Lars Tjelta Westlye

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.

This study compares thalamic nucleus volumes across multiple psychiatric and neurological disorders using MRI, finding significant volume reductions in specific regions associated with different conditions.

The paper uses a convolutional neural network on longitudinal MRI data to derive latent brain representations linked to adolescent phenotypic variation.

The paper establishes a normative reference for large-scale human brain dynamics using resting-state fMRI data from over 10,000 individuals, revealing systematic reorganization across development and ageing and disorder-specific deviations in mental health conditions.

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This study uses Mendelian Randomization to identify causal relationships between neuroimaging phenotypes and bipolar disorder, finding that white matter tract variations have larger causal effects on BD.

The paper links pregnancy- and birth-related risk factors to multivariate cortical brain MRI features in children using statistical analysis methods.

This paper addresses structured missingness in biomedical data by introducing a hierarchical imputation framework and advocating for alternative evaluation metrics.

The paper uses normative modeling to assess cerebellar morphology differences in 15q11.2 CNV carriers compared to non-carriers, finding modest and individualized effects.

This study shows brain age metrics from MRI do not reliably predict future brain changes but highlight modality-specific differences as potential cross-sectional markers.

The paper examines white matter volume deviations in psychiatric disorders using normative modeling, finding heterogeneous patterns with some aggregation in autism and schizophrenia.

The paper examines how white matter microstructure, assessed via diffusion MRI, correlates with age, BMI, blood pressure, and genetic factors across different life stages.

The paper identifies extensive phenotypic and genetic links between systemic metabolism and brain morphology using UK Biobank data.

This paper improves cerebrovascular brain age predictions by harmonizing arterial spin labeling (ASL) features across multiple cohorts using various methods, showing that ASL-specific harmonization enhances model performance.

The paper maps cerebellar anatomical variations across the lifespan and clinical illnesses, revealing heterogeneity in conditions like autism, schizophrenia, and Alzheimer's.

The paper examines how different types of early-life adversity relate to brain age gaps in children, finding some types are linked to younger-looking brains and others to older-looking brains.

The paper shows that shuffling FreeSurfer data across versions improves brain age prediction performance and generalizability.

This study examines how brain-age differences (PAD) change over time after traumatic brain injury (TBI) using a large multi-cohort dataset.

This paper extends hierarchical Bayesian regression to model non-Gaussian data with skewness and kurtosis, showing improved performance on neuroimaging datasets.

The paper evaluates age-related changes in brain asymmetry using MRI data from 48,040 UK Biobank participants and introduces hemispheric brain age as a marker of asymmetry.

This study identifies genetic overlap between functional brain connectivity and six psychiatric disorders using multivariate genome-wide association analysis in 30,701 UK Biobank individuals.

This study examines structural brain heterogeneity in schizophrenia using multimodal MRI data from the ENIGMA dataset, finding higher frontotemporal heterogeneity and left parahippocampal homogeneity in patients.

The paper examines how genetic and brain similarities predict childhood anthropometric and socioeconomic traits using a mixed model framework in the ABCD cohort.

This paper evaluates brain age predictions across repeated time points and finds small within-subject correlations, field strength influence, and model biases.

This study shows that free-water volume fraction in white matter increases non-linearly with age, particularly after 60, with region-specific variations and reduced variability when using FW-compensated MR signals.

This study identifies 72 genetic loci associated with basal ganglia volumes and finds significant genetic overlaps with Parkinson's disease, schizophrenia, and migraine.

This study compares data-driven and theory-based biomarkers for predicting chronic motor outcomes after stroke, finding data-driven models perform better, especially when using structural disconnection measures.

The paper analyzes white matter aging using diffusion MRI data from UK Biobank, finding that fornix microstructure is central to brain age prediction.