Dynamic neural systems underlying socioemotional function

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PROJECT SUMMARY This is an application for a Pathway to Independence Award (K99/R00) from Dr. Lorenzo Pasquini, a postdoctoral scholar in clinical neuroimaging at the Memory and Aging Center (MAC), University of California, San Francisco (UCSF). Dr. Pasquini is an early-career neuroscientist investigating the neural systems underlying socioemotional symptoms in neurodegenerative diseases. Dr. Pasquini has a strong background in neuroscience, clinical neuroimaging, and machine-learning techniques, but he requires mentored research and high-level training in four different areas to become established as an independent investigator. The training and research program outlined in this proposal will provide Dr. Pasquini with the support necessary to accomplish the following goals: 1) gain extended expertize in the clinical manifestation of socioemotional symptoms among elderly populations; 2) achieve proficiency in analysis of multimodal dynamic systems through the application of cutting-edge machine-learning algorithms; 3) get proficiency in the analysis of autonomic physiological recordings; and 4) develop an independent research career niche based on scientific productivity and grant applications. To achieve these goals, Dr. Pasquini has assembled a mentorship team including a primary mentor, Dr. William Seeley, a behavioral neurologist with deep expertize in neuroanatomy and neuroimaging of neurodegenerative diseases; a co-mentor, Dr. Virginia Sturm, a clinical psychologist who investigates autonomic and socioemotional deficits across distinct dementia syndromes; a second co-mentor, Dr. Manish Saggar, a computational neuroscientist developing computational methods to map dynamic brain activity in healthy and psychiatric populations; and a significant contributor, Dr. Isabel Allen, a statistician expert in machine-learning. This proposal describes the application of innovative techniques that aim to elucidate how the autonomic system and the brain dynamically interact to shape emotions and social behavior in healthy controls and patients with neurodegenerative syndromes. By leveraging the neuroanatomical and autonomic deficits found in behavioral variant frontotemporal dementia (bvFTD), Dr. Pasquini seeks to identify the fundamental properties of neural systems underlying socioemotional well-being, with important implications for psychiatry where the neurobiology underlying affective disorders is not well understood. Dr. Pasquini will first delineate deficits in dynamic brain network organization in patients with bvFTD and explore the relationship to socioemotional symptoms (Aim 1). Dr. Pasquini will proceed by identifying deficits in dynamic autonomic outflow in patients with bvFTD and assess the neural correlates through separately acquired neuroimaging (Aim 2). Finally, Dr. Pasquini will capitalize on multimodal simultaneous acquisitions of autonomic outflow and brain network imaging acquired in healthy older controls to explore how both systems dynamically interact to sustain human emotions and social behavior (Exploratory Aim 3). The proposed training will allow Dr. Pasquini to develop a scientific niche and conduct his research with world-class mentorship, paving the way for his career as an independent researcher.