School of Psychology, University of Birmingham, Birmingham, United Kingdom.
School of Psychology, University of Birmingham, Birmingham, United Kingdom.
Biol Psychiatry. 2015 Jun 1;77(11):989-96. doi: 10.1016/j.biopsych.2014.10.023. Epub 2014 Nov 11.
Investigation of aberrant large-scale brain networks offers novel insight into the role these networks play in diverse psychiatric disorders such as schizophrenia. Although studies report altered functional brain connectivity in participants at ultra-high risk (UHR) for psychosis, it is unclear whether these alterations extend to structural brain networks.
Whole-brain structural covariance patterns of 133 participants at UHR for psychosis (51 of whom subsequently developed psychosis) and 65 healthy control (HC) subjects were studied. Following data preprocessing (using VBM8 toolbox), the mean signal in seed regions relating to specific networks (visual, auditory, motor, speech, semantic, executive control, salience, and default-mode) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain signal and each seed region for UHR and HC individuals. The UHR participants who transitioned to psychosis were compared with the UHR participants who did not.
Significantly reduced structural covariance was observed in the UHR sample compared with the HC sample for the default-mode network, and increased covariance was observed for the motor and executive control networks. When the UHR participants who transitioned to psychosis were compared with the UHR participants who did not, aberrant structural covariance was observed in the salience, executive control, auditory, and motor networks.
Whole-brain structural covariance analyses revealed subtle changes of connectivity of the default-mode, executive control, salience, motor, and auditory networks in UHR individuals for psychosis. Although we found significant differences, these are small changes and tend to reflect largely intact structural networks.
对异常大规模脑网络的研究为这些网络在精神分裂症等多种精神障碍中的作用提供了新的见解。尽管研究报告称精神病超高风险（UHR）参与者的功能性大脑连接发生改变，但尚不清楚这些改变是否扩展到结构大脑网络。
研究了 133 名精神病 UHR 参与者（其中 51 名随后发展为精神病）和 65 名健康对照组（HC）参与者的全脑结构协变模式。在对数据进行预处理（使用 VBM8 工具箱）后，提取与特定网络（视觉、听觉、运动、言语、语义、执行控制、突显和默认模式）相关的种子区域的平均信号，并对 UHR 和 HC 个体的全脑信号与每个种子区域之间的关联进行协方差分析。与未发展为精神病的 UHR 参与者相比，发展为精神病的 UHR 参与者在突显、执行控制、听觉和运动网络中观察到异常的结构协变。
与 HC 组相比，UHR 组的默认模式网络的结构协变显著降低，运动和执行控制网络的协变增加。与未发展为精神病的 UHR 参与者相比，发展为精神病的 UHR 参与者的突显、执行控制、听觉和运动网络中观察到异常的结构协变。
全脑结构协变分析显示，精神病 UHR 个体的默认模式、执行控制、突显、运动和听觉网络的连接存在细微变化。尽管我们发现了显著差异，但这些变化很小，且往往反映了结构网络基本完整。
