ArticleImaging neuroscience (Cambridge, Mass.)2025
Open-source platforms to investigate analytical flexibility in neuroimaging.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- AutoML-Multiverse: An Instability-Aware Framework for Quantifying Analytic Variability in Alzheimer's Disease Machine-Learning Studies.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Researchers in brain imaging have access to a multitude of analysis tools, many of which carry out the same or similar tasks but yield different results when applied to the same data. This analytical flexibility often undermines reproducibility and raises concerns about the robustness of neuroimaging studies. However, the array of software packages to investigate and address analytical flexibility is decentralized, scattered, and not well documented. Consequently, researchers often lack the necessary information and protocols to buttress the reliability of their findings across analytical tools. This review catalogs and describes software platforms (i.e., software or computational libraries) that can be used to address result variability arising from computational pipelines and environments and explores the use of computing platforms and neuroimaging pipeline frameworks in addressing this issue. This study offers guidance to the research community on accessing, understanding, and utilizing these platforms to address brain imaging analytical flexibility. Additionally, the article provides specific recommendations tailored to different user groups, considering the tools they intend to use with these platforms and their computational constraints.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.