Front. Artif. Intell.Frontiers in Artificial IntelligenceFront. Artif. Intell.2624-8212Frontiers Media S.A.61424510.3389/frai.2021.614245Artificial IntelligenceCorrectionCorrigendum: A Frequency-Domain Machine Learning Method for Dual-Calibrated fMRI Mapping of Oxygen Extraction Fraction (OEF) and Cerebral Metabolic Rate of Oxygen Consumption (CMRO2)Germuska et al.Corrigendum: CMRO2 Frequency-Domain Machine LearningGermuskaMichael1*ChandlerHannah Louise1OkellThomas2FasanoFabrizio3TomassiniValentina145MurphyKevin1WiseRichard G.156
Cardiff University Brain Research Imaging Centre (CUBRIC), Department of Psychology, Cardiff University, Cardiff, United Kingdom
FMRIB, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
Siemens Healthcare Ltd., Camberley, United Kingdom
Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom
Department of Neuroscience, Imaging and Clinical Sciences, “G. D’Annunzio University” of Chieti-Pescara, Chieti, Italy
Institute for Advanced Biomedical Technologies, “G. D’Annunzio University” of Chieti-Pescara, Chieti, Italy
Edited by:Shuihua Wang, University of Leicester, United Kingdom
Reviewed by:Daniel Bulte, University of Oxford, United Kingdom
*Correspondence: Michael Germuska, germuskam@cardiff.ac.uk
This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence
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A Corrigendum on A Frequency-Domain Machine Learning Method for Dual-Calibrated fMRI Mapping of Oxygen Extraction Fraction (OEF) and Cerebral Metabolic Rate of Oxygen Consumption (CMRO2) by Germuska M., Chandler H., Okell T., Fasano F., Tomassini V., Murphy K., Wise R. G. (2020). Front. Artif. Intell. 3:12. doi: 10.3389/frai.2020.00012calibrated-fMRIoxygen extraction fractionCMRO2OEFmachine learning
In the original article, there was a mistake in Table 3 as published. The labels for [Hb] and OEF0 were swapped. The corrected Table 3 appears below.
Results of a bivariate regression of OEF0 against CBF0 and [Hb] for 30 healthy volunteers analyzed with the ML (ensemble of MLPs) and rNLS fitting methods.
Predictor
ML β1 (p value)
rNLS β1 (p value)
[Hb]
−1.42 (0.001)
−2.23 (0.001)
CBF
−0.07 (0.44)
−0.37 (0.005)
Intercept
61.95 (<0.001)
89.48 (<0.001)
In the original article, there was an error. Reference to Table 3 in the text had the labels for [Hb] and OEF0 swapped. A correction has been made to Results, In-vivo, paragraph 5:
“Table 3 reports the results of a bivariate regression of OEF against [Hb] and CBF for both analysis methods. The slopes of the relationship between OEF and [Hb] are similar to that reported in healthy subjects by Ibaraki et al. (2010), −1.75 Hb (g/dL). As per Ibaraki et al. the relationship between CBF and OEF did not reach significance (p = 0.44) for the ML approach, however a significant negative correlation was observed in the rNLS analysis (p = 0.005). A univariate analysis of CMRO2,0 against CBF0 is consistent with that observed in healthy controls by Powers et al. (2011) (β1 = 0.2) for both analysis methods, β1 = 0.32 (p < 0.001) and β1 = 0.24 (p < 0.001) for the ML and rNLS approaches respectively.”
The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.