Esten Høyland Leonardsen

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.

The paper shows cortical thickness changes occur years before high amyloid levels in Alzheimer's disease, challenging the view that amyloid is the first imaging marker.

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 uses machine learning to classify successful and failed stopping trials in the stop signal task using EEG data, finding that the N2-P3 complex and other features contribute to classification 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 shows that higher genetic risk for Alzheimer's disease is linked to faster brain volume loss and memory decline in healthy adults.

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.

The paper estimates brain age gap (BAG) using neural networks and identifies eight genomic regions associated with BAG, finding causal relationships with Alzheimer's and bipolar disorder but not Parkinson's, depression, or schizophrenia.

This study finds that adult brain age is more influenced by early life factors like birth weight and polygenic scores than by longitudinal brain decline.