Dynamic neural systems underlying socioemotional function
社会情绪功能背后的动态神经系统
基本信息
- 批准号:10598662
- 负责人:
- 金额:$ 24.9万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-08-01 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:Affective SymptomsAgingAlgorithmsApplications GrantsAreaArousalAutonomic nervous systemAwardBehavioralBiometryBrainBrain regionBreathingCaliforniaClinicalComputing MethodologiesDataDementiaDeteriorationDiagnosticDiseaseEducational StatusElderlyEmotionsEmpathyExhibitsFutureGalvanic Skin ResponseGoalsHealthHumanImageImpairmentLesionLinkMachine LearningMagnetic Resonance ImagingMapsMemoryMental disordersMentorsMentorshipModelingMood DisordersMoodsNerve DegenerationNeuroanatomyNeurobiologyNeurodegenerative DisordersNeurologicNeurologistNeurosciencesPathway interactionsPatientsPersonal SatisfactionPhasePhysiologicalPhysiologyPilot ProjectsPlayPopulationProductivityPropertyPsychiatryPsychologistRegulationResearchResearch PersonnelRestRoleRunningSamplingSan FranciscoScanningSeriesSeveritiesShapesSocial BehaviorSymptomsSyndromeSystemTechniquesTimeTrainingUniversitiesVirginiaWorkbasebehavioral variant frontotemporal dementiacareercohesiondynamic systemfundamental researchgeriatric neuropsychiatryhealthy agingimprovedinnovationmachine learning algorithmmultimodal datamultimodalitynervous system disorderneural correlateneuroimagingneuromechanismprogramsrecruitrelating to nervous systemskills
项目摘要
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.
项目概要
这是博士后 Lorenzo Pasquini 博士的独立之路奖 (K99/R00) 申请
加州大学旧金山分校记忆与衰老中心 (MAC) 临床神经影像学学者
(加州大学旧金山分校)。 Pasquini 博士是一位职业生涯早期的神经科学家,研究潜在的神经系统
神经退行性疾病的社会情绪症状。 Pasquini 博士在以下领域拥有深厚的背景:
神经科学、临床神经影像和机器学习技术,但他需要指导研究和
在四个不同领域接受高水平培训,成为一名独立调查员。培训和
本提案中概述的研究计划将为帕斯奎尼博士提供完成所需的支持
目标如下:1)获得有关社会情绪症状临床表现的广泛专业知识
老年人口; 2)通过应用熟练掌握多模态动态系统的分析
尖端的机器学习算法; 3)熟练掌握自主生理分析
录音; 4)基于科学生产力和资助开发独立的研究职业定位
应用程序。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、
William Seeley 博士是一位行为神经学家,在神经解剖学和神经影像学方面拥有深厚的专业知识
神经退行性疾病;共同导师弗吉尼亚·斯特姆博士是一位研究自主神经的临床心理学家
以及不同痴呆症综合征的社会情感缺陷;第二位共同导师 Manish Saggar 博士
计算神经科学家开发计算方法来绘制健康和健康大脑的动态大脑活动
精神病人群;机器学习领域的统计学专家 Isabel Allen 博士是该研究的重要贡献者。
该提案描述了创新技术的应用,旨在阐明自主系统如何
大脑动态地相互作用,塑造健康对照者和患有此病的患者的情绪和社会行为
神经退行性综合症。通过利用行为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),Pasquini 博士试图确定神经元的基本特性
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
潜在的情感障碍尚不清楚。帕斯奎尼博士将首先描述动态大脑的缺陷
bvFTD 患者的网络组织并探讨其与社会情绪症状的关系(目标 1)。
Pasquini 博士将继续识别 bvFTD 患者动态自主神经流出的缺陷并评估
通过单独获取的神经影像来建立神经关联(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态同步采集
探索两个系统如何动态交互以维持人类情感和社会行为的控制
(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领域并开展他的研究
在世界一流的指导下进行研究,为他作为独立研究员的职业生涯铺平了道路。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Lorenzo Pasquini其他文献
Lorenzo Pasquini的其他文献
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{{ truncateString('Lorenzo Pasquini', 18)}}的其他基金
Dynamic neural systems underlying socioemotional function
社会情绪功能背后的动态神经系统
- 批准号:
10623239 - 财政年份:2020
- 资助金额:
$ 24.9万 - 项目类别:
Dynamic neural systems underlying socioemotional function
社会情绪功能背后的动态神经系统
- 批准号:
10227218 - 财政年份:2020
- 资助金额:
$ 24.9万 - 项目类别:
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 - 财政年份:2020
- 资助金额:
$ 24.9万 - 项目类别:
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