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Predicting Conversion to Psychosis in Clinical High Risk Patients using Resting-State Functional MRI Features

机译:使用休息状态功能MRI特征预测临床高危患者的心理学症转化

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Recent progress in artificial intelligence provides researchers with a powerful set of machine learning tools for analyzingbrain imaging data. In this work, we explore a variety of classification algorithms and functional network featuresderived from resting-state fMRI data collected from clinical high-risk (prodromal schizophrenia) patients and controls,trying to identify features predictive of conversion to psychosis among a subset of CHR patients. While there are manyexisting studies suggesting that functional network features can be highly discriminative of schizophrenia whenanalyzing fMRI of patients suffering from the disease vs controls, few studies attempt to explore a similar approach toactual prediction of future psychosis development ahead of time, in the prodromal stage. Our preliminary resultsdemonstrate the potential of fMRI functional network features to predict the conversion to psychosis in CHR patients.However, given the high variance of our results across different classifiers and subsets of data, a more extensiveempirical investigation is required to reach more robust conclusions.
机译:人工智能的最新进展为研究人员提供了一套强大的机器学习工具,用于分析脑成像数据。在这项工作中,我们探索了各种分类算法和功能网络功能从临床高风险(前romal精神分裂症)患者和控制中收集的休息状态FMRI数据源自休息状态数据,试图识别在CHR患者的子集中识别预测转换对精神病的特征。虽然有很多现有研究表明功能网络特征可以高度辨别精神分裂症时分析患有疾病的患者与控制的患者,很少有研究试图探索类似的方法前期未来精神病发展的实际预测,在产前阶段。我们的初步结果展示FMRI功能网络特征的潜力,以预测CHR患者在心理学转化。然而,鉴于我们的结果对不同分类器和数据子集的结果高,更广泛实证调查需要达到更强大的结论。

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