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{"title"=>"A New Functional MRI Approach for Investigating Modulations of Brain Oxygen Metabolism", "type"=>"journal", "authors"=>[{"first_name"=>"Valerie E.M.", "last_name"=>"Griffeth", "scopus_author_id"=>"38662560400"}, {"first_name"=>"Nicholas P.", "last_name"=>"Blockley", "scopus_author_id"=>"25724144300"}, {"first_name"=>"Aaron B.", "last_name"=>"Simon", "scopus_author_id"=>"55349368400"}, {"first_name"=>"Richard B.", "last_name"=>"Buxton", "scopus_author_id"=>"7102032452"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pmid"=>"23826367", "pui"=>"369208969", "scopus"=>"2-s2.0-84879522926", "doi"=>"10.1371/journal.pone.0068122", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84879522926"}, "id"=>"0fe05feb-b376-3140-8c7b-45f23c60053f", "abstract"=>"Functional MRI (fMRI) using the blood oxygenation level dependent (BOLD) signal is a common technique in the study of brain function. The BOLD signal is sensitive to the complex interaction of physiological changes including cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral oxygen metabolism (CMRO2). A primary goal of quantitative fMRI methods is to combine BOLD imaging with other measurements (such as CBF measured with arterial spin labeling) to derive information about CMRO2. This requires an accurate mathematical model to relate the BOLD signal to the physiological and hemodynamic changes; the most commonly used of these is the Davis model. Here, we propose a new nonlinear model that is straightforward and shows heuristic value in clearly relating the BOLD signal to blood flow, blood volume and the blood flow-oxygen metabolism coupling ratio. The model was tested for accuracy against a more detailed model adapted for magnetic fields of 1.5, 3 and 7T. The mathematical form of the heuristic model suggests a new ratio method for comparing combined BOLD and CBF data from two different stimulus responses to determine whether CBF and CMRO2 coupling differs. The method does not require a calibration experiment or knowledge of parameter values as long as the exponential parameter describing the CBF-CBV relationship remains constant between stimuli. The method was found to work well for 1.5 and 3T but is prone to systematic error at 7T. If more specific information regarding changes in CMRO2 is required, then with accuracy similar to that of the Davis model, the heuristic model can be applied to calibrated BOLD data at 1.5T, 3T and 7T. Both models work well over a reasonable range of blood flow and oxygen metabolism changes but are less accurate when applied to a simulated caffeine experiment in which CBF decreases and CMRO2 increases.", "link"=>"http://www.mendeley.com/research/new-functional-mri-approach-investigating-modulations-brain-oxygen-metabolism", "reader_count"=>45, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>15, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>2, "Student > Master"=>5, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>15, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>2, "Student > Master"=>5, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>4, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>7, "Agricultural and Biological Sciences"=>3, "Neuroscience"=>8, "Sports and Recreations"=>1, "Physics and Astronomy"=>9, "Psychology"=>6, "Chemistry"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Neuroscience"=>{"Neuroscience"=>8}, "Chemistry"=>{"Chemistry"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>9}, "Psychology"=>{"Psychology"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "Austria"=>1, "United Kingdom"=>1, "Germany"=>1}, "group_count"=>0}

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• http://doi.org/10.1089/brain.2014.0294
• http://doi.org/10.1152/jn.00352.2017
• http://doi.org/10.14814/phy2.12378
• http://doi.org/10.1016/j.neuroimage.2017.12.049
• http://doi.org/10.1016/j.neuroimage.2015.07.059
• http://doi.org/10.1016/j.neuroimage.2015.02.068
• http://doi.org/10.1016/j.neuroimage.2016.01.001
• http://doi.org/10.1098/rstb.2015.0359
• http://doi.org/10.1016/j.neuroimage.2017.07.034
• http://doi.org/10.3389/fnins.2014.00139
• http://doi.org/10.1016/j.neuroimage.2016.01.021
• http://doi.org/10.1002/jmri.25537
• http://doi.org/10.1016/j.neuroimage.2016.10.004
• http://doi.org/10.1002/nbm.3275
• http://doi.org/10.1016/j.neuroimage.2018.10.032
• http://doi.org/10.1002/hbm.22608
• http://doi.org/10.1016/j.neuroimage.2014.10.003
• http://doi.org/10.1016/j.neuroimage.2015.03.080

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1103995"], "description"=>"1<p>Using the heuristic model proposed here with <i>α_v</i> = 0.2,</p>2<p>Using the Davis model with α = 0.2 and β = 1.3;</p>3<p>Using the Davis model with α = 0.13 and β = 0.92;</p>4<p>Using the Davis model with α = 0.38 and β = 1.5.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "calculations", "calibrated-bold", "applied", "pre-", "post-caffeine"], "article_id"=>733792, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.t003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_CMRO_2_calculations_by_different_models_using_the_calibrated_BOLD_approach_applied_to_pre_and_post_caffeine_data_/733792", "title"=>"Comparing ΔCMRO₂ calculations by different models using the calibrated-BOLD approach applied to pre- and post-caffeine data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103993"], "description"=>"<p>Input parameters to the detailed model.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology"], "article_id"=>733790, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Input_parameters_to_the_detailed_model_/733790", "title"=>"Input parameters to the detailed model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103990"], "description"=>"<p>Eleven input parameters to the detailed model were varied around reasonable values as defined in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t001\" target=\"_blank\">Tables 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t002\" target=\"_blank\">2</a> while other parameters were held constant at the best guess of physiology. The activation and ideal hypercapnia experiments were simulated for each of these physiological states at (A,B) 1.5T, (C,D) 3T and (E,F) 7T. The true CMRO₂ change with activation is shown as a dashed line, while the bars showing the range of calculated values is shaded from dark to light for increasing values of the associated physiological parameter. Davis model parameters α and β were adjusted for B₀ as noted in the figure. In the heuristic model, α_v = 0.2 across all B₀. (A,C,E) Accuracy of the models at <i>n</i> = 2 (%ΔCBF = 50%) and variable B₀. (B,D,F) Accuracy of the models at <i>n</i> = −1 (%ΔCBF = −25%) and variable B₀. Note values for α and β in the Davis model are consistent for each B₀.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "davis", "applied", "calibrated", "estimating", "variability", "physiological"], "article_id"=>733787, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.g002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heuristic_vs_B_0_adjusted_Davis_model_applied_to_the_calibrated_BOLD_experiment_Estimating_916_CMRO_2_calculation_bias_due_to_variability_in_physiological_parameters_/733787", "title"=>"Heuristic vs. B₀-adjusted Davis model applied to the calibrated BOLD experiment: Estimating %ΔCMRO₂ calculation bias due to variability in physiological parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103989"], "description"=>"<p>The DBM was used to simulate BOLD data from changes in CBF and set values of <i>n</i>. 10,000 simulations were performed using the ranges for the model inputs noted in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t001\" target=\"_blank\">Tables 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t002\" target=\"_blank\">2</a>. The data was compared to a reference of <i>n_ref</i> = 2. Inset histograms show the distribution of δS ratios for a non-linear CBF ratio of 0.5. At 1.5T (A) and 3T (B), the ratio method separates the data well while predicting <i>n_x</i> = <i>n_ref</i> data will fall along the line of identity. At 7T (C), the ratio method does not perform as well, particularly for <i>n_x</i> = <i>n_ref</i> for which the data deviates from the line of identity. (D) Application of the ratio method to data examining the effect of visual stimulus contrast on the coupling of CBF and CMRO₂ in 9 subjects. 100% contrast flickering checkerboard was used as the reference with results showing that 1% contrast has a significantly lower <i>n</i>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "cbf"], "article_id"=>733786, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ratio_method_for_analysis_of_combined_BOLD_948_S_and_CBF_data_/733786", "title"=>"The ratio method for analysis of combined BOLD (δS) and CBF data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103997", "https://ndownloader.figshare.com/files/1103998", "https://ndownloader.figshare.com/files/1103999", "https://ndownloader.figshare.com/files/1104000", "https://ndownloader.figshare.com/files/1104001", "https://ndownloader.figshare.com/files/1104002", "https://ndownloader.figshare.com/files/1104003", "https://ndownloader.figshare.com/files/1104004"], "description"=>"<div><p>Functional MRI (fMRI) using the blood oxygenation level dependent (BOLD) signal is a common technique in the study of brain function. The BOLD signal is sensitive to the complex interaction of physiological changes including cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral oxygen metabolism (CMRO₂). A primary goal of quantitative fMRI methods is to combine BOLD imaging with other measurements (such as CBF measured with arterial spin labeling) to derive information about CMRO₂. This requires an accurate mathematical model to relate the BOLD signal to the physiological and hemodynamic changes; the most commonly used of these is the Davis model. Here, we propose a new nonlinear model that is straightforward and shows heuristic value in clearly relating the BOLD signal to blood flow, blood volume and the blood flow-oxygen metabolism coupling ratio. The model was tested for accuracy against a more detailed model adapted for magnetic fields of 1.5, 3 and 7T. The mathematical form of the heuristic model suggests a new ratio method for comparing combined BOLD and CBF data from two different stimulus responses to determine whether CBF and CMRO₂ coupling differs. The method does not require a calibration experiment or knowledge of parameter values as long as the exponential parameter describing the CBF-CBV relationship remains constant between stimuli. The method was found to work well for 1.5 and 3T but is prone to systematic error at 7T. If more specific information regarding changes in CMRO₂ is required, then with accuracy similar to that of the Davis model, the heuristic model can be applied to calibrated BOLD data at 1.5T, 3T and 7T. Both models work well over a reasonable range of blood flow and oxygen metabolism changes but are less accurate when applied to a simulated caffeine experiment in which CBF decreases and CMRO₂ increases.</p></div>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "mri", "investigating", "modulations"], "article_id"=>733794, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0068122.s001", "https://dx.doi.org/10.1371/journal.pone.0068122.s002", "https://dx.doi.org/10.1371/journal.pone.0068122.s003", "https://dx.doi.org/10.1371/journal.pone.0068122.s004", "https://dx.doi.org/10.1371/journal.pone.0068122.s005", "https://dx.doi.org/10.1371/journal.pone.0068122.s006", "https://dx.doi.org/10.1371/journal.pone.0068122.s007", "https://dx.doi.org/10.1371/journal.pone.0068122.s008"], "stats"=>{"downloads"=>11, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_New_Functional_MRI_Approach_for_Investigating_Modulations_of_Brain_Oxygen_Metabolism_/733794", "title"=>"A New Functional MRI Approach for Investigating Modulations of Brain Oxygen Metabolism", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103992"], "description"=>"<p>The maximum BOLD signal results from complete elimination of dHb, which can be accomplished by increasing CBF, decreasing CMRO₂ and/or increasing PaO₂. Here the BOLD signal is shown as a function of SvO₂ at (A) 1.5T, (B) 3T, and (C) 7T. Three mechanisms of dHb reduction are included: hyperoxia combined with CBF increase (blue), CBF increase only (green) and CMRO₂ cessation (red). Also included is a simulation for ΔCBF = 100% and PaO₂ = 390 mmHg consistent with findings from Gauthier et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone.0068122-Gauthier1\" target=\"_blank\">[42]</a>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "dhb"], "article_id"=>733789, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulating_the_maximum_BOLD_signal_through_dHb_elimination_/733789", "title"=>"Simulating the maximum BOLD signal through dHb elimination.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103991"], "description"=>"<p>Simulated calibrated BOLD calculations were made for the best guess of physiology and imaging parameters noted in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t001\" target=\"_blank\">Tables 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068122#pone-0068122-t002\" target=\"_blank\">2</a>. (A–D) Calculations in the absolute ΔCMRO_2­ error are shown at 1.5T for the 1.5T-adjusted Davis model, the heuristic model, the Davis model with α = 0.2 and β = 1, and the free parameter Davis model with α and β fitted as noted. Similar calculations are shown for 3T (E–H) and 7T (I–L).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology"], "article_id"=>733788, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Absolute_error_in_CMRO_2_173_calculations_/733788", "title"=>"Absolute error in ΔCMRO_2­ calculations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-27 03:31:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1103996"], "description"=>"<p>Input parameter to the detailed model that are sensitive to B₀.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "biophysics", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "neurophysiology", "neurology", "parameter"], "article_id"=>733793, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Valerie E. M. Griffeth", "Nicholas P. Blockley", "Aaron B. Simon", "Richard B. Buxton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068122.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Input_parameter_to_the_detailed_model_that_are_sensitive_to_B_0_/733793", "title"=>"Input parameter to the detailed model that are sensitive to B₀.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-27 03:31:49"}

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Relative Metric

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F1000Prime