The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

中文翻译：

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大脑活动连接性共变的动态：精神分裂症的病理生理学


人脑是一个动态系统，融合了局部活动的进化和大脑交互的重新配置。反复出现的大脑模式（被视为“大脑状态”）揭示了对大脑疾病（尤其是精神分裂症）病理生理学的新见解。然而，之前的研究只关注大脑活动或连接的动态，忽略了它们之间的时间协同进化。在这项工作中，我们建议捕捉具有共变活动连接的动态大脑状态，并探讨与精神分裂症相关的大脑异常。我们发现基于状态的活动和连接性表现出高度的对应性，其中强而对抗的连接性伴随着整个大脑的强烈低频波动，而弱且稀疏的连接性则伴随着弱低频波动。此外，图形分析表明，连接网络效率与大脑活动的波动相关，并且这种关联在不同的大脑状态下是不同的。与健康对照者相比，精神分裂症患者处于弱连接和激活的大脑状态的时间更长，但处于强连接和激活的大脑状态的时间更少。精神分裂症患者在“强”状态下的丘脑区域也表现出较低的效率。有趣的是，一种大脑状态的非典型占有率与个人注意力表现相关。我们的研究结果在另一个独立的数据集中得到了复制，并使用不同的大脑分区方案进行了验证。 这些趋同的结果表明，大脑通过共变的活动和连接性自发地重新配置，这种共同进化的特性可能提供有关大脑疾病机制的有意义的信息，而这些信息是通过单独研究它们中的任何一个都无法观察到的。