融合神经网络与字典学习以实现功能连接组学数据的多维临床表征
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
AI总结：
本文提出融合神经网络与字典学习的统一优化框架，从静息态功能磁共振数据中联合学习基础网络、受试者载荷和预测权重，在52名ASD患者的多评分预测任务中优于现有方法。
摘要：
我们提出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基础网络和受试者特定载荷。这些受试者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常规矩阵分解目标的梯度信息相结合。该过程联合估计对临床严重程度最具信息量的基础网络、受试者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。我们的集成框架在十折交叉验证设置下优于最先进的方法，能够预测三种不同的临床严重程度指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
