Julia M. Stephen

This study maps the spatiotemporal dynamics of explore-exploit decisions in the human brain using MEG and POMDP modeling during a reinforcement learning task.

This paper introduces a nonlinear mixed model (NMM) for fMRI analysis to better capture nonlinear relationships and individual variability, showing improved model fit compared to linear models.

This study uses dynamic inter-modality source coupling to identify sex-specific brain structural-functional connectivity patterns in children, showing different cognitive predictions for males and females.

The paper links functional change patterns to brain structure using multimodal fusion in adolescents, revealing sex-specific differences in neural dynamics.

This study examines multisensory integration in schizophrenia patients, finding greater facilitation in synchronous conditions and heterogeneous patterns linked to cognitive decline and disorganization.

This study examines whether anterior pituitary volume mediates the relationship between DHEA levels and changes in dysregulation symptoms in adolescents.

This paper introduces DemoVAE, a variational autoencoder that generates synthetic fMRI data conditioned on demographics and removes demographic confounds from fMRI features.

The paper analyzes resting-state fMRI data across different frequency bins to assess functional network connectivity and finds distinct patterns at higher frequencies.

This paper introduces the spatial chronnectome, a method to analyze dynamic spatiotemporal brain network interactions, revealing transient network changes and cognitive relevance in schizophrenia.

This study evaluates the replicability of dynamic functional network connectivity patterns across 7500 fMRI datasets, finding robust and reproducible connectivity states.