Department of Psychiatry, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada.
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA.
Schizophr Res. 2019 Dec;214:24-33. doi: 10.1016/j.schres.2018.01.006. Epub 2018 Feb 3.
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.
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.
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.
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.
尽管精神分裂症谱系障碍（SSD）和精神病性双相障碍的临床病程和结局轨迹存在差异，但它们具有许多遗传和神经生物学特征。我们研究了基于 SSD 和精神病性双相障碍患者三重网络系统（默认模式、突显和中央执行网络）内结构和连接的诊断分类潜力。
估计了三个大网络的 8 个节点之间的定向静态连接及其动态方差。从 57 名患者（38 名 SSD 和 19 名双相障碍和精神病）中获得了去卷积静息状态功能磁共振成像时间序列的多元自回归模型。我们使用 2/3 的患者进行分类器的训练和验证，其余 1/3 作为独立的保留测试数据用于性能估计。
当将三重网络节点的有效连接和形态计量学与症状评分相结合时，精神病性双相障碍和 SSD 之间的区分度很高（综合平衡准确率为 96.2%；双相准确率为 100%，SSD 准确率为 92.3%）。与双相障碍和精神病患者相比，SSD 患者表现出更高的紊乱临床严重程度和突显与执行网络之间有效连接的更高变异性。
我们的结果支持这样一种观点，即研究网络水平的连接模式不仅可以阐明 SSD 的病理生理学，还可以提供一种极好的临床实用性措施，以识别精神病患者中的离散诊断/预后群体。
