Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis

Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ignoring the mining of individual information and the...

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Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering Jg. 33; S. 1550 - 1561
Hauptverfasser: Li, Yueying, Zhang, Xiaotong, Guan, Shihan, Ma, Guolin, Kong, Youyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2025
Schlagworte:
Adolescent
Algorithms
Attention Deficit Disorder with Hyperactivity - diagnosis
Autism Spectrum Disorder - diagnosis
Autoencoder
Autoencoders
Brain modeling
Brain network
Child
Data models
Decoding
Diseases
Electroencephalography
Female
functional connectivity
Functional magnetic resonance imaging
graph convolutional network
Humans
Image edge detection
Image reconstruction
Imaging
Machine Learning
Male
Medical diagnosis
Neural Networks, Computer
neurodevelopmental disorder diagnosis
Neurodevelopmental Disorders - diagnosis
population diseases diagnosis
ISSN:1534-4320, 1558-0210, 1558-0210
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Zusammenfassung:Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ignoring the mining of individual information and the exploration of inter-individual associations in population. To solve these problems, this work proposes a novel approach for detecting abnormal neural circuits associated with brain diseases, named Topology-guided Graph Masked autoencoder Learning method (TGML), which focuses on individual representation and intra-population association, to achieve the effective diagnosis of brain diseases within the population. Concretely, the TGML comprises 1) the t opology- g uided g roup a ssociation m odule (T<inline-formula> <tex-math notation="LaTeX">{G}^{{2}} </tex-math></inline-formula>AM) that reconstructs the edges and update the initial population graph, 2) the i ntra- p opulation i nteraction m asked a uto e ncoder network (IPI_MAE) captures the discriminative characteristics of subjects based on the novel Masked Autoencoder, which incorporates traditional masked autoencoders into a task-related process. The proposed method is evaluated on two neurodevelopmental disorder diagnosis tasks of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). The results show that the proposed TGML achieves significant improvements and surpasses the state-of-the-art methods.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2025.3562662