Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
Department of Psychiatry, University of Western Ontario, London, Ontario, Canada.
JAMA Psychiatry. 2018 Jun 1;75(6):613-622. doi: 10.1001/jamapsychiatry.2018.0391.
There is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not.
To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals.
DESIGN, SETTING, AND PARTICIPANTS: This investigation was a cross-sectional magnetic resonance imaging (MRI) study with follow-up assessment to determine the transition status of CHR individuals. Participants were recruited from a specialized clinic for the early detection of psychosis at the Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland. Participants included individuals in the following 4 study groups: 44 healthy controls (HC group), 63 at-risk mental state (ARMS) individuals without later transition to psychosis (ARMS-NT group), 16 ARMS individuals with later transition to psychosis (ARMS-T group), and 38 antipsychotic-free patients with first-episode psychosis (FEP group). The study dates were November 2008 to November 2014. The dates of analysis were March to November 2017.
Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures.
The 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedges g range, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%).
These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.
迫切需要提高临床仪器预测精神病高危（CHR）人群精神病发作的有限预后准确性。到目前为止，还没有可靠的生物学标志物来区分哪些 CHR 个体将发展为精神病，哪些不会。
探讨精神病早期基于图的脑回连接组的异常，并检验这种基于系统的方法预测 CHR 个体向精神病过渡的准确性。
设计、地点和参与者：这是一项横断面磁共振成像（MRI）研究，随访评估以确定 CHR 个体的过渡状态。参与者来自瑞士巴塞尔大学精神病学系（UPK）专门的精神病早期检测诊所。参与者包括以下 4 个研究组：44 名健康对照（HC 组）、63 名无后期精神病发作的高危精神状态（ARMS）个体（ARMS-NT 组）、16 名后期精神病发作的 ARMS 个体（ARMS-T 组）和 38 名未服用抗精神病药物的首发精神病患者（FEP 组）。研究日期为 2008 年 11 月至 2014 年 11 月。分析日期为 2017 年 3 月至 11 月。
构建了基于脑回的结构协变网络（连接组），以量化整体整合、分离和小世界特性。使用功能数据分析评估了网络密度范围内各网络测量的组间差异。使用极端随机树算法和重复 5 倍交叉验证来区分 ARMS-T 个体和 ARMS-NT 个体。进行置换检验以评估分类性能测量的显著性。
4 个研究组包括 161 名参与者，平均年龄（标准差）为 24.0（4.7）至 25.9（5.7）岁。ARMS-T 组和 FEP 组的小世界特性降低，两组的整合减少，分离增加（Hedges g 范围为 0.666-1.050）。使用连接组特性作为特征，得到了良好的分类性能（准确率为 90.49%，平衡准确率为 81.34%，阳性预测值为 84.47%，阴性预测值为 92.18%，敏感性为 66.11%，特异性为 96.58%，曲线下面积为 88.30%）。
这些发现表明，在发展为精神病的患者中，皮质折叠的协调发育存在整合不良。这些结果进一步表明，基于脑回的连接组可能是一种很有前途的方法，可以从解剖数据中生成基于系统的测量值，从而提高 CHR 个体向精神病过渡的个体预测能力。
