# Dynamic neural systems underlying socioemotional function

> **NIH NIH R00** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $94,765

## Abstract

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 ...

## Key facts

- **NIH application ID:** 11089708
- **Project number:** 3R00AG065457-05S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Lorenzo Pasquini
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $94,765
- **Award type:** 3
- **Project period:** 2020-08-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11089708

## Citation

> US National Institutes of Health, RePORTER application 11089708, Dynamic neural systems underlying socioemotional function (3R00AG065457-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11089708. Licensed CC0.

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