Evdokia A. Anagnostou

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.

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 identifies DDX53 genetic variants in individuals with ASD, showing a link between DDX53 and ASD, and highlights implications for genetic testing and mouse modeling.

This study performs an X chromosome-wide association study (XWAS) in ASD, identifying 59 variants associated with ASD, including novel candidate genes like FGF13.

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

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

The paper maps neuron-specific protein networks of autism risk genes, identifies shared biological mechanisms, and links them to disease-relevant pathologies.

The paper develops a genome-wide rare variant score linked to morphological subtypes of ASD and finds higher scores in dysmorphic ASD cases.

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%.

This study examines chronic intranasal oxytocin's effects on neuroanatomy and behavior in three autism-related mouse models, finding limited effects and no consistent response patterns.

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

This study identifies subtle cardiac abnormalities in ASD mouse models, highlighting heterogeneity and potential shared mechanisms with CHD.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

The paper examines hemispheric asymmetry in structural covariance networks in ASD across 43 datasets, finding altered topological features in specific brain regions.

The paper identifies a recurrent SHANK3 frameshift variant in Autism Spectrum Disorder, showing de novo mutations and variable expressivity.

The paper integrates brain imaging and behavioral data to identify transdiagnostic subgroups in children with ASD, ADHD, or OCD, finding stable groups based on cortical thickness and inattention symptoms.

This study examines the boundary sharpness coefficient (BSC) as a measure of cortical microstructure in ASD, finding age, sex, and regional differences in BSC that correlate with clinical measures.

This study identifies altered structural brain asymmetry in autism spectrum disorder using a large-scale meta-analysis of MRI data from 1,774 ASD cases and 1,809 controls.