ArticleNature communications2022
A guided multiverse study of neuroimaging analyses.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Biomarkers of stroke recovery using EEG-based resting-state functional connectivity: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration.Psychophysiology · 2025Pooled it
- Temporal stability of functional network configurations and its relationship to cognition and psychopathology in early psychoses.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Go figure: transparency in neuroscience images preserves context and clarifies interpretation.Nature methods · 2026Review
- From topography to connectome: Towards an integrated understanding of the resting brain.bioRxiv : the preprint server for biology · 2026Article
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Article
- 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
- Maximum entropy model reveals frequent brain state switching in a multiversal brain function analysis in early psychoses.bioRxiv : the preprint server for biology · 2026Article
- Multiverse analysis of machine learning: classification between groups defined by suicidal ideation screening status using acoustic features in college students.Frontiers in psychology · 2026Article
- A data-driven algorithm to determineMagnetic resonance in medicine · 2026Article
- The Systematic Multiverse Analysis Registration Tool for defining multiverse analyses.Royal Society open science · 2025Article
- Estimating the extent and sources of model uncertainty in political science.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- HCP Multi-Pipeline: a derived dataset to investigate analytical variability in fMRI.Scientific data · 2025Article
- Review
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Reproducibility of resting-state functional connectivity in healthy aging and brain injury: A mini-multiverse analysis.Network neuroscience (Cambridge, Mass.) · 2025Article
- A sensitivity analysis of preprocessing pipelines: Toward a solution for multiverse analyses.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.Imaging neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.
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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.