BCI-based feedback system to promote cognitive control of craving

基于BCI的反馈系统促进对渴望的认知控制

基本信息

批准号：
8792615
负责人：
Muhammad Adeel Parvaz
金额：
$ 5.51万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-21 至 2015-12-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8792615
关键词：
Algorithms Attention Base of the Brain Biomedical Engineering Brain Brain region Classification Clinical Cocaine Cocaine Dependence Coupled Cues Data Data Analyses Detection Disease Dopamine Drug Addiction Drug Modulation Drug usage EP300 gene Electroencephalogram Emotional Emotions Engineering Environment Event-Related Potentials Feedback Frequencies Goals Hypersensitivity Image Impairment Individual Intervention Laboratories Measures Mental Health National Research Service Awards Neurons Neurosciences Operant Conditioning Outcome Measure Patients Pattern Pharmaceutical Preparations Placebos Population Positron-Emission Tomography Psychophysiology Reaction Time Relapse Reporting Research Research Proposals Research Training Rewards Scientist Self Administration Self-control as a personality trait Stimulus Supervision System Techniques Testing Therapeutic Intervention Time Training Treatment outcome Work auditory feedback base behavior influence brain computer interface clinically relevant cocaine exposure cocaine use cognitive control craving cue reactivity data acquisition design drug craving experience improved individualized medicine motivational processes motor control multidisciplinary neurofeedback non-drug non-invasive system novel prevent reinforcer relating to nervous system response signal processing success treatment center treatment program visual feedback

项目摘要

DESCRIPTION (provided by applicant): At the core of drug addiction is impairment in the brain's reward system where hypersensitivity to drug- related stimuli comes at the expense of insufficient salience attributed to all other non-drug-related reinforcers. This hypersensitivity t drug-related stimuli renders individuals with cocaine use disorder (CUD) particularly vulnerable to craving (and drug use), especially when presented with drug-related cues. Nevertheless, when instructed to volitionally inhibit cue-induced craving in a laboratory environment, some CUD reported lower craving and showed decreased activity in the brain regions that process the motivational value of rewards including drug-related cues (orbitofrontal cortex), thereby retaining some level of control over their drug- related cue reactivity. We propose to capitalize on this willed control of craving, using electroencephalogram (EEG) and subsequently ascertained event-related potentials (ERP) techniques, coupled with a Brain- Computer Interface (BCI) based real-time feedback system, to help bolster such cognitive control in drug addiction. In the current proposal, we aim to test the hypothesis that, when asked to volitionally reappraise drug stimuli, CUD will be able to modulate functionally significant drug-cue-induced electrocortical markers. We also hypothesize that providing a real-time feedback (generated by a BCI platform with advanced signal processing algorithms) of one's own EEG/ERP neuronal markers of drug-cue reactivity will be associated with reduced drug-seeking and craving and enhanced inhibitory control in treatment-seeking CUD. Given that EEG- and ERP-based BCI systems are non-invasive, ambulatory and affordable, this proposal has important clinical implications. In particular, once tested and validated, this system could be implemented in treatment centers for reducing drug-cue reactivity/drug-seeking/craving and enhancing self-control. Because a BCI-based real- time feedback system incorporates each patient's own brain signature of illness, it could be used to design an individually tailored treatment program to prevent relapse. Thus, given my background in biomedical engineering and graduate research experience in EEG data acquisition, signal processing, data analysis and the support, expertise and extensive supervision of the sponsor and the co-sponsors, I believe I am at a unique juncture in undertaking this multidisciplinary and technologically cutting-edge research proposal. Therefore, obtaining NRSA support to integrate my course work, research experience with the proposed research training plan will help me develop as an independent scientist, with a niche in implementing engineering principles to advance clinical and interventional neuroscience.

描述（由申请人提供）：在药物成瘾的核心是大脑奖励系统中的损害，在大脑奖励系统中，对药物相关刺激的超敏反应是造成所有其他非药水相关增强剂的显着性的代价。这种高敏性T药物相关的刺激使患有可卡因使用障碍（CUD）的个体特别容易受到渴望（和吸毒）的影响，尤其是在与药物有关的提示呈现时。然而，当指示在实验室环境中自愿抑制提示引起的渴望时，一些CUD报告渴望较低，并且在大脑区域的活性下降，这些活动会处理奖励的动机价值，包括与药物相关的提示（Orbitofrontal Cortex），从而保持对其与药物相关的提示反应性的一定程度的控制。我们建议利用这种对渴望的渴望控制，并随后确定与事件相关的电位（ERP）技术，再加上基于脑计算机界面（BCI）的实时反馈系统，以帮助增强药物成瘾中这种认知控制。在当前的提案中，我们旨在检验以下假设：当被要求进行自愿重新分配药物刺激时，CUD将能够调节功能上显着的药物诱导的电程标记物。我们还假设，提供一个人自己的EEG/EEG/ERP神经元标记的实时反馈（由具有先进信号处理算法的BCI平台生成的药物 - 提示反应性标记将与降低药物寻求药物，渴望和增强的抑制性控制有关的治疗方法。鉴于基于EEG和ERP的BCI系统是无创，门诊和负担得起的，因此该提案具有重要的临床意义。特别是，一旦经过测试和验证，该系统就可以在治疗中心实施，以降低药物 - 寻求反应性/寻求药物/渴望并增强自我控制。由于基于BCI的实时反馈系统结合了每个患者自己的疾病大脑签名，因此可以用来设计一个单独量身定制的治疗计划，以防止复发。因此，鉴于我在脑电图数据获取，信号处理，数据分析以及对赞助商和共同发起人的支持，专业知识和广泛监督方面的生物医学工程和研究生研究经验的背景下，我相信我在进行这项多学科和技术尖端的研究方面处于独特的关注。因此，获得NRSA支持以整合我的课程工作，与拟议的研究培训计划的研究经验将帮助我发展为独立科学家，并在实施工程原理方面的利基市场，以提高临床和介入的神经科学。