Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.
Cereb Cortex. 2021 May 10;31(6):2822-2833. doi: 10.1093/cercor/bhaa390.
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
最近的研究发现功能磁共振成像（fMRI）的测试-重测信度较低，这引起了研究人员的严重关注，但这些研究大多集中在单个 fMRI 特征（例如，静息态连接图中的单个连接）的可靠性上。同时，神经影像学研究人员越来越多地采用多元预测模型，该模型可以聚合大量特征的信息来预测感兴趣的结果，但这些模型的预测结果的测试-重测信度尚未得到系统研究。在这里，我们应用 10 种预测建模方法对人类连接组计划数据集的静息态连接图进行分析，以预测 61 个结果变量。与单个静息态连接的平均可靠性相比，我们发现，在评估的所有 10 种建模方法中，预测模型的预测结果的平均可靠性显著更高。此外，在所有扫描和处理选择（即扫描长度、屏蔽阈值、基于体素和基于表面的处理）中都观察到了一致性的提高。对于最可靠的方法，尽管并非完全如此，但预测结果的可靠性大多处于“良好”范围（高于 0.60）。最后，我们确定了三种有助于解释为什么预测模型的预测结果比单个成像特征具有更高可靠性的机制。我们的结论是，研究人员可以通过更多地使用预测模型来提高测试-重测信度。
