ReviewNature communications2024
Why experimental variation in neuroimaging should be embraced.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Dynamic and Static Resting-State Functional Connectivity of Canonical Networks in Military and Civilian Populations With Posttraumatic Stress Disorder and/or Mild Traumatic Brain Injury.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Optimizing functional connectivity scanning conditions for predicting autistic traits.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.Nature. Mental health · 2026Article
- Variation in moment-to-moment brain state engagement follows a consistent trajectory during development.Neuron · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- BrainScape: An open-source framework for integrating and preprocessing anatomical MRI datasets.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Pipeline variability: Friend or foe, depending on the goal: Comment on Germani et al. 2025.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Open-source platforms to investigate analytical flexibility in neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Revisiting the interpretation of axon diameter mapping using higher-order signal representations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.
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.