Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Germany.
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
Hum Brain Mapp. 2018 Feb;39(2):644-661. doi: 10.1002/hbm.23870. Epub 2017 Nov 3.
Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients.
精神分裂症是一种严重的精神疾病，其默认模式网络（DMN）的高度关联出现明显中断。该规范网络与其他网络之间的相互作用可能有助于目标导向行为，因此其干扰是精神分裂症病理的候选神经特征。先前的研究报告了 DMN 内的超连接和低连接，DMN 与多模态显着性网络（SN）和背侧注意网络（DAN）的连接都增加和减少。本研究使用多站点数据集（n = 325）中的数据衍生网络图谱和多元模式学习算法，系统地重新研究了精神分裂症患者的网络中断。使用无约束脑状态的静息状态波动来估计功能连接，并且使用个体之间的局部体积差异来估计 DMN、SN 和 DAN 内和之间的结构共现。在大脑结构和功能中，使用网络耦合的稀疏逆协方差估计来表征健康参与者和精神分裂症患者，并识别具有统计学意义的组间差异。证据并未证实 DMN 的骨干是精神分裂症中大脑功能障碍的主要驱动因素。相反，功能和结构异常经常位于 DMN 核心之外，例如在前颞顶联合区和楔前叶。此外，功能协变分析强调了 DMN-DAN 耦合的功能障碍，而结构协变结果强调了 DMN-SN 耦合的异常。我们的研究结果重新定义了 DMN 核心及其在精神分裂症中的作用。因此，我们强调了大规模神经相互作用作为有效生物标志物的重要性，以及如何针对单个患者调整精神保健的指示。
