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Michael Germuska1*
Hannah Louise Chandler1
Thomas Okell2Fabrizio Fasano3
Valentina Tomassini1,4,5
Kevin Murphy1
Richard G. Wise1,5,6- 1Cardiff University Brain Research Imaging Centre (CUBRIC), Department of Psychology, Cardiff University, Cardiff, United Kingdom
- 2FMRIB, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- 3Siemens Healthcare Ltd., Camberley, United Kingdom
- 4Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom
- 5Department of Neuroscience, Imaging and Clinical Sciences, “G. D’Annunzio University” of Chieti-Pescara, Chieti, Italy
- 6Institute for Advanced Biomedical Technologies, “G. D’Annunzio University” of Chieti-Pescara, Chieti, Italy
A Corrigendum on
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.2020.00012
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.
TABLE 3
TABLE 3. 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.
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.
Keywords: calibrated-fMRI, oxygen extraction fraction, CMRO2, OEF, machine learning
Citation: Germuska M, Chandler HL, Okell T, Fasano F, Tomassini V, Murphy K and Wise RG (2021) Corrigendum: A Frequency-Domain Machine Learning Method for Dual-Calibrated fMRI Mapping of Oxygen Extraction Fraction (OEF) and Cerebral Metabolic Rate of Oxygen Consumption (CMRO2). Front. Artif. Intell. 4:614245. doi: 10.3389/frai.2021.614245
Received: 05 October 2020; Accepted: 29 June 2021;
Published: 12 July 2021.
Edited by:
Shuihua Wang, University of Leicester, United KingdomReviewed by:
Daniel Bulte, University of Oxford, United KingdomCopyright © 2021 Germuska, Chandler, Okell, Fasano, Tomassini, Murphy and Wise. 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) and the copyright owner(s) 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.
*Correspondence: Michael Germuska, Z2VybXVza2FtQGNhcmRpZmYuYWMudWs=