Dr. Meredith Lotz Wallace's long-term research goal is to become a psychiatric biostatistician who develops and applies novel methods to aid researchers in attaining the goals of the NIMH Research Domain Criteria (RDoC) project. The RDoC project suggests that mental disorders may be ideally characterized by multiple dimensions of signs and symptoms (e.g., brain circuitry, physiology, behavior, and self-report);however, this new paradigm poses substantial challenges because most existing statistical methods were not designed to accommodate the resulting mixture of data types. Dr. Wallace's proposed Mentored Research Scientist Development (K01) award is the first step towards her long-term research goal;it will provide the training and support she needs to develop statistical methods that will make the RDoC project a more viable research framework. The overall aim of Dr. Wallace's K01 is to develop and apply novel clustering methods that incorporate multiple types of intensively measured data.
In Aim 1, Dr. Wallace will develop clustering methods that incorporate intensively measured data captured through polysomnography, actigraphy, and daily self-report measures. These methods will be developed under two different theoretical frameworks to enhance their generalizability to applications beyond those addressed specifically in the K01.
In Aim 2, Dr. Wallace will apply her methods to a data base containing multiple dimensions of observations from over 1000 individuals with a variety of levels of disturbances related to loss, anxiety, and sleep/wake regulation. The methods'application will reveal multidimensional profiles that may cut across the current DSM-based diagnoses. A study of these profiles will clarify the extent to which the multiple dimensions of signs and symptoms lie on a true continuum or separate into discrete classes. Although the candidate's background in biostatistics has primed her to begin this research, its successful completion ultimately hinges on additional training in three areas: 1) mathematical foundations of methods for clustering and intensively measured data, 2) negative valence systems (e.g., loss and anxiety), and 3) sleep/wake regulation. This training will be achieved through tutorials, directed readings, formal coursework, hands-on clinical work, and professional conferences. To achieve her training goals, Dr. Wallace has assembled a strong team of clinical and methodological mentors and consultants. The clinical mentoring team is led by Dr. Ellen Frank, an internationally recognized expert in mood-related disorders. The methodological mentoring team is led by Dr. Satish Iyengar, a leader the field of psychiatric statistics with expertise in clustering and modeling intensively measured data. The training and research resulting from this project will provide Dr. Wallace with the experience and skills she needs to submit an R01 in the fourth year of the K01. This R01 will allow Dr. Wallace to expand on her proposed methods and apply them to develop RDoC-based multidimensional profiles based on additional dimensions and domains of study.

Public Health Relevance

Recent developments in neuroscience and psychology have led researchers to endeavor to characterize mental disorders based multiple dimensions of signs and symptoms (e.g., neurobiological, physiological, behavioral, and self-report). The proposed work will (1) develop new clustering methods that can incorporate the variety of types of intensively measured data that result from observing multiple dimensions of signs and symptoms, and (2) apply the methods to reveal multidimensional profiles that may cut across current categorical diagnoses, thereby suggesting innovative ways of characterizing mental disorders that may eventually lead to more specific and effective treatments.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Scientist Development Award - Research & Training (K01)
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Adult Psychopathology and Disorders of Aging Study Section (APDA)
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Wynne, Debra K
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University of Pittsburgh
Schools of Medicine
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