ArticleIEEE transactions on medical imaging2020
Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- MambaKAN: An Interpretable Framework for Alzheimer's Disease Diagnosis via Selective State Space Modeling of Dynamic Functional Connectivity.Brain sciences · 2026Article
- Four-Source Voxel-Level 3D-CNN With Hierarchical Feature Fusion of rs-fMRI and sMRI Derivatives for ADHD Classification.IEEE open journal of engineering in medicine and biology · 2026Article
- Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.Brain informatics · 2025Article
- Abnormal Functional Connectivity of the Primary Sensory Network in Autism Spectrum Disorder: Sex Differences, Early Overdevelopment, and Clinical Significance.Brain and behavior · 2025Article
- SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.Pattern recognition · 2025Article
- Domain adaptation in small-scale and heterogeneous biological datasets.Science advances · 2024Review
- Interactive computer-aided diagnosis on medical image using large language models.Communications engineering · 2024Article
- Review
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.NeuroImage. Clinical · 2024Article
- Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder.Molecular psychiatry · 2023Review
- Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.Human brain mapping · 2023Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder.Frontiers in human neuroscience · 2023Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
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Authors and funding
6 authors.
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Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early intervention of ASD. While multi-site data increase sample size and statistical power, they suffer from inter-site heterogeneity. To address this issue, we propose a multi-site adaption framework via low-rank representation decomposition (maLRR) for ASD identification based on functional MRI (fMRI). The main idea is to determine a common low-rank representation for data from the multiple sites, aiming to reduce differences in data distributions. Treating one site as a target domain and the remaining sites as source domains, data from these domains are transformed (i.e., adapted) to a common space using low-rank representation. To reduce data heterogeneity between the target and source domains, data from the source domains are linearly represented in the common space by those from the target domain. We evaluated the proposed method on both synthetic and real multi-site fMRI data for ASD identification. The results suggest that our method yields superior performance over several state-of-the-art domain adaptation methods.
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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.