Multivariate Machine Learning to Characterize Opioid-induced Alterations in the Brain in Chronic Pain

多变量机器学习表征阿片类药物引起的慢性疼痛大脑变化

基本信息

批准号：
10643854
负责人：
Behnaz Jarrahi
金额：
$ 17.59万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10643854
关键词：
Address Affect American Amygdaloid structure Anterior Award Behavior Behavioral Brain Caring Chronic Chronic low back pain Clinical Clinical Trials Cognitive Corpus striatum structure Data Data Analyses Decision Making Development Dopamine Dopamine D1 Receptor Dopamine D2 Receptor Drug usage Epidemic Epidemiologist Epidemiology Executive Dysfunction Faculty Functional Magnetic Resonance Imaging Funding Future Goals Health Hyperalgesia Iatrogenesis Imaging Techniques Infrastructure Insula of Reil Investigation Link Long-Term Effects MRI Scans Machine Learning Magnetic Resonance Imaging Measures Mediating Medical Mentors Molecular Morphology Multimodal Imaging National Institute of Drug Abuse Neuropsychological Tests Neuropsychology Nucleus Accumbens Opiate Addiction Opioid Opioid Analgesics Pain Pain Measurement Pain Research Pain management Patient Self-Report Patients Pattern Pharmaceutical Preparations Play Positron-Emission Tomography Prefrontal Cortex Prevalence Principal Investigator Psyche structure Psychophysics Questionnaires Raclopride Recovery Research Research Personnel Research Project Grants Resolution Rest Rewards Role Sensory Short-Term Memory Societies Specialist Strategic Planning Structure Substance Use Disorder Substance abuse problem Symptoms System Techniques Testing Thick Time Training abuse liability addiction brain circuitry brain morphology chronic pain chronic pain patient chronic painful condition classification algorithm clinical application cognitive control cognitive function cognitive performance cognitive process cohort data modeling dopamine system executive function experience flexibility gray matter imaging scientist innovation machine learning classifier multimodal neuroimaging neural neuroimaging neurotransmission opioid tapering opioid therapy opioid use pain patient pain processing patient oriented prescription opioid receptor binding recruit response responsible research conduct risk mitigation skills substance use tool

项目摘要

PROJECT SUMMARY/ABSTRACT Prescription opioids are a potent class of drugs for treating pain. However, growing body of research has described iatrogenic consequences of long-term (> 90 days) opioid use in patients with chronic pain including hyperalgesia and impaired executive function. Dopamine is a critical modulator of executive function. While changes in pain and behavior have been noted, little is known about the brain’s morphology, neural and dopaminergic activity that change over time with long-term prescription opioid use. Consistent with the NIDA Strategic Plan objective 1.3, this K25 proposal seeks to “establish the effects of drug use, addiction, and recovery on brain circuits, behavior, and health” using neuroimaging-informed tools. Specifically, the present study combines multiple levels of investigation, including structural and functional Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Quantitative Sensory Testing (QST) and neuropsychological assessments of executive function, and employ machine learning techniques for analysis to identify the effects of long-term prescription opioid use on the brain in chronic pain patients. The applicant will use her advanced quantitative skills in neuroimaging data analysis and modeling to training in QST, and experience in cognitive neuropsychology, epidemiology of chronic pain and addiction to develop an independent research plan in translational pain and successfully compete for future R01 funding. To achieve the training needed to facilitate this investigation, the applicant has consulted with an expert in chronic pain research and opioid therapy, a substance abuse specialist, a neuropsychologist, an epidemiologist, an imaging scientist, and a machine learning leader in neuroimaging field to develop an innovative study and training plan. 40 patients with a diagnostically homogeneous chronic pain condition (i.e., chronic low back pain; CLBP) on long-term opioid therapy, as compared to 40 opioid-naïve CLBP patients, will be studied to achieve the following Aims: 1) Measure pain, cognitive performance, neural and dopaminergic activity during concurrent pain and executive function task fMRI-PET to characterize the effects of opioids on pain processing and executive function in CLBP; 2) measure intrinsic brain activity during resting state fMRI-PET to identify intrinsic brain alterations associated with long-term opioid use in CLBP; and 3) apply high-resolution structural MRI to measure opioid-induced morphological changes in CLBP. This research is innovative in its use of combined QST and neuro- psychological measures with multimodal imaging and sophisticated statistical approaches. It is significant because of its comprehensive approach towards addressing the NIDA Strategic Plan objective. Findings stand to inform medical decision-making regarding pain care and opioid prescription, as well as risk mitigation strategies. The research, training and results obtained will provide a platform for applicant’s long-term scientific research goal of becoming an independent R01-funded, faculty-level principal investigator performing translational pain research aimed at developing neuroimaging tools to have clinical application.

项目概要/摘要处方阿片类药物是一类有效治疗疼痛的药物，但越来越多的研究表明。描述了慢性疼痛患者长期（> 90 天）使用阿片类药物的医源性后果，包括痛觉过敏和执行功能受损。而多巴胺是执行功能的关键调节剂。人们已经注意到疼痛和行为的变化，但对大脑的形态、神经和功能知之甚少。多巴胺能活性随长期处方阿片类药物使用而变化，与 NIDA 一致。战略计划目标 1.3，这项 K25 提案旨在“确定吸毒、成瘾和康复的影响具体来说，本研究使用神经影像学工具来研究大脑回路、行为和健康。结合了多个层次的研究，包括结构和功能磁共振成像 (MRI)，正电子发射断层扫描 (PET)、定量感官测试 (QST) 和神经心理学评估执行功能，并利用机器学习技术进行分析以确定效果申请人将使用她的先进技术来研究长期使用处方阿片类药物对慢性疼痛患者大脑的影响。神经影像数据分析和建模的定量技能以及 QST 培训的经验认知神经心理学、慢性疼痛和成瘾的流行病学制定独立研究计划并成功竞争未来的 R01 资金以实现促进所需的培训。在本次调查中，申请人咨询了慢性疼痛研究和阿片类药物治疗方面的专家，药物滥用专家、神经心理学家、流行病学家、成像科学家和机器神经影像领域的学习领导者为 40 名患者制定了创新的研究和培训计划。长期使用阿片类药物诊断出的同质性慢性疼痛（即慢性腰痛；CLBP）与 40 名未使用阿片类药物的 CLBP 患者相比，将研究该疗法以实现以下目标：1) 测量并发疼痛和执行期间的疼痛、认知表现、神经和多巴胺能活动功能任务 fMRI-PET 来表征阿片类药物对 CLBP 疼痛处理和执行功能的影响； 2) 测量静息态 fMRI-PET 期间的内在大脑活动，以识别相关的内在大脑变化 CLBP 中长期使用阿片类药物；3) 应用高分辨率结构 MRI 来测量阿片类药物引起的 CLBP 的形态变化这项研究的创新之处在于联合使用了 QST 和神经- 通过多模态成像和复杂的统计方法进行心理测量。因为它采用了全面的方法来实现 NIDA 战略计划的目标。为有关疼痛护理和阿片类药物处方以及风险缓解的医疗决策提供信息研究、培训和取得的成果将为申请人的长期科学发展提供平台。研究目标是成为一名独立的 R01 资助的、教职级的首席研究员转化疼痛研究旨在开发神经影像工具以进行临床应用。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Effects of Cardiorespiratory and Motor Skill Fitness on Intrinsic Functional Connectivity of Neural Networks in Individuals with Parkinson's Disease.

心肺和运动技能健身对帕金森病患者神经网络内在功能连接的影响。

DOI：
发表时间：
2021
期刊：
影响因子：
0
作者：
Jarrahi, Behnaz;McEwen, Sarah C;Holschneider, Daniel P;Schiehser, Dawn M;Petkus, Andrew J;Gomez, Megan E;Van Horn, Jack D;Filoteo, Vincent;Jakowec, Michael W;Petzinger, Giselle M
通讯作者：
Petzinger, Giselle M

Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project.

患有急性疼痛的健康成年人动态功能网络连接的改变：人类连接组项目的发现。

DOI：
发表时间：
2023-07
期刊：
影响因子：
0
作者：
Jarrahi; Behnaz
通讯作者：
Behnaz

Thalamic volume mediates associations between cardiorespiratory fitness (VO2max) and cognition in Parkinson's disease.

丘脑容量介导帕金森病心肺健康 (VO2max) 和认知之间的关联。

DOI：
10.1016/j.parkreldis.2021.03.019
发表时间：
2021-05
期刊：
Parkinsonism & related disorders
影响因子：
4.1
作者：
Petkus AJ;Jarrahi B;Holschneider DP;Gomez ME;Filoteo JV;Schiehser DM;Fisher BE;Van Horn JD;Jakowec MW;McEwen SC;Petzinger G
通讯作者：
Petzinger G