CONTINUOUS REMOTE MONITORING OF NEUROPHYSIOLOGIC IMMERSION ACCURATELY PREDICTS MOOD

Mental health professionals have relied primarily on clinical evaluations to identify in vivo pathology. As a result, mentalhealth is largely reactive rather than proactive. In an effort to proactively assess mood, we collected continuous neurophysiologicdata for ambulatory individuals 8-10 hours a day at 1Hz for 3 weeks (N=24). Data were obtained using a commercial neuroscience platform (Immersion Neuroscience) that quantifies the neural value of social-emotional experiences. These data were related to self-reported mood and energy to assess their predictive accuracy. Statistical analyses identifiedneurophysiologic troughs by the length and depth of social-emotional events with low value and neurophysiologic peaks as thecomplement. Participants in the study had an average of2.25 (SD = 3.70, Min = 0, Max = 25) neurophysiologic troughs per day and 3.28 (SD = 3.97, Min= 0, Max = 25) peaks. The number of troughs and peaks predicted daily mood with 90% accuracy using least squares regressions and machine learning models. The analysis also showed that men were more prone to low mood compared to women. Our approach indicates that a simple count variable derived from a commercially-available technology may be aviable way to assess low mood and low energy in populations vulnerable to mood disorders. In addition, peak Immersion events, which are mood-enhancing, may be an effective measure of thriving in adults.