ArticleBMC neuroscience2024
Autism spectrum disorders detection based on multi-task transformer neural network.
Article in BMC neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Pooled it
- AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- A Multi-Atlas Dynamic Connectivity Transformer Fused with 4D Spatiotemporal Modeling for Autism Spectrum Disorder Recognition.Brain sciences · 2026Article
- Age-stratified multimodal MRI and machine learning to explore autism-related brain characteristics in youth.Frontiers in psychiatry · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- An efficient Alzheimer's disease detection by NV classifier with BWTDL approach using MRI image.BMC medical imaging · 2025Article
- Computational Neuroscience's Influence on Autism Neuro-Transmission Research: Mapping Serotonin, Dopamine, GABA, and Glutamate.Biomedicines · 2025Review
- The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review.Turkish archives of pediatrics · 2025Article
- Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.