Nandakumar Naresh, Hsu David, Ahmed Raheel, Venkataraman Archana
Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
Department of Neurology, University of Wisconsin School of Medicine, USA.
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230831. Epub 2023 Sep 1.
Localizing the epileptogenic zone (EZ) is a critical step in the treatment of medically refractory epilepsy. Resting-state fMRI (rs-fMRI) offers a new window into this task by capturing dynamically evolving co-activation patterns, also known as connectivity, in the brain. In this work, we present the first automated framework that uses dynamic functional connectivity from rs-fMRI to localize the EZ across a heterogeneous epilepsy cohort. Our framework uses a graph convolutional network for feature extraction, followed by a transformer network, whose attention mechanism learns which time points of the rs-fMRI scan are important for EZ localization. We train our framework on augmented data derived from the Human Connectome Project and evaluate it on a clinical epilepsy dataset. Our results demonstrate the clear advantages of our convolutional + transformer combination and data augmentation procedure over ablated and comparison models.
定位致痫区（EZ）是药物难治性癫痫治疗中的关键步骤。静息态功能磁共振成像（rs-fMRI）通过捕捉大脑中动态演变的共激活模式（也称为连接性），为这项任务提供了一个新窗口。在这项工作中，我们提出了首个自动化框架，该框架利用rs-fMRI的动态功能连接性在异质性癫痫队列中定位EZ。我们的框架使用图卷积网络进行特征提取，随后是一个Transformer网络，其注意力机制学习rs-fMRI扫描的哪些时间点对EZ定位很重要。我们在源自人类连接组计划的增强数据上训练我们的框架，并在临床癫痫数据集上对其进行评估。我们的结果证明了我们的卷积+Transformer组合以及数据增强程序相对于消融模型和比较模型的明显优势。
