Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition.

中文翻译：

---

有风险精神状态受试者的个体化精神病预测

精神病的早期干预策略将显着受益于可靠的预后生物标志物的鉴定。模式分类方法已表明在临床和家族高危人群中早期诊断精神病发作的可行性。在这里，我们有兴趣使用来自前瞻性 FePsy (Fruherkennung von Psychosen) 研究的具有精神病临床高风险的独立队列来复制我们之前的分类结果。由线性支持向量机 (SVM) 和递归特征选择 (RFE) 组成的相同的基于神经解剖学的模式分类管道在预测后期精神病发作方面的准确度达到了 74%。这一发现背后的区别性神经解剖学模式由跨越所有四个叶和小脑的许多大脑区域组成。