Data_Sheet_1_Learning Cortical Parcellations Using Graph Neural Networks.pdf

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.

深度学习已被广泛应用于磁共振成像（Magnetic Resonance Imaging，MRI）领域，涵盖图像采集加速、图像去噪、组织分割与疾病诊断等诸多应用场景。卷积神经网络（Convolutional Neural Networks）因MRI数据具备规则采样的空间与时间特性，在MRI数据分析中展现出尤为突出的应用价值。然而，脑成像领域的技术进展催生了基于网络与基于表面的分析方法，这类方法通常更适合在图域中进行表征。在本研究中，我们提出了一种通用型皮层分割（cortical segmentation）方法：该方法可利用常规MRI预处理过程中便捷计算得到的静息态功能连接（resting-state connectivity）特征，以及一组对应的训练标签，为全新的MRI数据生成皮层分区（cortical parcellations）结果。我们将图神经网络（Graph Neural Networks，GNN）领域的最新研究进展应用于皮层表面分割任务，借助静息态功能连接特征学习人类新皮层的离散映射图谱。研究发现，图神经网络能够精准学习脑功能连接（functional brain connectivity）的低维表征，且该表征可自然推广至新数据集的皮层映射任务中。在对算法类型、网络架构以及训练特征进行优化后，相较于已发表的皮层分区方案，我们的方法实现了79.91%的平均分类准确率。我们还阐释了训练与测试数据时长、网络架构以及算法选择等超参数设置如何对模型性能产生影响。