Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.
Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.
复制标题
可解释的多模态深度字典学习捕获三种功能磁共振成像范式的发育差异。
DOI：
10.1109/tbme.2023.3244921
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wang,Yuping
中科院分区：
文献类型：
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作者：
Yang,Lan;Qiao,Chen;Zhou,Huiyu;Calhoun,VinceD;Stephen,JuliaM;Wilson,TonyW;Wang,Yuping
基于多模态的方法通过整合互补信息，为神经科学研究显示出巨大的潜力。有较少的多模态工作集中在大脑发育的变化。MethodsWe提出了一个可解释的多模态深度词典学习方法，以揭示不同模态的共性和特异性，它学习共享字典和模态-基于多模态数据的特定稀疏表示及其稀疏深度自动编码器的编码。结果通过将两个任务和静息状态期间收集的三个fMRI范例视为模态，我们应用所提出的方法对多模态数据，以确定大脑发育的差异。结果表明，该模型不仅可以实现更好的重建性能，但在重现模式产生的年龄相关的差异。具体来说，儿童和年轻人都喜欢在两个任务中切换状态，而在休息时保持在一个特定的状态，但不同的是，儿童具有更多的分散功能连接模式，而年轻人具有更多的集中功能连接模式。多模态数据及其编码被用于训练共享字典和模态特定稀疏表示。识别大脑网络差异有助于了解神经回路和大脑网络如何随着年龄的增长而形成和发展。
ObjectiveMultimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes.MethodsWe propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder.ResultsBy regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. The results show that the proposed model can not only achieve better performance in reconstruction, but also yield age-related differences in reoccurring patterns. Specifically, both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns.Conclusion and SignificanceTo uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age.