CRCN: Dissecting Neural Circuits for Acute Pain
CRCN: Dissecting Neural Circuits for Acute Pain
批准号:
9313960
负责人:
Zhe Sage Chen
金额:
$37.08万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-07-15 至 2020-05-31
关键词:
Absence of pain sensationAcute PainAdverse effectsAffectiveAnalgesicsAnteriorAreaBase of the BrainBehaviorBiological AssayBrainBrain regionCellsClinical TreatmentCodeComb animal structureDataData AnalysesDeep Brain StimulationDetectionElectrophysiology (science)EngineeringGoalsHealthHumanIndividualInstructionLifeMethodsModelingMolecularMotivationNeuraxisNeuronsOutcomePainPain managementPatientsPatternPeripheralPharmacologyPhysiologyPlayPopulationPrefrontal CortexRattusReportingResearchRodent ModelRoleSensorySignal TransductionSomatosensory CortexSpinalStatistical MethodsStimulusSynapsesSystemTechniquesTestingTherapeuticTimeUse EffectivenessWorkarmbasebrain machine interfacecentral paincingulate cortexclinical Diagnosiscomputational neurosciencedesignexperiencehuman imagingimaging studyneural circuitneuroimagingneuromechanismneuroregulationnext generationnoveloptogeneticspain behaviorpain symptomprototyperelating to nervous systemtemporal measurementtooltranslational impact
中文摘要
疼痛是一种多维体验,包括感觉和情感成分。人体成像
研究已经确定了关键皮层区域的活动模式,这些区域可以编码不同的疼痛
但目前还不清楚疼痛如何在单个神经元水平上可靠地编码
或神经元群体。初级躯体感觉皮层(S1)被认为是重要的,
虽然疼痛的感觉辨别方面,但已知前扣带皮层(ACC)在疼痛的感觉辨别方面起着关键作用。
在疼痛的情感动机体验中的作用。然而,影像学研究不能提供因果关系,
电路和行为之间的关系,并进一步受到时间分辨率差的限制。因此
对生理学上急性疼痛的神经编码的完整理解仍然缺失。神经调节是一种
疼痛治疗的潜在选择;但目前的技术,如脑深部电刺激(DBS)缺乏最佳的
靶向并且需要具有不期望的副作用的持续刺激。我们将使用老鼠模型来揭示疼痛
关键中枢神经回路的机制,并开发基于需求的脑机接口(BMI),
集成了疼痛信号的及时检测和精确的暂时镇痛控制。在目标1中,我们将确定
编码急性疼痛的皮层回路我们将同时收集S1和ACC合奏录音
从自由行为的大鼠,并在单细胞和群体水平上表征其放电模式。在
目的2,我们将确定中枢与外周镇痛策略如何改变中枢疼痛回路
使用光遗传学和药理学方法。在目标3中,我们将开发可靠的计算
基于来自中枢疼痛回路的神经系综记录解码急性疼痛的策略,
在目标4中,我们将开发一种实时闭环BMI系统,用于通过以下方式调节急性疼痛:
将神经解码的检测臂与中枢神经刺激的治疗臂相结合。我们将测试
使用已建立的疼痛行为测定其有效性。这些结果将使我们能够分析
神经回路和机制的急性疼痛,并提供了一个模板,为下一代的需求为基础的
疼痛治疗
相关性(见说明):
本项目旨在剖析急性疼痛的回路机制,并开发闭环BMI系统,
疼痛控制我们将结合联合收割机实验,计算和工程技术来解码急性疼痛
信号,并将其应用于开发使用神经刺激进行疼痛调制的实时BMI系统。的
拟议的研究不仅将揭示急性疼痛的重要机制,
提示针灸治疗燕麦失智症。
英文摘要
Pain is a multidimensional experience that includes sensory and affective components. Human imaging
studies have identified patterns of activities within key cortical areas that can encode different pain
experiences, but it remains unclear how pain can also be encoded reliably at the level of individual neurons
or populations of neurons. The primary somatosensory cortex (S1) has been thought to be important in the
sensory-discriminative aspect of the pain, yet the anterior cingulate cortex (ACC) is known to play a crucial
role in the affective-motivational experience of pain. However, imaging studies cannot provide causal
relationship between circuits and behavior and are further limited by poor temporal resolution. Therefore, a
complete understanding of neural codes for acute pain in physiology remains missing. Neuromodulation is a
potential option for pain treatment; but current techniques such as deep brain stimulation (DBS) lack optimal
targets and require constant stimulation with undesired side effects. We will use a rat model to uncover pain
mechanisms of key central neural circuits and develop a demand-based brain-machine interface (BMI) that
integrates timely detection of the pain signal and precise temporal analgesic control. In Aim 1, we will identify
cortical circuitry for encoding acute pain. We will collect simultaneous S1 and ACC ensemble recordings
from freely behaving rats and characterize their firing patterns at both single cell and population levels. In
Aim 2, we will determine how the central pain circuitry is altered by central vs. peripheral analgesic strategy
using optogenetic and pharmacological approaches. In Aim 3, we will develop reliable computational
strategies to decode acute pain based on neural ensemble recordings from the central pain circuits involving
S1 and ACC. In Aim 4, we will develop a real-time closed-loop BMI system for modulating acute pain by
combining a detection arm of neural decoding with a therapeutic arm of central neurostimulation. We will test
its effectiveness using established pain behavior assays. Together, these results will enable us to dissect
neural circuits and mechanisms for acute pain and provide a template for next-generation demand-based
pain treatment.
RELEVANCE (See Instructions):
This project is aimed to dissect circuit mechanisms of acute pain and develop closed-loop BMI system for
pain control. We will combine experimental, computational and engineering techniques to decode acute pain
signals and apply them to develop real-time BMI system for pain modulation using neurostimulation. The
proposed research will not only reveal important mechanisms of acute pain, but will also provide new
insiahts on theraoeutic treatment of oain analaesia.
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CRCN: Dissecting Neural Circuits for Acute Pain
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批准号:9242180
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项目类别:
-
资助金额:$37.08万
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财政年份:2016
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负责人:Zhe Sage Chen
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依托单位:
海外基金