Ramyead Avinash, Studerus Erich, Kometer Michael, Uttinger Martina, Gschwandtner Ute, Fuhr Peter, Riecher-Rössler Anita
a University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection , Basel , Switzerland.
b Neuropsychopharmacology and Brain Imaging Research Unit, University Hospital of Psychiatry , Zurich , Switzerland.
World J Biol Psychiatry. 2016 Jun;17(4):285-95. doi: 10.3109/15622975.2015.1083614. Epub 2015 Oct 9.
This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation.
Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis.
Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.
本研究调查异常神经振荡（已证明先于明显精神病发作）是否可用于临床高危患者精神病的个体化预测。
我们使用机器学习算法通过检测β和γ振荡的特定模式来评估精神病的个体化预测。在53名未使用过抗精神病药物且有精神病临床高危风险的患者身上训练并测试预测模型。其中，18名患者后来转变为精神病。所有患者至少随访3年。为了如实估计泛化能力，使用重复嵌套交叉验证在未见过的测试病例中评估模型的预测性能。
从电流源密度（CSD；曲线下面积[AUC]=0.77）可预测向精神病的转变，但从滞后相位同步性数据（LPS；AUC=0.56）则无法预测。结合这两种模式并未提高预测准确性（AUC=0.78）。左颞上回、左顶下小叶和楔前叶对精神病的预测贡献最大。
我们的结果表明，从临床静息态脑电图提取的CSD测量值有助于在单受试者水平上改善对精神病的预测。
