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Article

MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification

1
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
2
School of Clinical Medicine, Jiangsu Health Vocational College, Nanjing 210000, China
3
School of Mechanical and Electronic Engineering, College of Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2025, 15(10), 1086; https://doi.org/10.3390/brainsci15101086
Submission received: 6 September 2025 / Revised: 1 October 2025 / Accepted: 2 October 2025 / Published: 8 October 2025
(This article belongs to the Special Issue Advances in Emotion Processing and Cognitive Neuropsychology)

Abstract

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant neurological plasticity in early childhood, where timely interventions like behavioral therapy, language training, and social skills development can mitigate symptoms. Contributions: We introduce a novel Multi-Atlas Guided Multi-View Contrast Learning (MAMVCL) framework for ASD classification, leveraging functional connectivity (FC) matrices from multiple brain atlases to enhance diagnostic accuracy. Methodology: The MAMVCL framework integrates imaging and phenotypic data through a population graph, where node features derive from imaging data, edge indices are based on similarity scoring matrices, and edge weights reflect phenotypic similarities. Graph convolution extracts global field-of-view features. Concurrently, a Target-aware attention aggregator processes FC matrices to capture high-order brain region dependencies, yielding local field-of-view features. To ensure consistency in subject characteristics, we employ a graph contrastive learning strategy that aligns global and local feature representations. Results: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 85.71%, outperforming most existing methods and confirming its effectiveness. Implications: The proposed model demonstrates superior performance in ASD classification, highlighting the potential of multi-atlas and multi-view learning for improving diagnostic precision and supporting early intervention strategies.
Keywords: autism spectrum disorder (ASD); population graph; graph contrastive learning; classification autism spectrum disorder (ASD); population graph; graph contrastive learning; classification

Share and Cite

MDPI and ACS Style

Yin, Z.; Xu, F.; Ma, Y.; Huang, S.; Ren, K.; Zhang, L. MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification. Brain Sci. 2025, 15, 1086. https://doi.org/10.3390/brainsci15101086

AMA Style

Yin Z, Xu F, Ma Y, Huang S, Ren K, Zhang L. MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification. Brain Sciences. 2025; 15(10):1086. https://doi.org/10.3390/brainsci15101086

Chicago/Turabian Style

Yin, Zuohao, Feng Xu, Yue Ma, Shuo Huang, Kai Ren, and Li Zhang. 2025. "MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification" Brain Sciences 15, no. 10: 1086. https://doi.org/10.3390/brainsci15101086

APA Style

Yin, Z., Xu, F., Ma, Y., Huang, S., Ren, K., & Zhang, L. (2025). MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification. Brain Sciences, 15(10), 1086. https://doi.org/10.3390/brainsci15101086

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