We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral data. The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings. These subject-specific features are simultaneously input into a neural network that predicts multidimensional clinical information. Our novel optimization framework combines the gradient information from the neural network with that of a conventional matrix factorization objective. This procedure collectively estimates the basis networks, subject loadings, and neural network weights most informative of clinical severity. We evaluate our combined model on a multi-score prediction task using 52 patients diagnosed with Autism Spectrum Disorder (ASD). Our integrated framework outperforms state-of-the-art methods in a ten-fold cross validated setting to predict three different measures of clinical severity.

翻译：我们提出了一个统一的优化框架，将神经网络与字典学习相结合，以建模静息态功能磁共振成像与行为数据之间的复杂交互。字典学习目标将患者相关矩阵分解为一组共享的基础网络和个体特定的载荷。这些个体特定特征同时输入到一个神经网络中，用于预测多维临床信息。我们新颖的优化框架将来自神经网络的梯度信息与传统矩阵分解目标的梯度信息相结合。该过程共同估计最能反映临床严重程度的基础网络、个体载荷和神经网络权重。我们在一个多评分预测任务上评估了我们的组合模型，使用了52名被诊断为自闭症谱系障碍（ASD）的患者。在十折交叉验证设置中，我们的集成框架在预测三种不同的临床严重程度指标方面优于现有最先进的方法。