Characterizing constructs of motivation and the midbrain dopaminergic system in depression with ultra-high field MRI

用超高场 MRI 表征抑郁症患者的动机和中脑多巴胺能系统的结构

基本信息

项目摘要

Project Summary/Abstract This research project aims to characterize the broad cognitive construct of motivation in healthy individuals and patients with major depressive disorder (MDD) and to examine underlying dopaminergic network connectivity stemming from the ventral tegmental area (VTA) using ultra-high field 7-Tesla MRI. Motivational deficits represent a core feature of MDD and are associated with the debilitating symptom of anhedonia. An established measure of motivation, the Effort Expenditure for Rewards Task (EEfRT), which maps onto the reward valuation construct of the Research Domain Criteria (RDoC) framework, does not capture internally-generated motivation, known as volition in classic psychology models. Therefore the dimensional construct of internally-generated motivation has not yet been empirically explored in MDD. Neurally, motivation for rewards, a subjective measure of volition and anhedonia have each been separately linked to VTA projections to nucleus accumbens and medial prefrontal cortex, and insula. However, VTA circuitry has not been fully examined in relation to motivation constructs in MDD, partly due to the limited feasibility of discerning VTA with 3-Tesla MRI. We therefore aim to address these scientific gaps in our understanding of the construct of motivation and VTA circuitry in MDD. We will employ a novel objective measure of internally-generated motivation, an established measure of external motivation (EEfRT) and a validated ultra-high field 7- Tesla MRI protocol for imaging VTA circuitry in patients with MDD and healthy individuals. This combination of novel and established cognitive measures, and high- resolution MRI will allow precise insight into the neurocognitive mechanisms of MDD. Accompanying career development training plans will fine-tune skills in neuroimaging and cognitive neuroscience and facilitate new skills in clinical research methods. With diverse expert mentorship, specific training goals include: 1) Development of skills in clinical diagnostics and phenotyping for translational patient-oriented research; 2) Refinement of existing skills in multi-modal ultra-high field MRI; 3) Refinement of skills in cognitive and computational neuropsychiatry.
项目摘要/摘要 本研究旨在刻画学生对动机的广泛认知结构。 健康人和严重抑郁障碍(MDD)患者并检查 腹侧被盖区的多巴胺能网络连接 (VTA)使用超高场7-特斯拉磁共振成像。动机缺陷代表了 MDD和与快感缺乏的衰弱症状有关。一个老牌的 激励措施,奖励任务的努力支出(EEFRT),它映射 在研究领域标准(RDoC)框架的报酬估值结构上, 没有捕捉到内部产生的动机,在经典心理学中称为意志 模特们。因此,内生动机的维度结构没有 但在MDD中得到了经验上的探索。从神经上讲,奖励的动机是一种主观的 意志力和快感缺乏的测量分别与VTA预测有关 伏隔核和内侧前额叶皮质,以及脑岛。然而,VTA电路 在与MDD中的激励结构相关的方面没有得到充分的检查,部分原因是 3-Tesla MRI诊断室上性心动过速的可行性有限。因此,我们的目标是解决这些问题 我们对动机和VTA回路结构的理解上的科学空白 MDD。我们将采用一种新的客观衡量内部产生的动机,以及 已建立的外部动机测量(EEFRT)和经过验证的超高场7- 特斯拉磁共振成像技术在MDD患者和健康体检者VTA回路成像中的应用 个人。这种新颖和成熟的认知测量方法的结合,以及高度的 分辨率磁共振成像将使人们能够准确地了解MDD的神经认知机制。 附带的职业发展培训计划将微调神经成像方面的技能 和认知神经科学,并促进临床研究方法的新技能。使用 多样化的专家指导,具体的培训目标包括:1)在 面向患者的翻译研究的临床诊断和表型分析;2) 改进多模式超高场磁共振成像的现有技能;3)改进 认知和计算神经精神病学。

项目成果

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Laurel Sophia Morris其他文献

Laurel Sophia Morris的其他文献

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{{ truncateString('Laurel Sophia Morris', 18)}}的其他基金

Neural Circuit-Specific Mechanisms of Ketamine's Effect on Anhedonia and Anxiety in Depression Using Ultra-High Field 7-Tesla MRI
使用超高场 7 特斯拉 MRI 研究氯胺酮对抑郁症快感缺乏和焦虑影响的神经回路特异性机制
  • 批准号:
    10713827
  • 财政年份:
    2023
  • 资助金额:
    $ 5.35万
  • 项目类别:
Characterizing constructs of motivation and the midbrain dopaminergic system in depression with ultra-high field MRI
用超高场 MRI 表征抑郁症患者的动机和中脑多巴胺能系统的结构
  • 批准号:
    10439716
  • 财政年份:
    2019
  • 资助金额:
    $ 5.35万
  • 项目类别:
Characterizing constructs of motivation and the midbrain dopaminergic system in depression with ultra-high field MRI
用超高场 MRI 表征抑郁症患者的动机和中脑多巴胺能系统的结构
  • 批准号:
    10166618
  • 财政年份:
    2019
  • 资助金额:
    $ 5.35万
  • 项目类别:
Characterizing constructs of motivation and the midbrain dopaminergic system in depression with ultra-high field MRI
用超高场 MRI 表征抑郁症患者的动机和中脑多巴胺能系统的结构
  • 批准号:
    9806575
  • 财政年份:
    2019
  • 资助金额:
    $ 5.35万
  • 项目类别:

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