The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

中文翻译：

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通过实时神经反馈直接调节异常脑网络连接

包括自闭症谱系障碍 (ASD) 在内的神经精神疾病的静息状态网络之间存在异常连接模式已经得到很好的证实。ASD 的传统治疗方法是有限的，并且没有解决异常的网络结构。使用实时 fMRI 神经反馈，我们直接训练了 ASD 参与者的三个大脑节点，其中异常连接已被证明与症状严重程度相关。实时增强了所需的网络连接模式，而参与者并不知道正在进行培训。这种训练方案在网络层面产生了巨大的、显着的长期相关性变化，全脑分析显示最大的变化集中在被训练的区域。在对照组中没有发现这些变化。