  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
NeuroSynth: a collection of generative models of normative regional volumetric
features derived from structural brain imaging. NeuroSynth 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 NeuroSynth, 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 NeuroSynth 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/neuro-synth.
