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

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis 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...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 11; s. 15182 - 15194
Hlavní autoři: Liu, Mianxin, Zhang, Han, Shi, Feng, Shen, Dinggang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2024
Témata:
Aged
Alzheimer Disease - diagnosis
Alzheimer Disease - diagnostic imaging
Atlases as Topic
Brain - diagnostic imaging
Brain Diseases - diagnostic imaging
Brain disorder
brain multiscale hierarchy
Deep Learning
Feature extraction
Female
functional connectivity network (FCN)
Functional magnetic resonance imaging
graph convolutional neural network
Head
Humans
Image Processing, Computer-Assisted - methods
Imaging
Learning systems
Magnetic heads
Magnetic Resonance Imaging - methods
Male
Neural Networks, Computer
Neuroimaging
Neuroimaging - methods
ISSN:2162-237X, 2162-2388, 2162-2388
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Popis
Shrnutí:Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis 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 (AP)." Accordingly, we propose a multiscale-atlases-based hierarchical graph convolutional network (MAHGCN), built on the stacked layers of graph convolution and the AP, 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 the 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 a better understanding of the neuropathology of brain disorders. The codes for MAHGCN are publicly available at " https://github.com/MianxinLiu/MAHGCN-code ."
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3282961