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      A Frequency-Domain Machine Learning Method for Dual-Calibrated fMRI Mapping of Oxygen Extraction Fraction (OEF) and Cerebral Metabolic Rate of Oxygen Consumption (CMRO 2)

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          Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO 2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO 2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO 2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data ( n = 30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO 2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

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          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Random forests

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              Extremely randomized trees


                Author and article information

                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                31 March 2020
                : 3
                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
                Author notes

                Edited by: Shuihua Wang, University of Leicester, United Kingdom

                Reviewed by: Yi Chen, Nanjing Normal University, China; Blaise Frederick, Harvard Medical School, United States

                *Correspondence: Michael Germuska germuskam@ 123456cardiff.ac.uk

                This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence

                Copyright © 2020 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.

                Page count
                Figures: 6, Tables: 3, Equations: 3, References: 48, Pages: 12, Words: 7961
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 104943/Z/14/Z
                Award ID: 200804/Z/16/Z
                Award ID: 203139/Z/16/Z
                Funded by: Higher Education Funding Council for Wales 10.13039/501100000383
                Funded by: Royal Academy of Engineering 10.13039/501100000287
                Artificial Intelligence
                Original Research


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