Disorganized Gyrification Network Properties During the Transition to Psychosis
Disorganized Gyrification Network Properties During the Transition to Psychosis
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DOI：
10.1001/jamapsychiatry.2018.0391
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发表时间：
2018-06-01
期刊：
影响因子：
25.8
通讯作者：
Schmidt, Andre
中科院分区：
文献类型：
--
作者：
Das, Tushar;Borgwardt, Stefan;Schmidt, Andre
迫切需要提高临床仪器预测精神病临床高危（CHR）个体精神病发病的有限预后准确性。到目前为止，还没有建立可靠的生物学标记来描述CHR个体是否会发展为精神病。目的：研究精神病早期基于图形的旋转连接体的异常，并测试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、环境和参与者本研究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。参与者从瑞士巴塞尔大学精神病学系（Universitare Psychiatry Kliniken [UPK]）精神病早期检测专业诊所招募。参与者包括以下4个研究组的个体：44名健康对照组（HC组），63名有危险精神状态（ARMS）的个体（ARMS- nt组），16名有危险精神状态（ARMS- t组）的个体（ARMS- t组），38名无抗精神病首发精神病患者（FEP组）。研究时间为2008年11月至2014年11月。分析日期为2017年3月至11月。主要结果和测量方法：构建基于网格化的结构协方差网络（连接组）来量化全球整合、隔离和小世界性。使用跨网络密度范围的功能数据分析来评估网络测量的组差异。使用具有重复5倍交叉验证的极端随机树算法来描述ARMS-T个体和ARMS-NT个体。进行排列检验以评估分类性能指标的显著性。结果4个研究组包括161名参与者，平均（SD）年龄在24.0（4.7）至25.9（5.7）岁之间。在ARMS-T和FEP组中，小世界性减少，并且与两组的整合性降低和隔离性增加有关（Hedges g范围，0.666-1.050）。以连接体属性为特征，获得了较好的分类效果（准确率90.49%，平衡准确率81.34%，阳性预测值84.47%，阴性预测值92.18%，敏感性66.11%，特异性96.58%，曲线下面积88.30%）。结论和相关性这些研究结果表明，在精神病患者中，皮质折叠的协调发展存在较差的整合。这些结果进一步表明，基于回转体的连接体可能是一种很有前途的方法，可以从解剖学数据中产生基于系统的测量，以提高CHR个体向精神病过渡的个体预测。
IMPORTANCE 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.OBJECTIVES 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 investigationwas 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 (Universitare 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.MAIN OUTCOMES AND MEASURES 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.RESULTS 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%).CONCLUSIONS AND RELEVANCE 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.