Elizabeth A. Kelley

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.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

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.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

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

This study identifies 135 ASD-associated protein-coding genes using whole-genome sequencing data from 11,312 individuals and exome sequencing from over 18,000 trios, with a false discovery rate of 80%.

The paper examines associations between externalizing/internalizing behaviors and cortico-amygdalar connectivity in children with NDDs and TDC, finding no significant linear relationships.