Lukas Nics

The paper investigates the cellular and mitochondrial factors influencing regional glucose metabolism in the human cortex using multimodal data integration.

The paper presents an open-access framework for PET attenuation correction using pseudo-CT methods with a database of MRI-CT scans, showing that UCL achieves the lowest quantification error.

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 examines how acute psychosocial stress reconfigures brain metabolism and network organization using PET/MRI, revealing changes in the default mode network and its interactions with other networks.

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 paper compares molecular connectivity (MC) and covariance (mCov) methods using fPET data to assess metabolic associations between brain regions, finding that different methods perform better at varying temporal resolutions.

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.

The paper introduces a standardized nomenclature for PET-based molecular connectivity and covariance to improve reproducibility and interpretation across imaging modalities and radiotracers.

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.