  Objective: fMRI and derived measures such as functional connectivity (FC)
have been used to predict brain age, general fluid intelligence, psychiatric
disease status, and preclinical neurodegenerative disease. However, it is not
always clear that all demographic confounds, such as age, sex, and race, have
been removed from fMRI data. Additionally, many fMRI datasets are restricted to
authorized researchers, making dissemination of these valuable data sources
challenging. Methods: We create a variational autoencoder (VAE)-based model,
DemoVAE, to decorrelate fMRI features from demographics and generate
high-quality synthetic fMRI data based on user-supplied demographics. We train
and validate our model using two large, widely used datasets, the Philadelphia
Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for
Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates
group differences in fMRI data while capturing the full breadth of individual
variations. Significantly, we also find that most clinical and computerized
battery fields that are correlated with fMRI data are not correlated with
DemoVAE latents. An exception are several fields related to schizophrenia
medication and symptom severity. Conclusion: Our model generates fMRI data that
captures the full distribution of FC better than traditional VAE or GAN models.
We also find that most prediction using fMRI data is dependent on correlation
with, and prediction of, demographics. Significance: Our DemoVAE model allows
for generation of high quality synthetic data conditioned on subject
demographics as well as the removal of the confounding effects of demographics.
We identify that FC-based prediction tasks are highly influenced by demographic
confounds.
