Jan Egil Nordvik

This study finds that smaller spared subcortical nuclei are linked to worse sensorimotor outcomes after stroke, varying by time since stroke and type of impairment.

The paper assesses LTP-like visual cortical plasticity in schizophrenia and bipolar disorder using VEP modulation, finding impaired N1b modulation in SZ and BDI but not BDII, linked to symptom severity.

This study evaluates the effectiveness of combining cognitive training with tDCS in stroke survivors, finding significant training effects but no additional benefit from tDCS, and no strong link between pre-training brain activation and cognitive gains.

This study investigates the relationship between post-stroke fatigue and brain disconnectivity in chronic stroke patients, finding no significant associations with disconnectome measures or lesion characteristics.

This study evaluates TVA parameters in chronic stroke patients to assess short-term memory capacity, processing speed, and perceptual threshold, showing group differences and cognitive training benefits.

This study uses advanced diffusion models and brain-age prediction to analyze white matter microstructure changes across adult lifespan, finding that advanced models provide sensitive measures of age-related changes.

The paper analyzes functional brain networks in sub-acute stroke patients and healthy controls during rest and attentive tracking, finding task-dependent connectivity changes but no group classification differences.

The paper examines VEP modulation in 415 healthy individuals, finding significant effects for several components and age-related correlations, supporting VEP as a measure of synaptic plasticity.

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

This study uses diffusion tensor imaging and machine learning to predict brain age in schizophrenia and bipolar disorder patients, finding age overestimation in both groups compared to controls.

The paper identifies genetic loci associated with variability in brain volumes and thickness, showing they are distinct from loci affecting mean volumes.

This study tests if brain age prediction can predict cognitive outcomes and response to training in stroke patients, but finds no significant associations.

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 finds that multiple sclerosis patients have higher brain age gaps and accelerated brain aging rates compared to healthy controls using MRI and machine learning.

This study uses drift diffusion modeling to identify the cognitive phenotype of post-stroke fatigue by analyzing response time data from the Attention Network Test.

The paper develops tissue-specific brain age prediction models using diffusion tensor imaging and morphometry to assess cognitive aging patterns and their deviations from normal trajectories.

This study identifies 15 genome-wide significant loci associated with hippocampal subfield volumes in 21,297 individuals, revealing distinct genetic determinants for different subfields.

The paper introduces ATLAS, a dataset of 304 T1-weighted MRIs with manual lesion segmentations to improve lesion segmentation algorithm accuracy.