Mechanical restriction of intracortical vessel dilation by brain tissue sculpts the hemodynamic response

Abstract

Understanding the spatial dynamics of dilation in the cerebral vasculature is essential for deciphering the vascular basis of hemodynamic signals in the brain. We used two-photon microscopy to image neural activity and vascular dynamics in the somatosensory cortex of awake behaving mice during voluntary locomotion. Arterial dilations within the histologically-defined forelimb/hindlimb (FL/HL) representation were larger than arterial dilations in the somatosensory cortex immediately outside the FL/HL representation, demonstrating that the vascular response during natural behaviors was spatially localized. Surprisingly, we found that locomotion drove dilations in surface vessels that were nearly three times the amplitude of intracortical vessel dilations. The smaller dilations of the intracortical arterioles were not due to saturation of dilation. Anatomical imaging revealed that, unlike surface vessels, intracortical vessels were tightly enclosed by brain tissue. A mathematical model showed that mechanical restriction by the brain tissue surrounding intracortical vessels could account for the reduced amplitude of intracortical vessel dilation relative to surface vessels. Thus, under normal conditions, the mechanical properties of the brain may play an important role in sculpting the laminar differences of hemodynamic responses.

Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal

“…Because the resistance of a blood vessel depends on its diameter, these dilations cause a sharp reduction of vascular resistance, which increases blood velocity and flux. This increase in arterial diameter and blood flow is relatively rapid, occurring within less than a second following the stimulation (Chen et al, 2011; Drew et al, 2011; Gao et al, 2015; Kim et al, 2013). Arterial dilation and increased blood flow result in an elevated influx of oxygenated hemoglobin that exceeds the oxygen demand of the surrounding neural tissue (Fox and Raichle, 1986).…”

“…A cranial window may also be implanted at this time. After allowing adequate time for surgical recovery, we begin the habituation to head-fixation, which can be done on treadmill that allows locomotion (Dombeck et al, 2010; Gao et al, 2015; Gao and Drew, 2014; Huo et al, 2014; Rosenegger et al, 2015; Tran and Gordon 2015), or body restraining cylinders or slings (Berwick et al, 2002; Drew et al, 2011) (Fig. 8).…”

Functional hyperemia drives fluid exchange in the paravascular space

“…To study the flow of CSF in the PVS driven by functional hyperemia, we imposed arterial wall motion in our model that matched those observed in awake mice during a typical functional hyperemic event [34][35][36] (Fig 4a). The mathematical formulation of this problem is identical to the previous simulation, with the exception that the arterial wall movement was given by a typical vasodilation profile instead of a heartbeat-driven peristaltic wave (Fig 4a).…”

“…We simultaneously imaged processes of Thy1-expressing neurons and blood vessel diameters via intravenous injection of Texas-red dextran (Fig 5b). Arterioles in the somatosensory cortex dilate during spontaneous locomotion events due to increases in local neural activity 35 , so we imaged these vessels that will be naturally subject to large vasodilation. We performed piecewise, iterative motion correction of the collected images relative to the center of the artery (see Methods) in order to robustly measure the displacement of brain tissue during arterial dilations.…”

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

“…The problem with the first three models is that they only considered temporal variations in the BOLD signal across the cortical depth, disregarding any spatial effects. This cannot be used to explain hemodynamic waves in the brain (Aquino et al, 2012;Gao et al, 2015;Gravel et al, 2017;Hindriks et al, 2019). On the other hand, the work of Puckett et al (2016) was based on our cortical hemodynamic model but introduced advancements by not implementing averaging over z, allowing estimations of BOLD versus levels of the cortical depth and with each level defined by independent spatiotemporal hemodynamic processes.…”