Precision Functional Neuroimaging Core

精密功能神经影像核心

• 批准号: 10411713
• 负责人: Danhong Wang
• 金额: $ 35.33万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411713
• 关键词: Address; Anatomy; Area; Award; Behavior; Cerebral cortex; Clinical; Computer Models; Corpus striatum structure; Data; Decision Making; Dictionary; Functional Magnetic Resonance Imaging; Funding; Goals; Grain; Human; Impairment; Individual; Insula of Reil; Machine Learning; Maps; Measures; Meta-Analysis; Methods; Network-based; Neurons; Noise; Participant; Pattern; Positioning Attribute; Property; Protocols documentation; Publishing; Rest; Shapes; Signal Transduction; Techniques; Technology; Testing; Therapeutic Effect; Work; advanced analytics; association cortex; base; behavioral impairment; denoising; experimental study; flexibility; improved; innovation; inter-individual variation; neural circuit; neural correlate; neuroimaging; nonhuman primate; novel; response

Abstract: Functional neuroimaging experiments utilizing both resting state fMRI and task-based fMRI are proposed by individual projects across the Center, to accurately characterize the OCDnet. Given the substantial inter-individual variability of the OCDnet, these projects require advanced analytical approaches that can reliably and accurately map the OCD nodes in each participant. The central goal of the present core is to support these projects in acquiring and analyzing functional neuroimaging data using the cutting-edge techniques to achieve subject-level precision. Aim 1: The core will develop and implement individualized targeting strategies to identify homologous, subject-specific regions and networks based on resting-state fMRI. We will use a “maximizing between-subject homology” strategy and parcellate each subject's cerebral cortex into very fine-grained functional clusters, each of which may consist of a single region or of several discrete regions. We will also identify homologous hub regions in humans based on connectivity patterns observed in non-human primates (NHPs). Aim 2: The core will develop and implement highly innovative denoising technologies that will dramatically improve the SNR of both task-based fMRI and resting state fMRI data. The denoising technology will be key to obtaining reliable task-induced functional responses not only at the individual subject level but also at the single trial level. Aim 3: The core will assist projects in continuing to implement the advanced functional MRI neuroimaging protocols developed during the previous funding period, that maximize within-individual signal properties and minimize anatomical distortion.

翻译后摘要：利用静息状态fMRI和基于任务的fMRI功能神经成像实验， 由整个中心的各个项目提出，以准确地描述OCDnet的特征。鉴于 OCDnet的个体间差异很大，这些项目需要先进的分析方法 可以可靠和准确地映射每个参与者中的OCD节点。本核心的中心目标是 支持这些项目，使用最先进的神经成像技术获取和分析功能性神经成像数据， 技术，以达到主题级精度。目标1：核心将开发和实施个性化 靶向策略，以确定同源的，主题特定的区域和网络的基础上休息状态功能磁共振成像。 我们将使用“最大化受试者之间的同源性”的策略， 分成非常细粒度的功能簇，每个功能簇可以由单个区域或几个离散的区域组成， 地区我们还将根据在基因组中观察到的连接模式来识别人类中的同源枢纽区域。 非人类灵长类动物（NHP）。目标2：核心将开发和实施高度创新的去噪 这些技术将极大地提高基于任务的功能磁共振成像和静息状态功能磁共振成像数据的信噪比。的 去噪技术将是获得可靠的任务诱导功能反应的关键，不仅在 个体受试者水平，但也在单一试验水平。目标3：核心将协助项目继续 实施在以前的资助期间开发的高级功能性MRI神经成像协议 周期，最大化个体内信号特性并最小化解剖失真。