A transformer model for learning spatiotemporal contextual representation in fMRI data.
A transformer model for learning spatiotemporal contextual representation in fMRI data.
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DOI：
10.1162/netn_a_00281
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发表时间：
2023
影响因子：
4.7
通讯作者：
Obradovic, Zoran
中科院分区：
文献类型：
--
作者：
Asadi, Nima;Olson, Ingrid R;Obradovic, Zoran
表示学习是各种复杂现象的数据驱动建模的核心组成部分。学习上下文信息表示可以特别有利于功能磁共振成像数据的分析，因为在这样的数据集的复杂性和动态依赖性。在这项工作中，我们提出了一个框架的基础上Transformer模型学习嵌入的功能磁共振成像数据的时空背景信息的数据考虑在内。这种方法将大脑区域的多变量BOLD时间序列及其功能连接网络同时作为输入，以创建一组有意义的特征，这些特征可以用于各种下游任务，如分类，特征提取和统计分析。建议的时空框架使用的注意力机制，以及图形卷积神经网络，共同注入上下文信息的动态时间序列数据和它们的连接到表示。我们证明了这个框架的好处，通过将其应用到两个静息状态的功能磁共振成像数据集，并提供了进一步的讨论，它的各个方面和优势，它超过了一些其他常用的架构。
Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. We demonstrate the benefits of this framework by applying it to two resting-state fMRI datasets, and provide further discussion on various aspects and advantages of it over a number of other commonly adopted architectures.