Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis

磁共振弥散成像 计算机科学 节点(物理) 双线性插值 图形 人工神经网络 人工智能 光学(聚焦) 代表(政治) 模式识别(心理学) 理论计算机科学 磁共振成像 计算机视觉 工程类 放射科 政治 法学 医学 结构工程 政治学 物理 光学
作者
Jiashuang Huang,Luping Zhou,Lei Wang,Daoqiang Zhang
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:39 (7): 2541-2552 被引量:67
标识
DOI:10.1109/tmi.2020.2973650
摘要

Brain network provides essential insights in diagnosing many brain disorders. Integrative analysis of multiple types of connectivity, e.g, functional connectivity (FC) and structural connectivity (SC), can take advantage of their complementary information and therefore may help to identify patients. However, traditional brain network methods usually focus on either FC or SC for describing node interactions and only consider the interaction between paired network nodes. To tackle this problem, in this paper, we propose an Attention-Diffusion-Bilinear Neural Network (ADB-NN) framework for brain network analysis, which is trained in an end-to-end manner. The proposed network seamlessly couples FC and SC to learn wider node interactions and generates a joint representation of FC and SC for diagnosis. Specifically, a brain network (graph) is first defined, where each node corresponding to a brain region is governed by the features of brain activities (i.e., FC) extracted from functional magnetic resonance imaging (fMRI), and the presence of edges is determined by neural fiber physical connections (i.e., SC) extracted from Diffusion Tensor Imaging (DTI). Based on this graph, we train two Attention-Diffusion-Bilinear (ADB) modules jointly. In each module, an attention model is utilized to automatically learn the strength of node interactions. This information further guides a diffusion process that generates new node representations by considering the influence from other nodes as well. After that, the second-order statistics of these node representations are extracted by bilinear pooling to form connectivity-based features for disease prediction. The two ADB modules correspond to the one-step and two-step diffusion, respectively. Experiments on a real epilepsy dataset demonstrate the effectiveness and advantages of our proposed method.
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