ArticleMedical image analysis2023
Computing personalized brain functional networks from fMRI using self-supervised deep learning.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts.IEEE transactions on medical imaging · 2026Article
- Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics.JMIR AI · 2025Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.bioRxiv : the preprint server for biology · 2025Article
- Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.bioRxiv : the preprint server for biology · 2025Article
- Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.Medical image analysis · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- 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
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Article
- Prior-guided individualized thalamic parcellation based on local diffusion characteristics.Human brain mapping · 2024Article
- Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.Frontiers in neuroscience · 2023Article
- Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.NeuroImage · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.
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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.