FEBRUARY 28, 2025
Joshua Curtiss, PhD
Assistant Professor
Applied Psychology Department
Northeastern University
About the Presentation: Emotional disorders, such as depression and anxiety, are among the primary causes of disability and impairment worldwide. Unfortunately, response to “gold-standard” evidence-based cognitive behavioral therapy is modest at best (response rates = 49.5% – 65.2%). A primary reason for the limited progress in improving treatment for emotional disorders is our problematic reliance on traditional nosologies (e.g., DSM-5) that fail to capture both the dynamic temporal complexity underlying emotional disorders and the substantial individual differences in symptom presentation occurring between patients. One pathway forward includes embracing a precision medicine approach that uses computational modelling to capture the idiographic, dynamic, and multidimensional nature of emotional disorders. This talk will explore the following topics: 1) how network science and dynamical systems frameworks can provide a temporal model of emotional disorders, 2) how machine learning and temporal extensions of predictive modelling can improve predictions of clinically meaningful outcomes (e.g., treatment response, future symptom progression, etc.), and 3) future directions on how these methods can facilitate the development of more personalized treatments using individual-level time-series data. Throughout my talk, I will also emphasize how disparate data streams (e.g., ecological momentary assessment, sensor data, neuroimaging data, etc.) can provide unique insights in understanding the dynamics of emotional disorders and improving predictions of disease related outcomes. A better understanding of the dynamics and complexity underlying emotional disorders is needed to promote more accurate models of disease pathology, which can facilitate better forecasting of disease progression and promote more tailored precision medicine treatments at the individual level.
About the Presenter: Dr. Joshua Curtiss is an Assistant Professor in the Applied Psychology Department at Northeastern University, a faculty member of the Center for Cognitive Brain Health (CBH) at Northeastern University, and an Associate Researcher at Massachusetts General Hospital (MGH) with the Depression and Research Clinical Program. He received his PhD in clinical psychology from Boston University under the mentorship of Dr. Stefan Hofmann, and he completed his pre-doctoral internship and post-doctoral fellowship at MGH. As a clinical scientist, his research interests pertain to leveraging state-of-the-art statistical approaches to address questions relating to the nosology and treatment of emotional disorders. Specifically, his research embraces statistical procedures that foster idiographic and precision medicine approaches to clinical psychology, such as intensive time-series research designs, network science, and machine learning. With an abiding interest in emotional psychopathology, Dr. Curtiss’ current research uses intensive, multi-modal longitudinal data such as ecological momentary assessment and passive sensor data to investigate the network dynamics of affect in emotional psychopathology. Additionally, Dr. Curtiss has maintained an active line of research in affective science, emotion regulation, and mindfulness in the context of emotional psychopathology. His research has been funded by grants such as a NIMH K23 Grant, Kaplan Award, American Foundation for Suicide Prevention Focus Grant, and American Psychological Association Research Dissertation Award.