ArticleBrain imaging and behavior2016
Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder.
Article in Brain imaging and behavior, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis.Rambam Maimonides medical journal · 2019Article
- Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease.Brain imaging and behavior · 2019Article
- Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder.Translational psychiatry · 2017Review
- Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.Brain imaging and behavior · 2017Article
- Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.Human brain mapping · 2017Article
- Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism.PloS one · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals.
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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.