Cargando…

Objective Bayesian fMRI analysis—a pilot study in different clinical environments

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 negati...

Descripción completa

Detalles Bibliográficos
Autores principales: Magerkurth, Joerg, Mancini, Laura, Penny, William, Flandin, Guillaume, Ashburner, John, Micallef, Caroline, De Vita, Enrico, Daga, Pankaj, White, Mark J., Buckley, Craig, Yamamoto, Adam K., Ourselin, Sebastien, Yousry, Tarek, Thornton, John S., Weiskopf, Nikolaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4428130/
https://www.ncbi.nlm.nih.gov/pubmed/26029041
http://dx.doi.org/10.3389/fnins.2015.00168
_version_ 1782370845114499072
author Magerkurth, Joerg
Mancini, Laura
Penny, William
Flandin, Guillaume
Ashburner, John
Micallef, Caroline
De Vita, Enrico
Daga, Pankaj
White, Mark J.
Buckley, Craig
Yamamoto, Adam K.
Ourselin, Sebastien
Yousry, Tarek
Thornton, John S.
Weiskopf, Nikolaus
author_facet Magerkurth, Joerg
Mancini, Laura
Penny, William
Flandin, Guillaume
Ashburner, John
Micallef, Caroline
De Vita, Enrico
Daga, Pankaj
White, Mark J.
Buckley, Craig
Yamamoto, Adam K.
Ourselin, Sebastien
Yousry, Tarek
Thornton, John S.
Weiskopf, Nikolaus
author_sort Magerkurth, Joerg
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4428130
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-44281302015-05-29 Objective Bayesian fMRI analysis—a pilot study in different clinical environments Magerkurth, Joerg Mancini, Laura Penny, William Flandin, Guillaume Ashburner, John Micallef, Caroline De Vita, Enrico Daga, Pankaj White, Mark J. Buckley, Craig Yamamoto, Adam K. Ourselin, Sebastien Yousry, Tarek Thornton, John S. Weiskopf, Nikolaus Front Neurosci Neuroscience 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. Frontiers Media S.A. 2015-05-12 /pmc/articles/PMC4428130/ /pubmed/26029041 http://dx.doi.org/10.3389/fnins.2015.00168 Text en Copyright © 2015 Magerkurth, Mancini, Penny, Flandin, Ashburner, Micallef, De Vita, Daga, White, Buckley, Yamamoto, Ourselin, Yousry, Thornton and Weiskopf. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Magerkurth, Joerg
Mancini, Laura
Penny, William
Flandin, Guillaume
Ashburner, John
Micallef, Caroline
De Vita, Enrico
Daga, Pankaj
White, Mark J.
Buckley, Craig
Yamamoto, Adam K.
Ourselin, Sebastien
Yousry, Tarek
Thornton, John S.
Weiskopf, Nikolaus
Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title_full Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title_fullStr Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title_full_unstemmed Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title_short Objective Bayesian fMRI analysis—a pilot study in different clinical environments
title_sort objective bayesian fmri analysis—a pilot study in different clinical environments
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4428130/
https://www.ncbi.nlm.nih.gov/pubmed/26029041
http://dx.doi.org/10.3389/fnins.2015.00168
work_keys_str_mv AT magerkurthjoerg objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT mancinilaura objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT pennywilliam objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT flandinguillaume objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT ashburnerjohn objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT micallefcaroline objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT devitaenrico objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT dagapankaj objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT whitemarkj objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT buckleycraig objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT yamamotoadamk objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT ourselinsebastien objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT yousrytarek objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT thorntonjohns objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments
AT weiskopfnikolaus objectivebayesianfmrianalysisapilotstudyindifferentclinicalenvironments