ArticleNeuroinformatics2019
Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling.
Article in Neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility.NeuroImage · 2023Pooled it
- Go figure: transparency in neuroscience images preserves context and clarifies interpretation.Nature methods · 2026Review
- Task-evoked deactivations: dissociation between BOLD fMRI and FDG.bioRxiv : the preprint server for biology · 2026Article
- Large reaching datasets quantify the impact of age, sex/gender, and experience on motor control.Communications psychology · 2026Article
- White matter microstructural and macrostructural profiles during midlife reveal sex differences between men and women at different menopausal stages.Scientific reports · 2025Article
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- Article
- Evidence of functional connectivity disruptions between auditory and non-auditory regions in adolescents living with HIV.Frontiers in systems neuroscience · 2025Article
- How Distributed Subcortical Integration of Reward and Threat May Inform Subsequent Approach-Avoidance Decisions.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Change in striatal functional connectivity networks across 2 years due to stimulant exposure in childhood ADHD: results from the ABCD sample.Translational psychiatry · 2024Observational
- Change in Striatal Functional Connectivity Networks Across Two Years Due to Stimulant Exposure in Childhood ADHD: Results from the ABCD Sample.medRxiv : the preprint server for health sciences · 2024Article
- Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure.Molecular psychiatry · 2024Article
- Neural response to monetary incentives in acquired adolescent depression after mild traumatic brain injury: Stage 2 Registered Report.Brain communications · 2024Article
- Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging.Human brain mapping · 2024Article
- APOE ε4 influences within and between network functional connectivity in posterior cortical atrophy and logopenic progressive aphasia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Reduced vmPFC-insula functional connectivity in generalized anxiety disorder: a Bayesian confirmation study.Scientific reports · 2023Article
- Visual recognition in rhesus monkeys requires area TE but not TEO.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Hierarchical Bayesian modeling of the relationship between task-related hemodynamic responses and cortical excitability.Human brain mapping · 2023Article
- Longitudinal volumetric evaluation of hippocampus and amygdala subregions in recent trauma survivors.Molecular psychiatry · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.
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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.