Jessica Jones

The paper uses DyNeMo to analyze MEG data from children and adults during emotional face processing, revealing distinct developmental trajectories in brain network activation and connectivity.

This study identifies unique functional connectivity patterns in autism associated with five sensory processing phenotypes using machine learning.

This study uses deep learning to predict internalizing problems from brain structure in youth, finding good performance in neurodevelopmental conditions but sub-optimal in the general population.

The paper examines multisensory integration network (MSN) functional connectivity in neurodevelopmental disorders and finds that sensory characteristics, not diagnoses, drive the observed neural patterns.

The paper identifies molecular pathways associated with neurodevelopmental disorder subgroups using neuroimaging data from mice and humans.

The paper examines age-related changes in brain structure and function across two neurodiverse datasets, finding differences linked to demographic and clinical factors.