论文标题
Meta-Reggnn：使用图形神经网络和元学习来预测口头和全尺度智力得分
Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning
论文作者
论文摘要
从人脑结构中解密智力对于检测特定神经系统疾病至关重要。最近，功能性脑连接已成功地用于预测行为得分。然而，一方面，最先进的方法忽略了连接组的拓扑特性，另一方面，无法解决高主体间脑异质性。为了解决这些局限性，我们提出了一个新的回归图神经网络，即元学习，即元reggnn来预测脑连接组的行为评分。我们提出的回归GNN的参数是明确训练的，因此少数梯度步骤与较小的训练数据量相结合可以产生良好的概括，从而无法看到脑连接。我们关于言语和全尺度智能商（IQ）预测的结果都超过了神经型和自闭症谱系障碍同伙的现有方法。此外，我们表明我们提出的方法可确保概括性，尤其是对自闭症患者。我们的Meta-Reggnn源代码可在https://github.com/basiralab/meta-reggnn上找到。
Decrypting intelligence from the human brain construct is vital in the detection of particular neurological disorders. Recently, functional brain connectomes have been used successfully to predict behavioral scores. However, state-of-the-art methods, on one hand, neglect the topological properties of the connectomes and, on the other hand, fail to solve the high inter-subject brain heterogeneity. To address these limitations, we propose a novel regression graph neural network through meta-learning namely Meta-RegGNN for predicting behavioral scores from brain connectomes. The parameters of our proposed regression GNN are explicitly trained so that a small number of gradient steps combined with a small training data amount produces a good generalization to unseen brain connectomes. Our results on verbal and full-scale intelligence quotient (IQ) prediction outperform existing methods in both neurotypical and autism spectrum disorder cohorts. Furthermore, we show that our proposed approach ensures generalizability, particularly for autistic subjects. Our Meta-RegGNN source code is available at https://github.com/basiralab/Meta-RegGNN.
