Reetta K. Kälviäinen

The paper develops a custom microelectrode array for recording extracellular action potentials and local field potentials from human hippocampal slices to study temporal lobe epilepsy.

The paper develops a protocol to characterize epileptogenic tissue using multimodal data from surgical samples, aiming to improve diagnosis and treatment of drug-resistant epilepsy.

The paper examines associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

This study conducts a genome-wide meta-analysis across three biobanks to identify new genetic loci associated with lumbar disc herniation, expanding the known genetic landscape.

This study shows that epilepsy polygenic risk scores (PRSs) can predict epilepsy risk across lifetimes and after seizure events in a large Finnish cohort.

This study examines age-related changes in grey and white matter in TLE patients across the adult lifespan, finding accelerated aging effects after age 55.

The paper demonstrates the feasibility of conducting clinical recall studies from the FinnGen biobank to collect customized clinical data from participants with Alzheimer's disease or mild cognitive disorder.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

This study identifies divergent patterns of atypical cortical asymmetry and regional atrophy in temporal lobe epilepsy using MRI data from a large multi-site cohort.

This study links structural network changes in epilepsy syndromes to risk gene expression patterns using ENIGMA data.

The paper identifies microglial activation as a factor contributing to cortical thinning in epilepsy by integrating neuroimaging and gene expression data.

The paper introduces a multivariate GWAS framework that identifies functional variants and disease associations by transforming multivariate results into univariate phenotypes for follow-up analysis.

This study identifies network associations with atrophy patterns in epilepsy using neuroimaging and connectome analysis across 1,021 patients and 1,564 controls.