Dessa Bergen-Cico (PH) PI, Rachel Razza (HDFS) co-I, Mark Costa (Newhouse) co-I, Leanne Hirschfield (Newhouse) co-I, Qiu Wang (SOE) co-I.
Intramural Sponsored Project – 2018-2019 SU CUSE Grant – Interdisciplinary innovation, $29,629
Clinical research has explored the efficacy of mindfulness-based interventions for a range of physical and mental health outcomes including posttraumatic stress (PTS); however, little is known about the neural and cognitive mechanisms of change resulting from mindfulness-based practices. Objective measures of changes in cognitive and neural networks associated with mindfulness-based practices would be valuable tools for researchers to expand understanding of the specific mechanisms of neural change associated with mindfulness practices, and to provide objective measures of PTS. Using state-of-the-art technology, we will use machine-learning algorithms to analyze neural network and brain wave data in order to develop predictive models which distinguish brain states for traumatic stress symptoms and its’ associated impact on attention, working memory, and emotional regulation. Using functional near-infrared spectroscopy (fNIRS), electroencephalographic (EEG) data, and physiological measures (e.g. HR, GSR) we will measure neural, cognitive and attention measures for 30 participants at risk for traumatic stress (Veterans, first responders, and people impacted by community violence) and conduct longitudinal analysis of potential changes following a mindfulness-based intervention. Participants will be randomized into the mindfulness-based intervention cohort (n=15) or the wait list control cohort (n=15). We will assess changes in neural and biometric measures within each cohort and compare between-cohort differences (intervention vs control). Through the use of bioinformatics this research aims to 1.) measure neural and physiological responses to cognitive tasks to determine which measures are correlated to posttraumatic stress 2.) identify potential mechanisms of change in neural networks and biomarkers associated with mindfulness practice, and 3.) develop testbed environments using VR/xR ecological environments to support highly controlled mindfulness experiments.