Avram J. Holmes

The paper identifies attentional lateralization phenotypes and multidomain brain organization profiles linked to heritable structural and functional connectivity patterns.

97% of 184 high-impact biomedical AI studies compare cross-validated models with tests assuming fold independence; in 69% of re-analyzable studies a significant comparison does not survive correction.

This study identifies two subtypes of psychosis using SNF, showing distinct cortical and cellular signatures linked to different clinical and neuroanatomical features.

The paper examines brain network dynamics in individuals with and without psychiatric diagnoses, finding that these dynamics can differentiate symptom profiles more effectively than diagnostic categories.

The paper investigates how neuroanatomical variability relates to impulsivity in youth using data from the ABCD Study, finding sex-specific and dynamic associations across multiple brain features.

This paper improves brain connectome analysis by incorporating global and local features, enhancing individual identification and phenotype prediction.

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

This study identifies genetic and cellular factors associated with age-related changes in white matter microstructure using GWAS and transcriptomic data from UK Biobank.

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 shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

The paper examines how stable and changing brain network features predict cognitive development in adolescents using rs-fMRI and cognitive data from the ABCD Study.

The paper identifies latent impulsivity dimensions in adolescents using PCA and links them to substance use and brain network connectivity in the ABCD study.

The paper develops a framework using optimal control theory to estimate brain structure-function interactions from MRI data, showing correlations between self-inhibition parameters and neural timescales across species.

This study identifies molecular and cellular asymmetries in the human brain that underpin lateralized functions like language and visuospatial attention using multimodal data integration.

This paper introduces DELSSOME, a deep learning framework that accelerates biophysical brain circuit model optimization, enabling faster parameter estimation and lifespan trajectory analysis of cortical E/I ratios.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

This study examines the association between parenthood and brain function in mid- to late-life, finding that higher parity is linked to increased functional connectivity and counteracts age-related declines.

The paper shows that cell type distributions in the cortex are spatially aligned with functional organization patterns from fMRI.

The paper examines how specific lifetime stressor characteristics and brain network connectivity predict anxiety and depression symptoms in adolescents.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This paper identifies widespread functional connectivity differences in cocaine use disorder (CUD) and links these patterns to dopamine D2/3 receptor densities across the brain.

The paper links ventral attention network connectivity to cortical maturation and cognitive ability in children using independent datasets.

This study shows that sex and gender are associated with distinct functional brain networks in children, with sex linked to unimodal networks and gender to heteromodal networks.

The paper examines sex differences in functional brain network connectivity associated with psychotic-like experiences in children using the ABCD Study dataset.

This paper adapts meta-matching frameworks to translate phenotypic prediction models from large to small MRI datasets, showing significant performance improvements over classical methods.

The paper introduces multilayer meta-matching to improve phenotypic prediction in small datasets by translating models from multiple large datasets, outperforming classical methods and transfer learning.

The paper estimates the whole-cortex excitation-inhibition ratio using fMRI and shows it declines during youth, with lower ratios linked to better cognitive performance in children.

The paper shows that atypical language lateralization is linked to global shifts in cortical functional organization, with genetic factors contributing to these differences.

The paper shows that brain network features predicting internalizing and externalizing traits differ between children and adults, with more similarity within categories in children and distinct features in adults.

This study investigates the relationship between prediction accuracy and feature importance reliability in neuroimaging data, showing that feature importance reliability can be high with sufficient sample size and is positively correlated with prediction accuracy.