REAL-TIME LOCALIZED NEUROMODULATION IN MAJOR DEPRESSION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 背景：功能神经成像技术在抑郁症研究中的应用在帮助确定其神经相关性以及提供该疾病的神经情感模型方面具有关键作用。然而，这种方法已经暴露出两个缺点。首先，研究人员没有区分抑郁症的神经相关性和神经因素，后者在产生和维持抑郁症状方面涉及更关键的因素。这一区别对于创建信息丰富且有用的抑郁症神经情感模型至关重要。其次，抑郁症的功能神经成像研究对抑郁症患者的生活质量影响有限。这项研究旨在研究实时局部神经反馈训练在抑郁症中分离关键神经因素的使用，并评估实时神经反馈作为一种新的治疗方法在抑郁症治疗中的潜在有效性。 方法和初步数据：我们开发了一种能够实时提取和分析血氧水平依赖(BOLD)功能磁共振数据的软硬件配置。目前，该系统可以从感兴趣的区域和整个大脑提取并显示不断更新的粗体信号。这些信息可以被呈现给参与者，然后他们可以通过反复试验和错误，学习调节给定感兴趣区域内的活动。此外，还可以生成并实时显示反映感兴趣对比的统计地图。

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.