Evidence map›Paper›PMID 26029041›Full record

ArticleFrontiers in neuroscience2015

Objective Bayesian fMRI analysis-a pilot study in different clinical environments.

Joerg Magerkurth, Laura Mancini, William Penny, Guillaume Flandin, John Ashburner, Caroline Micallef, Enrico De Vita, Pankaj Daga, Mark J White, Craig Buckley and 5 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Joerg MagerkurthDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK ; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.
Laura ManciniDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
William PennyWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.
Guillaume FlandinWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.
John AshburnerWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.
Caroline MicallefDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
Enrico De VitaDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
Pankaj DagaCentre for Medical Image Computing, University College London London, UK.
Mark J WhiteDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
Craig BuckleySiemens Healthcare Camberley, UK.
Adam K YamamotoDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
Sebastien OurselinCentre for Medical Image Computing, University College London London, UK.
Tarek YousryNeuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
John S ThorntonDepartment for Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London London, UK.
Nikolaus WeiskopfWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.

Funding

Medical Research Council MR/K000608/1Wellcome Trust 091593
6 · The paper itself

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.

Indexed as

bayesian statisticseffect sizefalse negativefalse positiveinterventional MRImotor cortexneurosurgical planningpassive fMRI

Identifiers

PMID26029041
PMCPMC4428130

What OpenQuestion holds

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Registered trials

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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.