This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical structural features and a multi-task coactivation network in the Adolescent Brain Cognitive Development (ABCD) Study. It proposes a generative model that integrates effects of spatially-varying inputs and a network-valued input using deep neural networks to capture complex non-linear and spatial associations with the output. The method models spatial smoothness, accounts for subject heterogeneity and complex associations between network and spatial images at different scales, enables accurate inference of each images effect on the output image, and allows prediction with uncertainty quantification via Monte Carlo dropout, contributing to one of the first Explainable AI (XAI) frameworks for heterogeneous imaging data. The model is highly scalable to high-resolution data without the heavy pre-processing or summarization often required by Bayesian methods. Empirical results demonstrate its strong performance compared to existing statistical and deep learning methods. We applied the XAI model to the ABCD data which revealed associations between cortical features and ALFF throughout the entire brain. Our model performed comparably to existing methods in predictive accuracy but provided superior uncertainty quantification and faster computation, demonstrating its effectiveness for large-scale neuroimaging analysis. Open-source software in Python for XAI is available.

翻译：本文针对青少年脑认知发展（ABCD）研究中静息态功能磁共振成像中空间索引低频振荡幅度（ALFF）的建模挑战，将其建模为皮层结构特征与多任务共激活网络的函数。文章提出一种生成模型，该模型利用深度神经网络整合空间变化输入与网络值输入的影响，以捕捉与输出之间复杂的非线性及空间关联。该方法能够建模空间平滑性，考虑受试者异质性以及网络与空间图像在不同尺度下的复杂关联，实现对每幅图像对输出图像影响的精确推断，并支持通过蒙特卡洛丢弃法进行不确定性量化的预测，从而为异构影像数据构建了首批可解释人工智能（XAI）框架之一。该模型对高分辨率数据具有高度可扩展性，无需贝叶斯方法通常所需的大量预处理或汇总步骤。实证结果表明，与现有统计及深度学习方法相比，本模型性能优异。我们将该XAI模型应用于ABCD数据，揭示了整个大脑皮层特征与ALFF之间的关联。在预测准确性方面，我们的模型与现有方法表现相当，但提供了更优的不确定性量化及更快的计算速度，证明了其在大规模神经影像分析中的有效性。用于XAI的Python开源软件已发布。