Franziskus Liem

The paper compares three WMH segmentation algorithms on MRI data, finding BIANCA and UBO Detector outperform FreeSurfer in accuracy and correlation with Fazekas scores.

The paper predicts future cognitive decline using non-brain and multimodal brain imaging data in healthy and pathological aging, finding that non-brain data and structural MRI improve predictions.

This paper introduces fMRIPrep, a tool for preprocessing task-based fMRI data, and demonstrates its integration into a workflow for statistical analysis.

The paper combines MEG, MRI, and fMRI to predict age as a surrogate biomarker, showing additive effects and demonstrating robustness to missing data.

The paper examines hemispheric asymmetries in EEG and fMRI related to approach and avoidance behavior, finding no significant links to eating behavior or BMI.

The paper describes a collaborative neuroimaging initiative that brought together researchers to analyze 10,000+ MRI connectivity datasets and released 8,000+ MRI connectome maps.

The paper examines how resting heart rate variability (HRV) relates to brain structure and function across different age groups, finding age-dependent changes in functional connectivity.

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

This study predicts brain-age from multimodal imaging data and finds it captures cognitive impairment with robustness to motion and generalizability across datasets.