基于放宽阈值高斯过程先验的可扩展标量-图像皮层表面回归
Scalable Scalar-on-Image Cortical Surface Regression with a Relaxed-Thresholded Gaussian Process Prior
AI总结：
针对大规模ABCD fMRI数据集分析挑战，提出基于放宽阈值高斯过程先验的可扩展贝叶斯标量-图像回归模型，通过变分近似和Karhunen-Loève展开降低计算成本，在特征选择上引入灵活性，并在模拟和ABCD研究中验证了其高效性与准确性。
摘要：
为应对分析大规模青少年大脑认知发展（ABCD）fMRI数据集的挑战（涉及超过5000名受试者和海量神经影像数据），我们提出了一种可扩展的贝叶斯标量-图像回归模型，以实现计算可行性和效率。该模型采用放宽阈值高斯过程（RTGP），集成了能够同时进行硬阈值和软阈值处理的分段平滑、稀疏且连续的函数。该方法在标量-图像回归的特征选择中引入了额外的灵活性，并通过采用变分近似和利用高斯过程的Karhunen-Loève展开实现了可扩展的后验计算。这一进展大幅降低了大规模贝叶斯空间模型中皮层表面数据逐顶点分析的计算成本。通过广泛的模拟研究和对ABCD研究的实际应用，验证了模型的参数估计、预测准确性及特征选择性能。在此，我们进行了回归分析，将智力得分与基于任务的fMRI数据相关联，并考虑了包括年龄、性别和父母教育水平在内的混杂因素。该验证突显了我们的模型在处理大规模神经影像数据时保持计算可行性和准确性的能力。
英文摘要：
In addressing the challenge of analysing the large-scale Adolescent Brain Cognition Development (ABCD) fMRI dataset, involving over 5,000 subjects and extensive neuroimaging data, we propose a scalable Bayesian scalar-on-image regression model for computational feasibility and efficiency. Our model employs a relaxed-thresholded Gaussian process (RTGP), integrating piecewise-smooth, sparse, and continuous functions capable of both hard- and soft-thresholding. This approach introduces additional flexibility in feature selection in scalar-on-image regression and leads to scalable posterior computation by adopting a variational approximation and utilising the Karhunen-Loève expansion for Gaussian processes. This advancement substantially reduces the computational costs in vertex-wise analysis of cortical surface data in large-scale Bayesian spatial models. The model's parameter estimation and prediction accuracy and feature selection performance are validated through extensive simulation studies and an application to the ABCD study. Here, we perform regression analysis correlating intelligence scores with task-based functional MRI data, taking into account confounding factors including age, sex, and parental education level. This validation highlights our model's capability to handle large-scale neuroimaging data while maintaining computational feasibility and accuracy.
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