Spatiotemporal BOLD dynamics from a poroelastic hemodynamic model

Drysdale, P.M.; Huber, Jacqueline; Robinson, P. A.; Aquino, Kevin

doi:10.1016/j.jtbi.2010.05.026

Cited by 51 publications

(66 citation statements)

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“…These plots were constructed using the empirically 503 derived velocity, damping, and amplitude estimates (Fig. 3) The spatiotemporal dynamics found in this study are consistent with a poroelastic 510 model of hemodynamics (Drysdale et al, 2010). In particular, hemodynamic 511 waves were observed and could be characterized with velocity and damping 512 coefficients which are within predicted ranges (Aquino et al 2014 At present there is still significant debate about the general feasibility of using 543 fMRI to infer depth-dependent neuronal activity.…”

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confidence: 67%

“…We also relate our empirical measurements to a recent physiologically-based 80 model of the HRF (Drysdale et al, 2010) that approximates cortical tissue as a 81 porous elastic medium enabling the hemodynamics to be explained in a 82 continuous mean-field manner. An important finding from this work was that 83 waves of hemodynamic activity were predicted and supported by results in 84 primary visual cortex (Aquino et al, 2012).…”

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The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex

et al. 2016

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Schira, M. M. (2016). The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage, 139 (October), 240-248.The spatiotemporal hemodynamic response function for depthdependent functional imaging of human cortex AbstractThe gray matter of human cortex is characterized by depth-dependent differences in neuronal activity and connections (Shipp, 2007) as well as in the associated vasculature (Duvernoy et al., 1981). The resolution limit of functional magnetic resonance imaging (fMRI) measurements is now below a millimeter, promising the non-invasive measurement of these properties in awake and behaving humans (Muckli et al., 2015; Olman et al., 2012;Ress et al., 2007). To advance this endeavor, we present a detailed spatiotemporal hemodynamic response function (HRF) reconstructed through the use of high-resolution, submillimeter fMRI. We decomposed the HRF into directions tangential and perpendicular to the cortical surface and found that key spatial properties of the HRF change significantly with depth from the cortical surface. Notably, we found that the spatial spread of the HRF increases linearly from 4.8mm at the gray/white matter boundary to 6.6mm near the cortical surface. Using a hemodynamic model, we posit that this effect can be explained by the depth profile of the cortical vasculature, and as such, must be taken into account to properly estimate the underlying neuronal responses at different cortical depths. The gray matter of human cortex is characterized by depth-dependent 25 differences in neuronal activity and connections (Ship, 2007) as well as in the 26 associated vasculature (Duvernoy et al., 1981). The resolution limit of functional 27 magnetic resonance imaging (fMRI) measurements is now below a millimeter, 28 promising the non-invasive measurement of these properties in awake and 29 behaving humans (Muckli et al., 2015; Olman et al., 2012;Ress et al., 2007). To 30 advance this endeavor, we present a detailed spatiotemporal hemodynamic 31 response function (HRF) reconstructed through the use of high-resolution, 32 submillimeter fMRI. We decomposed the HRF into directions tangential and 33 perpendicular to the cortical surface and found that key spatial properties of the 34 HRF change significantly with depth from the cortical surface. Notably, we found 35 that the spatial spread of the HRF increases linearly from 4.8 mm at the 36 gray/white matter boundary to 6.6 mm near the cortical surface. Using a 37 hemodynamic model, we posit that this effect can be explained by the depth 38 profile of the cortical vasculature, and as such, must be taken into account to 39properly estimate the underlying neuronal responses at different cortical depths. 40 41

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The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex

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“…Previous models used oscillatory descriptions to model the flow response. 28,29 Here, we provide a physiologic basis for this oscillatory character in terms of energy exchange between the kinetic energy of moving arterial blood, and energy stored in the compliance of the downstream venous vasculature.…”

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“…Note that this equation is similar to those used in previous modeling efforts that included a damped harmonic oscillator to describe the flow response. 28,29 In our case, the transient arterial response is modeled by closing the switch across R 1b briefly, representing a vasodilation that decreases the upstream vascular resistance ( Figure 1C). Because of inertial effects, this vasodilation creates a positive pressure fluctuation.…”

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Model of the Transient Neurovascular Response Based on Prompt Arterial Dilation

Kim

Khan

Thompson

et al. 2013

J Cereb Blood Flow Metab

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Brief neural stimulation results in a stereotypical pattern of vascular and metabolic response that is the basis for popular brainimaging methods such as functional magnetic resonance imagine. However, the mechanisms of transient oxygen transport and its coupling to cerebral blood flow (CBF) and oxygen metabolism (CMRO 2 ) are poorly understood. Recent experiments show that brief stimulation produces prompt arterial vasodilation rather than venous vasodilation. This work provides a neurovascular response model for brief stimulation based on transient arterial effects using one-dimensional convection-diffusion transport. Hemoglobin oxygen dissociation is included to enable predictions of absolute oxygen concentrations. Arterial CBF response is modeled using a lumped linear flow model, and CMRO 2 response is modeled using a gamma function. Using six parameters, the model successfully fit 161/166 measured extravascular oxygen time courses obtained for brief visual stimulation in cat cerebral cortex. Results show how CBF and CMRO 2 responses compete to produce the observed features of the hemodynamic response: initial dip, hyperoxic peak, undershoot, and ringing. Predicted CBF and CMRO 2 response amplitudes are consistent with experimental measurements. This model provides a powerful framework to quantitatively interpret oxygen transport in the brain; in particular, its intravascular oxygen concentration predictions provide a new model for fMRI responses. Flow & Metabolism (2013) 33, 1429-1439 doi:10.1038/jcbfm.2013; published online 12 June 2013 Journal of Cerebral BloodKeywords: cerebral blood flow; cerebral hemodynamics; mathematical modeling; neurovascular coupling; neurovascular unit INTRODUCTION Neural activity causes local changes in cerebral oxygen consumption (CMRO 2 ), blood flow (CBF), and deoxyhemoglobin concentration. 1,2 This combination of neurometabolic and neurovascular coupling enables the use of hemodynamic imaging methods to infer brain activity. Of particular interest is the response to brief periods (few seconds) of neural activity, the hemodynamic response function (HRF). The stereotypical HRF permits the use of powerful linear analysis methods.Despite the utility of the HRF for quantifying brain activity, the mechanisms of transient oxygen transport remain poorly understood. There are at least three controversial issues. First, the role of venous volume changes is not clear, particularly in response to brief neural stimulation. [3][4][5] Second, there is disagreement about the relative role of capillaries and arterioles in oxygen transport to tissue. [6][7][8] Third, experiments in oxygen mass balance have sometimes shown that metabolic consumption is insufficient to account for global oxygen loss from the vasculature. It was therefore suggested that tissues at or adjacent to the vascular wall may consume the missing oxygen. 9,10 Thus, questions remain about oxygen transport both in steady state and as a consequence of brief stimulation.Various computational models have attempted to explain t...

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“…There is potential for further advancement of the neurohemodynamic model, for example, by including assumptions about how astrocytes mediate the signal from neurons that cause dilatation of arterioles and resulting increase in blood flow considering the finite range of astrocyte projections (Drysdale et al, 2010). It is pointed out by the authors of this article that the distribution of effective connectivity between neurons and flow regulation points has not been measured experimentally yet, but is an area of active research interest [see also Aquino and associates (2012)].…”

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The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

et al. 2013

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Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro-to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) (www.thevirtualbrain.org), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the modelbased simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

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Spatiotemporal BOLD dynamics from a poroelastic hemodynamic model

Cited by 51 publications

References 31 publications

The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex

The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex

Model of the Transient Neurovascular Response Based on Prompt Arterial Dilation

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

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