Precision Functional Neuroimaging Core

精密功能神经影像核心

• 批准号: 10594027
• 负责人: LISA D NICKERSON
• 金额: $ 34.09万
• 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/10594027
• 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; 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.

摘要：利用静息态fMRI和任务型fMRI进行功能神经成像实验