A Model of the Hemodynamic Response and Oxygen Delivery to Brain

Zheng, Ying; Martindale, John; Johnston, David; Jones, Myles; Berwick, Jason; Mayhew, John E. W.

doi:10.1006/nimg.2002.1078

Cited by 164 publications

(144 citation statements)

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“…A deeper question is to ask why n is not equal to 1, because this is the central phenomenon underlying the BOLD effect. This is an area of active research (Aubert and Costalat, 2002;Buxton, 2004;Buxton and Frank, 1997;Gjedde et al, 1991Gjedde et al, , 2002Hayashi et al, 2003;Hyder et al, 1998;Woo and Hathout, 2001;Zheng et al, 2002), and as this work is refined it can be included as a central step in modeling the path from stimulus to BOLD response.…”

Section: Modeling the Hemodynamic Responsementioning

confidence: 99%

Modeling the hemodynamic response to brain activation

et al. 2004

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Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extraction fraction (E) and cerebral blood volume (CBV); (2) the balloon model, proposed to describe the transient dynamics of CBV and deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO 2 ) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses, makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease. D

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Section: Modeling the Hemodynamic Responsementioning

confidence: 99%

Modeling the hemodynamic response to brain activation

et al. 2004

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“…In these models, variations in oxygen extraction are explicitly linked to the flow rate by a relationship analogous to the well-known model of Renkin-Crone (Renkin, 1959;Crone, 1963). Neuro-vascular coupling (i.e., the link between neuronal activation and changes in flow) are schematically modeled either by directly prescribing the temporal dynamics of the flow entering the system (Buxton et al, 1998;Aubert and Costalat, 2002;Valabrègue et al, 2003) or by prescribing an ad hoc non-linear relationship between the time-course of the imposed stimulus and the inlet flow (Zheng et al, 2002;Buxton et al, 2004).…”

Section: Future Directionsmentioning

confidence: 99%

Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: Flow variations induced by global or localized modifications of arteriolar diameters

Lorthois¹,

Cassot²,

Lauwers³

2011

NeuroImage

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. In a companion paper (Lorthois et al., Neuroimage, in press), we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking into account the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, volumes of functional vascular territories, regional flow at voxel and network scales, etc. Using the same approach, we study flow reorganizations induced by global arteriolar vasodilations (an isometabolic global increase in cerebral blood flow). For small to moderate global vasodilations, the relationship between changes in volume and changes in flow is in close agreement with Grubb's law, providing a quantitative tool for studying the variations of its exponent with underlying vascular architecture. A significant correlation between blood flow and vascular structure at the voxel scale, practically unchanged with respect to baseline, is demonstrated. Furthermore, the effects of localized arteriolar vasodilations, representative of a local increase in metabolic demand, are analyzed. In particular, localized vasodilations induce flow changes, including vascular steal, in the neighboring arteriolar trunks at small distances (b 300 μm), while their influence in the neighboring veins is much larger (about 1 mm), which provides an estimate of the vascular point spread function. More generally, for the first time, the hemodynamic component of various functional neuroimaging techniques has been isolated from metabolic and neuronal components, and a direct relationship with several known characteristics of the BOLD signal has been demonstrated. IntroductionThere is increasing recognition that, by its structural implication in neuro-vascular coupling, underlying vascular architecture is a key player in hemodynamically based functional imaging techniques (including fMRI and PET) (Harrison et al., 2002;d'Esposito et al., 2003;Logothetis and Wandell, 2004;Lauwers et al., 2008;Weber et al., 2008). In particular, the spatial resolution and specificity of such techniques are bound to the density of blood capillary vasculature and blood flow regulating structures (Harel et al., 2006). This is especially important in the context of high field fMRI Harel et al., 2006), because, despite a dramatic increase in the theoretical spatial resolution obtained by using higher magnetic fields, associated with other hardware advancements, "the spatial extent of the hemodynamic response ultimately will impose a fundamental limitation on the spatial resolution of fMRI modalities" ).However, little is known about the fundamentals of the relationship between vascular structure and hemodynamic changes induced by brain activation. A growing body of evidence indicates that neurons, glia, and cerebral blood vessels, actin...

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“…Moreover, each component may be nonlinear in its intensity response relationships and temporal integration properties (Birn, Saad and Bandettini, 2001;Bandettini and Ungerleider, 2001; see Boynton et al, 1996). The energy required to generate both the intracellular changes in potential and the axonal spikes make metabolic demands M(t) on the cellular energy mechanisms that activate the hemodynamic response of the blood H(t), both of which are also nonlinear processes (Buxton et al, 1998;Zheng et al, 2002) leading to the paramagnetic BOLD response Y(t). We therefore need an account of such multiple nonlinearities of the stimulus-evoked BOLD signal.…”

Section: Introductionmentioning

confidence: 99%

Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses

2008

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This is the published version of the paper.This version of the publication may differ from the final published version. Permanent repository link A R T I C L E I N F O A B S T R A C TArticle history: IntroductionFunctional magnetic-resonance imaging (fMRI) based on the blood oxygenation-level dependent (BOLD) signal is a powerful technique for studying the neural response in the cortex. Most previous work has been based on the assumption that the BOLD signal is a unitary response to a single form of stimulus S(t), usually in the form of a linear convolution of a hemodynamic response function (HRF) with the temporal waveform of the stimulus time sequence (Bandettini et al., 1993;Friston et al., 1994;Boynton et al., 1996). In most cases, it is assumed that the HRF incorporates both the cortical response to the stimulus and the hemodynamic response of the blood to the oxygen requirements of the cortical response. However, the situation may be much more complicated, with multiple components of neural response to the stimulus weighted differentially across the cortex (Zacks et al., 2001;d'Avossa et al., 2003;Bellgowan et al., 2003;Calhoun et al., 2004a;Fox et al., 2005), such that any one voxel contains contributions from more than one neural time course. If these time course variations are small relative to the BOLD temporal resolution, they may be negligible for an overall analysis of response strength, but the cited studies show non-negligible variations that require a more detailed analysis. For this reason, consistent with Friston et al. (1998Friston et al. ( , 2000, Buxton et al. (2004) and others, we restrict the term HRF to the dynamics of the blood response to the metabolic demands of the neural processing and consider the neural response to the stimulus as a separate process. This leads to the model of the BOLD mechanisms shown in Fig. 1.

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A Model of the Hemodynamic Response and Oxygen Delivery to Brain

Cited by 164 publications

References 28 publications

Modeling the hemodynamic response to brain activation

Modeling the hemodynamic response to brain activation

Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: Flow variations induced by global or localized modifications of arteriolar diameters

Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses

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