论文标题
深层的混合模型，以整合多模式和动态连接，以预测自闭症的光谱级缺陷
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
论文作者
论文摘要
我们提出了一个集成的深层框架，该框架共同模拟了来自静止状态功能MRI（RS-FMRI）连接性和扩散张量成像（DTI）拖拉术的互补信息，以提取疾病的预测生物标志物。我们框架的生成部分是结构规范化的动态词典学习（SR-DDL）模型，该模型将动态的RS-FMRI相关矩阵分解为共享基础网络的集合，并随时间变化的患者特定负载。该基质分解由DTI拖拉矩阵指导，以学习解剖学知情的连接曲线。我们框架的深处是LSTM-ANN块，该模块对患者SR-DDL载荷的时间演变进行了建模，以预测多维临床严重性。我们的耦合优化程序共同估计了基础网络，患者特定的动态载荷和神经网络权重。我们在57例被诊断患有自闭症谱系障碍（ASD）的患者中验证了多得分预测任务的框架。我们的混合模型在五倍的交叉验证设置中优于最先进的基线，并提取ASD中脑功能障碍的可解释的多模式神经特征。
We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease. The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings. This matrix factorization is guided by the DTI tractography matrices to learn anatomically informed connectivity profiles. The deep part of our framework is an LSTM-ANN block, which models the temporal evolution of the patient sr-DDL loadings to predict multidimensional clinical severity. Our coupled optimization procedure collectively estimates the basis networks, the patient-specific dynamic loadings, and the neural network weights. We validate our framework on a multi-score prediction task in 57 patients diagnosed with Autism Spectrum Disorder (ASD). Our hybrid model outperforms state-of-the-art baselines in a five-fold cross validated setting and extracts interpretable multimodal neural signatures of brain dysfunction in ASD.
