Functional connectivity network (FCN) data from functional magnetic resonance
imaging (fMRI) is increasingly used for the diagnoses of brain disorders.
However, state-of-the-art studies used to build the FCN using a single brain
parcellation atlas at a certain spatial scale, which largely neglected
functional interactions across different spatial scales in hierarchical
manners. In this study, we propose a novel framework to perform multiscale FCN
analysis for brain disorder diagnosis. We first use a set of well-defined
multiscale atlases to compute multiscale FCNs. Then, we utilize biologically
meaningful brain hierarchical relationships among the regions in multiscale
atlases to perform nodal pooling across multiple spatial scales, namely
"Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based
Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers
of graph convolution and the atlas-guided pooling, for a comprehensive
extraction of diagnostic information from multiscale FCNs. Experiments on
neuroimaging data from 1792 subjects demonstrate the effectiveness of our
proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal
stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum
disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All
results show significant advantages of our proposed method over other competing
methods. This study not only demonstrates the feasibility of brain disorder
diagnosis using resting-state fMRI empowered by deep learning, but also
highlights that the functional interactions in the multiscale brain hierarchy
are worth being explored and integrated into deep learning network
architectures for better understanding the neuropathology of brain disorders.

本研究提出了一种基于多尺度图谱和深度学习的方法，使用具有生物意义的多尺度图谱计算多尺度功能连接网络，并结合多尺度网络中区域之间的生物意义关系进行诊断信息提取，以实现更好地理解脑部疾病的神经病理学。实验证明该方法在阿尔茨海默病、轻度认知障碍和自闭症谱系障碍的诊断中比其他竞争方法更具优势。

使用多尺度图谱构建的分层图卷积网络用于脑障碍诊断和功能连接

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity

There have been several attempts to use deep learning based on brain fMRI
signals to classify cognitive impairment diseases. However, deep learning is a
hidden black box model that makes it difficult to interpret the process of
classification. To address this issue, we propose a novel analytical framework
that interprets the classification result from deep learning processes. We
first derive the region of interest (ROI) functional connectivity network (FCN)
by embedding functions based on their similar signal patterns. Then, using the
self-attention equipped deep learning model, we classify diseases based on
their FCN. Finally, in order to interpret the classification results, we employ
a latent space item-response interaction network model to identify the
significant functions that exhibit distinct connectivity patterns when compared
to other diseases. The application of this proposed framework to the four types
of cognitive impairment shows that our approach is valid for determining the
significant ROI functions.

本文提出一种基于深度学习和功能连接网络分析的认知障碍疾病分类方法，通过自注意力模型和潜空间项目 - 响应交互网络模型解释分类结果，发现与其他疾病不同的显著功能连接模式。