Cohort 1

Awardee Organization(s): Cogwear LLC
Principal Investigator(s): David Yonce, MS, MBA
Official Project Title: Physiological Detection and Monitoring of Alzheimer’s Disease
AITC Partner: PennAITech
Website(s): www.cogweartech.com

Cogwear has developed a wireless dry-sensor EEG wearable that can easily collect clinical-grade brainwave data anywhere, anytime with comfort and no limits on mobility. Initially applied to behavioral health, here we propose to extend our platform to early detection and trending of dementia and Alzheimer’s Disease (AD) based upon brain physiology. With the ability to sense EEG from the frontal and temporal lobes, the parts of the brain that regulate short and long-term memory, planning, and executive functions, our system can detect EEG changes implicated in dementia and AD: slowing, reduced complexity, decrease in synchronization, and neuromodulatory deficits.
Our project will focus on two components: migrating our wearable to a soft goods form factor with downsized electronics more appropriate for in-clinic and home use and begin to develop the EEG signal processing and applications to quantitatively detect and trend brain processes associated with dementia and AD. Deliverables will include an advanced prototype and pilot testing of algorithms with humans in a small sample of healthy and AD/ADRD patients.
Our expectation is that these algorithms will ultimately show efficacy to detect presymptomatic brain changes, allowing intervention by caregivers to prepare patients and families. Further, because subtle EEG shifts can be indicative of changing disease states, we can provide quantitative trending of AD based upon brain physiology, providing new methods to titrate pharmaceuticals and evaluate disease treatments. Through earlier detection and enhanced monitoring, our goal is to better support patients and their families by enabling more years of high-functioning and independent living.

Awardee Organization(s): University of Pennsylvania Frontotemporal Degeneration Center
Principal Investigator(s): Emma Rhodes, PhD
Official Project Title: Feasibility of Digital Monitoring to Detect Autonomic Markers of Empathy Loss in bvFTD
AITC Partner: PennAITech
Website(s): www.med.upenn.edu/ftd
Loss of empathy is a core symptom of behavioral variant frontotemporal dementia (bvFTD) that negatively impacts daily functioning and is highly distressing to families and caregivers. Scientific research has struggled to understand the specific causes of empathy loss in bvFTD. A relatively unexplored but promising avenue of scientific inquiry is the role of autonomic nervous system (ANS) arousal in empathy loss in bvFTD. The ANS is comprised of two complementary subsystems, the sympathetic and parasympathetic nervous systems, which operate together to regulate an individual’s level of physiologic arousal in response to emotional cues from the environment. Patients with bvFTD show abnormalities in autonomic arousal that are linked to symptoms of social dysfunction, including loss of empathy, but this line of research has been hindered by reliance on traditional methods of measuring autonomic arousal, namely hard-wired EKG and skin conductance sensors, which restrict the movement of the patient and are sensitive to motion effects. Recent advances in wearable smartwatch technology allow for precise, unobtrusive measurement of autonomic arousal with built-in motion sensors that more accurately capture key arousal variables, such as respiratory sinus arrythmia and skin conductance. Use of smartwatch technology will advance our understanding of physiologic mechanisms of empathy loss in bvFTD and other neuropsychiatric symptoms in ADRD and identify potential treatment targets. The overarching goal of this project is to test the feasibility of using a smartwatch to capture abnormalities in autonomic arousal in bvFTD and validate digital markers of autonomic abnormalities against behavioral measures of empathy loss.
In this project, we will use these findings to develop in-home conversational technologies that use prompts to structure care conversations between older adults and their caregivers.
We contribute a nuanced dyadic perspective to care relationships as most care research focuses solely on caregiver perspectives. We also extend conversational technology research beyond information seeking to include more social uses by developing conversational technology applications with mainstream voice technologies (e.g., Amazon Alexa) to support improved care relationships, social and emotional well-being, and quality of life.