REAL-TIME LOCALIZED NEUROMODULATION IN MAJOR DEPRESSION

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心机构，不一定为研究者机构。背景资料：应用功能性神经影像学技术对抑郁症的研究在帮助识别其神经相关性以及为该疾病的神经情感模型提供信息方面至关重要。然而，这种方法出现了两个缺点。首先，研究人员没有区分抑郁症的神经相关神经因素，更重要的是参与生产和维持抑郁症的神经。这种区别对于创建信息丰富和有用的抑郁症神经情感模型至关重要。其次，抑郁症的功能性神经影像学研究对患有这种疾病的人的生活质量影响有限。本研究旨在研究使用实时局部神经反馈训练来隔离抑郁症中的关键神经因素，并评估实时神经反馈作为治疗抑郁症的新型治疗程序的潜在有效性。 方法和初步数据：我们已经开发了一个软件和硬件配置能够提取和分析血氧水平依赖（BOLD）功能磁共振成像数据在真实的时间。目前，该系统可以从感兴趣区域和整个大脑中提取并显示连续更新的BOLD信号。该信息可以呈现给参与者，然后参与者可以通过反复试验和错误来学习调节给定感兴趣区域内的活动。此外，可以生成并真实的显示反映感兴趣对比度的统计图。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Background: The application of functional neuroimaging techniques to the study of major depressive disorder has been critical in helping to identifying its neural correlates as well as in informing neuroaffective models of the disorder. Two shortcomings of this approach have emerged, however. First, investigators have not distinguished neural correlates of depression from neural factors that are involved more critically in producing and sustaining depressive symptomatology. This distinction is essential in creating informative and useful neuroaffective models of depression. Second, functional neuroimaging studies of depression have had limited impact on the quality of life among those suffering from the disorder. This study was designed both to examine the use of real-time localized neurofeedback training to isolate crucial neural factors in depression, and to assess the potential effectiveness of real-time neurofeedback as a novel therapeutic procedure in the treatment of depression. Methods and preliminary data: We have developed a software and hardware configuration capable of extracting and analyzing blood oxygen level dependent (BOLD) fMRI data in real time. Currently, this system can extract and display continuously updated BOLD signal both from a region of interest and from the whole brain. This information can be presented to participants who can then, through iterative trial and error, learn to modulate activity within a given region of interest. In addition, statistical maps reflecting contrasts of interest can be generated and displayed in real time.