Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables-the levels of data, spatial scales, idiographic vs population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmark analyses using a locally collected functional magnetic resonance imaging dataset (N = 124) involving an experimental thermal pain task. The results demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.

中文翻译：

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解码疼痛：揭示影响基于神经影像的疼痛模型性能的因素。


基于神经影像的疼痛生物标志物与机器学习技术相结合，已证明在解码疼痛强度和诊断临床疼痛状况方面具有潜力。然而，对不同建模选项如何影响模型性能的系统评估仍有待探索。本研究呈现了综合文献调查和基准分析的结果。我们对之前发表的 57 篇文章进行了调查，其中包括基于神经影像学的疼痛预测模型，根据以下建模变量（数据水平、空间尺度、具体模型与总体模型以及样本量）比较分类和预测性能。研究结果揭示了对具有全脑特征的群体水平建模的偏好，这与神经影像生物标志物的临床转化目标相一致。然而，有限数量的独立测试结果阻碍了对不同建模选项影响的系统评估。这促使我们使用本地收集的功能磁共振成像数据集（N = 124）进行基准分析，涉及实验性热痛任务。结果表明，数据级别、空间尺度和样本大小会显着影响模型性能。具体来说，合并更多与疼痛相关的大脑区域、增加样本量、在训练期间平均更少的数据和在测试期间平均更多数据可以提高性能。这些发现为开发基于神经影像的生物标志物提供了有用的指导，强调了战略选择建模方法以构建性能更好的神经影像疼痛生物标志物的重要性。然而，这些发现对临床疼痛的普遍性需要进一步研究。