ArticleIEEE transactions on bio-medical engineering2019
Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
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- Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Deep adversarial learning identifies ADHD-specific associations between apoptotic genes and white matter microstructure in frontal-striatum-cerebellum circuit.Translational psychiatry · 2025Article
- Length of postoperative stay prediction in elderly patients with hip fractures based on machine learning.Frontiers in medicine · 2025Article
- Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.IEEE transactions on medical imaging · 2024Article
- Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.NeuroImage · 2024Article
- Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.Allergy, asthma & immunology research · 2024Article
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
- Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.IEEE transactions on bio-medical engineering · 2023Article
- Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment.Circulation research · 2023Review
- A joint multi-modal learning method for early-stage knee osteoarthritis disease classification.Heliyon · 2023Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.Medical image analysis · 2022Article
- Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders.Frontiers in neuroscience · 2022Article
- Impact ofFrontiers in aging neuroscience · 2022Article
- Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.IEEE transactions on medical imaging · 2021Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
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5 authors.
Funding
Abstract
objectiveMulti-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aims to develop a neural network framework to extract phenotype related cross-data relationships and use it to study the brain development.
methodsWe propose a novel method, deep collaborative learning (DCL), to address the limitation of existing methods. DCL first uses a deep network to represent original data and then seeks their correlations, while also linking the data representation with phenotypical information.
resultsWe studied the difference of functional connectivity (FCs) between different age groups and also use FCs as a fingerprint to predict cognitive abilities. Our experiments demonstrated higher accuracy of using DCL over other conventional models when classifying populations of different ages and cognitive scores. Moreover, DCL revealed that brain connections became stronger at adolescence stage. Furthermore, DCL detected strong correlations between default mode network and other networks which were overlooked by linear canonical correlation analysis, demonstrating DCL's ability of detecting nonlinear correlations.
conclusionThe results verified the superiority of DCL over conventional data-fusion methods. In addition, the stronger brain connection demonstrated the importance of adolescence stage for brain development. SIGNIFICANCE: DCL can better combine complex correlations between multiple data sets in addition to their fitting to phenotypes, with the potential to overcome the limitations of several current data-fusion models.
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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.