  Generative AI models hold great potential in creating synthetic brain MRIs
that advance neuroimaging studies by, for example, enriching data diversity.
However, the mainstay of AI research only focuses on optimizing the visual
quality (such as signal-to-noise ratio) of the synthetic MRIs while lacking
insights into their relevance to neuroscience. To gain these insights with
respect to T1-weighted MRIs, we first propose a new generative model,
BrainSynth, to synthesize metadata-conditioned (e.g., age- and sex-specific)
MRIs that achieve state-of-the-art visual quality. We then extend our
evaluation with a novel procedure to quantify anatomical plausibility, i.e.,
how well the synthetic MRIs capture macrostructural properties of brain
regions, and how accurately they encode the effects of age and sex. Results
indicate that more than half of the brain regions in our synthetic MRIs are
anatomically accurate, i.e., with a small effect size between real and
synthetic MRIs. Moreover, the anatomical plausibility varies across cortical
regions according to their geometric complexity. As is, our synthetic MRIs can
significantly improve the training of a Convolutional Neural Network to
identify accelerated aging effects in an independent study. These results
highlight the opportunities of using generative AI to aid neuroimaging research
and point to areas for further improvement.
