Anders Eklund

This study investigates cognitive symptoms in post-COVID-19 condition with fatigue, finding subtle neurobiological changes not captured by standard assessments.

This paper develops a deep learning method to enhance the spatial resolution of diffusion MRI data, achieving better results than spline interpolation on Human Connectome Project data.

This paper develops CNN encoders for parametric t-SNE to analyze EEG data, producing low-dimensional representations that capture EEG characteristics and generalize to new data.

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

This paper introduces an anisotropic spatial filtering method for smoothing fMRI data in white matter, showing improved detection of anisotropic activations compared to isotropic Gaussian filters.

This study uses diffusion-informed white matter harmonics to analyze spatial dynamics of fMRI data, revealing subtle spatial modulations under functional load.

This paper uses convolutional neural networks to upsample or restore EEG channels, showing better performance than interpolation methods.

This paper introduces a Bayesian model for fMRI analysis that uses Gaussian process priors to estimate brain activity and hemodynamics without assuming a linear time-invariant system.

This paper introduces anatomically informed Bayesian spatial models for fMRI analysis that improve posterior probability maps by incorporating anatomical structure.

The paper shows that group differences in motion scrubbing can inflate false positives in functional connectivity studies due to differences in temporal auto correlation modeling.

This study evaluates six phase encoding based methods for correcting susceptibility distortions in diffusion MRI and finds DR-BUDDI and TOPUP to be the most accurate.

This paper quantifies uncertainty in MAP-MRI derived metrics using residual bootstrap methods, showing that resampling within each shell generates larger uncertainty than using all shells.

This study uses CycleGAN to reconstruct facial features from anonymized MRI data, showing face blurring is insufficient while face removal offers better but still reversible anonymization.

The paper revisits cluster inference methods in fMRI, finding that data cleaning and two-sided tests reduce false positive rates, and estimates 10% of fMRI studies use problematic methods.

The paper introduces a Bayesian non-central chi regression model for fMRI and DWI data, showing improved accuracy in localizing brain activity and estimating diffusion metrics compared to Gaussian models.

The paper introduces a Bayesian heteroscedastic GLM for fMRI data to better handle motion spikes, showing it detects more brain activity by modeling time-varying variance.

This paper introduces BIDS Apps, a framework for creating portable neuroimaging analysis tools compatible with BIDS data, improving reproducibility and ease of use.