Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition.
Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition.
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DOI：
10.1001/archgenpsychiatry.2009.62
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发表时间：
2009-07
影响因子：
--
通讯作者：
Gaser, Christian
中科院分区：
文献类型：
--
作者：
Koutsouleris, Nikolaos;Meisenzahl, Eva M.;Davatzikos, Christos;Bottlender, Ronald;Frodl, Thomas;Scheuerecker, Johanna;Schmitt, Gisela;Zetzsche, Thomas;Decker, Petra;Reiser, Maximilian;Moeller, Hans-Juergen;Gaser, Christian
识别个体在发展精神病的高风险依赖于前驱症状学。最近，机器学习算法已经成功地用于基于磁共振成像的神经精神患者群体的诊断分类。确定多变量神经解剖模式分类是否有助于识别精神病不同高危精神状态（ARMS）的个体，并能够在个体水平上预测疾病转移。对早期或晚期ARMS患者与健康对照（hc）的结构磁共振成像数据进行多变量神经解剖模式分类。在4年的临床随访后，通过对过渡到精神病的个体与未过渡到hc的个体的基线成像数据进行分类，评估该方法的预测能力。通过交叉验证和对45例新发hcc的独立队列进行分类来估计分类的可泛化性。德国慕尼黑路德维希-马克西米利安大学精神病学和心理治疗系。第一个分类分析包括20名早期和25名晚期高危个体和25名匹配的hcc。第二次分析包括15个有转变的人，18个没有转变的人，17个匹配的人。特异性，敏感性和准确性的分类。第一次分析的3组交叉验证分类准确率为86% （hcc vs其余），91%（早期风险个体vs其余）和86%（晚期风险个体vs其余）。第二次分析的准确性为90% （hcc vs .其余），88%（转移个体vs .其余）和86%（无转移个体vs .其余）。独立hcc在96%（第一次分析）和93%（第二次分析）病例中被正确分类。通过评估全脑神经解剖异常的模式，可以在个体基础上可靠地识别不同的arms及其临床结果。这些模式可以作为临床医生指导精神病前驱期早期检测的有价值的生物标志物。
Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Specificity, sensitivity, and accuracy of classification. The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis.
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影响因子：
4.5
作者：
Job, DE;Whalley, HC;Lawrie, SM
通讯作者：
Lawrie, SM
影响因子：
10.5
作者：
Johns, LC;Cannon, M;Meltzer, H
通讯作者：
Meltzer, H
影响因子：
10.6
作者：
Fu, Cynthia H. Y.;Mourao-Miranda, Janaina;Brammer, Michael J.
通讯作者：
Brammer, Michael J.
DOI：
10.1007/s00406-004-0508-z
发表时间：
2004-04-01
影响因子：
4.7
作者：
Häfner, H;Maurer, K;Wölwer, W
通讯作者：
Wölwer, W
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC