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 3 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.

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的现象已得到充分证实。ASD中的传统治疗方法是有限的，并且不能解决异常的网络结构。使用实时功能磁共振成像神经反馈，我们直接对ASD参与者的3个脑结点进行了训练，其中异常连接已显示出与症状严重程度相关。实时增强了所需的网络连接模式，而参与者没有意识到进行培训的情况。这种训练方案在网络级别产生了重大的，长期的显着变化，并且全脑分析显示，最大的变化集中在被训练的领域。在对照组中未发现这些变化。