This project uses computationally optimized tDCS to investigate the role of motivation in healthy cognitive aging. We use memory tests and MRI to evaluate the effects of three different tDCS protocols in healthy adults. One of the protocols is individually optimized for each participant using MRI-based head models. This project is a collaboration with the Interdisciplinary Affective Science Lab at NU and the Martino Center at MGH. The project is funded by an R21 award from the National Institute on Aging.

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Transcranial current stimulation (tCS) has relatively poor focality and is only used to target superficial brain regions. Transcranial temporal interference stimulation (tTIS) is a recently developed method 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 enormous interest from researchers, but since optimization algorithms for tCS do not apply to tTIS, stimulation design has been based mostly on intuition. In a collaboration with the University of Utah, we developed novel simulation and optimization methods for tTIS and published an extensive human modeling study that showed for the first time that direct excitation of neurons with tTIS is not possible, but focal and deep neuromodulation may be achievable. We are currently combining these simulation results with neuron models to investigate mechanisms, and are conducting experimental studies to quantify the effects of model-based tTIS with TMS-EMG and EEG in healthy volunteers and non-human primates. We recently received an R01 award from the National Institute of Neurological Disorders and Stroke to support this work.

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 will 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 will be evaluated using data from EEG and cognitive assessments. This project is a collaboration with the Consortium on Technology for Proactive Care at NU and is funded by a Northeastern University Tier1 award.

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Interventions for stroke and epilepsy would benefit from the ability to stimulate selectively deep below the cortical surface, but this has only been possible by placing an electrode at the target. In a collaboration with the Universities of Washington and Utah, we are developing and evaluating optimization methods to steer currents from subdural electrode grids (electrocorticography, ECoG) and needle-like leads (stereotactic EEG, sEEG) to remote brain targets. Imaging data acquired after implantation of patients by surgeons in Washington and Utah is sent to the engineering team in Utah, who build a head model, which is then sent to UMass, where we perform optimization. We have streamlined this process such that optimization results can be sent back to the surgical team within three days of implantation. We published a sensitivity and validation study of our modeling pipeline for ECoG, and are repeating this for sEEG. The majority of this work was supported by an NSF CRCNS grant.

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 modeled and optimized invasive TTF for GBM. In a recently published study we showed that invasive TTF has great potential as a future therapy for GBM. We are further developing this technique.