Deep Representations for Time-varying Brain Datasets
Deep Representations for Time-varying Brain Datasets
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DOI：
10.1145/3534678.3539301
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Sikun Lin;Shuyun Tang;Scott T. Grafton;Ambuj K. Singh
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文献类型：
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作者：
Sikun Lin;Shuyun Tang;Scott T. Grafton;Ambuj K. Singh
找到大脑动态活动的适当表示对于许多下游应用至关重要。由于其高度动态的性质，时间平均功能磁共振成像（功能磁共振成像）只能提供潜在大脑活动的狭隘视图。以前的工作缺乏学习和解释大脑结构中潜在动态的能力。本文构建了一个高效的图神经网络模型，该模型将区域映射的 fMRI 序列和从 DWI（扩散加权成像）获得的结构连接作为输入。我们通过学习样本级自适应邻接矩阵并执行新颖的多分辨率内部簇平滑，找到了潜在大脑动态的良好表示。我们还通过集成梯度对输入进行归因，这使我们能够推断（1）每个任务高度参与的大脑连接和子网络，（2）表征任务的成像序列的时间关键帧，以及（3）区分个体受试者的子网络。这种识别表征异构任务和个体信号状态的关键子网络的能力对于神经科学和其他科学领域非常重要。大量的实验和消融研究证明了我们提出的方法在时空图信号建模方面的优越性和效率，并对大脑动力学进行了深刻的解释。
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can only provide a narrow view of underlying brain activities. Previous works lack the ability to learn and interpret the latent dynamics in brain architectures. This paper builds an efficient graph neural network model that incorporates both region-mapped fMRI sequences and structural connectivities obtained from DWI (diffusion-weighted imaging) as inputs. We find good representations of the latent brain dynamics through learning sample-level adaptive adjacency matrices and performing a novel multi-resolution inner cluster smoothing. We also attribute inputs with integrated gradients, which enables us to infer (1) highly involved brain connections and subnetworks for each task, (2) temporal keyframes of imaging sequences that characterize tasks, and (3) subnetworks that discriminate between individual subjects. This ability to identify critical subnetworks that characterize signal states across heterogeneous tasks and individuals is of great importance to neuroscience and other scientific domains. Extensive experiments and ablation studies demonstrate our proposed method's superiority and efficiency in spatial-temporal graph signal modeling with insightful interpretations of brain dynamics.