ArticleScientific data2024
Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.Frontiers in medicine · 2025Review
- Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study.Frontiers in neuroinformatics · 2025Article
- Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. Successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, requires large amounts of data for model building and optimization. To help overcome such limitations in the context of brain MRI, we present GenMIND: a collection of generative models of normative regional volumetric features derived from structural brain imaging. GenMIND models are trained on real brain imaging regional volumetric measures from the iSTAGING consortium, which encompasses over 40,000 MRI scans across 13 studies, incorporating covariates such as age, sex, and race. Leveraging GenMIND, we produce and offer 18,000 synthetic samples spanning the adult lifespan (ages 22-90 years), alongside the model's capability to generate unlimited data. Experimental results indicate that samples generated from GenMIND align well with the distributions observed in real data. Most importantly, the generated normative data significantly enhances the accuracy of downstream machine learning models on tasks such as disease classification. Dataset and the generative models are publicly available.
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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 Socratic graph.