The neurobiological mechanisms of untreated pain and depression to relapse risk in substance dependence
未经治疗的疼痛和抑郁导致物质依赖复发风险的神经生物学机制
基本信息
- 批准号:10417041
- 负责人:
- 金额:--
- 依托单位:
- 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:2020-04-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:21 year oldAdultAlcoholsAlgorithmsBehaviorBehavioralBrainBrain DiseasesCause of DeathClinicalCognitionCommunicationComputer AnalysisComputer ModelsComputing MethodologiesDataDiseaseFunctional Magnetic Resonance ImagingGlutamatesGoalsIndividualInterdisciplinary StudyKnowledgeMachine LearningMeasuresMental DepressionMentorsMethodsModelingMultimodal ImagingN-acetylaspartateNeurobiologyNeuronsOutcomePainParticipantPatient Self-ReportPatternPersonsPsychiatryQuestionnairesRelapseReportingResearchResearch PersonnelRestSamplingScientistSeveritiesSubstance AddictionSubstance Use DisorderSymptomsSynaptic plasticityTechniquesTestingTrainingUnited StatesVeteransWorkalcohol use disorderbasebehavior measurementbiosignaturecareerchronic depressionchronic painclinical practicecomorbid depressioncomorbiditydepressive symptomsdual diagnosiseffective therapyevidence baseindividualized medicinemachine learning algorithmmachine learning methodmagnetic resonance spectroscopic imagingmilitary veteranmultidimensional datamultimodal neuroimagingneural circuitneural patterningneurobiological mechanismneurochemistryneuroimagingneuromechanismpain symptomprecision medicinepredictive modelingprogramsrecruitrelapse riskrelating to nervous systemsymptomatologytreatment responseunsupervised learning
项目摘要
It is well known that alcohol use disorder (AUD) is highly co-morbid with both depression and
chronic pain. Chronic pain and depression are particularly high and debilitating in the veteran
population. The majority of research in AUD has been in samples of participants without co-
occurring disorders since research typically excludes common co-morbidities from recruitment.
Therefore, we would benefit enormously from studies where we include these co-morbidities,
examine them, and build a neural signature (constellation of neuroimaging and clinical
symptoms) of chronic pain and depression in AUD as a model to test with advanced machine
learning algorithms. What is currently unknown is the neurobiological and neurochemical
patterns that form a brain signature (the neural circuitry) of depression and of chronic pain within
AUD. This study will use multi-modal neuroimaging data and behavioral symptomology
measures to attain the overall objective of this proposal, which is to delineate the separate and
overlapping contributions of co-morbid depression and chronic pain brain signatures on the
neural signature of AUD. We will use advanced computational modeling algorithms (machine
learning) of clinical and multi-modality neuroimaging data. Results from this proposal will
provide a deeper understanding of AUD neurobiology and will identify a pattern of neural
circuitry that signifies depression versus chronic pain in AUD neurobiology as the scientific basis
for individualized precision medicine treatment approaches that target AUD co-morbidities of
depression and chronic pain.
My overarching career goal as an independent investigator is to build a multidisciplinary
research program on neuroimaging of substance use disorders. I will achieve this by clarifying
brain mechanisms contributing to co-occurring symptomology (depression, chronic pain) that
often presents in substance use disorders and particularly high and debilitating in Veterans. A
better understanding of to what extent behavior and co-morbid symptomatology relates to brain
neurobiology could facilitate more accurate predictive modeling of individual treatment response
and relapse using multi-modal imaging and advanced computational analyses methods.
My short-term research goals for this career mentored proposal are to identify the separate
and overlapping neural mechanisms of co-morbidities (depression and chronic pain) prevalent
in alcohol use disorders, and relate these mechanisms to behavior and relapse risk in Veterans.
These goals will be accomplished using state-of-the art multi-modal neuroimaging techniques
(whole-brain magnetic resonance (MR) spectroscopic imaging, resting state functional MR
imaging) which will be directly related to clinical/behavioral measures and self-report
questionnaires of depression and pain, combined with the use of advanced computational
analysis methods such as machine learning techniques of neuroimaging and clinical measures.
众所周知,酒精使用障碍(AUD)与抑郁症和抑郁症高度共病。
慢性疼痛。退伍军人的慢性疼痛和抑郁症尤其严重且使人衰弱
人口。 AUD 的大部分研究都是在没有共同参与的参与者样本中进行的。
由于研究通常将常见的合并症排除在招募范围之外。
因此,我们将从包含这些合并症的研究中受益匪浅,
检查它们,并建立一个神经特征(神经影像学和临床的星座)
以 AUD 为模型,用先进机器测试慢性疼痛和抑郁症的症状)
学习算法。目前未知的是神经生物学和神经化学
形成抑郁症和慢性疼痛的大脑特征(神经回路)的模式
澳元。这项研究将使用多模式神经影像数据和行为症状学
实现本提案总体目标的措施,即划定单独和
共病抑郁症和慢性疼痛大脑特征对大脑特征的重叠贡献
AUD 的神经特征。我们将使用先进的计算建模算法(机器
临床和多模态神经影像数据的学习)。该提案的结果将
提供对 AUD 神经生物学的更深入了解,并将识别神经模式
以 AUD 神经生物学为科学基础的表示抑郁与慢性疼痛的电路
针对 AUD 合并症的个体化精准医疗治疗方法
抑郁症和慢性疼痛。
作为一名独立调查员,我的总体职业目标是建立一个多学科的
物质使用障碍的神经影像学研究计划。我将通过澄清来实现这一目标
导致同时出现的症状(抑郁、慢性疼痛)的大脑机制
经常出现在物质使用障碍中,尤其是在退伍军人中,这种情况严重且使人衰弱。一个
更好地了解行为和共病症状与大脑的关系程度
神经生物学可以促进更准确的个体治疗反应预测模型
使用多模态成像和先进的计算分析方法进行复发。
我对这个职业指导提案的短期研究目标是确定单独的
并存疾病(抑郁症和慢性疼痛)的重叠神经机制普遍存在
酒精使用障碍,并将这些机制与退伍军人的行为和复发风险联系起来。
这些目标将使用最先进的多模式神经影像技术来实现
(全脑磁共振 (MR) 波谱成像、静息态功能 MR
成像),这将与临床/行为测量和自我报告直接相关
抑郁和疼痛问卷,结合先进的计算技术
分析方法,例如神经影像的机器学习技术和临床测量。
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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Donna Murray其他文献
Donna Murray的其他文献
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{{ truncateString('Donna Murray', 18)}}的其他基金
The neurobiological mechanisms of untreated pain and depression to relapse risk in substance dependence
未经治疗的疼痛和抑郁导致物质依赖复发风险的神经生物学机制
- 批准号:
9891504 - 财政年份:2020
- 资助金额:
-- - 项目类别:
The neurobiological mechanisms of untreated pain and depression to relapse risk in substance dependence
未经治疗的疼痛和抑郁导致物质依赖复发风险的神经生物学机制
- 批准号:
10657480 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Self-Regulation and Neural Networks in Alcohol Use Disorders
酒精使用障碍中的自我调节和神经网络
- 批准号:
9145073 - 财政年份:2015
- 资助金额:
-- - 项目类别:
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