BACKGROUND Posterior cingulate cortex (PCC) is a key aspect of the default mode network (DMN). Aberrant PCC functional connectivity (FC) is implicated in schizophrenia, but the potential for PCC related changes as biological classifier of schizophrenia has not yet been evaluated. METHODS We conducted a data-driven approach using resting-state functional MRI data to explore differences in PCC-based region- and voxel-wise FC patterns, to distinguish between patients with first-episode schizophrenia (FES) and demographically matched healthy controls (HC). Discriminative PCC FCs were selected via false discovery rate estimation. A gradient boosting classifier was trained and validated based on 100 FES vs. 93 HC. Subsequently, classification models were tested in an independent dataset of 87 FES patients and 80 HC using resting-state data acquired on a different MRI scanner. RESULTS Patients with FES had reduced connectivity between PCC and frontal areas, left parahippocampal regions, left anterior cingulate cortex, and right inferior parietal lobule, but hyperconnectivity with left lateral temporal regions. Predictive voxel-wise clusters were similar to region-wise selected brain areas functionally connected with PCC in relation to discriminating FES from HC subject categories. Region-wise analysis of FCs yielded a relatively high predictive level for schizophrenia, with an average accuracy of 72.28% in the independent samples, while selected voxel-wise connectivity yielded an accuracy of 68.72%. CONCLUSION FES exhibited a pattern of both increased and decreased PCC-based connectivity, but was related to predominant hypoconnectivity between PCC and brain areas associated with DMN, that may be a useful differential feature revealing underpinnings of neuropathophysiology for schizophrenia.

中文翻译：

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异常的后扣带连接将首发精神分裂症与对照分类：机器学习研究

背景后扣带皮层(PCC) 是默认模式网络(DMN) 的一个关键方面。异常 PCC 功能连接 (FC) 与精神分裂症有关，但尚未评估 PCC 相关变化作为精神分裂症生物分类器的潜力。方法我们使用静息状态功能 MRI 数据进行数据驱动的方法来探索基于 PCC 的区域和体素 FC 模式的差异，以区分首发精神分裂症 (FES) 患者和人口统计学匹配的健康对照 (HC ）。通过错误发现率估计选择有区别的 PCC FC。基于 100 FES 与 93 HC 训练和验证梯度提升分类器。随后，使用在不同 MRI 扫描仪上获取的静息状态数据在 87 名 FES 患者和 80 名 HC 的独立数据集中测试了分类模型。结果 FES 患者 PCC 与额叶区、左侧海马旁区、左侧前扣带回皮层和右侧顶下小叶之间的连接性降低，但与左侧颞区的连接性过度。在区分 FES 与 HC 主题类别方面，预测体素聚类类似于按区域选择的大脑区域，与 PCC 功能连接。FC 的区域分析对精神分裂症产生了相对较高的预测水平，独立样本的平均准确率为 72.28%，而选定的体素连接的准确率为 68.72%。