The brain is a metabolically demanding organ, consuming 20% of the body’s energy supply despite being only 2% of its weight (Clark and Sokoloff, 1999; Raichle and Gusnard, 2002). To ensure an adequate supply of oxygen and nutrients, brain arterioles dilate transiently in response to local increases in neural activity – a process called functional hyperemia (Iadecola, 2017). These vascular dynamics underlie hemodynamic imaging techniques used to map regional neural activity, such as blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) (Logothetis and Wandell, 2004; Vanzetta et al., 2014; Raichle and Mintun, 2006; Howarth et al., 2021). Arteriole dynamics are also crucial in buffering the brain from fluctuations in systemic blood pressure (autoregulation), and in driving clearance of metabolic waste products from the brain along perivascular spaces (Cipolla, 2009; Zhao et al., 2015; Iadecola, 2017; Presa et al., 2020; van Veluw et al., 2020; Rasmussen et al., 2021). Proper regulation of arteriole dynamics is essential to healthy brain function, as deterioration of neurovascular coupling, cerebral perfusion, and brain clearance contributes to the pathogenesis of many neurologic disorders (Gorelick et al., 2011; Iadecola, 2013; Snyder et al., 2015; Sweeney et al., 2018; Rasmussen et al., 2021). Thus, there is widespread interest in understanding vascular dynamics and how neural activity interacts with and shapes hemodynamic responses.

In vivo studies of functional hyperemia typically engage vascular dynamics indirectly by activating neurons (e.g., with sensory stimulation), which in turn leads to the hemodynamic response. This makes it difficult to study intrinsic properties of the blood vessels independent from neuronal activity and neurovascular coupling pathways. For instance, impaired vascular responses in disease may be due to reduced neural activity, altered neurovascular coupling, or loss of compliance in the vascular wall. Similarly, the kinetics of arterial contraction and relaxation in vivo cannot be understood independently of the kinetics of the neurovascular coupling mechanisms. The need to modulate arteriole diameter and/or blood flow independently from neural activation has led to the use of pharmacological approaches or systemic manipulations (such as a CO₂ challenge or modulation of systemic blood pressure) (Kara and Friedlander, 1999; Leithner et al., 2010; Tarantini et al., 2015; Masamoto and Vazquez, 2018). However, these methods do not provide spatiotemporal specificity for microvessels in the brain. Intracortical injection of vasoactive substances improves spatial specificity (Cai et al., 2018), but remains low throughput and can act on nonvascular cell types. Due to these limitations, there is a need for methods to directly activate the microvasculature with high spatiotemporal resolution.

Recently, optogenetic techniques have been developed to control vessel diameter with light by expressing opsins in smooth muscle cells. Most studies to date have used wide-field activation of ChR2 with visible wavelength light. However, this technique affects vessels over a large region and predominantly activates the superficial vasculature, limiting the spatial resolution achieved (Wu et al., 2015; Zhang et al., 2015; Rorsman et al., 2018). Some studies have improved the spatial resolution of optogenetics by using visible wavelength lasers to create a focal point of activation, but this also activates primarily surface vessels (Hill et al., 2015; Mateo et al., 2017; Nelson et al., 2020). Achieving greater imaging depths has been attempted with fiber bundle probes (Kim et al., 2017) but this reduces imaging resolution and causes tissue damage. Two-photon optogenetic activation allows deeper imaging and more precise spatial control than single-photon activation. This method has been used to activate ChR2 in mural cells to examine contractility with single-vessel resolution (Hill et al., 2015; Tong et al., 2020a; Hartmann et al., 2021). However, imaging and excitation in these studies required use of the same laser, limiting the ability to separate regions of stimulation from observation. Further, these studies used the original ChR2 (H134R) variant, and opsins with improved two-photon cross-sections have since become available.

Here, we introduce an approach to constrict cerebral arterioles with high spatiotemporal resolution up to hundreds of microns below the surface. We use the red-shifted opsin ReaChR (Lin et al., 2013) which has strong photocurrents and a long activation wavelength well suited for two-photon stimulation. This method allows for constriction of individual or multiple branches of pial or penetrating arterioles by driving cell-specific depolarization of smooth muscle cells. By combining dual-light paths and independent focusing of the excitation and imaging lasers, we constricted vessels independently from the depth of the imaging plane, making this a useful tool to manipulate and monitor vessel diameter in three dimensions. This approach is advantageous over ChR2 activation of vascular smooth muscle because it can manipulate single vessels over larger cortical depths, and longer wavelengths of light are less likely to create spurious vascular changes independent of opsin expression (Rungta et al., 2017).

ReaChR potently depolarizes cells when illuminated with red light (Lin et al., 2013), and exhibits peak two-photon activation efficiency at ~1000 nm (Chaigneau et al., 2016). This makes it ideal for combined two-photon activation and imaging of cerebrovasculature, similar to the all-optical methods developed to activate neurons in vivo using long-wavelength light (~1040 nm) while simultaneously imaging with shorter wavelengths (~800–950 nm) (Packer et al., 2015; Carrillo-Reid et al., 2016; Shemesh et al., 2017; Mardinly et al., 2018; Marshel et al., 2019). To selectively express ReaChR in mural cells, we crossed floxed ReaChR reporter mice with PDGFRβ-Cre mice (see Methods; Figure 1A), which allows expression of ReaChR tagged with mCitrine in vascular smooth muscle cells and pericytes, with negligible off-target expression in neurons or astrocytes (Hartmann et al., 2015; Figure 1B). We imaged vasoconstrictive responses with two-photon microscopy while depolarizing the vascular mural cells either with a pulsed 1040 nm laser for two-photon activation, or a 594 nm light-emitting diode (LED) for single-photon activation (Figure 1C).

Figure 1

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Single-photon activation of ReaChR in vessel walls produces widespread vasoconstriction

We first examined ReaChR activation with single-photon stimulation using a 594 nm LED to stimulate vessels broadly across the cranial window. We found that a single 100 ms pulse of light led to the rapid and widespread constriction of pial arterioles, which then relaxed back to baseline values within 30 s of activation (Figure 2A–D; Video 1). We also observed a weaker constriction (~2–3%) in the pial venules (Figure 2A–D). Although some expression of the opsin was seen in venous mural cells (Figure 1B), the venous response was likely a passive deflation due to widespread reduction in blood flow caused by arteriole constriction given its slower time-course relative to arterioles (Masamoto and Vazquez, 2018).

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We also observed that penetrating arterioles at least 300 µm deep in the cortex constricted with single-photon activation (Figure 2E–G; Video 2). When we applied repeated trains of 100 ms pulses, we were able to sustain penetrating arteriole constriction at 10–20% below baseline levels for over a minute while simultaneously imaging in between each pulse train (Figure 2H, I; Video 3). To demonstrate that constricted arterioles reduced downstream microvascular flow, we performed a line scan on a penetrating arteriole and the first branch of the arteriole–capillary transition (ACT) zone (85 µm deep in the tissue), to gather a measurement of blood flow to the capillary bed (Figure 3). The number of red blood cells (seen as dark streaks in the line scan) was greatly reduced in the branch within 1 s of stimulation and flow completely stopped for approximately 2 s before returning to normal by 6.5 s poststimulation (Figure 3B). The penetrating arteriole rapidly constricted and the flow also stopped briefly around 2 s poststimulation (Figure 3B). Altogether, these results show that single-photon activation of ReaChR in smooth muscle cells causes rapid and robust vasoconstriction and reduced blood flow to the parenchyma.

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Two-photon activation of ReaChR-expressing mural cells produces focal vasoconstrictions

Two-photon light provides greater spatial resolution and targeting of deeper vessels compared to the single-photon activation achieved with the LED. To investigate the spatial control achievable with two-photon optogenetic activation of arterioles, we used a spatial light modulator (SLM) (Chen et al., 2018; Yang and Yuste, 2018) to split a single 1040 nm stimulation beam into multiple beamlets that could be focused on specific locations of interest. With this method, vasoconstriction was evoked only when the stimulation spots were contacting the vessel wall (Figure 4A–E). No vasoconstriction was evoked when the edges of the stimulation spots were 10 µm from the vessel wall, indicating high spatial precision in the X–Y plane. Stimulating pial arterioles led to robust constrictions that were constrained to a few tens of microns around the stimulation spots. We were able to constrict two neighboring branches of a pial arteriole, separated by ~100 µm, independently with no overlap of constrictive responses (Figure 4F–I; Videos 4 and 5). We also used this spatial precision to directly target venules independently of arterioles. Venule branches did not constrict to two-photon activation (Figure 4—figure supplement 1), supporting our assertion that the constriction seen earlier with single-photon activation (Figure 2B–D) was likely a passive effect of the widespread arteriolar constriction.

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Two-photon stimulation also allows targeting of individual vessels deeper in the cortex. We imaged a region with two penetrating arterioles 200 µm below the cortical surface. By splitting the stimulation beam and positioning spots over each vessel, we were able to constrict each vessel independently of the other (Figure 4J–M), showing precise control of vascular diameter between neighboring penetrating arterioles.

Minimal spread of constriction along pial arterioles regardless of laser power

We next assessed the spatial precision of constriction along the length of the vessel. The difference images in Figure 4B, C and G, H demonstrated that there was very limited spread of the constriction beyond the stimulation spots. We tested the effect of laser power on the spatial spread of constriction by stimulating with a broad range of power levels. We found that increasing the laser power led to a small increase in the spread of constriction. For example, a 30-fold increase in power (from 5 to 150 mW total power) led to ~threefold increase in the spread of constriction (from ~25 to ~75 µm) (Figure 5A–H). Increasing the power to ~230 mW at this site led to damage of the vessel wall. Although the threshold for damage varied depending on vessel size and the number and arrangement of the stimulation spots, we frequently observed that high powers (typically >200 mW total across four to six spots) on pial arterioles caused i.v. dye extravasation, and this was occasionally accompanied by widespread vasoconstriction (Figure 5—video 1). Laser-induced vascular injury was also sometimes observed at very high powers (>250 mW) in control mice expressing only YFP in mural cells (see Figure 5—videos 2; 3). However, at laser powers routinely used to induce vasoconstriction in ReaChR mice (<200 mW), we never observed constrictions in control mice (Figure 5—figure supplement 1; Figure 5—videos 4; 5). This confirms that the focal constrictions seen at lower laser powers are ReaChR dependent and not attributable to direct effects of light on blood vessels (Rungta et al., 2017; Rorsman et al., 2018).

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Stimulation power influences the axial resolution of photoactivation

The precision of two-photon photoactivation in 3D also depends on the resolution in the axial dimension, where the point-spread-function is larger than in the lateral dimension (Rickgauer and Tank, 2009; Packer et al., 2015). To measure the axial resolution, we used the SLM to position stimulation spots at multiple depth planes above and below a pial arteriole while imaging the center of the arteriole (Figure 6A, B). When spots were focused 100 µm above or below the vessel at 115 mW total power, the vessel constricted approximately 25–35% of the magnitude caused by direct stimulation of the vessel. At 150 µm below the vessel, constriction dropped to ~10% (Figure 6C, D). Constriction was greater when the stimulation was above the pial surface than when it was deeper in the tissue, likely due to increased light scattering when penetrating the cortical tissue (Figure 6D – compare ‘above pia’ with ‘parenchyma’).

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To ensure that the SLM was accurately focusing the laser power throughout the depth range, we bleached a fluorescent slide with SLM stimulation at the imaging plane and 200 µm above and below the imaging plane. This confirmed that the region of excitation was accurately shaped and placed regardless of the SLM focal plane (Figure 6—figure supplement 1).

Next, we tested the effect of laser power on the axial range of constriction. While the higher laser power used in Figure 6C, D showed substantial axial spread, lower laser powers (20–44 mW) resulted in pial arteriole constriction which was almost exclusively restricted to the stimulation plane, although the magnitude of the constrictive response was reduced (Figure 6E).

An important consideration in determining out-of-focal-plane activation, is that the cone of light extends laterally outside of the focal plane. This could lead to a greater lateral spread of activation outside the focal plane than seen within the plane in Figure 4E. Therefore, we repeated the lateral resolution test with high laser power (115 mW) at different depth planes. When the stimulation spots were laterally offset from the vessel wall, vasoconstriction was greater when stimulating 50–150 µm below the imaging plane than in the imaging plane. However, just a 10-µm lateral offset resulted in very small vasoconstriction at all depths except when very high power levels were used (200 mW) (Figure 6F).

Thus, for precise 3D targeting of vasoconstriction, care must be taken to avoid unwanted activation of vessels within the light cone but outside the focal plane. The range of out-of-focus activation will depend not only on the laser power applied, but also on the objective used, the orientation of the vessel relative to the light cone, and the size of the arteriole.

Vasoconstriction in the axial plane along penetrating arterioles

We next measured the spread of constriction in the axial plane through penetrating arterioles. We stimulated penetrating arterioles 200 µm below the surface with a range of powers and imaged in the stimulation plane and 150–175 µm superficial to that plane (Figure 7A–C). We found that lower powers (20–30 mW) could constrict penetrating arterioles in the stimulation plane with little spread to the superficial plane. With moderate powers (~70 mW), near maximal constriction could be achieved but this led to ~50% constriction in the superficial plane. With higher power (>150 mW), near maximal constriction could be achieved in both planes. Since penetrating arterioles supply columns of cortical microvasculature (Shih et al., 2013), the use of high powers to constrict a large span of the penetrating arteriole could be useful for modulating blood flow to a column of cortical tissue in a reversible manner (Figure 7—figure supplement 1).

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Optogenetic vasoconstriction is slower and smaller in microvessels

Previous work has shown that prolonged optogenetic stimulation of pericytes expressing ChR2 leads to slow constriction of capillaries (Nelson et al., 2020; Hartmann et al., 2021). We therefore tested the efficacy of ReaChR for optogenetic constriction of pericytes. We divided vessels into three categories: penetrating arterioles (zero order; covered by α-smooth muscle actin (SMA)-positive smooth muscle cells), ACT vessels (branch orders 1–4; covered by α-SMA-positive ensheathing pericytes), and capillaries (branch orders 5–9; covered by α-SMA-low/undetectable capillary pericytes)(Figure 8A; Hartmann et al., 2021). Brief pulses of single-photon excitation with the 594 nm LED led to ~14% constriction of penetrating arterioles, ~5% constriction of ACT vessels, and a small ~1% constriction of capillaries (Figure 8B). Single pulses of two-photon stimulation, which elicited robust constriction in penetrating arterioles, led to minimal (~1%) constriction of ACT vessels and no constriction in capillaries (Figure 8C). To determine if more prolonged stimulation of ReaChR would induce constriction, we applied repeated pulses of light with the SLM for 40 s (Figure 8D). While ACT vessels constricted by ~3–4%, capillaries still showed negligible constriction. We then considered the duty cycle of stimulation. Our prior studies stimulated pericytes using line scanning for near constant laser stimulation (Hartmann et al., 2021), whereas the SLM pulses used here were 100 ms with 0.5–1.5 s between pulses. Continuous line-scan stimulation at 1045 nm led to rapid constriction in penetrating arterioles and slower constrictions in the other vessel categories (Figure 8E). Consistent with our prior findings (Hartmann et al., 2021), capillaries exhibited a gradual constriction over 60 s of stimulation. ACT vessels exhibited a more rapid constriction in the early phase. Constriction of both capillaries and ACT vessels reached a maximum of ~10% (Figure 8E). These results show that near continuous stimulation provided by line scans is necessary to constrict the smallest microvessels.

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Faster vasoconstriction with mural cell activation of ReaChR compared to ChR2

We next compared the contractile kinetics of mural cells expressing ReaChR with our previous data from mice expressing ChR2 in mural cells (Hartmann et al., 2021; Figure 9A, B). We computed the rate of constriction over the first 10 s of stimulation for capillaries and ACT vessels (Figure 9C). Since penetrating arterioles expressing ReaChR constricted rapidly and their responses had nearly plateaued by 10 s, we computed the rate of constriction for the first 5 s of stimulation for the penetrating arterioles (Figure 9C). Capillaries showed similar, slow contractile dynamics with both opsins, but ACT vessels and penetrating arterioles exhibited more rapid initial constriction with ReaChR compared to ChR2 vessels.

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Optogenetic vasoconstriction is attainable in awake animals

To ensure that vessels could also be precisely targeted in awake animals, we replicated some key findings in awake mice. As in the anesthetized animals, brief pulses of light from the LED evoked rapid robust constrictions and the constrictions could be maintained with repeated light pulses (Figure 10A, B). Additionally, two-photon activation provided single-vessel precision of vasoconstriction (Figure 10C). In general, there was a slight decrease in constriction amplitude and faster return-to-baseline in awake versus anesthetized animals.

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Use of optogenetic stimulation to modulate sensory-evoked dilation

We next examined the utility of this technique to directly modulate the physiological dynamics of brain arterioles, thereby providing a novel means to dissociate vascular and neuronal responses in processes such as functional hyperemia and vasomotion. Modulation of physiological dynamics is possible because optogenetic activation of mural cells evokes rapid contraction within hundreds of milliseconds (Figure 11—figure supplement 1), compared to the slower dynamics of neurovascular coupling, which occurs on the order of seconds (Silva et al., 2000; Hillman, 2014). We directly compared the response latency of optogenetically evoked constriction to visually evoked dilation in the same penetrating arterioles, and found much faster responses to the optogenetic stimulation (0.6 s to reach 25% of peak constriction) than to the visual stimulus (3.4 s to 25% peak dilation; Figure 11A – magenta vs. blue).

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As a proof-of-principle experiment, we determined whether optogenetically driven constriction could alter sensory-evoked vasodilation. We presented 5 s of visual stimulation together with a brief 100 ms pulse of optogenetic stimulation. This led to a rapid constriction of the vessel followed by a dilation that was lower in amplitude than the dilation to the sensory stimulus alone (Figure 11A, B, green vs. blue). By reducing the visual stimulus duration from 5 to 2 s, the same light pulse completely suppressed the dilation phase of the vascular response following the initial rapid constriction (Figure 11A, B, red vs. blue). Similarly, using multiple pulses of light during the visual stimulation was also sufficient to eliminate the sensory-evoked vasodilation, as shown in population data across animals (Figure 11—figure supplement 2). Thus, vasodilation to a sensory stimulus can be negated by optogenetic ‘clamping’ of arteriole diameter.

Building on techniques recently developed for all-optical interrogation of neurons (Packer et al., 2015; Carrillo-Reid et al., 2016; Marshel et al., 2019), we introduce a technique for all-optical interrogation of the brain vasculature. We expressed the red-shifted opsin ReaChR in vascular mural cells to control arteriole diameter rapidly and reversibly over a range of spatiotemporal scales in vivo. Single-photon activation produced widespread vasoconstriction across the full cranial window, whereas two-photon activation provided single-vessel control of constriction in 3D. Single brief pulses of light could produce robust vasoconstriction with rapid onset, and repeated light pulses could maintain prolonged constriction. Optical stimulation of mural cells expressing ReaChR is therefore a powerful approach for studying arteriole vasoconstriction and the spatiotemporal modulation of cortical blood flow, and for dissociating neuronal and vascular activity in complex processes such as neurovascular coupling and vasomotion.

Prior studies have used the original variant of channelrhodopsin, ChR2 (H134R), to achieve vasoconstriction in vascular mural cells in vivo. One- and two-photon activation of ChR2 led to the gradual constriction of arterioles, requiring >20 s to reach a minimum diameter with near-constant activation (Hill et al., 2015; Tong et al., 2020a; Hartmann et al., 2021). In contrast, continuous activation of ReaChR in penetrating arterioles (using laser line scanning) led to a more rapid constriction, reaching a minimum in <15 s, that was greater in amplitude and returned to baseline more quickly. This suggests that ReaChR is superior to ChR2 for two-photon optogenetic studies of the vasculature, likely because of the higher photocurrent amplitude in ReaChR, compared to ChR2 (H134R), at their respective optimal excitation wavelengths (Lin et al., 2013). The improved tissue penetration of 1040 nm light used to activate ReaChR may also increase stimulation efficiency for parenchymal vessels (Chaigneau et al., 2016).

ReaChR is among the most potent red-shifted opsins for two-photon activation as shown in neurons (Chen et al., 2019), and its availability as a floxed mouse line allows vascular targeting experiments. Mural cells are not easily transduced in vivo using viral approaches. An additional advantage is that the optimal activation wavelength of ReaChR (>1000 nm) does not substantially interfere with imaging of common fluorophores, whereas two-photon ChR2 activation occurs around the imaging wavelengths used for calcium indicators such as Oregon Green or GCaMP (Prakash et al., 2012). This feature facilitates independent imaging of neural activity and vascular stimulation with separate laser lines.

The highly localized constriction seen following a focal activation differs from vasodilation responses seen during functional hyperemia. Following focal activation of neural tissue, vasodilatory signals propagate several millimeters from the activation site (Iadecola et al., 1997; Chen et al., 2011). This long-range propagation of dilation is attributed to endothelial cell signaling through gap junctions (Chen et al., 2014; Longden et al., 2017; Zechariah et al., 2020), and influences the spatial precision of vascular imaging (e.g., fMRI) signals (O’Herron et al., 2016; Rungta et al., 2018; Drew, 2019). The lack of propagation seen when directly activating smooth muscle cells supports the notion that endothelial rather than smooth muscle cells mediate the long-range propagation of signals through vessel walls. However, differences in the propagation characteristics of hyperpolarizing (dilatory) versus depolarizing (constrictive) signals along the vascular wall may also play a role in the propagation range of the signals.

We anticipate that this technique will be useful for studies requiring assessment of vascular dynamics in vivo. The ability to directly modulate vessel diameter and blood flow levels with high spatiotemporal precision could be useful for understanding vascular wall kinetics. We have shown that it is possible to study the contractile kinetics of different vessel types within the microvascular network. Our data confirm the distinct kinetics across arterioles, ACT, capillary and venous zones, with slow contraction occurring in capillaries covered by αSMA-low/undetectable pericytes. These slow kinetics have implications for how contraction of capillary pericytes contribute to blood flow regulation, which is relevant to maintenance of basal capillary flow and flow heterogeneity (Hartmann et al., 2021; Berthiaume et al., 2022; Hartmann et al., 2022).

The ability to concurrently monitor neural and/or astrocytic activity alongside vessels during vascular manipulation opens additional opportunities to study questions on neuro–glia–vascular coupling. For example, offsetting stimulus-evoked dilations (Figure 11) while monitoring neural activity can shed light on how neuronal function depends on functional hyperemia (Moore and Cao, 2008). Neural circuits are extremely complex with many different subtypes of neurons playing different roles. These subtypes have been shown to have different metabolic sensitivities and thus, may be differentially affected by blocking functional hyperemia (Kann, 2016). Additionally, the energy budgets of different cellular functions within neurons are quite different (Howarth et al., 2012) and reducing available energy by blocking functional hyperemia could lead to differing degrees of dysfunction across key cellular processes (e.g., re-establishing the membrane potential, recycling neurotransmitters). These changes could lead to altered circuit activity which could have profound consequences for neural processing. Furthermore, it has been shown that there is a steep gradient of oxygen moving away from penetrating arterioles, and so neurons at greater distances from vessels may be differentially affected by blocking the hyperemic response (Devor et al., 2011).

Optical control of arteriole tone can also be used to study ‘vasculo-neuronal coupling’, a process where alteration in vessel tone modifies neural and astrocyte signals. Theoretical (Moore and Cao, 2008) and brain slice (Kim et al., 2016) studies have proposed the existence of such processes. The direct, precise control of vessel diameter afforded by optogenetics provides a tool to study neuro–glia–vascular interactions in vivo. In addition, the role of vasomotion in clearance of metabolic waste through perivascular pathways could also be studied in more detail with spatiotemporal modulation of arteriole diameter in vivo (van Veluw et al., 2020).

Impaired functional hyperemia is seen in numerous neurologic diseases, including acute injuries caused by ischemia (Summers et al., 2017), and during progressive, age-related pathologies such as cerebral amyloid angiopathy and other small vessel diseases (Niwa et al., 2002; Park et al., 2014). It is currently difficult to pinpoint the basis of impaired neurovascular responses, as defects could arise from loss of neuronal activation, vascular reactivity, coupling between these two processes, or a combination thereof. The ability to selectively block the vascular response with optogenetics provides cleaner access for probing neurovascular physiology and pathophysiology in vivo, independent of neural activity.

Experiments were performed at the Medical University of South Carolina (MUSC) and Augusta University (AU). All surgical and experimental procedures were approved by the Institutional Animal Care and Use Committees of the Universities (current AU protocol #0982).

All data included in this study are presented in the figures. The source data for the average data points presented in the paper are given in the Source Data file.

1. Preprint

   1. Berthiaume AA
   2. Schmid F
   3. Stamenkovic S
   4. Coelho-Santos V
   5. Nielson CD
   6. Weber B
   7. Majesky MW
   8. Shih AY

   (2022) Pericyte remodeling is deficient in the aged brain and contributes to impaired capillary flow and structure

   bioRxiv.

   https://doi.org/10.1101/2022.04.28.489881

   • Google Scholar
2. 1. Cai C
   2. Fordsmann JC
   3. Jensen SH
   4. Gesslein B
   5. Lønstrup M
   6. Hald BO
   7. Zambach SA
   8. Brodin B
   9. Lauritzen MJ

   (2018) Stimulation-induced increases in cerebral blood flow and local capillary vasoconstriction depend on conducted vascular responses

   PNAS 115:E5796–E5804.

   https://doi.org/10.1073/pnas.1707702115

   • PubMed
   • Google Scholar
3. 1. Carrillo-Reid L
   2. Yang W
   3. Bando Y
   4. Peterka DS
   5. Yuste R

   (2016) Imprinting and recalling cortical ensembles

   Science 353:691–694.

   https://doi.org/10.1126/science.aaf7560

   • PubMed
   • Google Scholar
4. 1. Chaigneau E
   2. Ronzitti E
   3. Gajowa MA
   4. Soler-Llavina GJ
   5. Tanese D
   6. Brureau AYB
   7. Papagiakoumou E
   8. Zeng H
   9. Emiliani V

   (2016) Two-photon holographic stimulation of reachr

   Frontiers in Cellular Neuroscience 10:234.

   https://doi.org/10.3389/fncel.2016.00234

   • PubMed
   • Google Scholar
5. 1. Chen BR
   2. Bouchard MB
   3. McCaslin AFH
   4. Burgess SA
   5. Hillman EMC

   (2011) High-speed vascular dynamics of the hemodynamic response

   NeuroImage 54:1021–1030.

   https://doi.org/10.1016/j.neuroimage.2010.09.036

   • PubMed
   • Google Scholar
6. 1. Chen BR
   2. Kozberg MG
   3. Bouchard MB
   4. Shaik MA
   5. Hillman EMC

   (2014) A critical role for the vascular endothelium in functional neurovascular coupling in the brain

   Journal of the American Heart Association 3:e000787.

   https://doi.org/10.1161/JAHA.114.000787

   • PubMed
   • Google Scholar
7. 1. Chen IW
   2. Papagiakoumou E
   3. Emiliani V

   (2018) Towards circuit optogenetics

   Current Opinion in Neurobiology 50:179–189.

   https://doi.org/10.1016/j.conb.2018.03.008

   • PubMed
   • Google Scholar
8. 1. Chen IW
   2. Ronzitti E
   3. Lee BR
   4. Daigle TL
   5. Dalkara D
   6. Zeng H
   7. Emiliani V
   8. Papagiakoumou E

   (2019) in vivo submillisecond two-photon optogenetics with temporally focused patterned light

   The Journal of Neuroscience 39:3484–3497.

   https://doi.org/10.1523/JNEUROSCI.1785-18.2018

   • PubMed
   • Google Scholar
9. 1. Cipolla MJ

   (2009)

   The Cerebral Circulation

   Integrated systems physiology: from molecule to function, The Cerebral Circulation, San Rafael (CA), Morgan & Claypool Life Sciences, 10.4199/C00005ED1V01Y200912ISP002.

   • Google Scholar
10. Book

    1. Clark D
    2. Sokoloff L

    (1999)

    Basic Neurochemistry: Molecular, Cellular and Medical Aspects

    Philadelphia: Lippincott.

    • Google Scholar
11. 1. Devor A
    2. Sakadzic S
    3. Saisan PA
    4. Yaseen MA
    5. Roussakis E
    6. Srinivasan VJ
    7. Vinogradov SA
    8. Rosen BR
    9. Buxton RB
    10. Dale AM
    11. Boas DA

    (2011) “Overshoot” of o₂ is required to maintain baseline tissue oxygenation at locations distal to blood vessels

    The Journal of Neuroscience 31:13676–13681.

    https://doi.org/10.1523/JNEUROSCI.1968-11.2011

    • PubMed
    • Google Scholar
12. 1. Drew PJ

    (2019) Vascular and neural basis of the BOLD signal

    Current Opinion in Neurobiology 58:61–69.

    https://doi.org/10.1016/j.conb.2019.06.004

    • PubMed
    • Google Scholar
13. 1. Gorelick PB
    2. Scuteri A
    3. Black SE
    4. Decarli C
    5. Greenberg SM
    6. Iadecola C
    7. Launer LJ
    8. Laurent S
    9. Lopez OL
    10. Nyenhuis D
    11. Petersen RC
    12. Schneider JA
    13. Tzourio C
    14. Arnett DK
    15. Bennett DA
    16. Chui HC
    17. Higashida RT
    18. Lindquist R
    19. Nilsson PM
    20. Roman GC
    21. Sellke FW
    22. Seshadri S
    23. American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia

    (2011) Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association

    Stroke 42:2672–2713.

    https://doi.org/10.1161/STR.0b013e3182299496

    • PubMed
    • Google Scholar
14. 1. Hartmann DA
    2. Underly RG
    3. Grant RI
    4. Watson AN
    5. Lindner V
    6. Shih AY

    (2015) Pericyte structure and distribution in the cerebral cortex revealed by high-resolution imaging of transgenic mice

    Neurophotonics 2:041402.

    https://doi.org/10.1117/1.NPh.2.4.041402

    • PubMed
    • Google Scholar
15. 1. Hartmann DA
    2. Berthiaume AA
    3. Grant RI
    4. Harrill SA
    5. Koski T
    6. Tieu T
    7. McDowell KP
    8. Faino AV
    9. Kelly AL
    10. Shih AY

    (2021) Brain capillary pericytes exert a substantial but slow influence on blood flow

    Nature Neuroscience 24:633–645.

    https://doi.org/10.1038/s41593-020-00793-2

    • PubMed
    • Google Scholar
16. 1. Hartmann DA
    2. Coelho-Santos V
    3. Shih AY

    (2022) Pericyte control of blood flow across microvascular zones in the central nervous system

    Annual Review of Physiology 84:331–354.

    https://doi.org/10.1146/annurev-physiol-061121-040127

    • PubMed
    • Google Scholar
17. 1. Hill RA
    2. Tong L
    3. Yuan P
    4. Murikinati S
    5. Gupta S
    6. Grutzendler J

    (2015) Regional blood flow in the normal and ischemic brain is controlled by arteriolar smooth muscle cell contractility and not by capillary pericytes

    Neuron 87:95–110.

    https://doi.org/10.1016/j.neuron.2015.06.001

    • PubMed
    • Google Scholar
18. 1. Hillman EMC

    (2014) Coupling mechanism and significance of the BOLD signal: A status report

    Annual Review of Neuroscience 37:161–181.

    https://doi.org/10.1146/annurev-neuro-071013-014111

    • PubMed
    • Google Scholar
19. 1. Howarth C
    2. Gleeson P
    3. Attwell D

    (2012) Updated energy budgets for neural computation in the neocortex and cerebellum

    Journal of Cerebral Blood Flow and Metabolism 32:1222–1232.

    https://doi.org/10.1038/jcbfm.2012.35

    • PubMed
    • Google Scholar
20. 1. Howarth C
    2. Mishra A
    3. Hall CN

    (2021) More than just summed neuronal activity: how multiple cell types shape the BOLD response

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 376:20190630.

    https://doi.org/10.1098/rstb.2019.0630

    • PubMed
    • Google Scholar
21. 1. Iadecola C
    2. Yang G
    3. Ebner TJ
    4. Chen G

    (1997) Local and propagated vascular responses evoked by focal synaptic activity in cerebellar cortex

    Journal of Neurophysiology 78:651–659.

    https://doi.org/10.1152/jn.1997.78.2.651

    • PubMed
    • Google Scholar
22. 1. Iadecola C

    (2013) The pathobiology of vascular dementia

    Neuron 80:844–866.

    https://doi.org/10.1016/j.neuron.2013.10.008

    • PubMed
    • Google Scholar
23. 1. Iadecola C

    (2017) The neurovascular unit coming of age: A journey through neurovascular coupling in health and disease

    Neuron 96:17–42.

    https://doi.org/10.1016/j.neuron.2017.07.030

    • PubMed
    • Google Scholar
24. 1. Kann O

    (2016) The interneuron energy hypothesis: implications for brain disease

    Neurobiology of Disease 90:75–85.

    https://doi.org/10.1016/j.nbd.2015.08.005

    • PubMed
    • Google Scholar
25. 1. Kara P
    2. Friedlander MJ

    (1999) Arginine analogs modify signal detection by neurons in the visual cortex

    The Journal of Neuroscience 19:5528–5548.

    https://doi.org/10.1523/JNEUROSCI.19-13-05528.1999

    • PubMed
    • Google Scholar
26. 1. Kim KJ
    2. Ramiro Diaz J
    3. Iddings JA
    4. Filosa JA

    (2016) Vasculo-neuronal coupling: retrograde vascular communication to brain neurons

    The Journal of Neuroscience 36:12624–12639.

    https://doi.org/10.1523/JNEUROSCI.1300-16.2016

    • PubMed
    • Google Scholar
27. 1. Kim M
    2. Hong J
    3. Kim J
    4. Shin HJ

    (2017) Fiber bundle-based integrated platform for wide-field fluorescence imaging and patterned optical stimulation for modulation of vasoconstriction in the deep brain of a living animal

    Biomedical Optics Express 8:2781–2795.

    https://doi.org/10.1364/BOE.8.002781

    • PubMed
    • Google Scholar
28. 1. Leithner C
    2. Royl G
    3. Offenhauser N
    4. Füchtemeier M
    5. Kohl-Bareis M
    6. Villringer A
    7. Dirnagl U
    8. Lindauer U

    (2010) Pharmacological uncoupling of activation induced increases in CBF and CMRO2

    Journal of Cerebral Blood Flow and Metabolism 30:311–322.

    https://doi.org/10.1038/jcbfm.2009.211

    • PubMed
    • Google Scholar
29. 1. Lin JY
    2. Knutsen PM
    3. Muller A
    4. Kleinfeld D
    5. Tsien RY

    (2013) ReaChR: a red-shifted variant of channelrhodopsin enables deep transcranial optogenetic excitation

    Nature Neuroscience 16:1499–1508.

    https://doi.org/10.1038/nn.3502

    • PubMed
    • Google Scholar
30. 1. Logothetis NK
    2. Wandell BA

    (2004) Interpreting the BOLD signal

    Annual Review of Physiology 66:735–769.

    https://doi.org/10.1146/annurev.physiol.66.082602.092845

    • PubMed
    • Google Scholar
31. 1. Longden TA
    2. Dabertrand F
    3. Koide M
    4. Gonzales AL
    5. Tykocki NR
    6. Brayden JE
    7. Hill-Eubanks D
    8. Nelson MT

    (2017) Capillary K⁺-sensing initiates retrograde hyperpolarization to increase local cerebral blood flow

    Nature Neuroscience 20:717–726.

    https://doi.org/10.1038/nn.4533

    • PubMed
    • Google Scholar
32. 1. Mardinly AR
    2. Oldenburg IA
    3. Pégard NC
    4. Sridharan S
    5. Lyall EH
    6. Chesnov K
    7. Brohawn SG
    8. Waller L
    9. Adesnik H

    (2018) Precise multimodal optical control of neural ensemble activity

    Nature Neuroscience 21:881–893.

    https://doi.org/10.1038/s41593-018-0139-8

    • PubMed
    • Google Scholar
33. 1. Marshel JH
    2. Kim YS
    3. Machado TA
    4. Quirin S
    5. Benson B
    6. Kadmon J
    7. Raja C
    8. Chibukhchyan A
    9. Ramakrishnan C
    10. Inoue M
    11. Shane JC
    12. McKnight DJ
    13. Yoshizawa S
    14. Kato HE
    15. Ganguli S
    16. Deisseroth K

    (2019) Cortical layer-specific critical dynamics triggering perception

    Science 365:eaaw5202.

    https://doi.org/10.1126/science.aaw5202

    • PubMed
    • Google Scholar
34. 1. Masamoto K
    2. Vazquez A

    (2018) Optical imaging and modulation of neurovascular responses

    Journal of Cerebral Blood Flow and Metabolism 38:2057–2072.

    https://doi.org/10.1177/0271678X18803372

    • PubMed
    • Google Scholar
35. 1. Mateo C
    2. Knutsen PM
    3. Tsai PS
    4. Shih AY
    5. Kleinfeld D

    (2017) Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent “resting-state” connectivity

    Neuron 96:936–948.

    https://doi.org/10.1016/j.neuron.2017.10.012

    • PubMed
    • Google Scholar
36. 1. Moore CI
    2. Cao R

    (2008) The hemo-neural hypothesis: on the role of blood flow in information processing

    Journal of Neurophysiology 99:2035–2047.

    https://doi.org/10.1152/jn.01366.2006

    • PubMed
    • Google Scholar
37. 1. Nelson AR
    2. Sagare MA
    3. Wang Y
    4. Kisler K
    5. Zhao Z
    6. Zlokovic BV

    (2020) Channelrhodopsin excitation contracts brain pericytes and reduces blood flow in the aging mouse brain in vivo

    Frontiers in Aging Neuroscience 12:108.

    https://doi.org/10.3389/fnagi.2020.00108

    • PubMed
    • Google Scholar
38. 1. Niwa K
    2. Kazama K
    3. Younkin L
    4. Younkin SG
    5. Carlson GA
    6. Iadecola C

    (2002) Cerebrovascular autoregulation is profoundly impaired in mice overexpressing amyloid precursor protein

    American Journal of Physiology. Heart and Circulatory Physiology 283:H315–H323.

    https://doi.org/10.1152/ajpheart.00022.2002

    • PubMed
    • Google Scholar
39. 1. O’Herron P
    2. von der Heydt R

    (2011) Representation of object continuity in the visual cortex

    Journal of Vision 11:12.

    https://doi.org/10.1167/11.2.12

    • PubMed
    • Google Scholar
40. 1. O’Herron P
    2. Chhatbar PY
    3. Levy M
    4. Shen Z
    5. Schramm AE
    6. Lu Z
    7. Kara P

    (2016) Neural correlates of single-vessel haemodynamic responses in vivo

    Nature 534:378–382.

    https://doi.org/10.1038/nature17965

    • PubMed
    • Google Scholar
41. 1. O’Herron P
    2. Summers PM
    3. Shih AY
    4. Kara P
    5. Woodward JJ

    (2020) In vivo two-photon imaging of neuronal and brain vascular responses in mice chronically exposed to ethanol

    Alcohol 85:41–47.

    https://doi.org/10.1016/j.alcohol.2019.12.001

    • PubMed
    • Google Scholar
42. Software

    1. O’Herron P

    (2022) Vessel diameter code, version 1.0.0

    GitHub.

    https://github.com/poherron/Vessel-Diameter-Code
43. 1. Packer AM
    2. Russell LE
    3. Dalgleish HWP
    4. Häusser M

    (2015) Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo

    Nature Methods 12:140–146.

    https://doi.org/10.1038/nmeth.3217

    • PubMed
    • Google Scholar
44. 1. Park L
    2. Koizumi K
    3. El Jamal S
    4. Zhou P
    5. Previti ML
    6. Van Nostrand WE
    7. Carlson G
    8. Iadecola C

    (2014) Age-dependent neurovascular dysfunction and damage in a mouse model of cerebral amyloid angiopathy

    Stroke 45:1815–1821.

    https://doi.org/10.1161/STROKEAHA.114.005179

    • PubMed
    • Google Scholar
45. 1. Prakash R
    2. Yizhar O
    3. Grewe B
    4. Ramakrishnan C
    5. Wang N
    6. Goshen I
    7. Packer AM
    8. Peterka DS
    9. Yuste R
    10. Schnitzer MJ
    11. Deisseroth K

    (2012) Two-photon optogenetic toolbox for fast inhibition, excitation and bistable modulation

    Nature Methods 9:1171–1179.

    https://doi.org/10.1038/nmeth.2215

    • PubMed
    • Google Scholar
46. 1. Presa JL
    2. Saravia F
    3. Bagi Z
    4. Filosa JA

    (2020) Vasculo-neuronal coupling and neurovascular coupling at the neurovascular unit: impact of hypertension

    Frontiers in Physiology 11:584135.

    https://doi.org/10.3389/fphys.2020.584135

    • PubMed
    • Google Scholar
47. 1. Raichle ME
    2. Gusnard DA

    (2002) Appraising the brain’s energy budget

    PNAS 99:10237–10239.

    https://doi.org/10.1073/pnas.172399499

    • PubMed
    • Google Scholar
48. 1. Raichle ME
    2. Mintun MA

    (2006) Brain work and brain imaging

    Annual Review of Neuroscience 29:449–476.

    https://doi.org/10.1146/annurev.neuro.29.051605.112819

    • PubMed
    • Google Scholar
49. 1. Rasmussen MK
    2. Mestre H
    3. Nedergaard M

    (2021) Fluid transport in the brain

    Physiological Reviews 102:1025–1151.

    https://doi.org/10.1152/physrev.00031.2020

    • PubMed
    • Google Scholar
50. 1. Rickgauer JP
    2. Tank DW

    (2009) Two-photon excitation of channelrhodopsin-2 at saturation

    PNAS 106:15025–15030.

    https://doi.org/10.1073/pnas.0907084106

    • PubMed
    • Google Scholar
51. 1. Rorsman NJG
    2. Ta CM
    3. Garnett H
    4. Swietach P
    5. Tammaro P

    (2018) Defining the ionic mechanisms of optogenetic control of vascular tone by channelrhodopsin-2

    British Journal of Pharmacology 175:2028–2045.

    https://doi.org/10.1111/bph.14183

    • PubMed
    • Google Scholar
52. 1. Rungta RL
    2. Osmanski BF
    3. Boido D
    4. Tanter M
    5. Charpak S

    (2017) Light controls cerebral blood flow in naive animals

    Nature Communications 8:14191.

    https://doi.org/10.1038/ncomms14191

    • PubMed
    • Google Scholar
53. 1. Rungta RL
    2. Chaigneau E
    3. Osmanski BF
    4. Charpak S

    (2018) Vascular compartmentalization of functional hyperemia from the synapse to the pia

    Neuron 99:362–375.

    https://doi.org/10.1016/j.neuron.2018.06.012

    • PubMed
    • Google Scholar
54. 1. Shemesh OA
    2. Tanese D
    3. Zampini V
    4. Linghu C
    5. Piatkevich K
    6. Ronzitti E
    7. Papagiakoumou E
    8. Boyden ES
    9. Emiliani V

    (2017) Temporally precise single-cell-resolution optogenetics

    Nature Neuroscience 20:1796–1806.

    https://doi.org/10.1038/s41593-017-0018-8

    • PubMed
    • Google Scholar
55. 1. Shih AY
    2. Blinder P
    3. Tsai PS
    4. Friedman B
    5. Stanley G
    6. Lyden PD
    7. Kleinfeld D

    (2013) The smallest stroke: occlusion of one penetrating vessel leads to infarction and a cognitive deficit

    Nature Neuroscience 16:55–63.

    https://doi.org/10.1038/nn.3278

    • PubMed
    • Google Scholar
56. 1. Silva AC
    2. Lee SP
    3. Iadecola C
    4. Kim SG

    (2000) Early temporal characteristics of cerebral blood flow and deoxyhemoglobin changes during somatosensory stimulation

    Journal of Cerebral Blood Flow and Metabolism 20:201–206.

    https://doi.org/10.1097/00004647-200001000-00025

    • PubMed
    • Google Scholar
57. 1. Snyder HM
    2. Corriveau RA
    3. Craft S
    4. Faber JE
    5. Greenberg SM
    6. Knopman D
    7. Lamb BT
    8. Montine TJ
    9. Nedergaard M
    10. Schaffer CB
    11. Schneider JA
    12. Wellington C
    13. Wilcock DM
    14. Zipfel GJ
    15. Zlokovic B
    16. Bain LJ
    17. Bosetti F
    18. Galis ZS
    19. Koroshetz W
    20. Carrillo MC

    (2015) Vascular contributions to cognitive impairment and dementia including alzheimer’s disease

    Alzheimer’s & Dementia 11:710–717.

    https://doi.org/10.1016/j.jalz.2014.10.008

    • PubMed
    • Google Scholar
58. 1. Summers PM
    2. Hartmann DA
    3. Hui ES
    4. Nie X
    5. Deardorff RL
    6. McKinnon ET
    7. Helpern JA
    8. Jensen JH
    9. Shih AY

    (2017) Functional deficits induced by cortical microinfarcts

    Journal of Cerebral Blood Flow & Metabolism 37:3599–3614.

    https://doi.org/10.1177/0271678X16685573

    • PubMed
    • Google Scholar
59. 1. Sweeney MD
    2. Kisler K
    3. Montagne A
    4. Toga AW
    5. Zlokovic BV

    (2018) The role of brain vasculature in neurodegenerative disorders

    Nature Neuroscience 21:1318–1331.

    https://doi.org/10.1038/s41593-018-0234-x

    • PubMed
    • Google Scholar
60. 1. Tarantini S
    2. Hertelendy P
    3. Tucsek Z
    4. Valcarcel-Ares MN
    5. Smith N
    6. Menyhart A
    7. Farkas E
    8. Hodges EL
    9. Towner R
    10. Deak F
    11. Sonntag WE
    12. Csiszar A
    13. Ungvari Z
    14. Toth P

    (2015) Pharmacologically-induced neurovascular uncoupling is associated with cognitive impairment in mice

    Journal of Cerebral Blood Flow & Metabolism 35:1871–1881.

    https://doi.org/10.1038/jcbfm.2015.162

    • PubMed
    • Google Scholar
61. Preprint

    1. Tong L
    2. Yuan P
    3. Xue Y
    4. Chen M
    5. Chen F
    6. Bewersdorf J
    7. Zhou ZJ
    8. Grutzendler J

    (2020a) Single cell in vivo brain optogenetic stimulation by two-photon excitation fluorescence transfer

    bioRxiv.

    https://doi.org/10.1101/2020.06.29.179077

    • Google Scholar
62. 1. Tong L
    2. Hill RA
    3. Damisah EC
    4. Murray KN
    5. Yuan P
    6. Bordey A
    7. Grutzendler J

    (2020b) Imaging and optogenetic modulation of vascular mural cells in the live brain

    Nature Protocols 16:472–496.

    https://doi.org/10.1038/s41596-020-00425-w

    • PubMed
    • Google Scholar
63. 1. van Veluw SJ
    2. Hou SS
    3. Calvo-Rodriguez M
    4. Arbel-Ornath M
    5. Snyder AC
    6. Frosch MP
    7. Greenberg SM
    8. Bacskai BJ

    (2020) Vasomotion as a driving force for paravascular clearance in the awake mouse brain

    Neuron 105:549–561.

    https://doi.org/10.1016/j.neuron.2019.10.033

    • PubMed
    • Google Scholar
64. 1. Vanzetta I
    2. Slovin H
    3. Omer DB
    4. Grinvald A

    (2014) Columnar resolution of blood volume and oximetry functional maps in the behaving monkey

    Neuron 42:843–854.

    https://doi.org/10.1016/j.neuron.2004.04.004

    • PubMed
    • Google Scholar
65. 1. Wu Y
    2. Li SS
    3. Jin X
    4. Cui N
    5. Zhang S
    6. Jiang C

    (2015) Optogenetic approach for functional assays of the cardiovascular system by light activation of the vascular smooth muscle

    Vascular Pharmacology 71:192–200.

    https://doi.org/10.1016/j.vph.2015.03.006

    • PubMed
    • Google Scholar
66. 1. Yang W
    2. Yuste R

    (2018) Holographic imaging and photostimulation of neural activity

    Current Opinion in Neurobiology 50:211–221.

    https://doi.org/10.1016/j.conb.2018.03.006

    • PubMed
    • Google Scholar
67. 1. Zechariah A
    2. Tran CHT
    3. Hald BO
    4. Sandow SL
    5. Sancho M
    6. Kim MSM
    7. Fabris S
    8. Tuor UI
    9. Gordon GRJ
    10. Welsh DG

    (2020) Intercellular conduction optimizes arterial network function and conserves blood flow homeostasis during cerebrovascular challenges

    Arteriosclerosis, Thrombosis, and Vascular Biology 40:733–750.

    https://doi.org/10.1161/ATVBAHA.119.313391

    • PubMed
    • Google Scholar
68. 1. Zhang S
    2. Cui N
    3. Wu Y
    4. Zhong W
    5. Johnson CM
    6. Jiang C

    (2015) Optogenetic intervention to the vascular endothelium

    Vascular Pharmacology 74:122–129.

    https://doi.org/10.1016/j.vph.2015.05.009

    • PubMed
    • Google Scholar
69. 1. Zhao Z
    2. Nelson AR
    3. Betsholtz C
    4. Zlokovic BV

    (2015) Establishment and dysfunction of the blood-brain barrier

    Cell 163:1064–1078.

    https://doi.org/10.1016/j.cell.2015.10.067

    • PubMed
    • Google Scholar

Author details

1. Philip J O'Herron

   1. Department of Physiology, Augusta University, Augusta, United States
   2. Department of Neuroscience, Medical University of South Carolina, Charleston, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   poherron@augusta.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8137-9432

2. David A Hartmann

   1. Department of Neuroscience, Medical University of South Carolina, Charleston, United States
   2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Kun Xie

   Department of Physiology, Augusta University, Augusta, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Prakash Kara

   1. Department of Neuroscience, Medical University of South Carolina, Charleston, United States
   2. Department of Neuroscience, University of Minnesota, Minneapolis, United States
   3. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Resources, Software, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4285-1634

5. Andy Y Shih

   1. Department of Neuroscience, Medical University of South Carolina, Charleston, United States
   2. Center for Developmental Biology and Regenerative Medicine, Seattle Children’s Research Institute, Seattle, United States
   3. Department of Bioengineering, University of Washington, Seattle, United States
   4. Department of Pediatrics, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

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