our goal is to develop a digital biomarker for cognitive health. We do this by detecting cognitive changes in persons with mild cognitive impairment using continuously and passively captured smartphone data. We use mHealth, machine learning and cognitive modeling approaches to predict cognitive changes from interactions with mobile apps, mobility patterns (GPS) and motor behavior (typing and walking speed). The inferences are evaluated using data from EEG and lab-based cognitive assessments. We completed our first yearlong study with 22 participants and are currently analyzing the data. This project is a collaboration with the Consortium on Technology for Proactive Care at NU and was funded by a Northeastern University Tier1 award.

This project uses computationally optimized tDCS to investigate the role of motivation in healthy cognitive aging. We use memory tasks and structural and functional MRI to evaluate the effects of three different tDCS protocols in healthy adults. All protocols consist of 5 days of tDCS for 20 minutes per day, with different electrode configurations per protocol, one of which is individually optimized for each participant using MRI-based head models. We recently concluded a randomized double-blind placebo-controlled study with 64 participants and are currently analyzing the data. This project is a collaboration with the Interdisciplinary Affective Science Lab at NU and the Martino Center at MGH. The project was funded by an R21 award from the National Institute on Aging.