Constructing hierarchical attentive functional brain networks for early AD diagnosis

节点(物理) 人工智能 等级制度 计算机科学 代表(政治) 地图集(解剖学) 机器学习 古生物学 结构工程 政治 经济 法学 政治学 工程类 市场经济 生物
作者
Jianjia Zhang,Yunan Guo,Luping Zhou,Lei Wang,Wei‐Wen Wu,Dinggang Shen
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:94: 103137-103137 被引量:4
标识
DOI:10.1016/j.media.2024.103137
摘要

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.
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