Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.

中文翻译：

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功能，解剖和形态网络突显了基底节神经节-丘脑-皮层回路在精神分裂症中的作用。

电生理，功能和结构研究的证据表明，异常的大脑连通性在精神分裂症的病理生理中起重要作用。但是，大多数以前的研究仅关注单一模式，每种模式都有其自身的局限性。多模式组合可以更有效地利用各种信息，但是以前的多模式研究主要集中在提取局部特征上，而不是基于网络角度进行研究。这项研究包括135例精神分裂症患者和148个性别和年龄相匹配的健康对照者。功能磁共振成像，扩散张量成像和结构磁共振成像数据分别用于构建每个参与者的功能，解剖和形态网络。这些网络与机器学习结合使用，以识别更一致的大脑连接性生物标志物，并探索不同方式之间的关系。我们发现，尽管每种模式都有不同的连通性生物标志物，但收敛的模式是，它们都大部分位于基底神经节-丘脑-皮层回路内。此外，将这三种形态的生物标记物用作特征可获得最高的分类准确度（相对于单一形态而言为91.75％），这表明可以有效利用多种形态的组合来获得有关不同形态网络的补充信息；此外，该信息可以帮助区分患者。这些发现为精神分裂症的脱节假说提供了直接的证据，