We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. https://doi.org/10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.

我们应用光谱动态因果建模（Friston 等人在 Neuroimage 94:396–407. https://doi.org/10.1016/j.neuroimage.2013.12.009, 2014）来分析三个节点之间的有效连接差异在 31 名男性健康对照 (HC) 和 25 名诊断为精神分裂症 (SZ) 的男性患者的数据集中的静息状态网络（即默认模式网络、显着网络和背侧注意力网络）。患者显示从左海马 (LHC) 到以下区域的定向连接增加：背侧前扣带皮层 (DACC)、右前岛叶 (RAI)、左额眼区和双侧顶下沟 (LIPS & RIPS)，以及增加从右侧海马体 (RHC) 到以下区域的连接：双侧前岛叶 (LAI & RAI)、右额眼区和 RIPS。在深圳，阴性症状预测了 LHC 到 DACC、左顶下沟 (LIPAR) 和 RHC 的连接强度，而阳性症状预测了 LHC 到 LIPAR 和 RHC 到 LHC 的连接强度。这些结果强化了海马体连接障碍在 SZ 病理学中的关键作用及其作为疾病严重程度生物标志物的潜力。