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Found 6 papers, 0 papers with code
Mitigating analytical variability in fMRI results with style transfer
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines.
On the validity of fMRI studies with subject-level data processed through different pipelines
Here, we investigate the impact of combining subject-level data processed with different pipelines in between-group fMRI studies.
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD.
The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis
Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different analytical choices in the processing of the fMRI data.
Uncovering communities of pipelines in the task-fMRI analytical space
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline.
On the benefits of self-taught learning for brain decoding
We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks.
