Department of Psychology, Shanghai Normal University, Shanghai, China.
Section of Geriatric Psychiatry, Department of Psychiatry, University of Heidelberg, Germany.
Psychiatry Res Neuroimaging. 2023 Sep;334:111690. doi: 10.1016/j.pscychresns.2023.111690. Epub 2023 Jul 20.
Schizophrenia is a severe mental disease with significant morphometric reductions in gray matter volume and cortical thickness in a variety of brain regions. However, most studies only focused on the voxel level alterations in specific cerebral regions and ignored the spatial relationship between voxels. In the present study, we used a novel, data-driven technique-nonnegative matrix factorization (NMF) to group voxels with similar information into a network, and studied the structural covariance at the network level in schizophrenia. Our sample included 36 patients with schizophrenia and 21 healthy controls. Compared with healthy controls, patients with schizophrenia showed significant gray matter volume reductions in six structural covariance networks (dorsal striatum, thalamus, hippocampus-parahippocampus, supplementary motor area-fusiform, middle/inferior temporal network, frontal-parietal-occipital network). Our findings confirmed the assumption of a disturbance in the cortical-subcortical circuit in schizophrenia and suggested that NMF is a useful multivariate method to identify brain networks, which provides a new perspective to study the neural mechanism in schizophrenia.
精神分裂症是一种严重的精神疾病，其大脑多个区域的灰质体积和皮质厚度存在明显的形态计量学减少。然而，大多数研究仅集中在特定脑区的体素水平改变上，忽略了体素之间的空间关系。在本研究中，我们使用了一种新的数据驱动技术——非负矩阵分解（NMF），将具有相似信息的体素分组到一个网络中，并研究了精神分裂症患者在网络水平上的结构协方差。我们的样本包括 36 名精神分裂症患者和 21 名健康对照者。与健康对照组相比，精神分裂症患者在六个结构协变网络（背侧纹状体、丘脑、海马-旁海马、辅助运动区-梭状回、中/下颞叶网络、额顶枕叶网络）中表现出显著的灰质体积减少。我们的研究结果证实了精神分裂症中皮质-皮质下回路紊乱的假设，并表明 NMF 是一种识别脑网络的有用的多变量方法，为研究精神分裂症的神经机制提供了新的视角。
