ArticleNeuroImage2021
Deep learning for sex classification in resting-state and task functional brain networks from the UK Biobank.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.Journal of the American Statistical Association · 2026Article
- The progression of sex differences in brain networks across the lifespan.bioRxiv : the preprint server for biology · 2026Article
- Reply to Lockhart et al.: Advancing the understanding of sex differences in functional brain organization with innovative AI tools.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A benchmark of individual auto-regressive models in a massive fMRI dataset.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Sex-Related Disparities in the Resting State Functional Connectivity of the Locus Coeruelus and Salience Network in Preclinical Alzheimer's Disease.International journal of molecular sciences · 2023Article
- Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.Journal of personalized medicine · 2023Review
- Fine temporal brain network structure modularizes and localizes differently in men and women: insights from a novel explainability framework.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.PloS one · 2023Article
- Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease.Artificial intelligence in medicine · 2022Article
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction.Frontiers in neurology · 2022Article
- Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.Molecular autism · 2021Article
- Biobanking in the digital pathology era.Oncology research · 2021Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
2 authors.
Funding
Abstract
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by sex. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.
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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.