Bridging Scales to Understand Endogenous Neuromodulation and its Regulation

桥接尺度以了解内源性神经调节及其调节

基本信息

批准号：
10567073
负责人：
Rachel Alison Adcock
金额：
$ 59.34万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-08 至 2027-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10567073
关键词：
Address Back Behavior Behavioral Blood - brain barrier anatomy Brain Cell Nucleus Cognition Cognitive Complement Cues Decision Making Disadvantaged Dissociation Dopamine Dopaminergic Agents Education Evolution Foundations Functional Magnetic Resonance Imaging Goals Grant Hippocampus Human Imagery Individual Informal Social Control Intervention Knowledge Learning Machine Learning Measures Medial Memory Mental Health Mental disorders Methods Midbrain structure Modeling Motivation Neuromodulator Neurons Neurotransmitters Norepinephrine Participant Pattern Pharmaceutical Preparations Pharmacologic Substance Pharmacotherapy Physiological Positioning Attribute Regulation Resources Serotonin Signal Transduction Specific qualifier value Statistical Models Stream System Temporal Lobe Time Training Up-Regulation Work analytical method basal forebrain clinical application financial incentive improved learned behavior locus ceruleus structure method development motivated behavior multidimensional data multilevel analysis neural neural model neural network neurofeedback neuroimaging neuroregulation neurotransmitter release novel resilience response side effect spatiotemporal support network tool

项目摘要

Neuromodulatory nuclei detect and transform brain network activity into simpler signals, then send neurotransmitters back out to large-scale brain networks to change their function. Such nuclei are centrally implicated in mental disorders and adaptive resilience, and their regulation remains an untapped resource for interventions. The purpose of this grant is to understand how neuromodulatory nuclei detect and in turn influence distributed patterns of brain activity to impact behavior. To understand their regulation and effects on brain function, the investigative team has developed novel neuroimaging, behavioral, and analytic methods. These methods include: training participants to endogenously self-regulate dopaminergic midbrain, isolating distinct streams of information in the midbrain over multiple timescales, distinguishing behavioral contexts and network effects associated with univariate activation in neuromodulatory nuclei, and finally relating midbrain activation to memory-conducive states in medial temporal lobe memory systems. Our team has recently developed whole-brain analyses of real-time fMRI during midbrain neurofeedback and machine-learning tools for characterizing nonlinear latent dynamics from high-dimensional data. Now, with these tools, we can relate midbrain activation to whole brain states. We hypothesize 1) that distinct distributed spatiotemporal patterns precede and follow midbrain univariate activation, specify it uniquely among neuromodulatory nuclei, and distinguish sustained from transient midbrain responses; 2) that the evolution of these patterns over the training session will predict learning to upregulate midbrain, and 3) that endogenous midbrain regulation will predict brain and behavioral effects we and others have previously shown to be associated with midbrain activation and dopamine function. If the aims of this project are achieved, we will have introduced a multi-level model of the neural states that support midbrain activation, a complement of methods for regulating midbrain noninvasively, and an improved understanding of its impact on learning and motivated behavior. Reliable cognitive strategies for dynamically and selectively fine-tuning neural networks to suit behavioral contexts will lay the foundation for a wide array of interventions across educational and clinical applications.

神经调节核检测大脑网络活动并将其转化为更简单的信号，然后发送神经递质返回到大规模的大脑网络以改变其功能。这样的原子核位于中心与精神障碍和适应性恢复力有关，其调节仍然是未开发的资源干预措施。这笔赠款的目的是了解神经调节核如何检测并反过来影响大脑活动的分布式模式以影响行为。了解它们的调节和影响为了研究大脑功能，研究小组开发了新的神经影像、行为和分析方法。这些方法包括：训练参与者内源性地自我调节多巴胺能中脑，隔离中脑在多个时间尺度上的不同信息流，区分行为背景和与神经调节核的单变量激活相关的网络效应，最后与中脑相关激活内侧颞叶记忆系统中的记忆传导状态。我们团队最近开发了中脑神经反馈和机器学习工具期间实时功能磁共振成像的全脑分析用于表征高维数据的非线性潜在动力学。现在，有了这些工具，我们可以将中脑激活到全脑状态。我们假设 1) 不同的分布时空模式在中脑单变量激活之前和之后，在神经调节核中唯一指定它，并且区分持续和短暂的中脑反应； 2）这些模式的演变训练课程将预测学习上调中脑，并且 3）内源性中脑调节将预测我们和其他人之前已证明与中脑相关的大脑和行为影响激活和多巴胺功能。如果这个项目的目标得以实现，我们将引入多层次的支持中脑激活的神经状态模型，是调节中脑方法的补充非侵入性地，并更好地了解其对学习和动机行为的影响。可靠的动态地、选择性地微调神经网络以适应行为环境的认知策略将为教育和临床应用的广泛干预措施奠定基础。