Sebastian Klug

The paper examines network-specific metabolic costs of functional connectivity in the human brain using PET/MR data, revealing differences in metabolic demand across brain networks and changes in Alzheimer's disease.

This study evaluates the test-retest reliability of fPET and fMRI during reward processing, finding comparable reliability but differing in target areas and dynamic assessment capabilities.

This paper develops a noise correction method for high-temporal resolution fPET data, showing it improves metabolic connectivity estimation across different scanners and frequency bands.

This study evaluates motion correction techniques for high temporal resolution fPET and finds that neither deep learning nor sliding window averaging significantly improves motion artifact mitigation compared to standard methods.

This study evaluates filtering techniques for fPET data, finding that dNLM and MRI-MRF filters improve signal-to-noise ratio and test-retest reliability, with dNLM reducing sample size needs.

This paper introduces a method to assess cerebral glucose metabolism changes during tasks without blood sampling, using GLM beta values and percent signal change, showing strong correlation with standard CMRGlu measurements.

This study uses high-temporal resolution PET/MRI to show synchronized metabolic and hemodynamic brain responses during working memory tasks.

The paper investigates how metabolic demands of the posteromedial default mode network (DMN) are influenced by task-positive networks during attention-demanding tasks.

This study investigates how learning affects brain network interactions and metabolic demands, finding that resting-state and task-specific changes in functional connectivity and glucose metabolism are complementary for skill acquisition.