Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naive schizophrenia patients
Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naive schizophrenia patients
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DOI：
10.1017/s0033291718003781
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发表时间：
2019-12-01
影响因子：
6.9
通讯作者：
Glenthoj, Birte Y.
中科院分区：
文献类型：
--
作者：
Ebdrup, Bjorn H.;Axelsen, Martin C.;Glenthoj, Birte Y.
背景资料。大量的临床研究已经确定了客观的生物标志物，这些生物标志物在群体水平上将精神分裂症患者与健康对照区分开来，但目前的诊断系统仅包括临床症状。在这项研究中，我们调查了多模式数据上的机器学习算法是否可以作为临床翻译的框架。46名未服用抗精神病药物的首发精神分裂症患者和58名对照接受了神经认知测试、电生理学和磁共振成像(MRI)。患者在阿米舒利单用抗精神病药物治疗6周之前和之后进行了临床评估。采用9种不同结构的有监督机器学习算法，首先估计单峰诊断精度，然后估计多峰诊断精度。最后，我们探讨了症状缓解的可预测性。认知数据显著区分患者和对照组(准确率=60-69%；p值=0.0001-0.009)。电生理学、结构磁共振和扩散张量成像的准确性没有超过机会水平。包含认知的多模式分析加上其余三种模式中的一种或多种的任意组合并不比单独的认知更好。没有一种方法能预测症状缓解。在这项针对抗精神病药物患者的多变量和多模式研究中，只有认知显著区分了患者和对照组，而没有模式似乎能预测短期症状缓解。总体而言，这些发现增加了越来越多的人呼吁将认知包括在精神分裂症的定义中。为了在首发、抗精神病的精神分裂症患者中充分发挥机器学习算法的潜力，可能需要仔细选择基于独立数据的先验变量，以及纳入其他模式。
Background. A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.Methods. Forty-six antipsychotic-naive, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.Results. Cognitive data significantly dassified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.Conclusions. In this multivariate and multimodal study in antipsychotic-nalve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-nalve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.