Cortical information integration as a model for pain perception and behavior
皮质信息整合作为疼痛感知和行为的模型
基本信息
- 批准号:10205303
- 负责人:
- 金额:$ 197.82万
- 依托单位:
- 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:2021-09-22 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:Acute PainAddressAffectiveAlgorithmsAnatomyAnteriorAreaBehaviorBrainCellsCodeCognitiveComplexCouplingDataDetectionDevice DesignsDiagnosisEmotionalEngineeringFreund&aposs AdjuvantHumanImpairmentLocationMeasuresModelingMotorNeocortexNeuronal PlasticityNociceptionOutputPainPeripheralPrefrontal CortexProcessPublic HealthRattusRegulationResearchResolutionRodentRodent ModelRoleSchemeSensorySignal TransductionSomatosensory CortexSpecificityStatistical Data InterpretationStatistical MethodsStimulusTestingTimeTranslatingWithdrawalawakebasebrain computer interfacecentral painchronic paincingulate cortexcomputer frameworkdesignexperiencemachine learning methodmillisecondnerve injuryneuroimagingneurotransmissionnovelnovel diagnosticsnovel therapeutic interventionopioid overuseoptogeneticspain behaviorpain modelpain perceptionpain signalpredictive testrelating to nervous systemresponsesensory cortexsensory inputsensory integrationsensory neurosciencespared nervespatiotemporaltheoriestool
项目摘要
Sensory processing requires the interaction between external inputs and an internal brain state. Pain is a unique
sensory experience that is triggered by external signals, but is also strongly shaped by internal cognitive and
emotional variables. At the circuit level, there is not a single primary pain cortex; instead, a distributed network
of cortical areas process and regulate pain. For example, the primary somatosensory cortex (S1) is known to
process stimulus-evoked information, such as location and timing. The anterior cingulate cortex (ACC), in
contrast, gives rise to the aversive experience of pain and displays a high level of neuronal plasticity in the
chronic pain state. Meanwhile, the prefrontal cortex (PFC) can strongly modulate pain behaviors. However, the
mechanisms whereby these distributed cortical pain circuits integrate information remain largely unknown.
Thus, we propose a novel conceptual and computational framework for pain as a converging, temporally
specific, interaction among the S1, the ACC, and the PFC. This interaction can be described by a predictive
coding framework that combines feedforward inputs with top-down predictions dependent on prior aversive
experiences, and modulatory commands, based on neural activities in the S1, ACC and PFC. To test this
hypothesis, we will create a new set of tools for pain studies. We will design devices to accurately measure
pain responses; engineer closed-loop brain-computer interfaces (BCIs) to selectively perturb cortical circuits
during the precise time course of pain; and define novel statistical methods such as mechanistic mean-field
models to analyze dynamic cortical information integration, using local field potentials (LFPs) and ensemble
spikes. In Aim 1, we will identify the impact of the nociceptive information on central pain circuit dynamics. We
will characterize the directed information flow between the S1, ACC, and PFC (more specifically the prelimbic
PFC), before and after noxious stimulation. We will create closed-loop BCIs using a real-time pain detection
algorithm based on statistical analyses of simultaneous spikes and LFPs in the S1 and ACC to optogenetically
modulate the S1, and show that such perturbations disrupt the integration of signals from the ACC and PFC to
impact pain behaviors. We will also analyze how chronic pain alters predictive coding schemes and response
to acute pain. In Aim 2, we will use BCIs to test the impact of ACC modulation on neural activities in the S1 and
PFC, as well as on pain behaviors. More importantly, we will show that chronic pain can induce maladaptive
plasticity in the ACC, which in turn alters the information flow from the S1 and PFC to give rise to pain
anticipation and tonic pain – two examples of pain experience driven by an internal aversive state. In Aim 3, we
will show that BCI-driven modulation of PFC outputs can provide scalable regulation of the nociceptive
information flow from the S1 and ACC to alter pain behaviors. Further, we will show how such cortical
modulation is impaired by chronic pain.
感觉处理需要外部输入和大脑内部状态之间的相互作用。痛苦是独一无二的
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Zhe Sage Chen其他文献
Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility
内侧背侧丘脑调节任务不确定性以实现认知灵活性
- DOI:
10.1038/s41467-025-58011-1 - 发表时间:
2025-03-18 - 期刊:
- 影响因子:15.700
- 作者:
Xiaohan Zhang;Arghya Mukherjee;Michael M. Halassa;Zhe Sage Chen - 通讯作者:
Zhe Sage Chen
Prefrontal transthalamic uncertainty processing drives flexible switching
前额叶经丘脑不确定性处理驱动灵活切换
- DOI:
10.1038/s41586-024-08180-8 - 发表时间:
2024-11-13 - 期刊:
- 影响因子:48.500
- 作者:
Norman H. Lam;Arghya Mukherjee;Ralf D. Wimmer;Matthew R. Nassar;Zhe Sage Chen;Michael M. Halassa - 通讯作者:
Michael M. Halassa
Zhe Sage Chen的其他文献
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{{ truncateString('Zhe Sage Chen', 18)}}的其他基金
Predictive Biosignature for Endoscopic Therapy for Chronic Pancreatitis Pain
慢性胰腺炎疼痛内镜治疗的预测生物特征
- 批准号:
10794609 - 财政年份:2023
- 资助金额:
$ 197.82万 - 项目类别:
CRNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep
CRNS:睡眠期间海马-新皮质记忆编码的综合研究
- 批准号:
10401807 - 财政年份:2018
- 资助金额:
$ 197.82万 - 项目类别:
CRNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep
CRNS:睡眠期间海马-新皮质记忆编码的综合研究
- 批准号:
9920779 - 财政年份:2018
- 资助金额:
$ 197.82万 - 项目类别:
CRCN: Dissecting Neural Circuits for Acute Pain
CRCN:剖析急性疼痛的神经回路
- 批准号:
9313960 - 财政年份:2016
- 资助金额:
$ 197.82万 - 项目类别:
CRCN: Dissecting Neural Circuits for Acute Pain
CRCN:剖析急性疼痛的神经回路
- 批准号:
9242180 - 财政年份:2016
- 资助金额:
$ 197.82万 - 项目类别:
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