  Deep Learning (DL) models have gained popularity in neuroimaging studies for
predicting psychological behaviors, cognitive traits, and brain pathologies.
However, these models can be biased by confounders such as age, sex, or imaging
artifacts from the acquisition process. To address this, we introduce
'DeepRepViz', a two-part framework designed to identify confounders in DL model
predictions. The first component is a visualization tool that can be used to
qualitatively examine the final latent representation of the DL model. The
second component is a metric called 'Con-score' that quantifies the confounder
risk associated with a variable, using the final latent representation of the
DL model. We demonstrate the effectiveness of the Con-score using a simple
simulated setup by iteratively altering the strength of a simulated confounder
and observing the corresponding change in the Con-score. Next, we validate the
DeepRepViz framework on a large-scale neuroimaging dataset (n=12000) by
performing three MRI-phenotype prediction tasks that include (a) predicting
chronic alcohol users, (b) classifying participant sex, and (c) predicting
performance speed on a cognitive task called 'trail making'. DeepRepViz
identifies sex as a significant confounder in the DL model predicting chronic
alcohol users (Con-score=0.35) and age as a confounder in the model predicting
cognitive task performance (Con-score=0.3). In conclusion, the DeepRepViz
framework provides a systematic approach to test for potential confounders such
as age, sex, and imaging artifacts and improves the transparency of DL models
for neuroimaging studies.
