PROJECT SUMMARY
Emotional disorders are among the primary causes of disability and impairment worldwide. Predicting their future
course can be used to improve disease management and to deliver more timely and individually tailored
treatment. However, accurate forecasting is notoriously difficult. A major challenge is that the static features
traditionally used for prediction (i.e., cross-sectional symptom data) are not theoretically optimal for forecasting
disease progression. Overwhelming evidence demonstrates that emotional disorders are characterized by
behaviors that unfold dynamically over time. A more principled disorder conceptualization is needed that
emphasizes dynamic time-series features of emotional psychopathology. To address this gap, dynamical
systems and complex network theory have been proposed as theoretical models that can explain the dynamic
behavior of emotional disorders. In the current proposal, we will create a precision-medicine framework for
predicting emotional disorder symptoms that leverages theoretically relevant time-series measures using
modern “forecasting” machine learning methods. Patients with emotional disorders will be recruited to undergo
a 4-week ecological momentary assessment period during which symptom and affect data will be collected 5
times per day and passive sensor data will be continuously monitored. Aim 1 will evaluate the predictive utility of
“forecasting” machine learning using EMA-based dynamical systems and network features in predicting future
transdiagnostic symptom domains. Aim 2 will evaluate the added predictive utility of incorporating dynamic time-
series features from passive sensor data alongside EMA data in predicting future transdiagnostic symptom
domains. In Aim 3, we will develop dynamic time-series phenotypes of emotional disorders using both self-
reported EMA and sensor data. The current proposal will result in the first forecasting machine learning
framework designed to leverage temporally dynamic features for predicting the future course of symptoms,
thereby better capturing the dynamic nature of emotional psychopathology. This is significant as it facilitates
more accurate monitoring and just-in-time interventions for emotional disorders, alleviating one of the major
causes of disease and burden worldwide. Furthermore, a tailored training plan was created to cultivate
competencies in advanced time-series computational techniques and career development, including expertise
in (1) early warning signals in dynamical systems and network theory, (2) time-series machine learning
(forecasting), (3) unsupervised machine learning, (4) passive sensor data, and (5) grantsmanship and transition
to academic independence, with guidance from Dr. Paola Pedrelli (primary mentor), Dr. Richard McNally (co-
mentor), Dr. Jordan Smoller (co-mentor), and Drs. Rosalind Picard and Amanda Baker (consultants). Research
and training activities will harness the exceptional resources at Massachusetts General Hospital/Harvard Medical
School. This training plan will facilitate a future research program leveraging computational modeling to advance
precision medicine and personalized care for emotional disorders.