M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations
M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
N. S. D'Souza;M. B. Nebel;D. Crocetti;Joshua Robinson;S. Mostofsky;A. Venkataraman
中科院分区：
文献类型：
--
作者：
N. S. D'Souza;M. B. Nebel;D. Crocetti;Joshua Robinson;S. Mostofsky;A. Venkataraman
我们提出了一个多模态图卷积网络（M-GCN），它集成了静息状态fMRI连接和离散张量成像纤维束成像来预测表型指标。我们的专业M-GCN过滤器在主题结构连接体的指导下，对功能连接矩阵进行拓扑作用。结构信息的包含也充当正则化器，并有助于提取预测临床结果的丰富数据嵌入。我们对来自人类连接组项目的275名健康个体和来自内部数据的57名被诊断患有自闭症谱系障碍的个体验证了我们的框架，以分别预测认知测量和行为缺陷。我们证明了M-GCN在五倍交叉验证的环境中优于几种最先进的基线，并从健康和自闭症人群中提取预测性生物标志物。因此，我们的框架提供了代表性的可扩展性，以利用结构和功能的互补性，并在有限的训练数据的存在下将这些信息映射到表型测量。
We propose a multimodal graph convolutional network (M-GCN) that integrates resting-state fMRI connectivity and diffusion tensor imaging tractography to predict phenotypic measures. Our specialized M-GCN filters act topologically on the functional connectivity matrices, as guided by the subject-wise structural connectomes. The inclusion of structural information also acts as a regularizer and helps extract rich data embeddings that are predictive of clinical outcomes. We validate our framework on 275 healthy individuals from the Human Connectome Project and 57 individuals diagnosed with Autism Spectrum Disorder from an in-house data to predict cognitive measures and behavioral deficits respectively. We demonstrate that the M-GCN outperforms several state-of-the-art baselines in a five-fold cross validated setting and extracts predictive biomarkers from both healthy and autistic populations. Our framework thus provides the representational flexibility to exploit the complementary nature of structure and function and map this information to phenotypic measures in the presence of limited training data.