Jacob J. Michaelson

Polygenic score variants in HAQERs (Human Ancestor Quickly Evolved Regions, <0.1% of the genome) predict spoken language but not nonverbal IQ; language associations replicate in SPARK, ABCD and UK Biobank.

This study identifies genetic loci associated with migration distance and links them to neurodevelopmental genes, cognition, and economic growth.

This study identifies the transcriptional changes in the retrosplenial cortex during spatial memory consolidation using spatial transcriptomics and shows the role of excitatory neurons in memory formation.

The paper identifies eight sleep archetypes in autism using a large cohort and finds genetic associations with educational attainment, BMI, and ADHD, while suggesting distinct genetic drivers from the general population.

This study examines how prenatal stress affects dorsal striatum cell types and behaviors in mice, identifying gene expression changes linked to neurodevelopmental disorders.

NEUROeSTIMator uses deep learning to estimate neuronal activation from single-cell and spatial transcriptomic data, showing robustness across species and brain regions.

The paper examines polygenic scores' associations with gender diversity and mental health in two studies, finding stronger links with cognitive performance than neuropsychiatric conditions.

This study uses spatial transcriptomics to identify distinct gene expression signatures in hippocampal subregions during memory consolidation and shows their functional relevance.

This study identifies the gene expression changes in the human brain following electrical stimulation, revealing non-neuronal cell involvement and a conserved gene signature.

The paper examines how environmental adversity moderates polygenic score effects on childhood behavioral problems using data from two U.S. cohorts.

This study finds smaller cerebellar cortex volumes in bipolar disorder type I patients, with medication use potentially influencing cerebellar structure.

The paper investigates the genetic liabilities of autism subscales, finding heritable, pleiotropic traits with sex-specific effects and stronger genetic signals in the RBS-R instrument.

The paper identifies ER chaperone genes as effectors of long-term memory and shows their role in mitigating memory deficits in a tau-based ADRD mouse model.

This study shows that Akirin2 loss in excitatory cortical neurons leads to neurodegeneration via p53 activation, highlighting its role in neuronal maintenance and potential relevance to neurodegenerative diseases.

This study identifies 1,146 dysregulated genes in the hippocampus following acute sleep deprivation, revealing distinct transcriptomic changes in RNA splicing, cell adhesion, and synaptic processes.

This study shows that the CBP KIX domain is essential for long-term memory and circadian rhythms by regulating CREB and CLOCK target genes.

VCFdbR converts large-scale VCF files into SQLite databases for efficient variant querying and filtering.

This paper introduces forecASD, a machine learning method that identifies potential autism risk genes using genome-scale data, showing improved prediction of gene-disrupting mutations and differential expression.

The paper reports exome sequencing of 457 ASD families, identifying 10.4% with known ASD genes and 3.4% with possibly associated variants, and provides statistical support for 34 ASD risk genes using the TADA framework.

This study introduces BRAVA, a resource to estimate the spatio-temporal context of genetic mutations in the human brain, revealing both neurodevelopmental and late-onset components for schizophrenia-associated loci.

The paper identifies clinically relevant genetic dysfunction in ASD risk genes by analyzing whole-exome data from 2,381 individuals, finding associations between specific variants and cognitive symptoms.

TiSAn predicts tissue-specific relevance of genetic variants using machine learning, outperforming GenoSkyLine in prioritizing variants for heart and brain tissues.