OBJECTIVE Schizophrenia spectrum disorders (SSD) and psychotic bipolar disorder share a number of genetic and neurobiological features, despite a divergence in clinical course and outcome trajectories. We studied the diagnostic classification potential that can be achieved on the basis of the structure and connectivity within a triple network system (the default mode, salience and central executive network) in patients with SSD and psychotic bipolar disorder. METHODS Directed static connectivity and its dynamic variance was estimated among 8 nodes of the three large-scale networks. Multivariate autoregressive models of deconvolved resting state functional magnetic resonance imaging time series were obtained from 57 patients (38 with SSD and 19 with bipolar disorder and psychosis). We used 2/3 of the patients for training and validation of the classifier and the remaining 1/3 as an independent hold-out test data for performance estimation. RESULTS A high level of discrimination between bipolar disorder with psychosis and SSD (combined balanced accuracy = 96.2%; class accuracies 100% for bipolar and 92.3% for SSD) was achieved when effective connectivity and morphometry of the triple network nodes was combined with symptom scores. Patients with SSD were discriminated from patients with bipolar disorder and psychosis as showing higher clinical severity of disorganization and higher variability in the effective connectivity between salience and executive networks. CONCLUSIONS Our results support the view that the study of network-level connectivity patterns can not only clarify the pathophysiology of SSD but also provide a measure of excellent clinical utility to identify discrete diagnostic/prognostic groups among individuals with psychosis.

中文翻译：

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三重网络大脑系统内的有效连接可在单一受试者水平上将精神分裂症谱系障碍与精神病性双相障碍区分开来

目的 精神分裂症谱系障碍 (SSD) 和精神病性双相障碍具有许多遗传和神经生物学特征，尽管在临床过程和结果轨迹上存在差异。我们研究了基于三重网络系统（默认模式、显着性和中枢执行网络）内的结构和连通性在 SSD 和精神病性双相障碍患者中可以实现的诊断分类潜力。方法估计三个大型网络的8个节点之间的有向静态连通性及其动态方差。从 57 名患者（38 名患有 SSD，19 名患有双相情感障碍和精神病）获得去卷积静息状态功能磁共振成像时间序列的多变量自回归模型。我们将 2/3 的患者用于分类器的训练和验证，剩余的 1/3 作为独立的保持测试数据用于性能估计。结果当三重网络节点的有效连接和形态测量与症状评分相结合时，实现了双相情感障碍与精神病和 SSD 之间的高度区分（综合平衡准确度 = 96.2%；双相分类准确率为 100%，SSD 分类准确率为 92.3%） . SSD 患者与双相情感障碍和精神病患者被区分开来，因为它们表现出更高的临床严重程度的混乱和更高的显着性和执行网络之间的有效连接的可变性。