  We propose to apply non-linear representation learning to voxelwise rs-fMRI
data. Learning the non-linear representations is done using a variational
autoencoder (VAE). The VAE is trained on voxelwise rs-fMRI data and performs
non-linear dimensionality reduction that retains meaningful information. The
retention of information in the model's representations is evaluated using
downstream age regression and sex classification tasks. The results on these
tasks are highly encouraging and a linear regressor trained with the
representations of our unsupervised model performs almost as well as a
supervised neural network, trained specifically for age regression on the same
dataset. The model is also evaluated with a schizophrenia diagnosis prediction
task, to assess its feasibility as a dimensionality reduction method for
neuropsychiatric datasets. These results highlight the potential for
pre-training on a larger set of individuals who do not have mental illness, to
improve the downstream neuropsychiatric task results. The pre-trained model is
fine-tuned for a variable number of epochs on a schizophrenia dataset and we
find that fine-tuning for 1 epoch yields the best results. This work therefore
not only opens up non-linear dimensionality reduction for voxelwise rs-fMRI
data but also shows that pre-training a deep learning model on voxelwise
rs-fMRI datasets greatly increases performance even on smaller datasets. It
also opens up the ability to look at the distribution of rs-fMRI time series in
the latent space of the VAE for heterogeneous neuropsychiatric disorders like
schizophrenia in future work. This can be complemented with the generative
aspect of the model that allows us to reconstruct points from the model's
latent space back into brain space and obtain an improved understanding of the
relation that the VAE learns between subjects, timepoints, and a subject's
characteristics.
