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State-dependent Decision-making in Brainwide Neural Circuits

全脑神经回路中的状态相关决策

基本信息

批准号：
10294668
负责人：
ANNE KATHRYN CHURCHLAND
金额：
$ 379.57万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10294668
关键词：
Affect Anatomy Animals Area Axon Bar Codes Behavior Behavioral Biological Assay Brain Brain region Collaborations Collection Communication Computer Models Computing Methodologies Data Analyses Data Collection Data Science Core Data Set Decision Making Development Disease Environment Etiology Food Gene Expression Genetic Goals Histology Impairment Individual Infrastructure International Laboratories Learning Maps Measurement Measures Methods Modeling Molecular Mus Mutation Neurons Pattern Population Protocols documentation Reproducibility Research Research Personnel Rewards Schizophrenia Standardization Structure Testing Training Ultrasonography Wild Type Mouse Work addiction autism spectrum disorder cell type data infrastructure data sharing deep learning expectation experimental study flexibility large datasets large scale data mouse model neural circuit neuromechanism next generation novel open source tool optogenetics relating to nervous system theories tool

项目摘要

Summary/Abstract Animals constantly make decisions, such as how to evaluate a potential threat or where to look for food. Yet the same animal in the same environment can produce different decisions on different occasions, because its internal state interacts powerfully with external inputs to determine behavior. This proposal’s overarching goal is to understand how internal states influence decisions and to identify the underlying neural mechanisms. In a mouse decision-making task, these experiments will examine the effects of three types of internal state changes: those arising spontaneously with engagement and disengagement in a task, those resulting from changing expectations during the task, and those resulting from learning within and across days. To determine how internal states affect brain activity and behavior, the team will apply cutting-edge technical advances on a brainwide scale, including statistical tools to infer internal states from behavior; simultaneous recordings from large populations of neurons across many regions during behavior and during optogenetic perturbations; assays that map functionally and molecularly defined cell-type-specific, cross-region connectivity; and computational approaches to model how cross-region neural communication depends on internal states. These ambitious goals go beyond the capabilities of an individual laboratory and are ideally suited for an already- productive consortium. This team is part of the International Brain Laboratory, which has already developed a standardized mouse decision-making task and standardized methods for training, neural measurement, and data analysis, along with a working, scalable infrastructure for sharing data. The proposed research leverages this existing infrastructure and takes it in a new direction. Projects 1-5 will examine simultaneously recorded population activity, evaluate causality, study neural activity and behavior during learning in normal and autism model mice, identify cell types by measuring neuronal activity, gene expression, and axonal projection patterns in the same populations of neurons, and build a comprehensive computational model of all these experimental results. Cores A-D will support the collection, replicability, management, and analysis of the large datasets produced by this brainwide examination of neural circuits. Taken together, the proposed research will rigorously define the neural basis of multiple internal states and evaluate their impact on the flow of decision-relevant information through the brain. The results will greatly advance the field by generating a comprehensive, mechanistic understanding of how internal states are reflected in the brain, and how these states interact with external inputs to guide decisions. Moreover, the team will produce and disseminate open-source tools and protocols that will enable other laboratories to collect and manage large-scale datasets produced through brainwide measurements.

摘要/文摘