Dynamic neural systems underlying socioemotional function

社会情绪功能背后的动态神经系统

• 批准号: 10055555
• 负责人: Lorenzo Pasquini
• 金额: $ 12.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055555
• 关键词: Affective Symptoms; Aging; Algorithms; Applications Grants; Area; Arousal; Autonomic nervous system; Award; Behavioral; Biometry; Brain; Brain region; Breathing; California; Clinical; Computing Methodologies; Data; Dementia; Deterioration; Diagnostic; Disease; Educational Status; Elderly; Emotions; Empathy; Exhibits; Future; Galvanic Skin Response; Goals; Health; Human; Image; Impairment; Lesion; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Memory; Mental disorders; Mentors; Mentorship; Modeling; Mood Disorders; Moods; Nerve Degeneration; Neuroanatomy; Neurobiology; Neurodegenerative Disorders; Neurologic; Neurologist; Neurosciences; Pathway interactions; Patients; Personal Satisfaction; Phase; Physiological; Physiology; Pilot Projects; Play; Population; Productivity; Property; Psychiatry; Psychologist; Regulation; Research; Research Personnel; Rest; Role; Running; Sampling; San Francisco; Scanning; Series; Severities; Shapes; Social Behavior; Structure; Symptoms; Syndrome; System; Techniques; Time; Training; Universities; Virginia; Work; base; behavioral variant frontotemporal dementia; career; cohesion; dynamic system; fundamental research; geriatric neuropsychiatry; healthy aging; improved; innovation; machine learning algorithm; multimodal data; multimodality; nervous system disorder; neural correlate; neuroimaging; neuromechanism; programs; recruit; relating to nervous system; skills

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.

项目总结 这是博士后洛伦佐·帕斯奎尼博士颁发的独立之路奖(K99/R00)的申请书 加州大学旧金山分校记忆和衰老中心(MAC)临床神经影像学者 (加州大学旧金山分校)帕斯奎尼博士是一位职业生涯早期的神经学家，研究潜在的神经系统 神经退行性疾病的社会情绪症状。帕斯奎尼博士在 神经科学、临床神经成像和机器学习技术，但他需要有指导的研究和 在四个不同领域接受高水平培训，成为独立调查员。培训和 这项提案中概述的研究计划将为帕斯奎尼博士提供必要的支持，以实现 以下目标：1)在以下社会情绪症状的临床表现方面获得广泛的专业知识 老年人口；2)熟练运用以下方法分析多通道动力系统 先进的机器学习算法；3)精通自主神经生理分析 记录；以及4)根据科学生产力和拨款发展独立的研究职业 申请。为了实现这些目标，帕斯奎尼博士组建了一个导师团队，其中包括一名主要导师， 威廉·西利博士是一位行为神经学家，在神经解剖学和神经成像方面拥有深厚的专业知识 神经退行性疾病；共同导师，研究自主神经的临床心理学家弗吉尼亚·斯特姆博士 和不同痴呆症综合征的社会情绪缺陷；第二位共同导师曼尼什·萨加博士 计算神经学家开发计算方法来绘制健康和健康人的动态脑活动图 还有一个重要的贡献者，机器学习方面的统计学家伊莎贝尔·艾伦博士。 这项建议描述了创新技术的应用，旨在阐明自主系统是如何 在健康对照组和患有抑郁症的患者中，大脑动态地相互作用来塑造情绪和社会行为 神经退行性综合征。通过利用在行为学中发现的神经解剖学和自主神经缺陷 变异型额颞叶痴呆(BvFTD)，帕斯奎尼博士试图确定神经的基本特性 社会情绪健康的基础系统，对精神病学具有重要意义，其中神经生物学 潜在的情感障碍还没有被很好地理解。帕斯奎尼博士将首先描述动态大脑的缺陷 BvFTD患者的网络组织及其与社会情绪症状的关系(目标1)。 帕斯奎尼博士将继续识别bvFTD患者动态自主神经流出的缺陷，并评估 神经通过单独的获得性神经成像进行关联(目标2)。最后，帕斯奎尼博士将利用 健康老年人自主神经流出和脑网络成像的多模式同步采集 控件，以探索这两个系统如何动态交互以维持人类情绪和社会行为 (探索性目标3)。拟议的培训将使帕斯奎尼博士能够开发一个科学利基并进行他的 在世界级导师的指导下进行研究，为他作为独立研究员的职业生涯铺平了道路。