Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
Department of Psychology, Stanford University, Stanford, CA, USA.
Neuroimage. 2020 May 15;212:116684. doi: 10.1016/j.neuroimage.2020.116684. Epub 2020 Feb 27.
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
实时功能磁共振成像（rt-fMRI）神经反馈是一种非侵入性、非药物治疗工具，可用于训练行为和缓解临床症状。虽然以前的工作已经使用 rt-fMRI 来针对大脑活动或少数几个大脑区域之间的功能连接进行靶向治疗，但越来越多的证据表明，症状和行为源自许多不同大脑区域之间的相互作用。在这里，我们提出了一种新的 rt-fMRI 方法，即连接组神经反馈，其中间歇性反馈基于跨越数百个区域和数千个功能连接的复杂功能网络的强度。我们首先证明了实时计算全脑功能连接的技术可行性，并为实现连接组神经反馈提供了资源。接下来，我们表明，该方法可用于在任务执行过程中提供关于先前定义的持续注意力连接组模型 saCPM 的强度的准确反馈。尽管在我们最初的试点样本中，基于 saCPM 强度的神经反馈并没有提高扫描外注意力任务的表现，但未来的工作特征化网络目标、训练时长和反馈量对 rt-fMRI 效果的影响，可以为实验或临床试验设计提供信息。
