Heather M. Shappell

The paper identifies dynamic resting-state brain network markers associated with disruptive behavior problems in children using fMRI data from the ABCD Study.

This study investigates the feasibility of identifying participant identity and task-related neural patterns from single-frame functional brain connectivity data.

This study uses dynamic functional connectivity to compare brain state dynamics during mindfulness and control conditions after stress in moderate to heavy drinkers, finding differences in salience and default mode network activity.

This study uses brain network dynamics to predict future alcohol consumption in adolescents, finding sex-specific associations between network states and drinking behavior.

The paper extends Stochastic Actor-Oriented Models to account for edge uncertainty in dynamic networks, improving estimation accuracy with simulated and real brain network data.

MIND-Map is a toolbox that uses HSMM to estimate brain dynamic states, analyze their dynamics, and compare network dynamics between samples, with a method to determine the optimal number of states.

The paper examines how resting-state brain networks modify the relationship between amyloid beta and cognitive/physical function in older adults, suggesting brain networks as a buffer against amyloid's negative effects.

The paper examines how transdiagnostic symptom domains relate to head motion in children during multimodal neuroimaging.

The paper compares synchronous and asynchronous functional brain networks using fMRI data, finding differences in global and local efficiency and age-related interactions.

This paper introduces a mixed-modeling framework to analyze dynamic brain networks and their associations with phenotypic traits, demonstrating its use in linking fluid intelligence to dynamic connectivity patterns in resting-state fMRI data.

This paper improves state change estimation in dynamic functional connectivity using hidden semi-Markov models, showing better sojourn time estimation compared to hidden Markov models.