Aristotle N. Voineskos

This study examines BOLD signal variability in autism and schizophrenia, finding reduced variability in these groups compared to controls, linked to poorer cognitive and functional outcomes.

The paper uses fALFF to compare brain activity patterns in ASD, SSD, and TDC groups, finding shared neurobiological features and variability differences.

BOONStim uses Bayesian optimization to improve TMS targeting by integrating fMRI and TMS simulation tools, showing faster convergence and accuracy compared to grid optimization.

The paper examines individual variability in functional connectivity during rest and an emotional-processing task in schizophrenia spectrum disorders and controls, identifying predictors like diagnosis and social cognition.

The paper introduces SAN, a method to harmonize cortical thickness data across scanners by normalizing spatial autocorrelation, improving data quality and reproducibility.

This study investigates how structural brain alterations in schizophrenia relate to network architecture, finding hub susceptibility and shared epicenters across disease stages and disorders.

The paper introduces CLEAN-V, a method for testing variance components in neuroimaging data, showing improved power for detecting test-retest reliability and heritability.

This study compares volume-based, surface-based, and personalized surface-based approaches to identify dysconnectivity patterns in schizophrenia spectrum disorders using resting-state fMRI.

RELIEF removes inter-scanner biases in neuroimaging data by using multivariate factorization, improving data integration and statistical power.

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

The paper examines age-related variations in cortical thickness across different life stages using gene expression data from a large neuroimaging dataset.

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

This paper identifies potential nootropic drug targets by integrating large-scale cognitive GWAS and transcriptomic data to find credible druggable genes.

The paper analyzes brain activity variability across cognitive tasks using hierarchical clustering and dimensionality reduction, finding that individual differences are best represented multidimensionally.

The paper develops a transcriptome-based polygenic risk score (T-PRS) linking depression-related gene expression changes to neural face processing and anhedonia in a non-clinical sample.

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 identifies brain regions and genes associated with anorexia nervosa using transcriptomic data and genetic variants.

The paper examines sex differences in brain structure variability across the lifespan, finding greater regional variability in males and age-by-sex interactions in variability patterns.

The paper identifies structural neuroimaging signatures of anorexia nervosa symptoms and risk factors in a sample enriched for disease vulnerability using MRI and DTI data.

This study examines white matter microstructure changes in women with acute and remitted anorexia nervosa using diffusion MRI, finding increased diffusivity indices in affected groups compared to healthy controls.

The paper uses pleiotropic meta-analysis to identify genetic loci associated with cognition, education, and schizophrenia, revealing distinct biological mechanisms for concordant and discordant genetic correlations.

The paper introduces ciftify, a framework to adapt HCP surface-based analysis methods to legacy MR data using CIFTI format.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This study identifies 206 new genomic loci and 1,041 genes associated with intelligence using a large GWAS meta-analysis of 279,930 individuals.

This study introduces PINT, a method to identify individual-specific brain network variations in ASD and TD controls, finding greater variability in ASD and reduced hypo-connected regions after PINT application.

This study performs a large-scale GWAS of cognitive ability, identifies 70 genomic loci, and finds tissue-specific gene expression and potential drug targets.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.