Mahmood Usman, Fu Zening, Calhoun Vince, Plis Sergey
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
Georgia State University, Department of Computer Science, Atlanta, GA, USA.
Proc IEEE Int Conf Acoust Speech Signal Process. 2023 Jun;2023. doi: 10.1109/icassp49357.2023.10097126. Epub 2023 May 5.
Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a 'glass-box' deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain's intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson's correlation coefficients.
深度学习模型在许多任务中，尤其是与视觉相关的任务中，表现得与人类一样好甚至更好。几乎无一例外，这些模型都用于执行分类或预测。然而，深度学习模型通常具有黑箱性质，往往难以解释模型或其特征。缺乏可解释性限制了深度学习在神经成像等领域的应用，因为在这些领域结果必须是透明且可解释的。因此，我们提出了一种“白箱”深度学习模型，并将其应用于神经成像领域。我们的模型连续混合空间和时间维度，以估计大脑内在网络之间的动态连接性。我们的模型生成的可解释连接矩阵，在使用多个功能磁共振成像数据集的许多任务上，战胜了当前的先进模型。更重要的是，与诸如皮尔逊相关系数等静态方法不同，我们的模型能够估计基于任务的灵活连接矩阵。
