  One of the central objectives of contemporary neuroimaging research is to
create predictive models that can disentangle the connection between patterns
of functional connectivity across the entire brain and various behavioral
traits. Previous studies have shown that models trained to predict behavioral
features from the individual's functional connectivity have modest to poor
performance. In this study, we trained models that predict observable
individual traits (phenotypes) and their corresponding singular value
decomposition (SVD) representations - herein referred to as latent phenotypes
from resting state functional connectivity. For this task, we predicted
phenotypes in two large neuroimaging datasets: the Human Connectome Project
(HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the
importance of regressing out confounds, which could significantly influence
phenotype prediction. Our findings reveal that both phenotypes and their
corresponding latent phenotypes yield similar predictive performance.
Interestingly, only the first five latent phenotypes were reliably identified,
and using just these reliable phenotypes for predicting phenotypes yielded a
similar performance to using all latent phenotypes. This suggests that the
predictable information is present in the first latent phenotypes, allowing the
remainder to be filtered out without any harm in performance. This study sheds
light on the intricate relationship between functional connectivity and the
predictability and reliability of phenotypic information, with potential
implications for enhancing predictive modeling in the realm of neuroimaging
research.
