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Article

An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network

1
Faculty of Psychology, Beijing Normal University, Beijing 100875, China
2
College of Computer Science, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2025, 15(6), 569; https://doi.org/10.3390/brainsci15060569
Submission received: 25 April 2025 / Revised: 19 May 2025 / Accepted: 23 May 2025 / Published: 25 May 2025
(This article belongs to the Section Computational Neuroscience and Neuroinformatics)

Abstract

(1) Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Functional MRI (fMRI) has been widely applied to investigate brain functional abnormalities associated with ASD, yet challenges remain due to complex data characteristics and limited spatiotemporal information capture. This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. The method first uses the 3D-CNN to automatically extract high-dimensional spatial features directly from the raw 4D fMRI data. It then captures temporal dynamic properties through a designed segmented Long Short-Term Memory (LSTM) network. The concatenated spatiotemporal features are classified using Gradient Boosting Decision Trees (GBDTs), and finally, a voting mechanism is applied to determine whether the subject belongs to the ASD group based on the prediction results. This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches, achieving an average accuracy of 0.85. (4) Conclusions: Our method provides a new solution for ASD classification by leveraging the spatiotemporal information of 4D fMRI data, achieving a significant improvement in classification performance. These results not only offer a new computational tool for ASD diagnosis but also provide important insights into understanding its neurobiological mechanisms.
Keywords: autism spectrum disorder; machine learning; FMRI; ABIDE autism spectrum disorder; machine learning; FMRI; ABIDE

Share and Cite

MDPI and ACS Style

Liu, Z.; Chen, Y.; Dong, X.; Liu, J. An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network. Brain Sci. 2025, 15, 569. https://doi.org/10.3390/brainsci15060569

AMA Style

Liu Z, Chen Y, Dong X, Liu J. An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network. Brain Sciences. 2025; 15(6):569. https://doi.org/10.3390/brainsci15060569

Chicago/Turabian Style

Liu, Zhiling, Ye Chen, Xinrui Dong, and Jing Liu. 2025. "An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network" Brain Sciences 15, no. 6: 569. https://doi.org/10.3390/brainsci15060569

APA Style

Liu, Z., Chen, Y., Dong, X., & Liu, J. (2025). An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network. Brain Sciences, 15(6), 569. https://doi.org/10.3390/brainsci15060569

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