深度 sr-DDL：深度结构正则化动态字典学习，用于整合多模态与动态功能连接组学数据以实现多维临床表征
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations
AI总结：
本文提出深度 sr-DDL 框架，将结构正则化动态字典学习与 LSTM-ANN 深度网络联合优化，整合 rs-fMRI 和 DTI 多模态数据，用于预测认知及自闭症谱系障碍的多维临床评分，并在多个基准上优于现有方法。
摘要：
我们提出一种新颖的集成框架，联合建模来自静息态功能磁共振成像（rs-fMRI）连接性和弥散张量成像（DTI）纤维束成像的互补信息，以提取可预测行为的大脑连接性生物标志物。该框架将连接组学数据的生成模型与预测行为评分的深度网络耦合。生成组件是一个结构正则化动态字典学习（sr-DDL）模型，将动态 rs-fMRI 相关矩阵分解为共享基础网络集合和随时间变化的被试特异性载荷。我们使用 DTI 纤维束成像对该矩阵分解进行正则化，并学习解剖学信息指导的功能连接性特征。框架的深度组件是一个 LSTM-ANN 模块，利用被试特异性 sr-DDL 载荷的时间演化来预测多维临床表征。我们的联合优化策略同时估计基础网络、被试特异性时变载荷和神经网络权重。我们在人类连接组计划（HCP）数据库中的神经典型个体数据集上验证该框架以映射认知，并在诊断为自闭症谱系障碍（ASD）的个体上以五折交叉验证设置进行单独的多评分预测任务。我们的混合模型在临床结果预测上优于多种最先进方法，并学习到可解释的大脑组织多模态神经特征。
英文摘要：
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component 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 subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.
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。
