ArticleFrontiers in neuroscience2015
Objective Bayesian fMRI analysis-a pilot study in different clinical environments.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence for non-selective response inhibition in uncertain contexts revealed by combined meta-analysis and Bayesian analysis of fMRI data.Scientific reports · 2022Pooled it
- Evaluating Approaches for Inference Testing of Whole-Brain Densely Sampled Single-Subject Task fMRI Data.bioRxiv : the preprint server for biology · 2026Article
- Article
- Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference.Frontiers in neuroinformatics · 2021Article
- Acquisition of sensorimotor fMRI under general anaesthesia: Assessment of feasibility, the BOLD response and clinical utility.NeuroImage. Clinical · 2019Article
- Using SPM 12's Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users.Frontiers in neuroinformatics · 2018Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Functional MRI (fMRI) used for neurosurgical planning delineates functionally eloquent brain areas by time-series analysis of task-induced BOLD signal changes. Commonly used frequentist statistics protect against false positive results based on a p-value threshold. In surgical planning, false negative results are equally if not more harmful, potentially masking true brain activity leading to erroneous resection of eloquent regions. Bayesian statistics provides an alternative framework, categorizing areas as activated, deactivated, non-activated or with low statistical confidence. This approach has not yet found wide clinical application partly due to the lack of a method to objectively define an effect size threshold. We implemented a Bayesian analysis framework for neurosurgical planning fMRI. It entails an automated effect-size threshold selection method for posterior probability maps accounting for inter-individual BOLD response differences, which was calibrated based on the frequentist results maps thresholded by two clinical experts. We compared Bayesian and frequentist analysis of passive-motor fMRI data from 10 healthy volunteers measured on a pre-operative 3T and an intra-operative 1.5T MRI scanner. As a clinical case study, we tested passive motor task activation in a brain tumor patient at 3T under clinical conditions. With our novel effect size threshold method, the Bayesian analysis revealed regions of all four categories in the 3T data. Activated region foci and extent were consistent with the frequentist analysis results. In the lower signal-to-noise ratio 1.5T intra-operative scanner data, Bayesian analysis provided improved brain-activation detection sensitivity compared with the frequentist analysis, albeit the spatial extents of the activations were smaller than at 3T. Bayesian analysis of fMRI data using operator-independent effect size threshold selection may improve the sensitivity and certainty of information available to guide neurosurgery.
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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.