Multimodal Data Analysis and Integration

多模态数据分析与集成

• 批准号: 10639548
• 负责人: Ting Xu
• 金额: $ 32.15万
• 依托单位: NATHAN S. KLINE INSTITUTE FOR PSYCH RES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639548
• 关键词: Address; Anatomy; Biological Models; Brain; Cognitive; Computer Models; Data; Data Analyses; Diffusion; Dimensions; Electrodes; Electroencephalography; Ensure; Event; Frequencies; Functional Magnetic Resonance Imaging; Goals; Human; Image; Individual; Joints; Link; Macaca; Magnetic Resonance Imaging; Maps; Measurement; Methods; Modality; Modeling; Monkeys; Neurobiology; Output; Physiological; Property; Pupil; Reproducibility; Rest; Role; Signal Transduction; Surface; T2 weighted imaging; Testing; Work; data integration; design; distributed data; dynamic system; functional magnetic resonance imaging/electroencephalography; human data; improved; inter-individual variation; movie; multimodal data; multimodality; neural circuit; neural model; nonhuman primate; spatiotemporal

ABSTRACT: This Conte Center proposal seeks to elucidate the role of slow brain network fluctuations (SBNFs) across species and scales, and to establish computational models of neural circuit dynamics that characterize SBNFs. Data collected in Projects 1-3 will span multiple modalities (EEG, fMRI, ECoG/LFP, and single-unit recordings) and species (humans and nonhuman primates). The overarching goal of the Multimodal Data Analysis & Integration Core (Core-B) is to address the challenge of integrating data across these multiple modalities and analyses. To achieve this goal, Core-B will apply and develop multimodal alignment frameworks that establish a common space for analysis across modalities, individuals, and species (to be used in Projects 1-3), as well as integrate these data to facilitate computational modeling of neural dynamics (to be used in Project 4). Aim 1 will leverage the functional alignment framework to construct a common space for different modalities within- and between-subjects. Aim 2 will refine the cross-species alignment using the functional data from the common tasks to provide the spatial transformation to link human data in Projects 1-2 and macaque data in Project 3. In addition to optimizing cross-modal and cross-species alignment, Aim 3 will construct a framework for decomposing brain dynamics into spatiotemporal states and state-transitions derived jointly from EEG, fMRI, and physiological signals. We will also leverage the cross-species transformation between humans and macaques in Aim 2 to delineate the matched states across species and characterize the species-specific temporal configurations. This framework will provide a state-space in which brain dynamics and their causal properties can be interrogated across individuals and species. The alignment approach and aligned data generated by this Core will be applied to the curated, quality-checked outputs received from Core C after appropriate preprocessing (determined by domain experts from the individual Projects) and will be distributed to Projects 1-4.

摘要：孔特中心的这项提议试图阐明大脑网络缓慢波动的作用 (SBNFS)跨物种和尺度，并建立神经电路动力学的计算模型 描述SBNF的特征。在项目1-3中收集的数据将涵盖多种模式(EEG、fMRI、ECoG/LFP和 单一单位记录)和物种(人类和非人灵长类)。多式联运的首要目标 数据分析和集成核心(Core-B)旨在解决跨多个 模式和分析。为了实现这一目标，Core-B将应用和开发多模式调整 为跨模式、个人和物种(将使用)建立公共分析空间的框架 在项目1-3中)，以及整合这些数据以促进神经动力学的计算建模(将 在项目4中使用)。目标1将利用功能调整框架为 受试者内部和受试者之间的不同模式。目标2将使用 来自共同任务的职能数据，以提供空间转换，以便将项目1-2中的人类数据联系起来 和猕猴数据。除了优化跨模式和跨物种的比对，Aim 3还将 构建一个将脑动力学分解为时空状态和衍生的状态转换的框架 结合脑电、功能磁共振和生理信号。我们还将利用跨物种转型 在目标2中描述人类和猕猴之间的匹配状态，并描述 物种特定的时间配置。这一框架将提供一个状态空间，在其中大脑动力学 它们的因果属性可以在不同的个体和物种之间进行询问。对齐方法和 此核心生成的对齐数据将应用于从核心接收的经过策划、经过质量检查的输出 C经过适当的预处理(由来自各个项目的领域专家确定)，并将 分发给项目1-4。