Understanding overlap in resting state fMRI networks at the single cell level: a cross-species approach

了解单细胞水平静息态 fMRI 网络的重叠：跨物种方法

• 批准号: 10059107
• 负责人: Janine Diane Bijsterbosch
• 金额: $ 70.88万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10059107
• 关键词: Address; Affect; Animal Model; Animals; Architecture; Behavior; Biological; Brain; Cells; Cognition; Cognitive; Cognitive deficits; Collaborations; Coupling; Data; Electrodes; Electrophysiology (science); Functional Magnetic Resonance Imaging; Human; Image Analysis; Individual; Interdisciplinary Study; Investigation; Lesion; Life Style; Link; Location; Macaca; Magnetic Resonance Imaging; Measurement; Mental Processes; Methodology; Methods; Microelectrodes; Modeling; Monkeys; Neurons; Outcome; Oxygen; Parietal Lobe; Pattern; Play; Polarography; Primates; Resolution; Rest; Role; Signal Transduction; Site; Structure; Techniques; Testing; Time; Tissues; Variant; analytical method; awake; base; behavior test; blood oxygen level dependent; experimental study; follow-up; forging; imager; interdisciplinary collaboration; interest; neural circuit; nonhuman primate; novel; relating to nervous system; tool

Understanding overlap in resting state fMRI networks at the single cell level: a cross-species approach Abstract Resting state functional connectivity MRI (rsfcMRI) is a popular tool to investigate the intrinsic functional organization of the brain into large scale networks. Multiple different lines of investigation have pointed to the importance of densely interconnected `hub' regions for cognition and behavior. However, the functional architecture of cellular circuits in these hub regions is unknown. To study the cellular underpinnings of hub regions, we bring together an interdisciplinary research team to bridge across species and across scales. We start by generalizing recent advances in human fcMRI analyses across species to characterize individualized patterns of network overlap in rsfcMRI data from awake macaque monkeys (Aim 1). This allows us to identify regions of interest for recordings in this same animals from a hub region where two (or more) networks spatially overlap, and from two non-hub regions that strongly contribute to only one of the networks respectively. We then ask whether, at a finer cellular scale, there is true neural coupling between both networks in hub regions, or whether networks that appear spatially overlapping at the resolution of rsfcMRI data are in fact spatially interdigitated rather than overlapping at a finer scale (Aim 2). Lastly, we use electrophysiological recordings to determine whether individual neurons in hub regions integrate information from both overlapping networks (i.e. coupling), or whether neurons dynamically switch their network allegiance from one network to another over time (Aim 3). The outcomes of this proposal have important implications for the modeling and interpretation of human rsfcMRI data. This R34 proposal provides the opportunity to establish a new collaboration and validate our methodology across species. These factors are essential for the next stage of our project, a Targeted Brain Circuits Project R01 proposal, in which we will build on this line of investigation by bridging into behavior to study how fundamental principles of the brain circuits in hub regions form the biological basis of mental processes.

在单细胞水平上理解静息状态fMRI网络的重叠：一个跨物种的研究 方法 摘要 静息态功能连接MRI（rsfcMRI）是研究脑内神经元功能的常用工具。 将大脑的功能组织成大规模的网络。多条不同的 调查指出了密集互联的“枢纽”区域的重要性， 认知和行为。然而，这些中枢中的细胞电路的功能架构 地区不详。为了研究枢纽区域的细胞基础，我们将一个 跨学科研究团队跨越物种和规模。我们首先 概括了人类fcMRI分析在不同物种中的最新进展， 清醒猕猴rsfcMRI数据中网络重叠的个体化模式（Aim 1）。这使我们能够从集线器中识别相同动物的记录的感兴趣区域 两个（或多个）网络在空间上重叠的区域，以及两个 分别对其中一个网络有很大贡献。然后我们问，在一个更好的 在细胞尺度上，枢纽区域的两个网络之间存在真正的神经耦合，或者 在rsfcMRI数据的分辨率下出现空间重叠的网络实际上在空间上是重叠的。 在更精细的尺度上相互交叉而不是重叠（目标2）。最后，我们使用电生理学 记录以确定中枢区域中的单个神经元是否整合来自 重叠网络（即耦合），或者神经元是否动态切换其网络 随着时间的推移，从一个网络到另一个网络的忠诚度（目标3）。该提案的成果包括： 对人类rsfcMRI数据的建模和解释具有重要意义。R34 提案提供了建立新的合作和验证我们的方法的机会 跨越物种。这些因素对于我们项目的下一个阶段--目标大脑--至关重要 电路项目R 01提案，在该提案中，我们将在此调查基础上， 行为，以研究中枢区域大脑回路的基本原理如何形成 心理过程的生物学基础。

项目成果

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.

• DOI: 10.1016/j.neuroimage.2021.118513
• 发表时间: 2021-11
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Farahibozorg SR;Bijsterbosch JD;Gong W;Jbabdi S;Smith SM;Harrison SJ;Woolrich MW
• 通讯作者: Woolrich MW

Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates.

• DOI: 10.1002/hbm.25690
• 发表时间: 2022-02-01
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Dutt RK;Hannon K;Easley TO;Griffis JC;Zhang W;Bijsterbosch JD
• 通讯作者: Bijsterbosch JD

Shared brain and genetic architectures between mental health and physical activity.

• DOI: 10.1038/s41398-022-02172-w
• 发表时间: 2022-10-03
• 期刊: TRANSLATIONAL PSYCHIATRY
• 影响因子: 6.8
• 作者: Zhang, Wei;Paul, Sarah E.;Winkler, Anderson;Bogdan, Ryan;Bijsterbosch, Janine D.
• 通讯作者: Bijsterbosch, Janine D.

Piggybacking on big data.

• DOI: 10.1038/s41593-022-01058-w
• 发表时间: 2022-06
• 期刊: NATURE NEUROSCIENCE
• 影响因子: 25
• 作者: Bijsterbosch, Janine