Klaus Scheffler

This study shows that concept learning can rapidly alter early visual cortex responses and microstructure, independent of prior knowledge or hippocampal reactivation.

The paper evaluates quantitative MRI metrics in RRMS patients and healthy controls to identify white matter alterations using a multi-parametric pc-bSSFP framework.

This study examines how episodic memory network dysfunction relates to AD biomarkers, neurodegeneration, and cognition using longitudinal fMRI data from the DZNE study.

This study examines functional connectivity of thalamic nuclei during sensorimotor tasks using fMRI at 9.4 Tesla, finding that thalamic nuclei exhibit task-specific connectivity patterns rather than fixed roles.

UltraCortex provides high-resolution 9.4 T brain MRI data and validated segmentations, addressing gaps in existing repositories.

Cylarim enables unbiased laminar fMRI mapping by eliminating reliance on predefined ROIs, allowing large-scale, depth-resolved analysis of cortical activity.

This study compares the macrovascular contribution of different fMRI sequences at 9.4 Tesla, finding that bSSFP shows lower macrovascular signal dependence than EPI sequences.

The paper links cognitive reserve to brain activity during memory formation, showing it moderates the impact of Alzheimer's pathology on cognitive performance and predicts slower decline.

This paper introduces SMAS, a Bayesian VAE-based AD score from structural MRI data, showing strong correlations with cognitive decline and brain atrophy markers, and outperforming existing measures in predicting AD.

The paper examines if fractional anisotropy of the fornix mediates the link between Mediterranean diet adherence and verbal memory decline in older adults over four years.

This paper provides a protocol for 3D imaging of unstained human brain tissue using synchrotron radiation phase-contrast microtomography, enabling detailed anatomical analysis at the cellular level.

The paper identifies diagnostic images that correlate with Alzheimer's biomarkers and show differential neural processing in MCI patients compared to healthy controls.

This paper improves fMRI-based intelligence prediction by using semi-blind machine learning with educational level as supplementary data, achieving better accuracy even with small sample sizes.

This paper introduces a new fMRI method called SELINE to improve laminar-specific BOLD signal detection by reducing vein effects and improving spatial resolution.

The paper shows that sleep enhances memory retention and brain activity in specific regions for memories consolidated during wakefulness, and that sleep integrates memory systems complementary to wakefulness consolidation.

This study finds that lifelong musical activity is linked to better cognitive abilities and preserved brain structure in older adults, suggesting it may enhance cognitive resilience.

The paper uses Gaussian process multikernel modeling to predict memory performance and CSF biomarkers in ageing and Alzheimer's disease using MRI and other data.

The paper uses 9.4T fMRI to study motion signal integration in human V3A and V6+ areas across cortical depth, finding differential laminar processing for retinal and objective motion.

This paper develops a videography-based method to estimate skeletal pose and joint kinetics in freely-moving rodents, validated by direct limb placement measurements.

This paper introduces CBF-fMRI using phase-contrast imaging to map blood velocity in single vessels, showing comparable velocity changes in arterioles and venules.

This paper improves the sensitivity and resolution of in-vivo magnetic resonance current density imaging (MRCDI) through methodological advancements and validates it using phantom and human brain experiments.

The paper introduces a machine learning algorithm that predicts intelligence from task-based fMRI data with high accuracy and faster scan times than resting state fMRI.

The paper evaluates methods for predicting task activation maps from resting-state fMRI and finds that most underperform compared to simple baselines.

This study develops depth metrics to characterize tissue thickness and depth relationships in the human dorsal midbrain using functional MRI data.

This paper introduces two new implementations of eigenvector centrality mapping for fMRI data to address computational and interpretational limitations of existing methods.

The paper investigates the feasibility of functional MRI at ultralow magnetic field using changes in cerebral blood volume and predicts signal-to-noise ratios for different experimental conditions.

The paper assesses reproducibility in task-based fMRI by evaluating false negative rates across different sample sizes and introduces a new statistical method to improve reproducibility.