René S. Kahn

The paper examines the relationship between white matter microstructure, measured by fractional anisotropy, and transition to psychosis in individuals at clinical high risk, finding directionally consistent but small associations.

The paper examines longitudinal white matter changes in early psychosis using diffusion tensor imaging and finds no significant group differences or associations with symptom progression, but links antipsychotic medication dose to lower fractional anisotropy.

The paper maps gene expression and chromatin accessibility QTLs in fibroblasts from European and Latin American ancestries, finding high concordance and shared regulatory mechanisms.

This study links sulcal widening in schizophrenia to network hubs and genes involved in synaptic signaling and energy metabolism.

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

The paper develops a DNA methylation predictor for C9orf72 repeat expansion carriers using EPICv2 data, achieving 98.6% accuracy and identifying four carriers in an independent cohort.

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 morphometric similarity networks in health and schizophrenia, finding reduced default mode network similarity in schizophrenia but minimal individual deviations over time.

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 study compares neuronal phenotypes from monozygotic twins discordant for schizophrenia, finding differences in synaptic activity and neuronal maturation between affected and unaffected siblings.

The paper investigates epigenetic age acceleration in schizophrenia, finding age- and sex-specific effects and associations with polygenic risk scores.

This study uses bulk RNA-Seq from blood samples to estimate cell type proportions and eQTLs, revealing cell-type specific genetic associations with psychiatric disorders and medication effects.

This study examines circadian genetic architecture in fibroblasts to assess its contribution to bipolar disorder risk, finding limited gene expression patterns and no significant genetic associations with BD.

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

This study examines brain morphometric deviations in individuals at clinical high-risk for psychosis and finds they largely overlap with healthy variation and are not strongly associated with symptom severity or conversion to psychosis.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This study evaluates 21 machine learning algorithms for predicting brain age from sMRI data in youth, finding Extreme Gradient Boosting, Random Forest Regression, and Support Vector Regression with Radial Basis Function Kernel as the top performers.

This paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

Exomes of 24,248 schizophrenia cases and 97,322 controls implicate ultra-rare damaging variants in 10 genes, including GRIN2A and GRIA3, with odds ratios of 3 to 50.

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.

This study shows that reducing RNA-seq coverage while increasing sample size improves eQTL discovery power.

The paper examines longitudinal allometric scaling of sulcal morphology in health and schizophrenia, finding steeper age-related declines in SZ compared to healthy individuals.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

The paper analyzes the transcriptome of human microglia across brain regions, aging, and disease to identify genetic effects on gene expression and splicing, linking them to neurological disorders.

The paper examines sex differences in lifespan trajectories and variability of human sulcal and gyral morphology using longitudinal neuroimaging data.

The paper reports exome sequencing findings in bipolar disorder, identifying AKAP11 as a shared risk gene with schizophrenia through ultra-rare protein-truncating variants.

This study compares machine learning algorithms for predicting brain age in schizophrenia, finding that most linear methods perform similarly but OLS regression is less accurate, leading to variability in brainPAD estimates.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper identifies a subgroup of schizophrenia patients with extreme cognitive deficits by analyzing sulcal width dissimilarity using the PBSI-SW metric.