Bereavement is a potent and ubiquitous stressor. More than 10 million individuals experience the death a loved one each year in the United States, increasing risk for numerous psychiatric disorders, including complicated grief. Despite the substantial burden conferred by these disorders, we know little about their nature and etiology and, consequently, are limited in our ability to predict, prevent, and treat post-bereavement psychopathology. Recently, my colleagues and I proposed that bereavement-related mental disorders, such as complicated grief, are best understood as complex systems of mutually reinforcing symptoms. In two studies, we used the tools of network science to study the structure of the complicated grief symptom network. These studies allowed us to identify which symptoms are most central to the complicated grief network and provided some of the earliest evidence that understanding network topology can inform our understanding of the course of complicated grief. My core aim for this career development award is to become an expert in network science and to use the tools of network science to study complicated grief as a complex psychobiological system. The proposed research study will build on our previous research through four critical innovations. First, we will use intensive time-series data to estimate the intra-individual network structure for each patient, providing information about the unique psychobiological processes operating within that individual. Second, we will focus our analyses on the elements of the complicated grief syndrome that align with bereavement-related Research Domain Criteria (RDoC) constructs, linking our analyses to the broader effort to understand these individual components of psychopathology. Third, we will incorporate biological units of analysis into our intra-individual network and study how individual differences in physiological reactivity contribute to the complicated grief syndrome. Fourth, we will use intra-individual network parameters to predict the course of complicated grief over time. The approach taken in this study is innovative because it adopts a novel conceptual framework for understanding and studying psychiatric disorders that is rooted in the rapidly growing multi-disciplinary field of network science. It is significant because it will elucidate the patient-specific psychobiological mechanisms contributing to the development and onset of bereavement-related mental disorders. Moreover, it will introduce new tools into the field of psychiatry that will allow us to better predict, prevent, and treat psychopathology at the level of the individual patient, moving us closer to the aim of a precision medicine approach to psychiatry.

Public Health Relevance

Bereavement is a potent stressor that confers substantial risk for numerous psychiatric disorders, psychosocial impairment, physical morbidity, and mortality. In the proposed project, we will use a novel intra-individual network analysis approach to examine specific components of bereavement-related psychopathology and the patient-specific relationships among those components that lead them to cohere and persist as a syndrome. In doing so, this project will advance our understanding of the etiology of bereavement-related psychopathology while providing the foundation for a network-based, personalized medicine approach to predicting, preventing, and treating mental disorders.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Mentored Patient-Oriented Research Career Development Award (K23)
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Adult Psychopathology and Disorders of Aging Study Section (APDA)
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Chavez, Mark
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Massachusetts General Hospital
United States
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