Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation

决策过程中大脑动力学的预测模型及其使用分布式光遗传学刺激的验证

基本信息

批准号：
10240643
负责人：
Roozbeh Kiani
金额：
$ 66.72万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-25 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10240643
关键词：
Address Algorithms Area Attention BRAIN initiative Behavior Behavioral Biological Biophysics Brain Choice Behavior Collaborations Computer Models Data Data Analyses Decision Making Development Etiology Eye Movements Feedback Goals Individual Intervention Link Location Measures Mediating Mental Processes Methodology Methods Modeling Monitor Movement Neurons Non-linear Models Nonlinear Dynamics Output Parietal Parietal Lobe Pattern Performance Phase Physiologic pulse Play Population Primates Process Property Reporting Resolution Role Sensory Site Space Models Stimulus System Techniques Testing Time Training United States National Institutes of Health Validation Visual Fields Work base behavioral response computer framework experience experimental group experimental study flexibility frontal lobe improved loss of function millisecond multi-scale modeling neural circuit neural patterning neuroregulation nonhuman primate oculomotor optogenetics outcome prediction parietal-frontal circuits predictive modeling relating to nervous system response sensory input stem theories tool

项目摘要

Project Summary During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to sensory targets during selection can be strongly influenced by external stimulus properties (“bottom-up”) or internal goals based on previous experience (“top-down”). Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping time scales, how neural circuits mechanistically support top-down and bottom-up processing has been difficult to disentangle. This is because the neural circuits for spatial attention and selection are distributed across the frontal and parietal cortices and operate across multiple spatial scales spanning the activity of individual neurons and neuronal populations. In this Targeted Brain Circuit R01 Project proposal, an experimental group (Pesaran/NYU) and a theory group (Shanechi/USC) will use cutting-edge techniques developed under the NIH BRAIN Initiative support to validate predictive models of neuronal dynamics and test hypotheses about how frontal-parietal cortices perform attentional selection. A behavioral task that dissociates bottom up and top-down processing will let us define bottom-up and top-down target states. We will then build predictive models of neuronal dynamics within and between frontal and parietal cortex and empirically validate the models by stimulating neural activity to achieve the desired neural state. Aim 1 validates predictive models of local circuit dynamics. We will stimulate within PFC to achieve target states in PFC. Aim 2 validates predictive models of long-range circuit dynamics. We will stimulate sites in PPC that functionally connect to PFC in order to achieve target states in PFC. Aim 3 validates predictive models of distributed circuit dynamics. We will simultaneously stimulate both PFC and PPC to achieve the target states. In each case, successfully directing activity toward the target state will indicate the model is valid. If the target state reflects a causal role in attention, as opposed to correlating with attentional processes, we predict that behavioral choices will be biased. This proposal tackles several of the major topic areas of the BRAIN 2025 report. We will identify fundamental principles about circuit dynamics and functional connectivity for understanding the biological basis of mental processes through development of new theoretical and data analysis tools (Topic 5). We will produce a dynamic picture of the functioning brain by developing and applying improved methods for large-scale monitoring of neural activity (Topic 3). We will demonstrate causality by linking brain activity to behavior with precise interventional tools that change neural circuit dynamics (Topic 4). Recent years have seen dramatic advances in our ability to experimentally interface with the primate brain with increasing precision scale. A fruitful interplay between multiscale experiments and predictive modeling that we propose will let us test hypotheses about how flexible behaviors are controlled by large-scale neural circuits.

项目摘要在行为期间，动眼系统的任务是从不断变化的视野和将眼睛的移动转向这些位置。可能会受到外部刺激特性（“自下而上”）的强烈影响或以前的内部低音经验（“自上而下”）。跨越部分重叠的时间尺度，神经回路如何自上而下和自下而上处理已与疾病差异。选择分布在额叶和顶叶皮层上，并在多个空间尺度上运行跨越单个神经元和神经元种群的活性。提案，实验组（Pesaran/NYU）和理论组（Shanechi/USC）将使用尖端在NIH脑倡议支持下开发的技术以验证神经元的预测模型动力学和测试假设关于额叶皮层的行为选择解散自下而上的任务和自上而下的处理将使我们定义自下而上的目标国家。皮质和经验通过刺激神经活动以达到所需的神经状态来验证模型。 AIM 1验证了本地电路动力学的预测模型。 PFC中的状态2验证了远程电路动力学的预测模型。 PPC在功能上连接到PFC，以实现PFC中的目标状态。分布式电路动力学的模型。目标状态在每种情况下，成功定向活动状态将表明该模型是有效的。如果目标状态反映了在授权中的因果作用，而与与Attental过程相关，那么我们预测行为选择将有偏见。大脑2025报告。通过开发Newe Teority和数据来理解心理过程的生物学基础分析工具（主题5）。改进的大规模监测神经活动的方法（主题3）。将大脑活动与行为与聊天电路动态的精确干预工具联系起来（主题4）。近年来，我们与灵长类动物大脑与与灵长类动物的大脑进行实验的能力取得了巨大进步提高精度量表。提议将允许关于柔性行为的UST假设由大规模的神经回路控制。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.

DOI：
10.1038/s41467-020-20197-x
发表时间：
2021-01-27
期刊：
Nature communications
影响因子：
16.6
作者：
Abbaspourazad H;Choudhury M;Wong YT;Pesaran B;Shanechi MM
通讯作者：
Shanechi MM

Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation.

DOI：
10.1038/s41593-018-0171-8
发表时间：
2018-07
期刊：
Nature neuroscience
影响因子：
25
作者：
Pesaran B;Vinck M;Einevoll GT;Sirota A;Fries P;Siegel M;Truccolo W;Schroeder CE;Srinivasan R
通讯作者：
Srinivasan R

Multiregional communication and the channel modulation hypothesis.