Tristan Glatard

This study benchmarks four structural MRI foundation models for age, sex, and BMI prediction, finding that 3D-Neuro-SimCLR and AnatCL outperformed baselines overall.

The paper quantifies numerical variability in MRI analyses of Parkinson's disease and shows it can significantly affect statistical conclusions and p-values.

This study improves brain age prediction by integrating features from multiple segmentation pipelines, showing that variability across pipelines reflects structured demographic differences.

The paper develops a deep-learning model to translate spatial topography maps into individualized connectomes, confirming its accuracy and ability to retain brain-cognition associations.

The paper shows that untrained convolutional neural networks can extract features from structural MRI data that perform as well as or better than pretrained models in three tasks across three datasets.

The paper evaluates numerical variability in functional MRI graph measures using the fMRIPrep pipeline and finds measurable impact on network outcomes.

Styx and NiWrap provide language-native wrappers for command-line tools, reducing complexity in pipeline development and enhancing portability across programming languages and environments.

This study examines how different versions of FreeSurfer affect structural brain measure estimation in Parkinson's disease and shows significant software-induced variability impacting clinical outcomes.

This study tests the CRUNCH hypothesis in aging by comparing neural activation and behavior in semantic tasks across young and older adults, finding no support for the predicted compensatory effects.

This study replicates findings from Hanganu et al. (2014) on Parkinson's disease neuroimaging biomarkers using PPMI data, finding partial replication of cortical thinning and cognitive decline associations.

This study attempts to reproduce and replicate a machine learning model for predicting Parkinson's disease progression using MRI data, finding lower performance than the original study.

NeuroCI evaluates result variability across neuroimaging pipelines and datasets using continuous integration to improve reproducibility.

This paper investigates how numerical uncertainty in brain imaging pipelines affects connectome stability and shows that induced variance can improve network reliability and classification of individual differences.

The paper uses Monte Carlo Arithmetic to perturb structural connectome pipelines, creating augmented datasets that improve age classification performance across multiple classifiers and preprocessing strategies.

BigBrainWarp simplifies integration of BigBrain histology with neuroimaging by providing tools for registration and cytoarchitectural feature access.

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.

This paper evaluates MS lesion segmentation algorithms using a new open-science infrastructure, finding that automatic methods lag behind human experts in detection and delineation.

This paper outlines best practices for open and reproducible neuroimaging research using MRI, providing guidelines for data analysis, sharing, and reporting.

This paper introduces NIDM-Results, a standardized format for sharing neuroimaging statistical results, enabling reproducibility and meta-analysis.

The paper introduces BIDS, a standard for organizing MRI datasets to improve data sharing and processing.