The role of the cerebral capillaries in acute ischemic stroke: the extended penumbra model

Abstract

The pathophysiology of cerebral ischemia is traditionally understood in relation to reductions in cerebral blood flow (CBF). However, a recent reanalysis of the flow-diffusion equation shows that increased capillary transit time heterogeneity (CTTH) can reduce the oxygen extraction efficacy in brain tissue for a given CBF. Changes in capillary morphology are typical of conditions predisposing to stroke and of experimental ischemia. Changes in capillary flow patterns have been observed by direct microscopy in animal models of ischemia and by indirect methods in humans stroke, but their metabolic significance remain unclear. We modeled the effects of progressive increases in CTTH on the way in which brain tissue can secure sufficient oxygen to meet its metabolic needs. Our analysis predicts that as CTTH increases, CBF responses to functional activation and to vasodilators must be suppressed to maintain sufficient tissue oxygenation. Reductions in CBF, increases in CTTH, and combinations thereof can seemingly trigger a critical lack of oxygen in brain tissue, and the restoration of capillary perfusion patterns therefore appears to be crucial for the restoration of the tissue oxygenation after ischemic episodes. In this review, we discuss the possible implications of these findings for the prevention, diagnosis, and treatment of acute stroke.

Figures

Figure 1

Vascular changes in conditions that predispose to stroke. The figure illustrates the three levels at which the vascular system is affected in conditions predisposing to stroke. The schematic drawing is modified from a figure by Lanza and Crea. At the level of conduit vessels (A), blood flow can be restricted by atherosclerotic lesions (red arrow). At the level of resistance vessels (B), small vessel disease causes thickening of the vessel wall (red arrows). More peripherally, the capillary wall undergoes morphological changes, such as thickened basement membrane, pericapillary fibrosis, and pericyte loss in aging and a number of conditions predisposing to stroke (A, right)—see also Table 1. Images from Farkas et al. Ischemia can induce irreversible constrictions of capillary pericytes (B, right) and thereby disturb capillary flow patterns—from Yemisci et al.

Figure 2

Classical Bohr–Kety–Crone–Renkin (BKCR) flow-diffusion relation for oxygen. The classical BKCR curve shows the maximum amount of oxygen that can diffuse from a single capillary into tissue, for a given flow CMRO2max. The curve shape predicts three important properties of CMRO2max parallel-coupled capillaries: first, the curve slope decreases toward high perfusion values, making vasodilation increasingly inefficient as a means of improving tissue oxygenation toward high perfusion rates. This property is reflected at the macroscopic level in that CBF increases are generally several-fold larger than the corresponding increase in oxygen utilization. The resulting blood ‘hyperoxygenation' is thought to explain the blood oxygen level-dependent (BOLD) contrast mechanism. Second, if erythrocyte flows differ among capillary paths (case B) instead of being equal (case A), the net tissue oxygen availability declines. This is seen by using the BKCR curve to determine the net tissue oxygen availability resulting from the individual flows in case (B). The resulting net tissue oxygen availability is the weighted average of the oxygen availabilities for the two flows, labeled B in the plot. Note that the resulting tissue oxygen availability will always be less than that of the homogenous case, labeled A. Conversely, homogenization of capillary flows during hyperemia has the opposite effect, and serves to compensate for the first property. Third, if erythrocyte flows are hindered (rather than continuously redistributed) along single capillary paths (as indicated by slow-passing white blood cell (WBC) and rugged capillary walls) upstream vasodilation is likely to amplify the redistribution losses, as erythrocytes are forced through other branches at very high speeds, with negligible net oxygenation gains. CBF, cerebral blood flow; CMRO2max, cerebral metabolic rate of oxygen.

Figure 3

Changes in cerebral blood flow (CBF) and tissue oxygen tension that must accompany increasing levels of capillary dysfunction to maintain tissue oxygen availability before stroke. The figure displays the adaptations of CBF (second panel from the top) and PtO2 (third panel from the top) that are necessary to maintain tissue oxygen availability as capillary transit time heterogeneity (CTTH) levels (top panel) gradually increase, both during rest (upper graph in each panel) and during functional activation (lower graph in each panel). These changes are hypothesized to occur in relation to conditions that predispose to stroke—cf. Table 1—years before symptoms develop. The most important hemodynamic change occurs toward the end of Stage 1 when the increase in tissue oxygen availability during hyperemia is no longer sufficient to meet the metabolic needs of the tissue. Therefore, vasodilation during episodes of increased metabolic demands must be attenuated. This transition is hypothesized to mark the onset of neurovascular dysfunction, and of increased oxidative damage due to the accompanying release of reactive oxygen species (ROS). Later, vasodilation induced during rest must also be attenuated to maintain tissue oxygen availability above the needs of resting tissue. This is hypothesized to mark the onset of detectable reductions in cerebrovascular reserve capacity. As CTTH continues to increase, oxygen availability can be secured by attenuated CBF responses and more efficient blood-tissue concentration gradients—see text. This results in increasing maximum oxygen extraction fraction (OEFmax) values. The gradual reduction in metabolic reserve capacity is indicated in the lower panel: As CTTH increases, net oxygen extraction is increasingly limited by CBF as the oxygen extraction fraction approaches unity. In the stroke prone state, minor reductions in CBF or increases in CTTH are therefore predicted to result in neurologic symptoms as maximum cerebral metabolic rate of oxygen (CMRO2max) approaches the actual, metabolic needs of the tissue. HIF-1, hypoxia-inducible transcription factor 1.

Figure 4

Effects of mean transit time (MTT), capillary transit time heterogeneity (CTTH), and oxygen tension on oxygen extraction. (A, B) The x axis position is indicated by the value of both MTT (bottom x axis) and cerebral blood flow (CBF) (top x axis). (A) Contour plot of maximum oxygen extraction fraction (OEFmax) as a function of these parameters and the CTTH at a tissue oxygen tension of PtO2=26 mm Hg. The value of OEFmax is indicated by a color at the corresponding location in the (MTT, CTTH) plane. The OEFmax value corresponding to this color or location in the contour plot is most easily derived from the OEFmax values indicated on the two nearest solid black lines, which display OEFmax iso-contours. Note that the oxygen extraction efficacy always increases with increasing MTT and with decreasing CTTH. The image inserts show schematic capillary beds with homogenous capillary transit times (lower left insert) and with CTTH typical of resting brain (upper right insert). The arrow indicates the changes in MTT and CTTH that occur during functional activation: The reduction in CTTH that occurs in parallel with the arteriolar dilation partly maintains the oxygen extraction efficacy during hyperemia. The maximum supported cerebral metabolic rate of oxygen (CMRO2max) is shown in units of mL/100 mL per minute in (B). Recall that this figure is derived from (A) by multiplying its OEFmax values by Ca and CBF, assuming that the capillary transit times of erythrocytes are inversely proportional to CBV. Note that CMRO2max, and thereby tissue oxygen availability, always increases with decreasing flow heterogeneity. The physiologic CBF response to functional activation tends to reduce OEFmax in normal tissue, both due to the shorter MTT and due to a higher PtO2. Hemodynamic states above the yellow line in (B) are unique in that increases in their CBF (reductions in MTT) will lead to states of lower tissue oxygen availability: these states are referred to as having malignant CTTH. The horizontal arrow indicates the metabolic consequence of increased CBF without concomitant reductions in CTTH: in the absence of any homogenization of capillary flows, vasodilation would not lead to a net increase in the availability of oxygen in tissue. The origin of this paradox effect is illustrated by the insert in the upper right of (A). The oxygenation of blood along capillary paths in this insert was modeled according to in vivo erythrocyte velocity recordings in resting rat brain, and the single-capillary Bohr–Kety–Crone–Renkin (BKCR) model, and then indicated as a color code, ranging from fully oxygenated (left) to levels well below that of mixed venular blood (right) along some capillary paths—cf. also Figure 3 in Jespersen and Østergaard. Note that, along some capillary paths, flow velocities are so high that little oxygen is extracted from blood. In normal brain, hyperemia is accompanied by a reduction in CTTH (lower left insert in A). This homogenization decreases the number of capillary paths with high flow and limited oxygen extraction, causing net oxygen availability to increase as a function of CBF. If this heterogeneity persist during hyperemia, for example due to changes in capillary morphology or blood rheology, the effective shunting of oxygenated blood can become critical. As discussed in the text, vasodilation may in fact be attenuated by hypoxia-sensitive mechanisms as hemodynamic states approach this critical limit. Paradoxically, the neurovascular dysfunction and the reduced CVRC observed in conditions predisposing to stroke may therefore reflect adaptations to prevent critical hypoxia in cases of high CTTH. (C) This panel shows how tissue oxygen metabolism can be secured when CBF increases no longer lead to increased tissue oxygen availability. The figure shows net oxygenation as a function of tissue oxygen tension and CTTH for fixed MTT (CBF=60 mL/100 mL per minute; MTT 1.4 seconds) to illustrate how tissue metabolism, by reducing tissue oxygen tension, can facilitate net oxygen extraction in cases where capillary dysfunction cause critical levels of ‘shunting'—provided that CBF responses are attenuated. Note that a decrease in oxygen tension of 5 mm Hg can support a CMRO2 increase of 20%. This corresponds roughly to the additional energy requirements of neuronal firing.

Figure 5

Metabolic thresholds. The green iso-contour surface corresponds to the metabolic rate of contralateral tissue in patients with focal ischemia. The red plane marks the boundary, left of which vasodilation reduces tissue oxygen availability (malignant capillary transit time heterogeneity (CTTH)). The maximum value CTTH can attain at a PtO2 of 25 mm Hg if oxygen availability is to remain above that of resting tissue is indicated by the label A. As CTTH increases further, a critical limit is reached as PtO2 approaches zero—label B. At this stage, the metabolic needs of tissue cannot be supported unless mean transit time (MTT) is prolonged to a threshold of ∼4 seconds, corresponding to cerebral blood flow (CBF)=21 mL/100 mL per minute. If MTT increases instead, owing to limitations in blood supply, then the threshold corresponding to resting contralateral tissue is reached at MTT values of roughly 6.3 seconds, or CBF=13 mL/100 mL per minute, provided CTTH is negligible or moderate (label C). If, however, CTTH increases as a result of the reduction in CBF or because of per-ischemic changes in capillary patency (cf. Figure 1), then tissue oxygen availability cannot be maintained above the metabolic needs of resting tissue for CBF values below 21 mL/100 mL per minute. The blue arrows indicate how tissue hypoperfusion and capillary dysfunction causes MTT and CTTH to change, and tissue oxygen availability to approach the metabolic requirements of resting brain tissue (the green iso-contour).

Figure 6

Digital phantom simulation of the inherent bias of the Tmax parameter by the values of mean transit time (MTT) and capillary transit time heterogeneity (CTTH). (A) Forty-nine squares with ‘true' maximum oxygen extraction fraction (OEFmax) values corresponding to combinations of seven MTT values and seven CTTH values, cf. Figure 4A, are shown. Each square consists of 14-by-14 voxels, with associated concentration time curves characteristic of the tissue bolus passage which would result from the vascular retention caused by the corresponding values of MTT and CTTH. Random noise characteristic of magnetic resonance perfusion raw data was then added at each noise free, simulated data point, and the tissue curve analyzed by a customized algorithm, which determine OEFmax based on CTTH and MTT. (B) This panel shows OEFmax values estimated by this algorithm. They are seen to correspond well with the true values in panel A. We also determined the corresponding Tmax value using two common singular value decomposition based deconvolution techniques., (C, D) Maps of Tmax derived from the delay-corrected (C) and delay un-corrected (D) singular value decomposition algorithms. Note that the Tmax maps contain an incidental bias that mimics OEFmax.