Nils Inge Landrø

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 identifies genetic loci associated with brainstem volumes and finds genetic overlaps with psychiatric and neurological disorders.

This study models a network of depression symptoms and brain structures to explore their associations and clarify biological and symptomatic heterogeneity in depression.

This study uses machine learning on brain connectomics data to predict cognitive and mental health traits and their polygenic scores in a large population sample.

This study fuses structural and functional brain imaging data in depression using linked independent component analysis but finds limited associations with case-control status and symptom loads.

The paper estimates brain age gaps using MRI and machine learning in individuals with and without brain disorders, finding heritability and genetic overlap with common disorders.

This study shows that brain age gap from MRI correlates with processing speed in early multiple sclerosis.

The paper identifies symptom-based subgroups in depression and anxiety using clustering and finds distinct static brain connectivity patterns associated with these subgroups.