Temporal Dynamics of Neural Network Dysfunction in Depression

 描述（由申请人提供）：近年来，在重性抑郁症（MDD）的病理生理学的研究中出现了范式转变，因为理论模型越来越多地采用系统神经科学观点，认为这种疾病植根于大规模神经网络内部和之间的不平衡相互作用。事实上，美国国立卫生研究院的《神经科学研究蓝图》（www.neuroscienceblueprint.nih.gov/connectome/）强调，解剖学和功能连接性的研究是精神病学研究的一个关键前沿。然而，这项研究存在重大差距，特别是在（1）我们对时间的理解 网络功能障碍的维度，以及（2）网络功能障碍与认知情感缺陷的个体差异之间的关系。F32博士后奖学金将允许候选人研究MDD中网络功能的这些新维度。具体而言，参与执行控制的“额顶控制网络”（FPN）、参与内部心理状态的“默认模式网络”（DMN）和支持对外部世界的注意的“背侧注意网络”（DAN）之间的活动和连接变化可能与MDD以及作为该障碍的标志的沉思和负偏认知风格密切相关。根据先前的发现，拟议的研究旨在验证MDD成人将表现出（1）DMN和DAN之间“转换”频率降低的预测（2a）DMN内和FPN-DMN之间的超连通性，以及DAN内和FPN-DAN之间的低连通性;以及（2b）FPN-DMN和FPN-DAN之间过度的“稳定性”（脑系统之间相关活动的幅度和方向的稳定性），表示这些系统之间调节关系的灵活性降低。此外，拟议的研究旨在明确测试网络功能障碍与反刍和负性认知偏差的假定机制之间的关联，预测（3a）减少网络切换和过度稳定性将与情感信息的执行功能较差相关;（3b）这种执行缺陷将与负性偏见和反刍思维方式相关。在Diego Pizzagalli博士的指导下（初级导师），抑郁症临床神经科学领域的领导者，以及尤塔·约尔曼医生的会诊（抑郁症认知情感缺陷专家）和布莱斯·弗雷德里克博士（姆克林医院的首席磁共振成像（MRI）物理学家和MRI方法专家），候选人将接受设计和实施她的第一个独立功能MRI（fMRI）研究。此外，Susan Whitfield-Gabrieli博士（二级导师），基于网络的神经成像方法的先驱，将提供先进的功能磁共振成像分析技术和编程的指导。总之，拟议的培训计划将支持候选人调查MDD的神经网络基质，提高先进神经影像学研究的新技能，并建立独立研究计划。

 DESCRIPTION (provided by applicant): Recent years have witnessed a paradigm shift in the investigation of the pathophysiology of major depressive disorder (MDD), as theoretical models increasingly take a systems-neuroscience view of the disorder as being rooted in imbalanced interactions within and between large-scale neural networks. Indeed, the NIH Blueprint for Neuroscience Research (://www.neuroscienceblueprint.nih.gov/connectome/) emphasizes the study of anatomical and functional connectivity as a critical frontier in psychiatric research. However, significant gaps exist in this research, particularly in (1) our understanding of temporal dimensions of network dysfunction in MDD, and (2) the relationship between network dysfunction and individual differences in cognitive-affective deficits that characterize the disorder. An F32 postdoctoral fellowship would allow the candidate to investigate these novel dimensions of network functioning in MDD. Specifically, activity and connectivity changes between the "frontoparietal control network" (FPN) involved in executive control, the "default mode network" (DMN) involved in internal mentation, and the "dorsal attention network" (DAN) that supports attention to the external world, may be critically related to MDD and to the ruminative and negatively- biased cognitive style that is a hallmark of the disorder. In light of previous findings, the proposed study aims to test the predictions that adults with MDD will exhibit (1) reduced frequency of "switching" between DMN and DAN (spontaneous changes in activity within each network); (2a) hyperconnectivity within DMN and between FPN-DMN, and hypoconnectivity within DAN and between FPN-DAN; and (2b) excessive "stationarity" (stability in the magnitude and directionality of correlated activity between brain systems) between FPN-DMN and FPN-DAN, representing reduced flexibility in the regulatory relationships between these systems. In addition, the proposed study aims to explicitly test associations between network dysfunction and putative mechanisms of rumination and negative cognitive bias, predicting that (3a) reduced network switching and excessive stationarity will be associated with poorer executive functioning with emotional information; and that (3b) such executive deficits will be associated with a negatively biased and ruminative thinking style. With the mentorship of Dr. Diego Pizzagalli (primary mentor), a leader in the field of clinical neuroscience of depression, and the consultation of Dr. Jutta Joormann (expert in cognitive-affective deficits in depression) and Dr. Blaise Frederick (head magnetic resonance imaging (MRI) physicist at McLean Hospital and MRI methods expert) the candidate will receive training in designing and implementing her first independent functional MRI (fMRI) study. In addition, Dr. Susan Whitfield-Gabrieli (secondary mentor), a pioneer in network-based neuroimaging methods, will provide mentorship in advanced fMRI analytic techniques and programming. Together, the proposed training plan will support the candidate in investigating neural network substrates of MDD, sharpening new skills in advanced neuroimaging research, and building a program of independent research.