Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
复制标题
DOI：
10.1016/j.bpsc.2019.05.018
复制
发表时间：
2020-08-01
影响因子：
5.9
通讯作者：
Phelps, Elizabeth A.
中科院分区：
文献类型：
--
作者：
Boeke, Emily A.;Holmes, Avram J.;Phelps, Elizabeth A.
背景：精神病学领域长期以来一直在寻找能够客观诊断患者、预测治疗反应或识别疾病发病风险的生物标记物。然而，可靠的精神病学生物标记物尚未出现。最近应用机器学习技术开发基于神经成像的生物标记物已经产生了令人振奋的初步结果。然而，这一领域的许多工作都没有达到机器学习领域的最佳实践标准。对于焦虑的研究尤其如此，这给焦虑生物标记物的发展带来了不确定性。方法：我们应用机器学习工具从人类的神经成像测量中预测特质焦虑。使用脑基因组超结构计划的公开数据，我们通过k重交叉验证，比较了一套基于神经成像的机器学习模型在Discovery样本(n=531,307名女性)中预测焦虑的能力，并在一个坚持的、看不见的测试样本(n=348,209名女性)中测试了最终的模型(包括区域到区域的功能连接、杏仁核种子到体素的连接以及体积和皮质厚度数据的堆叠模型)。结果：尽管最好的模型能够预测Discovery样本内的焦虑(交叉验证R2为0.06，置换检验p<；.001)，坚持样本内的泛化检验失败(R-2，2.04，排列检验p.05)。结论：在本研究中，我们没有发现可泛化的焦虑生物标记物的证据。然而，我们鼓励其他研究人员利用大样本和适当的方法来研究这一主题，以阐明基于神经成像的焦虑生物标记物的潜力。
BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development.METHODS: We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimagingbased machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women).RESULTS: Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R-2 of 2.04, permutation test p..05).CONCLUSIONS: In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.