CRCNS: Optimization of closed-loop control of gamma oscillations

CRCNS：伽马振荡闭环控制的优化

• 批准号: 10636642
• 负责人: Satish S Nair
• 金额: $ 28.4万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-26 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10636642
• 关键词: Accounting; Address; Affect; Algorithms; Amygdaloid structure; Anatomy; Behavior; Behavioral; Biophysics; Brain; Brain region; Cell Nucleus; Cells; Collaborations; Communication; Detection; Emotional; Frequencies; Genetic; Goals; Implant; Individual; Individual Differences; Instruction; Interneurons; Intervention; Machine Learning; Mental Health; Methods; Modeling; Monitor; Neurons; Performance; Phase; Physiological; Property; Response to stimulus physiology; Rodent Model; Route; Scheme; Stimulus; Structure; System; Techniques; Testing; Time; Twin Multiple Birth; biophysical model; cognitive function; design; emotion regulation; emotional behavior; experience; experimental study; genetic approach; improved; in vivo; individual variation; insight; neurophysiology; novel; optogenetics; pharmacologic; predictive modeling; response; signal processing; social; vigilance

Throughout the brain, specialized systems carry out different but complementary functions, sometimes independently but often in cooperation. However, we do not understand how their activity is dynamically coordinated, and dysregulation of this is associated with many mental health conditions. Neuronal oscillations, which are detectable in local field potentials (LFPs) at various frequencies, are a promising target for this coordination. Gamma oscillations (40-100 Hz) in particular have been singled out since they enhance stimulus responses, facilitate interactions between brain regions, and are expressed ubiquitously across cortical and subcortical regions. Indeed, gamma oscillations occur in the basolateral nucleus of the amygdala (BL), an important regulator of emotional behaviors. BL gamma oscillations are enhanced during periods of heightened vigilance during a foraging task, following emotionally salient experiences, and upon presentation of socially-relevant stimuli. The variety of circumstances that engage it make it a promising target for interventions affecting emotional behaviors in general. However, technical challenges abound because gamma manifests as brief intermittent oscillatory bursts, layered atop numerous ongoing activities in other frequency bands. This precludes manipulating gamma exclusively with traditional pharmacological, optogenetic, or chemogenetic approaches, since these have substantial effects on ongoing non-gamma activities, and are delivered irrespective of whether gamma bursts are present or absent. To overcome this, a closed-loop algorithm was developed that monitors the LFP in real-time for gamma oscillations and delivers precisely timed optogenetic stimulation capable of enhancing or suppressing gamma strength on a cycle-by-cycle basis. While this improves upon the status quo,, further refinement is needed. Aim 1 of this proposal seeks to clarify how the gamma modulation technique operates via biophysically detailed modeling of the local circuits in the BL that generate gamma, the effects of optogenetic stimulation, and the closed-loop algorithm. Aim 2 designs better signal processing routines for detecting and parameterizing gamma in real-time. Aim 3 develops an approach to create customized biophysical models that reproduce the properties of gamma observed in individual subjects, which when combined with the results of Aims 1 and 2 should allow for optimized control over gamma oscillations in individual subjects. RELEVANCE (See instructions): Gamma oscillations occur in the basolateral amygdala, a brain region implicated in emotional regulation. By developing improved methods to manipulate these oscillations, we hope to better understand their function and improve our ability to control emotional states and behaviors.

在整个大脑中，专门的系统执行不同但互补的功能，有时 独立但经常合作。然而，我们不了解它们的活动是如何动态地 协调和失调与许多心理健康状况有关。神经元 振荡，这是可检测的局部场电位（LFP）在各种频率，是一个有前途的 这一协调的目标。伽马振荡（40-100 Hz）特别被挑选出来，因为 它们增强刺激反应，促进大脑区域之间的相互作用， 在皮层和皮层下区域无处不在。事实上，伽马振荡发生在基底外侧， 杏仁核（BL）是情绪行为的重要调节器。BL伽马振荡是 在觅食任务中提高警惕期间， 经验，并在呈现社会相关的刺激。各种各样的情况， 它使其成为影响一般情绪行为干预的一个有希望的目标。但技术 挑战比比皆是，因为伽马表现为短暂的间歇性振荡爆发， 在其他频段进行的许多活动。这排除了专门操纵伽马 与传统的药理学、光遗传学或化学遗传学方法相比，因为这些方法具有显著的生物学效应， 对正在进行的非伽马活动的影响，无论伽马爆发是否 出席或缺席。为了克服这一点，开发了一种闭环算法，该算法监测LFP， 实时进行伽马振荡并提供精确定时的光遗传学刺激，能够 在逐周期的基础上增强或抑制伽马强度。虽然这改善了 然而，还需要进一步完善。本提案的目标1旨在阐明伽马调制 该技术通过BL中产生伽马的局部电路的生物病理学详细建模来操作， 光遗传学刺激的效果和闭环算法。Aim 2设计更好的信号 用于实时检测和参数化伽马的处理例程。目标3提出了一种方法， 创建定制的生物物理模型，重现在个体中观察到的伽马特性， 与目标1和目标2的结果相结合， 个体受试者的伽马振荡。 相关性（参见说明）： 伽马振荡发生在基底外侧杏仁核，这是一个与情绪调节有关的大脑区域。 通过开发改进的方法来操纵这些振荡，我们希望更好地了解它们的特性。 功能和提高我们控制情绪状态和行为的能力。

项目成果

Reverse engineering information processing in lateral amygdala during auditory tones.

听觉音调期间外侧杏仁核的逆向工程信息处理。

• DOI: 10.1109/ner52421.2023.10123856
• 发表时间: 2023
• 期刊: International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering
• 作者: Glickert,Greg;Latimer,Ben;Sah,Pankaj;Nair,SatishS
• 通讯作者: Nair,SatishS

Predicting opioid use disorder before and after the opioid prescribing peak in the United States: A machine learning tool using electronic healthcare records.

在美国的阿片类药物处方峰之前和之后预测阿片类药物使用障碍：使用电子医疗记录的机器学习工具。

• DOI: 10.1177/14604582231168826
• 发表时间: 2023-04
• 期刊: Health informatics journal
• 影响因子: 3

Inferring Pyramidal Neuron Morphology using EAP Data.

• DOI: 10.1109/ner52421.2023.10123903
• 发表时间: 2023-04
• 期刊: International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering
• 作者: Chen, Ziao;Carroll, Matthew;Nair, Satish S.
• 通讯作者: Nair, Satish S.

Classification of Brainwaves Using Convolutional Neural Network.

使用卷积神经网络对脑电波进行分类。

• DOI: 10.23919/eusipco.2019.8902952
• 发表时间: 2019
• 期刊: Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)
• 作者: Joshi,SwapnilR;Headley,DrewB;Ho,KC;Paré,Denis;Nair,SatishS
• 通讯作者: Nair,SatishS

Domain-Specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities

• DOI: 10.1109/tkde.2021.3093350
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Yuanxun Zhang;P. Calyam;T. Joshi;Satish Nair;Dong Xu