Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.

中文翻译：

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使用机器学习和结构性神经影像学来检测首发性精神病：重新考虑证据

尽管人们对使用机器学习（ML）和神经影像学来检测个体水平的精神病有很高的兴趣，但是由于潜在的方法学问题可能会使现有文献膨胀，因此研究结果的可靠性尚不清楚。这项研究旨在阐明将ML应用于神经解剖学数据可以在多大程度上检测出首发性精神病（FEP），同时采取方法上的预防措施以避免过度乐观的结果。我们使用3个感兴趣的特征集测试了传统ML和一种称为深度学习（DL）的新兴方法：（1）基于表面的区域体积和皮质厚度，（2）基于体素的灰质体积（GMV）和（3 ）基于体素的皮层厚度（VBCT）。为了评估调查结果的可靠性，我们在5个独立的数据集中重复了所有分析，总计956名参与者（514名FEP和444名现场匹配对照）。通过嵌套交叉验证（CV）和跨站点CV评估了性能。基于表面的特征的精度范围从50％到70％；对于GMV，从50％到63％；VBCT从51％增至68％。将DL应用于基于表面的特征时，可以达到最佳精度（70％）；但是，这些模型在其他网站上的推广效果很差。这项研究的发现表明，当采取方法上的预防措施以避免过分乐观的结果时，在精神病早期阶段对个体进行检测比原先认为的更具挑战性。有鉴于此，我们认为应重新考虑当前对ML和结构性神经影像学的诊断价值的证据，以便作出更为谨慎的解释。