Espen Hagen

The paper describes the MoBaPsychGen pipeline for quality controlling and imputing genotyping data from the MoBa cohort, identifying extensive family relationships across 207,569 individuals.

Bivariate GSA-MiXeR is a tool that models genetic correlations between two traits to identify genomic regions associated with psychiatric and neurodegenerative disorders using single-cell RNA sequencing data.

This study identifies shared genetic loci between common epilepsies and general cognitive ability using LDSC, MiXeR, and conjFDR analyses.

The paper introduces trivariate MiXeR to quantify genetic overlap between three complex phenotypes using GWAS summary statistics, demonstrating its ability to detect complex patterns of genetic overlap.

MiXeR-Pred improves polygenic prediction by leveraging genetic overlap between complex phenotypes, outperforming primary GWAS and MTAG approaches.

The paper uses biophysical simulations of mouse primary visual cortex to show that current sinks and sources are shaped by both synaptic weights and dendritic placement, with distinct contributions from thalamocortical inputs and cortical feedback.

This study identifies 14 novel opioid use disorder loci and 7 shared loci with schizophrenia, bipolar disorder, and major depression using genome-wide association summary statistics.

This paper introduces a framework for predicting extracellular brain signals from neuronal network models, showing that linearized biophysical models can accurately capture spatiotemporal dynamics of these signals.

This paper introduces RippleNet, a deep LSTM-based model for detecting SPW-R events from LFP data, achieving high precision with minimal manual curation.

This paper presents a method to compute current dipoles from simulated neural activity to model EEG and MEG signals with biophysical detail.

This paper uses convolutional neural networks to estimate synaptic connection weights in a neuronal network model from local field potential (LFP) signals.

LFPy calculates extracellular brain signals from simulated neuron models using Python and NEURON.

The paper extends LFPy to model neural networks and compute LFP, ECoG, EEG, and MEG signals using biophysically accurate forward models and head models.

A dense multi-electrode array spike sorter that triages collisions, clusters clean waveforms with a coreset Dirichlet process mixture, then deconvolves, giving more stable clusters and faster template learning than KiloSort.