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A novel Bayesian approach to accounting for uncertainty in fMRI-derived estimates of cerebral oxygen metabolism fluctuations

Abstract:
Calibrated blood oxygenation level dependent (BOLD) imaging is a multimodal functional MRI technique designed to estimate changes in cerebral oxygen metabolism from measured changes in cerebral blood flow and the BOLD signal. This technique addresses fundamental ambiguities associated with quantitative BOLD signal analysis; however, its dependence on biophysical modeling creates uncertainty in the resulting oxygen metabolism estimates. In this work, we developed a Bayesian approach to estimating the oxygen metabolism response to a neural stimulus and used it to examine the uncertainty that arises in calibrated BOLD estimation due to the presence of unmeasured model parameters. We applied our approach to estimate the CMRO2 response to a visual task using the traditional hypercapnia calibration experiment as well as to estimate the metabolic response to both a visual task and hypercapnia using the measurement of baseline apparent R2' as a calibration technique. Further, in order to examine the effects of cerebral spinal fluid (CSF) signal contamination on the measurement of apparent R2', we examined the effects of measuring this parameter with and without CSF-nulling. We found that the two calibration techniques provided consistent estimates of the metabolic response on average, with a median R2'-based estimate of the metabolic response to CO2 of 1.4%, and R2'- and hypercapnia-calibrated estimates of the visual response of 27% and 24%, respectively. However, these estimates were sensitive to different sources of estimation uncertainty. The R2'-calibrated estimate was highly sensitive to CSF contamination and to uncertainty in unmeasured model parameters describing flow-volume coupling, capillary bed characteristics, and the iso-susceptibility saturation of blood. The hypercapnia-calibrated estimate was relatively insensitive to these parameters but highly sensitive to the assumed metabolic response to CO2.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2016.01.001

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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author


Publisher:
Elsevier
Journal:
NeuroImage More from this journal
Volume:
129
Pages:
198-213
Publication date:
2016-01-11
Acceptance date:
2016-01-01
DOI:
EISSN:
1095-9572
ISSN:
1053-8119


Language:
English
Keywords:
Pubs id:
pubs:597929
UUID:
uuid:03d9ec67-58af-4d59-b887-8cadd957bb80
Local pid:
pubs:597929
Source identifiers:
597929
Deposit date:
2016-02-29
ARK identifier:

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