  In human neuroscience, machine learning can help reveal lower-dimensional
neural representations relevant to subjects' behavior. However,
state-of-the-art models typically require large datasets to train, so are prone
to overfitting on human neuroimaging data that often possess few samples but
many input dimensions. Here, we capitalized on the fact that the features we
seek in human neuroscience are precisely those relevant to subjects' behavior.
We thus developed a Task-Relevant Autoencoder via Classifier Enhancement
(TRACE), and tested its ability to extract behaviorally-relevant, separable
representations compared to a standard autoencoder, a variational autoencoder,
and principal component analysis for two severely truncated machine learning
datasets. We then evaluated all models on fMRI data from 59 subjects who
observed animals and objects. TRACE outperformed all models nearly
unilaterally, showing up to 12% increased classification accuracy and up to 56%
improvement in discovering "cleaner", task-relevant representations. These
results showcase TRACE's potential for a wide variety of data related to human
behavior.

Neurons and Cognition;Machine Learning