Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples

TL;DR

This paper introduces GenMIND, a collection of generative models that produce synthetic brain MRI features to augment datasets, improving machine learning performance in neuroimaging applications.

Contribution

The paper presents GenMIND, a novel generative modeling framework trained on large-scale MRI data to generate realistic synthetic neuroimaging features for research and clinical use.

Findings

Generated data aligns well with real data distributions

Synthetic samples improve disease classification accuracy

Models can generate unlimited neuroimaging data across lifespan

Abstract

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary 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 agree with the distributions obtained from real data. Most importantly, the generated normative data significantly enhance the accuracy of downstream machine learning models on tasks such as disease classification. Data and models are available at: https://huggingface.co/spaces/rongguangw/GenMIND.