This is an application for a K01 award for Dr. Jesse Brown, a postdoctoral scholar in clinical neuroimaging at the University of California, San Francisco (UCSF) in the Memory and Aging Center (MAC). Dr. Brown is an early-career neuroscientist focusing on brain network neuroanatomy involved in neurodegenerative disease. This K01 award will provide Dr. Brown with the support necessary to accomplish the following goals: 1) gain experience in the network neuroanatomy of dementia, 2) achieve proficiency in PET data analysis with an emphasis on tau imaging, 3) become an expert in longitudinal statistical modeling, 4) expand knowledge of new MRI neuroimaging methods, and 5) develop an independent research career. To achieve these goals, Dr. Brown has assembled a mentorship team including a primary mentor, Dr. William Seeley, a behavioral neurologist who conducts neuroimaging and neuropathological studies on selective regional vulnerability in neurodegenerative disease; a co-mentor, Dr. Gil Rabinovici, a behavioral neurologist who investigates how molecular brain imaging techniques can be used to improve diagnostic accuracy in dementia; a collaborator, Dr. Howard Rosen, a behavioral neurologist who uses neuroimaging to track how neurodegenerative diseases affect the brain over time; a collaborator, Dr. John Kornak, a biostatistician who is an expert in longitudinal data analysis; and a collaborator, Dr. Christopher Hess, a neuroradiologist focused on the translational application of MR imaging techniques to brain degeneration. This proposal describes a multimodal neuroimaging approach to predict neurodegenerative disease progression in individual patients with frontotemporal dementia (FTD) and Alzheimer's disease (AD). The central hypothesis of this proposal is that each of these diseases originates in a selectively vulnerable brain region or ?epicenter? and spreads outwards along network connections, with affected regions first showing elevated tau protein binding, followed by an increased rate of gray matter loss, and eventually a high degree of cumulative atrophy. We will first develop methods to detect patient-tailored epicenters in FTD/AD patients with different clinical syndromes and use clustering methods to identify atrophy subtypes (Aim 1). We will then test a model predicting that as disease spreads from an epicenter throughout the network, nodes that become affected will show a greater longitudinal rate of atrophy before they show high cumulative atrophy (Aim 2). Finally, we will use 18F-AV1451 PET imaging to examine the relative timing of tau spread and regional atrophy spread from the epicenter. The goal of this project is to improve prognostic accuracy in individual dementia patients. This proposal includes innovative imaging and statistical methods that will help us evaluate different biomarkers of network-based neurodegenerative disease progression in a clinical trial. The K01 training will prepare Dr. Brown to build translational neuroimaging tools enabling next-generation monitoring of brain disease.

Public Health Relevance

We currently lack patient-tailored predictions of how neurodegenerative disease will spread in the brain. We now have the tools to design a better predictive model of disease progression which will yield more accurate personalized prognosis about cognitive decline, better inform targeted interventions for slowing disease progression, and tell clinicians where in the brain to look for signs of preservation in clinical trial treatments.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Scientist Development Award - Research & Training (K01)
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Neuroscience of Aging Review Committee (NIA)
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Hsiao, John
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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Toller, Gianina; Brown, Jesse; Sollberger, Marc et al. (2018) Individual differences in socioemotional sensitivity are an index of salience network function. Cortex 103:211-223
Staffaroni, Adam M; Brown, Jesse A; Casaletto, Kaitlin B et al. (2018) The Longitudinal Trajectory of Default Mode Network Connectivity in Healthy Older Adults Varies As a Function of Age and Is Associated with Changes in Episodic Memory and Processing Speed. J Neurosci 38:2809-2817
Perry, David C; Brown, Jesse A; Possin, Katherine L et al. (2017) Clinicopathological correlations in behavioural variant frontotemporal dementia. Brain 140:3329-3345