Gabriele Lohmann

UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

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

Beyond ROIs: Laminar fMRI Mapping with Cylarim

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

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

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.

The 3D Structural Architecture of the Human Hand Area is Non-Topographic

The paper uses 7T MRI to analyze 3D structural features in the human hand area of S1 and finds no topographic organization, unlike in some monkeys.

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

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.

Jumping over Baselines with New Methods to Predict Activation Maps from 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.

Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain

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

Inflated False Negative Rates Undermine Reproducibility In Task-Based fMRI

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.