Relationship Between Flow and Metabolism in BOLD Signals: Insights from Biophysical Models

In many physiological or pathological situations, the interpretation of BOLD signals remains elusive as the intimate link between neuronal activity and subsequent flow/metabolic changes is not fully understood. During the past decades, a number of biophysical models of the neurovascular coupling have been proposed. It is now well-admitted that these models may bridge between observations (fMRI data) and underlying biophysical and (patho-)physiological mechanisms (related to flow and metabolism) by providing mechanistic explanations. In this study, three well-established models (Buxton’s, Friston’s and Sotero’s) are investigated. An exhaustive parameter sensitivity analysis (PSA) was conducted to study the marginal and joint influences of model parameters on the three main features of the BOLD response (namely the principal peak, the post-stimulus undershoot and the initial dip). In each model, parameters that have the greatest (and least) influence on the BOLD features as well as on the direction of variation of these features were identified. Among the three studied models, parameters were shown to affect the output features in different manners. Indeed, the main parameters revealed by the PSA were found to strongly depend on the way the flow(CBF)-metabolism(CMRO2) relationship is implemented (serial vs. parallel). This study confirmed that the model structure which accounts for the representation of the CBF–CMRO2 relationship (oxygen supply by the flow vs. oxygen demand from neurons) plays a key role. More generally, this work provides substantial information about the tuning of parameters in the three considered models and about the subsequent interpretation of BOLD signals based on these models.

[1]  Lars Kai Hansen,et al.  Bayesian Model Comparison in Nonlinear BOLD fMRI Hemodynamics , 2008, Neural Computation.

[2]  H. Rabitz,et al.  General foundations of high‐dimensional model representations , 1999 .

[3]  R. Buxton,et al.  Modeling the hemodynamic response to brain activation , 2004, NeuroImage.

[4]  J. Mazziotta,et al.  Brain Mapping: The Methods , 2002 .

[5]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[6]  Jens Frahm,et al.  The post-stimulation undershoot in BOLD fMRI of human brain is not caused by elevated cerebral blood volume , 2008, NeuroImage.

[7]  R. Buxton The Elusive Initial Dip , 2001, NeuroImage.

[8]  I. Sobol Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[9]  D. V. Cramon,et al.  Investigating the post-stimulus undershoot of the BOLD signal—a simultaneous fMRI and fNIRS study , 2006, NeuroImage.

[10]  Nelson J. Trujillo-Barreto,et al.  Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism , 2008, NeuroImage.

[11]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[12]  P. Nunez,et al.  On the Relationship of Synaptic Activity to Macroscopic Measurements: Does Co-Registration of EEG with fMRI Make Sense? , 2004, Brain Topography.

[13]  Nelson J. Trujillo-Barreto,et al.  Identification and comparison of stochastic metabolic/hemodynamic models (sMHM) for the generation of the BOLD signal , 2009, Journal of Computational Neuroscience.

[14]  M. Raichle,et al.  The Effects of Changes in PaCO2 Cerebral Blood Volume, Blood Flow, and Vascular Mean Transit Time , 1974, Stroke.

[15]  B. Rosen,et al.  Evidence of a Cerebrovascular Postarteriole Windkessel with Delayed Compliance , 1999, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  O. Arthurs,et al.  How well do we understand the neural origins of the fMRI BOLD signal? , 2002, Trends in Neurosciences.

[17]  Thomas T. Liu,et al.  Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients , 2004, NeuroImage.

[18]  Zhenghui Hu,et al.  Nonlinear Analysis of BOLD Signal: Biophysical Modeling, Physiological States, and Functional Activation , 2007, MICCAI.

[19]  Seong-Gi Kim,et al.  Relative changes of cerebral arterial and venous blood volumes during increased cerebral blood flow: Implications for BOLD fMRI , 2001, Magnetic resonance in medicine.

[20]  Panos G Georgopoulos,et al.  Correlation method for variance reduction of Monte Carlo integration in RS‐HDMR , 2003, J. Comput. Chem..

[21]  R. Buxton,et al.  A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[22]  A K Liu,et al.  Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Karl J. Friston,et al.  Comparing hemodynamic models with DCM , 2007, NeuroImage.

[24]  J.-M. Lina,et al.  Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach , 2005, IEEE Transactions on Signal Processing.

[25]  Ying Zheng,et al.  A Model of the Hemodynamic Response and Oxygen Delivery to Brain , 2002, NeuroImage.

[26]  Nelson J. Trujillo-Barreto,et al.  Modelling the role of excitatory and inhibitory neuronal activity in the generation of the BOLD signal , 2007, NeuroImage.

[27]  Douglas C. Noll,et al.  Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations , 2006, NeuroImage.

[28]  Pierre J. Magistretti,et al.  Neuroenergetics Original Research Article Deciphering Neuron-glia Compartmentalization in Cortical Energy Metabolism , 2022 .

[29]  Robert Costalat,et al.  A Model of the Coupling between Brain Electrical Activity, Metabolism, and Hemodynamics: Application to the Interpretation of Functional Neuroimaging , 2002, NeuroImage.

[30]  Alison S. Tomlin,et al.  GUI-HDMR - A software tool for global sensitivity analysis of complex models , 2009, Environ. Model. Softw..

[31]  Herschel Rabitz,et al.  Global uncertainty assessments by high dimensional model representations (HDMR) , 2002 .

[32]  Robert G. Shulman,et al.  A BOLD search for baseline , 2007, NeuroImage.

[33]  T. L. Davis,et al.  Calibrated functional MRI: mapping the dynamics of oxidative metabolism. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[35]  David Poeppel,et al.  How can EEG/MEG and fMRI/PET data be combined? , 2002, Human brain mapping.

[36]  Olivier Faugeras,et al.  Using nonlinear models in fMRI data analysis: Model selection and activation detection , 2006, NeuroImage.

[37]  John R Fieberg,et al.  Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk , 2005 .

[38]  Peter Herman,et al.  Multimodal Measurements of Blood Plasma and Red Blood Cell Volumes during Functional Brain Activation , 2009, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.