Department of Psychology, Humboldt-Universität zu Berlin, Germany.
Department of Psychology, Humboldt-Universität zu Berlin, Germany; German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Germany.
Neurosci Biobehav Rev. 2024 May;160:105640. doi: 10.1016/j.neubiorev.2024.105640. Epub 2024 Mar 26.
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.
在开始治疗之前预测内化性精神障碍的治疗结果对于精准精神保健至关重要。在这方面，静息态功能连接 (rs-FC) 和机器学习经常显示出有希望的预测准确性。本系统评价和荟萃分析通过预测模型研究偏倚风险评估工具 (PROBAST) 评估了这些研究，并考虑了它们的偏倚风险。我们检查了来自 rs-FC 的特征的预测性能，确定了具有最高预测价值的特征，并评估了所采用的机器学习管道。我们于 2022 年 12 月 12 日在 Scopus、PubMed 和 PsycINFO 电子数据库中进行了检索，共纳入了 13 项研究。预测治疗结果的平均平衡准确性为 77%（95%CI：[72%-83%]）。大多数研究中，背外侧前额叶皮层的 rs-FC 具有较高的预测价值。然而，所有研究都存在较高的偏倚风险，影响了结果的可解释性。根据对研究中机器学习管道的全面探索提供了方法学建议，并讨论了潜在的有价值的发展。
