Mechanical restriction of intracortical vessel …pjd17/Drew_Lab/Publications_files/Gao...Mechanical restriction of intracortical vessel dilation by brain tissue ... to the cortex

NeuroImage 115 (2015) 162–176

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Mechanical restriction of intracortical vessel dilation by brain tissuesculpts the hemodynamic response

Yu-Rong Gao a,b, Stephanie E. Greene a, Patrick J. Drew a,b,c,⁎a Center for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, USAb Neuroscience Graduate Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USAc Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, USA

Abbreviations: 2PLSM, two-photon laser scanning mvolume; CSF, cerebrospinal fluid; fMRI, functional magnforelimb/hindlimb; GFAP, glial fibrillary acidic protein; Lpolished and reinforced thin-skull; ROI, region of interesspace.⁎ Corresponding author at: Department ofNeurosurgery

University Park, PA 16802, USA.E-mail address: [email protected] (P.J. Drew).

http://dx.doi.org/10.1016/j.neuroimage.2015.04.0541053-8119/© 2015 Published by Elsevier Inc.

a b s t r a c t
a r t i c l e i n f o
Article history:Received 26 January 2015Accepted 27 April 2015Available online 5 May 2015

Keywords:Two-photon microscopySomatosensory cortexVoluntary locomotionTissue mechanics

Understanding the spatial dynamics of dilation in the cerebral vasculature is essential for deciphering thevascular basis of hemodynamic signals in the brain. We used two-photon microscopy to image neural activityand 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 thanarterial dilations in the somatosensory cortex immediately outside the FL/HL representation, demonstratingthat the vascular response during natural behaviors was spatially localized. Surprisingly, we found that locomo-tion 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 imagingrevealed that, unlike surface vessels, intracortical vessels were tightly enclosed by brain tissue. A mathematicalmodel showed that mechanical restriction by the brain tissue surrounding intracortical vessels could accountfor the reduced amplitude of intracortical vessel dilation relative to surface vessels. Thus, under normal condi-tions, the mechanical properties of the brain may play an important role in sculpting the laminar differences ofhemodynamic responses.

© 2015 Published by Elsevier Inc.

Introduction

Increases in neural activity in the brain are usually followed by local-ized increases in blood flow and oxygenation (Raichle and Mintun,2006). Because hemodynamic signals are used to study cognition andperception in humans, it is important to understand the underlying vas-cular mechanism that causes these changes in blood flow in the awakebrain at the level of single vessels (Hillman, 2014). Blood is transportedto the cortex through a network of interconnected arteries on thesurface of the brain (Duvernoy et al., 1981; Blinder et al., 2010) whichfeed into penetrating arterioles that enter perpendicularly into thecortex. The penetrating arterioles then ramify into the capillary bed(Blinder et al., 2013). Deciphering which components of the cerebralvasculature mediate flow changes is critical for understanding the vas-cular mechanisms underlying hemodynamic signals (Kim and Ogawa,2012). Specifically, where and to what extent the vessels in the arterial

icroscopy; CBV, cerebral bloodetic resonance imaging; FL/HL,FP, local field potential; PoRTS,t; TiRS, Thresholding in Radon

, Pennsylvania StateUniversity,

tree dilatewill determine the spatial pattern of blood flow changes. Thishas practical bearing on fMRI imaging, as it is currently a matter of de-bate whether hemodynamic signals of surface vessels or intracorticalvessels are more reliable indicators of local neural activity (Zhao et al.,2006; Kim and Kim, 2010; Goense et al., 2012; Huber et al., 2013;Poplawsky and Kim, 2014).

Arteries are composed of an inner layer of endothelial cells, and anouter layer of smooth muscle cells which control the diameter of thevessel. Because the endothelial and smoothmuscle cells of blood vesselsare electrically coupled via gap junctions, extensive studies in the pe-ripheral vasculature (Segal and Duling, 1986; Bagher and Segal, 2011)and in the vasculature of the brain (Jensen and Holstein-Rathlou,2013) have established that vasodilation and vasoconstriction signalsare conducted or propagated through the vascular network. In thebrains of anesthetized animals (Iadecola et al., 1997; Erinjeri andWoolsey, 2002; Berwick et al., 2008; Chen et al., 2011; Chen et al.,2014), it has been shown that these dilations are conducted throughthe surface portion of arterial tree. Similarly, in vitro, dilation can beconducted upstream away from the site of initiation (Dietrich et al.,1996). There are no known active conductances in the vasculature,and the voltage–diameter relationship has a saturating non-linearityassociated with it (Wölfle et al., 2011), so the propagated dilation de-creaseswith distance from the site of initiation. Because of the long elec-trotonic length constants of arterioles (Segal and Duling, 1986; Bagher

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Table 1Number and depth of vessels imaged.

Animal Surface branch order0 Surface branch order1 0 b depth ≤ 50 μm

Number Peak dilation (%) Run fraction (%) Number Peak dilation (%) Run fraction (%) Number Peak dilation (%) Run fraction (%)

Arterioles C57 (9) 30 22.0% ± 7.4% 25.6% ± 13.6% 36 22.0 ± 7.2% 23.2% ± 9.5% 19 13.9% ± 7.3% 23.9% ± 12.8%Thy1-GCaMP3 (6) 9 22.9% ± 10.3% 24.0% ± 8.4% 11 20.8% ± 8.4% 21.2% ± 6.9% 8 10.4% ± 4.5% 25.7% ± 13.5%Sum 39 22.2% ± 8.0% 25.2% ± 12.5% 47 21.8% ± 7.4% 22.7% ± 9.0% 27 12.9% ± 6.7% 24.4% ± 12.8%

Animal Surface Branch Order0 Surface Branch Order1 0 b depth ≤ 50 μm


Venules C57 (9) 18 8.0% ± 4.8% 22.4% ± 10.0% 18 4.8% ± 5.4% 21.0% ± 6.5% 7 4.1% ± 1.4% 24.1% ± 10.2%Thy1-GCaMP3 (6) 4 4.9% ± 1.2% 22.6% ± 11.9% 3 3.0% ± 1.2% 17.6% ± 7.6% 4 3.9% ± 1.5% 24.0% ± 4.9%Sum 22 7.4% ± 4.5% 22.5% ± 10.0% 21 5.1% ± 1.8% 20.5% ± 6.6% 11 4.0% ± 1.3% 24.1% ± 8.3%

163Y.-R. Gao et al. / NeuroImage 115 (2015) 162–176

and Segal, 2011), these dilations propagate without loss of amplitudeover hundreds of microns or more. Since intracortical arterioles areembedded in brain tissue composed of astrocytes and neurons thatrelease vasodilatory substances (Attwell et al., 2010), it is thoughtthat vasodilation is initiated in intracortical vessels and then propa-gates up the vascular tree (Hamel, 2006; Tian et al., 2010). Laminardifferences in the amplitudes of cerebral blood volume responseshave been observed in several MRI studies (Zhao et al, 2006; Kimand Kim, 2010; Polimeni et al 2010; Goense et al., 2012; Poplawskyand Kim, 2014; Pucket, 2014), suggesting that there may be spatiallylocalized hemodynamic signals that could be useful for human neuro-imaging. However, all of these experiments were done in anesthetizedanimals, and anesthesia profoundly decreases the amplitude andslows the dynamics of hemodynamic responses (Martin et al., 2006a,2006b; Goense and Logothetis, 2008; Drew et al., 2011; Pisauroet al., 2013). To better interpret the signals coming from humanneuroimaging studies (Gardner, 2010; Kriegeskorte et al., 2010), it isimportant to understand the microvascular basis of hemodynamic inawake animals, preferably those performing voluntary behaviors(Huo et al., 2014; Huo et al., 2015).

Here,we investigated how the amplitudes of the dilations of individ-ual arterioles and venuleswere affected by cortical location and positionin the vascular network. Using two-photon laser scanning microscopy(2PLSM)(Shih et al., 2012a, 2012b), we quantified the amplitudes ofthe dilation of arterioles and venules at various cortical depths in theforelimb/hindlimb (FL/HL) representation and neighboring regions ofthe somatosensory cortex of mice during voluntary locomotion. Volun-tary locomotion has been used to study sensorimotor dynamics in theoptically accessible limb representations of the cortex (Dombeck et al.,2007; 2009). Because animals naturally and continually engage in vol-untary locomotion, it is an ethologically relevant behavior, and givesus insight into neurovascular coupling during normal behavior. Imagingof vessel dilation and neural activitywith a genetically-encoded calciumindicator showed that intracortical arteriole dilation was correlatedwith nearby neural activity. Surprisingly, we found that surface vesselsdilated substantially more than intracortical vessels. The difference indilation amplitudes between surface and intracortical arterioles wasnot due to the intracortical arterioles being more dilated at baseline,as intracortical and surface arterioles dilated the same amount whenthe mouse was anesthetized with isoflurane. Histological visualizationof smooth muscle and astrocytes revealed that penetrating arterioleenters the brain through a “pial funnel”, a narrowing of the Virchow–

Robin space (Cserr et al., 1986; Iliff and Nedergaard, 2013) as the vesselgoes deeper into the cortex. This implies that any dilation of theintracortical portion of the vessel would entail compression of the sur-rounding brain tissue. Using mathematical modeling, we showed thatthe mechanical restriction of dilation by brain tissue could account forthe difference in dilation amplitude between surface and intracorticalarterioles. Our results suggest that biomechanical constraints interactwith vasodilatory signals to drive large surface vessel dilations duringnatural sensorimotor behaviors.

Materials and methods

Animals and surgery

All surgical and experimental procedures were performed in accor-dance with NIH guidelines and approved by the Pennsylvania StateUniversity Institutional Animal Care and Use Committee (IACUC).Two-photon imaging subjects were 11 (8 male) 2–6 month oldC57/BL6 mice (Jackson Labs, Bar Harbor, ME) and 6 (6 male)2–6 month old Thy1-GCaMP3mice (Jackson Labs). As we found no dif-ferences in vessel dilation amplitudes and fraction of time spent loco-moting between C57/BL6 and Thy1-GCaMP3mice (Table 1), we pooledboth groups of mice for analysis. Polished and reinforced thin-skull(PoRTS) windows were implanted over the right somatosensory cortex(Drew et al., 2010; Shih et al., 2012a, 2012b). We purposely did not usecraniotomies, because they cause inflammation (Xu et al., 2007; Coleet al., 2011), and change the mechanical properties of the brain tissue,making it more compliant (Hatashita and Hoff, 1987). Animals wereallowed to recover for at least two days after the surgery before theywere habituated on the imaging setup, a spherical treadmill (60 mmdiameter) with one degree freedom, equipped with a rotation encoderto detect motion (Nimmerjahn et al., 2009; Gao and Drew, 2014).Mice were habituated for several days in 15 minutes sessions, up to 4times a day. Imaging sessions took place within 1 month of the windowimplantation surgery.

Two-photon microscopy

Animals were imaged using a two-photon microscope consisting ofa Movable Objective Microscope (Sutter Instruments, CA) and a MaiTaiHP laser (Spectraphysics, Mountain View, CA), controlled by MPScansoftware (Nguyen et al., 2006). A 20× 0.5 N.A. (Olympus, Center Valley,PA), or 20 × 1.0 N.A. (Olympus) water dipping objective was used forimaging. Before each imaging session, animalswere briefly anesthetizedwith isoflurane and were infraorbitally injected with 50 μL (50 mg/mL)fluorescein-conjugated dextran (70 kDa; Sigma, St. Louis, MO) orrhodamineB-conjugated dextran (70 kDa; Sigma). The laser was tunedto 800 nm for imaging fluorescein alone, and 910 nm for rhodamineB/GCaMP3 imaging. For isoflurane vasodilation experiments, mice wereplaced on a homoeothermic heating pad while anesthetized with 2%isoflurane in air. Imaging sessions typically lasted ~2 h. Each vesselwas imaged for approximately 15 min at ~8 frames/s. Penetratingvessels were imaged 30–250 μm below the pia. We were able to imagecapillaries clearly down to 200 μm through PoRTS windows with noloss of resolution (Supplementary Fig. 6; Supplementary Table 1). Arteri-oles and venules were identified morphologically (Blinder et al., 2010).

Image processing and data analysis

All reported summary numbers are mean ± standard deviation un-less otherwise indicated. All error bars or shaded areas in plots show

50 b depth ≤ 100 μm 100 b depth ≤ 150 μm 150 b depth Vessel groups Neural areas


27 9.1% ± 5.5% 21.3% ± 7.4% 13 8.0% ± 4.5% 27.0% ± 9.56% 9 7.2% ± 3.9% 25.6% ± 11.1% 36 –

12 7.9% ± 3.6% 23.9% ± 4.7% 3 7.9% ± 7.2% 30.0% ± 5.8% 1 5.60% 20.7% 18 2339 8.7% ± 4.9% 22.1% ± 6.7% 16 8.0% ± 4.8% 27.6% ± 8.9% 10 7.0% ± 3.7% 25.1% ± 10.6% 54 23

50 b depth ≤ 100 μm 100 b depth ≤ 150 μm 150 b depth Vessel groups Neural areas


12 3.5% ± 1.3% 22.7% ± 9.0% 14 3.4% ± 1.1% 22.9% ± 8.1% 6 4.4% ± 1.2% 29.2% ± 8.6% 25 –

6 4.0% ± 1.4% 25.4% ± 6.7% 4 3.4% ± 1.4% 20.8% ± 6.2% 1 4.0% 21.8% 7 1218 3.6% ± 1.3% 23.6% ± 8.2% 18 3.4% ± 1.2% 22.4% ± 7.9% 7 4.3% ± 1.1% 28.2% ± 8.3% 32 12

164 Y.-R. Gao et al. / NeuroImage 115 (2015) 162–176

one standard deviation. All data analysis was performed with Matlab(MathWorks) or SAS (SAS 9.3). All peak responses (vessel dilationsand neural [Ca2+] signals)were taken to be the 95th percentile of diam-eter or neural activity during an imaging session, and peak-to-peakresponses were the difference between the 5th and 95th percentiles ofdiameters.

For surface vessel and neuronal imaging, individual frames werealigned to remove motion artifacts in the x–y plane (Guizar-Sicairoset al., 2008; Drew et al., 2011). Visual inspection of movies, with nearbycapillaries as references, indicated that the z-axismotionwas b5 μm. Toquantify surface vessel diameter, a rectangular boxwasmanually drawnaround a short segment (2–5 μm long) of a vessel. Pixel intensity wasaveraged along the long axis of the vessel, and the diameter was calcu-lated from the full width at half-maximum. Vessel diameter fractionalchanges (ΔD/D0) were calculated by normalizing to the averagediameter during a ~10 second-long stationary period.

For penetrating vessels,where the cross-section of a vesselmaynot becircular and can change shape (Gao andDrew, 2014), the Thresholding inRadon space (TiRS) method (Gao and Drew, 2014) was used to ob-tain a more accurate and robust measure of vessel cross-sectionalarea. The TiRS algorithm essentially performs a full-width at halfmaximum measurement along every angle, making it very robust.Briefly, a square region of interest (ROI) enclosing the vessel of interestwas manually drawn. The images were first transformed into Radonspace, thresholded, and then transformed back to image space,where the vessel cross-sectional area was quantified after a secondthresholding. To facilitate comparison, diameters of penetrating vesselswere taken to be: diameter=2

ffiffiffiffiffiffiffiareaπ

p. As with surface vessels, diameters

were normalized by the stationary diameter baseline to get ΔD/D0.The TiRS algorithm has been validated to work well down to signal-to-noise ratios of 1 or less (Gao and Drew, 2014). The signal-to-noiseratio of the vessels imaged here were all significantly above this level(Supplementary Table 1).

For neural GCaMP3 signals, a rectangular ROI wasmanually selectedto enclose an area with robust GCaMP fluorescence signals, and a con-trol for background fluorescence ROI was drawn inside a vessel wherethere was no neural signal. Averaged pixel intensities inside the back-ground ROI were subtracted from the averaged pixel intensities insidethe neural ROI. Fluorescence fractional changes (ΔF/F0) were calculatedby normalizing to the same average fluorescence intensity during a~10 second long stationary period. The ΔD/D0 and ΔF/F0 time serieswere first filtered with a five-point median filter, and then low-passfiltered at 3 Hz (3rd order Butterworth) (Huo, et al., 2015).

To detect locomotion events, we first low-pass filtered the velocitysignal at 10 Hz, calculated the absolute value of acceleration, and thenbinarized the absolute acceleration signal, using a 10−5 cm/s2 threshold.For calculation of the locomotion triggered average, locomotion eventsseparated by b1 swere considered continuous. All locomotion triggeredevents were N5 s in duration, with N2 s of quiescence beforehand. Be-fore averaging, dilations or fluorescence changes were normalized to

the second before running initiation. The onset time of vessel dilationand neural activity increases were taken to be the x-axis intercept ofthe line given by the 20% and 80% points of the maximum of the aver-aged locomotion-triggered response (Tian et al., 2010). We imaged226 arterioles (138 in FL/HL representation and 88 in other somatosen-sory regions) and 117 venules (64 in FL/HL representation and 53 inother somatosensory regions) that had at least one five-second longlocomotion bout (mean number of bouts for arterioles: 4.4 ± 2.8;venules: 3.8 ± 2.7).

We fitted the dilation amplitude–depth relationship with a linearpiece-wise function with three free parameters: a maximal dilation(Dmax), a minimal dilation plateau (Dmin), and an inflection depth dI:

ΔDD0

¼ −s Dmax−Dminð Þzþ Dmax; z ≤ dIDmin; z N dI

�ð1Þ

whereΔDD0

is the vascular dilation amount and cortical depth is z. The slope,s, is determinedby the three free parameters. All the parameterswere cal-culated by minimizing the mean-square error between the raw data andestimates of the piece-wise fit using the Matlab function fminserchbnd inwhich eachof theparameter hadbounded constraints (10%≤Dmax≤40%,0% ≤ Dmin ≤ 20%, 0 μm ≤ dI ≤ 100 μm). To calculate the 95% confidenceinterval of the depth of the inflection point dI, 1000 bootstrap estimateswere generated. For each bootstrap, the number of points equal to theoriginal data set was randomly re-sampled with replacement.

Histology and anatomical reconstruction

To reconstruct the location of each vessel imaged with 2PLSM inthe somatosensory cortex, animals were transcardially perfused withheparin–saline and 4% paraformaldehyde (PFA) at the conclusion ofthe imaging. Fiduciary marks were made at the corners of the windowto reconstruct imaging sites relative to the functional areas. The brainwas extracted and sunk in PFA in 30% sucrose. The brain was sectionedand stained for cytochrome oxidase (CO) in order to visualize theforelimb, hindlimb, and vibrissae representations in layer IV (Drewand Feldman, 2009).

For anatomical visualization of arterioles and astrocytes we used 4(2 males) 1–3 month old C57/BL6 mice (Jackson Labs, Bar Harbor,ME). Mice were transcardially perfused with warm heparin–saline,followed by 4% PFA, then were flushed with warm heparin–saline. Toprevent the collapse of blood vessels, mice were then perfused with2% gelatin in PBS and placed in ice–water for 15 min (Tsai et al.,2009). The brains were placed in 4% PFA overnight. One brain waskept intact for two-photon imaging and three were coronally sectionedfor confocal imaging. The brain and the brain slices were washed inblocking buffer (BB) (5% goat serum and 1% Trition X-100 in 0.1 MPBS) for 1 h, and then stained with an anti-GFAP antibody (1:500Anti-Glial Fibrillary Acid Protein, Abcam, Cambridge, MA) in BB at 4 °Cfor 48 h. The samples were then rinsed for 30 min in BB four times,


and then stained with a secondary antibody (1:500 Goat Anti-RabbitAlexa 488, Abcam, Cambridge, MA) and 1:250 dilution of Rhodamine-conjugated phalloidin (Life Technologies, Carlsbad, CA) in BB at 4 °Cfor 48 h. The brain or brain slices were then rinsed for 30 min in BBtwice, and 30 min in PBS before being imaged using 2PLSM or OlympusFluoview confocal microscope (Olympus Corporation, Tokyo, Japan).

Mechanical properties of brain tissue and blood vessels

Young's moduli, E, were calculated from shear moduli, G, using therelationship:

E ¼ 2G 1þ νð Þ ð2Þ

where v is Poisson's ratio, taken to be 0.45 for brain tissue (Mousaviet al., 2014). The Young's (or elastic)modulus of a material is a constantthat relates an applied pressure to the amount of deformation.Materialswith higher Young's moduli, such as bone, are stiffer than those withlow Young's moduli, such as adipose tissue. Young's moduli have unitsof pressure. Published estimates of bulk shear moduli for brain tissuespan a wide range, from 1.5 to 19 kPa (Pattison et al., 2010; Weaveret al., 2012), yielding calculated Young's moduli between 4–55 kPa.We used a value in themiddle of this range, the average shear modulusmeasured in (Pattison et al., 2010), 7 kPa, for our calculations, giving20.3 kPa for the Young'smodulus of the brain tissue. Themoduli obtain-ed for the brain tissue in vivo using magnetic resonance elastographyare 10–100× larger than those obtained from ex vivo slices (Franze,2011). While these discrepancies may reflect methodological issues,the differences are likely caused by the confinement by the skull alongwith the incompressibility of brain tissue, as well as the changes causedby the sacrifice of the animal and damage by the slicing procedure,making the in vivo measurements more applicable for our case.

The pressure–strain modulus of a vessel, Ep, is related to theincremental modulus (equivalent to the Young's modulus of the vesselwall), Einc, by the formula (Ethier and Simmons, 2008):

Ep ¼ Einc1− 1− 2T

D0

� �2

2 1−ν2� �

1− 2TD0

� �2 ð3Þ

Einc is the quantity measured in most experiments examining vesselelasticity. T is the thickness of vessel wall, and D0 is the vessel diameterat rest. Taking Einc to be 0.5 × 106 dyn/cm2 (Hajdu and Baumbach,1994) for relaxed arteries at b50 mm Hg internal pressure (Lipowsky,2005), T/D0=0.05 (Hajdu and Baumbach, 1994), and v=0.45,we calcu-late Ep to be 7.4 kPa. If we take a higher estimate of Einc, 2 × 106 dyn/cm2

(Coulson et al., 2004), Ep is 29.4 kPa. The mechanical properties of braintissue in vivo have only been measured for bulk tissue, which may differfrommicroscopic values in the upper cortex.

Results

We performed two-photon imaging of neural activity and vascularresponses in the somatosensory cortex of awake mice that were head-fixed on a spherical treadmill (Dombeck et al., 2007; Huo et al., 2014).Voluntary locomotion is a natural sensorimotor behavior that drives alarge and robust increase in blood flow and volume (Huo et al., 2015)and neural activity (Chapin and Woodward, 1981; Dombeck et al.,2007; Huo et al., 2014) in the limb representations in the somatosensorycortex.We imaged a total of 245 arteriole and 124 venule segments in 17mice through Polished and Reinforced Thin-Skull (PoRTS) windows(Drew et al., 2010; Shih et al., 2012a, 2012b). Using cytochrome oxidasestaining (Drew and Feldman, 2009), we reconstructed imaging sitelocations relative to the forelimb/hindlimb (FL/HL) representation, andseparated vessels into those located in the FL/HL or other somatosensoryrepresentations, allowing us to detect region-specific differences invessel responsiveness.

Neurovascular coupling during voluntary locomotion

We first asked if increases in neural activity during voluntary loco-motion were accompanied by dilation of a nearby penetrating arteriole.To answer this question, we simultaneously imaged penetrating arteri-oles and neuronal [Ca2+] with 2PLSM in Thy1-GCaMP3 transgenic mice(Chen et al., 2012) 50–150 μm below the pia. These mice expressGCaMP3 primarily in layer V pyramidal neurons, allowing us to detectcalcium signals from the apical tufts (Xu et al., 2012) of these neuronsin the superficial layers (Fig. 1A, inset). We found that voluntary loco-motion drove an increase in neural activity in the somatosensory cortex,followed by dilation of nearby arterioles (Figs. 1A&B). In both the FL/HLregion and other surrounding somatosensory regions, locomotion trig-gered an increase in neuropil fluorescence which was of comparableamplitude as those obtained by other groups using Thy1-GCaMP3 mice(Chen et al., 2012; Issa et al., 2014). The neuropil signal will contain sig-nals from axonal (Lecoq et al., 2009; Glickfeld et al., 2013) and dendriticactivity (Tian et al., 2009), which should reflect the local field potential(LFP). The LFP is correlatedwith hemodynamic signals (Logothetis et al.,2001; Viswanathan and Freeman, 2007; Chaigneau et al., 2007). The cal-cium responses of the neuropil in other somatosensory regions werenot significantly different than those in the FL/HL representation(Fig. 1D). The increases in neural activity in both regions were followedby prompt dilation of the nearby arterioles (Fig. 1C), with a 0.35 ±1.15 second delay relative to neural activity increases in the FL/HLrepresentation and a −0.05 ± 1.26 s delay in the other somatosensoryregions. These simultaneous measurements of neural activity and arte-riole diameter demonstrated that increases in neural activity were ac-companied by dilation of nearby intracortical arterioles. We theninvestigated how these evoked dilations propagated through the vascu-lar network.

Locomotion-induced vasodilation was dependent on vessel depth, locationin the vascular tree, and cortical region

To determine how location in the vascular tree and in the somato-sensory cortex affected voluntary locomotion induced vessel dilations,we imaged arterioles and venules from the cortical surface down to250 μmbelow the pia, both inside and outside the FL/HL representation.Because conducted dilatory signals have been shown to attenuatewhenthey travel through vessel branch points (Segal and Neild, 1996), wesegregated the vessels on the pial surface by the number of junctionsbetween them and the penetrating arteriole. We defined penetratingarteriole/ascending venule branches as the part of the pial vesselnetwork that were directly connected to intracortical arterioles orvenuleswithout an intervening junction, and amain branch as a portionof vessel that was at least one junction away from the penetrating orascending vessel (Supplementary Fig. 1).

Surprisingly, we found that the locomotion-induced dilations ofintracortical arterioles and venules were dramatically smaller than thedilations of surface arterioles and venules. A typical example of thedepth-dependence of dilation of an arteriole from a C57/BL6 mouse inthe FL/HL region is shown in Fig. 2A. In this example arteriole, locomo-tion induced dilations throughout the arterial tree, but the amplitude ofarteriole dilation decreased with increasing depth below the pia. Theevoked dilations were largest at the surface (N20%), decreasing to~10% at a depth of 50 μm below the pia, and the portion 200 μmbelow the pia barely responded to locomotion (~5%) (Fig. 2D, left). Asimilar difference between the response amplitudes of intracorticaland surface venules was observed as well (Supplementary Fig. 2). Inthese example vessels, the deeper portions of penetrating arteriolesand ascending venules did not dilate asmuch during bouts of voluntarylocomotion as the more superficial portion of the same vessel and thesurface branches.

We quantified the relationship between depth and dilation ampli-tude across the population of imaged arterioles and venules, both inside

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arteriole ΔD/D0

Fig. 1. Imagingneurovascular coupling in the somatosensory cortexduring voluntary locomotion. A) Example diameter changes (ΔD/D0 (%), in red) of an intracortical arteriole and calciumfluorescence changes in nearby neuropil (ΔF/F0 (%), in green) in the FL/HL region during voluntary locomotion. Locomotion events are denoted by black dots. Increases in neuropilfluorescence preceded the dilation of the arteriole. Inset: 2PLSM image of this example arteriole and its surrounding neuropil, 60 μm below the pial surface from a Thy1-GCaMP3mouse. Green box designates the neuropil ROI in which [Ca2+] signals were quantified. Red box encloses the measured penetrating arteriole. Scale bar 20 μm. B) Locomotion-triggeredaverage of vessel dilations and neuropil fluorescence increases from the example shown in A. Black bar denotes locomotion period. C) Population locomotion-triggered averages ofneuropil fluorescence signal and arteriole dilation in the FL/HL representation (15 neuropil-arteriole pairs, left) and other regions (7 neuron-arteriole pairs, right) of somatosensory cortexfrom 6 Thy1-GCaMP3mice. D) Left, comparison of the locomotion-triggered neuropilfluorescence averaged over 1–4 s after locomotion onset between the FL/HL region and other regions.Right, Plot of locomotion-triggered neuropil response onset times in the FL/HL region and other regions of somatosensory cortex. The response of the neuropil outside the FL/HLrepresentation was not significantly different from the FL/HL region (averaged ΔF/F0%: 10.8 ± 6.7% (FL/HL region), 9.2 ± 5.7% (other regions), p = 0.14, paired t-test) and had a similarlatency (onset time:−0.11 ± 1.19 s (FL/HL region), 0.53 ± 1.16 s (other regions), p = 0.13, paired t-test).


and outside the FL/HL representation, in order to reveal vessel-locationand somatosensory-region differences. As the vessel dilation ampli-tudes and fraction of time spent locomoting were similar in both wild-type C57/BL6 and Thy1-GCaMP3 mice (Table 1), we combined the twogroups for analysis purposes. Both surfacemain branches and penetrat-ing branches of arterioles had significantly larger peak dilations duringlocomotion than intracortical arterioles, both inside and outside theFL/HL representation (Figs. 3A & C). This difference in the amplitude ofthe responses of surface and intracortical arterioles was also apparentif the dilations were averaged 1–4 s after the initiation of locomotion(Supplementary Fig. 3), and if the dilations were calculated as peak-to-peak differences over the whole imaging session (SupplementaryFig. 4), indicating that the laminar difference of dilations was a generalfeature of the cerebral vascular system during locomotion. Lastly, thedifference in dilation amplitude was substantial even within the first100 μm cortex, as the peak dilation of all surface vessels in the FL/HLwas significantly larger than intracortical vessels within 100 μm of thesurface (21.2 ± 7.9% vs. 10.4 ± 6.0%; p b 10−5, paired t-test).

In addition to the differences in locomotion-induced vessel dilationamplitudewith depth, the dilation amplitude depended on the positionin the somatosensory cortex. At all depths, the dilations of vessels in theFL/HL representation elicited by locomotion were significantly largerthan those at the same depth in other somatosensory regions (Figs. 3A& C). On the surface, both the arteriole main branches and penetratingbranches in the FL/HL region exhibited significantly larger peak dila-tions than their counterparts outside the FL/HL representation(Fig. 3C) and intracortical arterioles showed stronger locomotion-evoked peak dilations in the FL/HL representation than intracorticalarterioles in other regions (Fig. 3C).We fit the peak dilation–depth rela-tionship using a piecewise linear model, where the peak dilation wasassumed to decrease linearly from a peak Dmax, with a slope s, with

depth, z, below the cortical surface until an inflection point dI afterwhich it is assumed to be constant, Dmin. For the arterioles in theFL/HL region the best fit was given by: Dmax = 24.0%; Dmin = 8%;s = −0.21%/μm; dI = 76.8 ± 19.3 μm, with r = 0.75. The 95% confi-dence interval of the inflection depth for arteries in the FL/HL represen-tation obtained by bootstrapping was 36.0 μm to 114.3 μm. For thearterioles in the other regions, the fit was: Dmax = 17.0%; Dmin = 6.7%;s = −0.14%/μm; dI = 72.0 ± 29.8 μm, with r = 0.39. For the venulesin the FL/HL region, the best fit was: Dmax = 7.3%; Dmin = 3.7%;s = −0.06%/μm; dI = 56.6 μm, with r = 0.57, while in the otherregions, the fit was: Dmax = 5.9%; Dmin = 3.1%; s = −0.04%/μm;dI = 76.6 μm, with r = 0.62.

The fitting showed that the peak dilation of the locomotion-triggered average of both arterioles and venules dropped with increas-ing distance from the pial surface, until it reached an inflection pointapproximately 50–80 μm below the surface, beyond which it was flat(Figs. 3B & E).

The significantly larger dilation of arterioles inside the FL/HL repre-sentation than those outside the FL/HL representation was surprising,as the large majority of the vessels outside the FL/HL representationwere still very close to the FL/HL representation (Fig. 3B, average arteri-ole distance to the border of FL/HL: 0.52 ± 0.27 mm, average venuledistance: 0.50±0.29mm, 100% arterioleswere b1mm from the borderof FL/HL region, and 95.7% venules b1mm from the border), suggestingthat the hemodynamic response was relatively spatially specific. Thusthe amplitude of dilation of an arteriole reflected both its positionin the vascular tree, as well as its location in the body map in thesomatosensory cortex. Similar depth dependencies were seenwhen the locomotion-triggered response was averaged 1–4 s afterlocomotion initiation (Supplementary Fig. 3) or the differences inthe peak-to-peak responses across the entire imaging session were


used (Supplementary Fig. 4), again indicating the depth dependencewas not due to differences in temporal dynamics of the dilatory re-sponse across depths.

Since intracortical arterioles tended to be slightly larger than surfacevessels (Supplementary Fig. 5), we wanted to test if this size difference

could account for difference in dilation amplitude of surface andintracortical vessels. Smaller surface arterioles are known to bemore re-active and dilatemore in response to sensory stimulation and hypercap-nia (Lee et al., 2001; Drew et al., 2011; Huo et al., 2015), though it is notknown if this is the case for intracortical arterioles. We performed a


multivariate regression of peak dilation against depth and resting diam-eter of the arteriole to determine if diameter differences could accountfor the differences in dilation. The fit yielded the relationship:

ΔDpeak

D¼ −0:11z−0:36Dþ 0:264 ð4Þ

where zwas the depth below the surface andDwas the vessel diameter.There was no significant interaction between cortical depth and vesseldiameter (p = 0.3). With resting diameters typically spanning a20 μm range (10–30 μm, Supplementary Fig. 5), and depth rangingover 200 μm (Fig. 3A), the effect of depth was approximately 3× largerthan any diameter-related reactivity effects (0.11/μm × 200 μm vs.0.36/μm × 20 μm). From these results, we see that the intracorticalpart of the vessels dilated substantially less than the surface part ofthe same vessels, and that this difference was largely attributable tothe depth of the vessel, not size differences.

We also quantified the dilation latencies for surface and intracorticalarterioles. We found no significant differences in the onset latencies forintracortical and surface vessels in either area (Supplementary Fig. 5,Fig. 3D). As venous dilations were very small (typically ~5% at thepeak) and slow, taking tens of seconds to reach their peak (Drewet al., 2011; Huo et al., 2015), we were not able to accurately calculatetheir onset time. Averaged across all arterioles, the mean onset timewas 0.32 ± 0.66 s. The near simultaneous dilation of surface andintracortical vessels was consistent with previous studies showing es-sentially no latency differences between surface and intracortical vesseldilation (Hirano et al., 2011; Nizar et al., 2013; Sekiguchi et al., 2014)(but see (Tian et al., 2010)) and the known rapid (several mm/s) con-duction of vasodilation (Hilton, 1959; Emerson and Segal, 2000; Chenet al., 2011). Our measurement of the variability of vasodilation onsettimes (standard deviations of 0.55, 0.57, 0.61 s for surface main branch,penetrating branch and main intracortical vessels, respectively in theFL/HL representation) was in agreement with other single vessel mea-surements, as well as with human fMRI studies. Nizar et al. (2013)report a standard deviation of 0.6 s in the onset time of vasodilation(calculated from their report of a standard error of 0.1 s over 36wild-type animals). Larger variability in the onset the hemodynamic re-sponse, on the order of seconds, has been seen in human and primatefMRI studies (Handwerker et al., 2004; Watanabe et al., 2013).

The smaller dilation of intracortical vessels, relative to surfacevessels was surprising and counterintuitive. First, propagated dilationscan travel several millimeters with little attenuation (Bagher andSegal, 2011), due to the millimeter-scale length constants of vessels.The dilation did not attenuate much in the surface arteriole network(Fig. 3C), and the dilation propagated very rapidly (Fig. 3D), both sug-gesting that the entire arterial network supplying the somatosensorycortex was strongly electrically coupled (Segal, 2005). Secondly, allknown anatomical evidence pointed to the vasodilatory signal originat-ing in the cortex (reviewed by Hamel (2006)), so we would expect theintracortical vessels to be dilatedmore, as they are closer to the origin ofthe vasodilatory input. Because the dynamics and magnitudes of thedilations we observed in the surface arterioles and venules duringvoluntary locomotion were very similar to those evoked by passivestimulation (Drew et al., 2011), it is unlikely that this dilation pattern

Fig. 2. Locomotion-driven dilations of an example arteriole imaged atmultiple depths below thesured at the surface and atmultiple depths below the cortical surface. The top panel shows the dB) enters into the brain to become intracortical vessel. The lower panels show the diameter chathe pia. Black dots below each panel correspond to locomotion events. The asterisksmark the timat each depth was performed sequentially, so locomotion patterns differ at different imaging dpoints from which images are taken. Scale bars 20 μm. Colored boxes in the top image correspof images are the depth below the surface at which the vessel was imaged. Colored contourtwo relative to the images. Drest and Dloc are the diameters from the above images during rest arelative to rest. C) Photo of PoRTS window with histologically identified somatosensory represebox encloses the area imagedwith 2PLSM that contains the arteriole inA. Scale bar 1mm. D) Locsurface. Each colored curve was taken from the corresponding time series of the example arte

was a unique feature of voluntary locomotion. Because the observedfits of arterial and venous depth dependence of dilation were very sim-ilar across cortical regions and vessel types, it is likely that the depth de-pendence of dilation in arterioles and venules share a common origin.

No difference in maximal dilation between surface and intracortical vessels

One possible origin of the smaller dilation of the intracortical arteri-oles relative to surface arterioleswas that the intracortical portion of thearteriole might have already been dilated by ongoing neural activity inthe awake brain, reducing its dilatory range. This saturation of dilationmight be caused by the tonic action of vasodilatory substances releasedfrom active neurons deep within the brain, where neuronal density ishigher (Tsai et al., 2009). If the deep vessels were already dilated duringnormal wakefulness, then global vasodilation should cause them todilate proportionally less than surface vessels.

To test the hypothesis that deep vessels were already dilated whenthemice were quietly resting between locomotion bouts, we comparedthe peak diameters of individual vessels during locomotion with theirdiameters during anesthesia with isoflurane. We used isoflurane ratherthan CO2 for vasodilation for several reasons. Isoflurane is a potent vaso-dilator (Archer et al., 1987; Todd and Drummond, 1984; Weeks et al.,1990), directly acting on smooth muscles (Flynn et al., 1992) byblocking calcium channels (Buljubasic et al., 1992; Akata et al., 2003)and opening potassium channels (Kokita et al., 1999). These same path-ways are activated in smoothmuscles by neural activity (Bolotina et al.,1994; Filosa et al., 2006). At the dose used here (2%), isofluranecompletely occludes sensory-evoked vasodilation (Blinder et al.,2013), consistentwith isoflurane acting via the same pathway as neuralactivity. In contrast to isoflurane-induced vasodilation, CO2-induced di-lation has an additive interaction with neurally evoked vessel dilations(Corfield et al., 2001; Martin et al., 2006a, 2006b), meaning that CO2 di-lates cerebral vessels via a different mechanism than sensory-evokedneural activity. Additionally, levels of CO2 that induce vasodilationcause anxiety in mice (Taugher et al., 2014), which will drive locomo-tion, confounding the interpretation.

We compared the peak dilations in the locomotion-triggeredresponse to the dilation elicited by isoflurane (Fig. 4). Both surface andintracortical arterioles were significantly more dilated by isofluranethan by locomotion (Fig. 4A). Interestingly, we found that isofluranedilated surface and intracortical arterioles the same amount (~40%),consistent with the observation that there was smooth muscle onboth intracortical and surface arterioles (Supplementary Fig. 7). A simi-lar pattern was found for veins, where intracortical venules dilated bythe same amount as surface venules (Fig. 4B). This result has severalimplications. First, it shows that intracortical and surface vessels havethe same maximal dilatory capacity, and the difference in surface andintracortical dilation amplitudes is not due to a baseline dilation orsaturation of the intracortical vessels. It is also a positive control of ourability to resolve equivalent changes in intracortical vessels diameterwith two-photon microscopy. It is known that isoflurane-inducedvasodilation causes a substantial rise in intracranial pressure(Todd and Drummond, 1984; Scheller et al., 1988), meaning thatthis un-physiologically large dilation of cerebral vessels will causecompression of the brain tissue. Since intracortical arterioles in the

surface. A) Voluntary-locomotion induced diameter changes of an example arteriolemea-iameter changes of three surface branches. The penetrating branch (enclosed by red box innges of this penetrating arteriole in the brain during locomotion from 50 to 200 μmbelowe pointswhere the images of standing and locomoting in Bwere taken. Note that imagingepths. B) 2PLSM images of the example arteriole at multiple depths. Asterisks show timeond to the colored diameter percentage traces in the top panel of A. Numbers on the tops show vessel outlines detected with the TiRS algorithm, and are enlarged by a factor ofnd locomotion, respectively.ΔD/D0 is the diameter percentage changes during locomotionntations overlaid. HL: hindlimb region, FL: forelimb region, Vi: vibrissae region. The blackomotion-triggered average of vessel diameter changes at different depth below the corticalriole in A. Black bars show locomotion.


FL/HL representation were not maximally dilated during locomotion,this suggests that the observed depth-dependence of the dilation wasnot due to the intracortical arterioles being closer to maximal dilation,

or having a more limited range of dilation but rather the dilation ofintracortical arterioles was restricted under normal physiologicalconditions.

Fig. 4.Vasodilation by isoflurane reveals that intracortical vessels were notmaximally dilated during locomotion. A) Plot shows the peak percentage change in diameter of arterioles insidethe FL/HL region during locomotion and during isoflurane treatment. The percentage change of surface arterioles during locomotion (26.2% ± 7.8%) was significantly different from thatunder isoflurane (43.7%± 27.7), (n= 18, p= 0.01, paired t-test). The diameter change of intracortical arterioles during isoflurane treatment (47.7% ± 26.3) was significantly larger thanthat during locomotion (12.1% ± 8.0%), (n = 29, p = 4 × 10−9, paired t-test). Isoflurane caused a similar dilation in surface and intracortical arterioles (p = 0.62, paired t-test). B) Plotshows the peak percentage change in diameter of venules inside the FL/HL region during locomotion and during isoflurane treatment. The diameter change of surface venules during lo-comotion (7.01% ± 2.2%) was not significantly different from that during isoflurane treatment (10.7% ± 9.8%) (n = 8, p = 0.34, paired t-test). However, for the intracortical venules,isoflurane (12.2% ± 10.5%) dilated the vessels significantly more than locomotion (3.4% ± 1.17%) (n = 11, p = 0.02, paired t-test). The dilation caused by isoflurane in surface andintracortical venules was not significantly different (p = 0.78, paired t-test).


Intracortical vessels, but not surface vessels, were enclosed by brain tissue

We then asked if the smaller dilations of intracortical vessels couldbe due to restriction of dilation by the surrounding brain tissue. Totest this hypothesis, we visualized smooth muscles and astrocytes inan intact brain ex-vivo. This approach was used in order to maintainthe vascular ultrastructure. We filled the vasculature with gelatin tomaintain vascular tone (Tsai et al., 2009; Blinder et al., 2013), labeledthe actin-rich smooth muscle that surrounds the arterioles withrhodamine-conjugated phalloidin and astrocytes that ensheath the ves-sel (Abbott et al., 2006; Takano et al., 2006; Koehler et al., 2009) with ananti-GFAP antibody. This allowed us to use 2PLSM to image the interfacebetween brain tissue and the penetrating arterioles up to 100 μmbelowthe surface (Fig. 5). The imaging depth was restricted by scattering andthe penetration of the antibody (Tsai et al., 2009). Rings of smoothmus-cle were visible around surface and intracortical arterioles. Consistentwith previous studies (Zilles et al., 1991; Drewet al., 2010), we observedGFAP expression on the pial surface. Where the penetrating arterioleenters the brain, we observed a ‘pial funnel’ (Fig. 5A), a narrowing ofthe Virchow–Robin space between the vessel and the brain that is filledwith CSF (Cserr et al., 1986; Iliff et al., 2012). As the vessel went deeperbelow the pial surface, the space between the brain and vessel was re-duced, until the astrocytes were in nearly direct contact with the vessel.This narrowing of the Virchow–Robin space took place less than 100 μmbelow the surface, consistent with the inflection point of the dilationamplitude of vessels (Fig. 3B, Supplementary Figs. 3 & 4). Between thebrain tissue and the intracortical vessels (deeper than 50 μm) therewas a very small (~1 μm) perivascular space (Virchow–Robin space)(Zhang et al., 1990; Iliff et al., 2012; Thrane et al., 2013) (Fig. 5B,Supplementary Fig. 7). It seems unlikely that distortion by fixationwould cause these effects as the close apposition of glial endfeet and

Fig. 3. Amplitude of locomotion-evoked dilations depended on vessel location and depth below t(left) and outside (right) the FL/HL representation. Insets show the color codes for the locatio50 b depth ≤ 100 μm, 150 μm: 100 b depth ≤ 150 μm, 200 μm: depth N 150 μm). B) The peak dweremade in the FL/HL region (left) and the other regions (right). Only the penetratingbranchesthese arterioles in the somatosensory cortex relative to the FL/HL representation. Scale bar 1 mmlations of the arteriole surface penetrating branches, main branches, and intracortical arterioles inin the vascular tree, vessels in FL/HL region had larger dilations than the corresponding vessels in op=0.0019;main branch: 23.9%±7.3% (FL/HL region), 17.8%±5.5% (other regions), p=0.0019;paired t-test). In both regions, the dilation of surface penetrating arterioles was significantly lp = 3.9 × 10−9, paired t-test). D) Comparison of the onset time of the three arteriole groups as isurface penetrating branches (FL/HL region: 0.16 ± 0.57 s (penetrating branch), 0.46 ± 0.55 s ((main branch), p=0.003, paired t-test), but therewasno significant onset difference between surfp=0.20, 0.19±0.80 s (other regions), p=0.59, paired t-test). E) The peakdilation of each venuleoutside are light blue (right). The inset shows the locations of these venules in the somatosensor

vessels that we observed has been seen inmany in vivo imaging studies(Takano et al., 2006; Mccaslin et al., 2011; Iliff et al., 2012; Thrane et al.,2013). These anatomical observations indicate that dilations of theintracortical vessels that we observed would require compression ofthe surrounding brain tissue, which is not the case for the surfacevessels. This mechanical interaction between the intracortical bloodvessel and the surrounding brain could be the cause of the differencebetween the dilation amplitude of surface and intracortical vessels.We next sought to determine if mechanical restriction by brain tissuewas physically plausible by using a biomechanical model.

Mathematical modeling of the mechanical restriction of intracortical vesseldilation by brain tissue

For mechanical restriction of the blood vessels to be a viable expla-nation for the differences in surface and intracortical vessel dilation,the brain would have to be stiff enough to resist intracortical vesseldilation, but not stiff enough to completely prevent it. While ex vivomeasurements of brain tissue mechanical properties have indicatedthat brain tissue is very soft (having a Young's modulus of severalhundred kiloPascals (Franze et al., 2012)), in vivo measurements haveconsistently shown that under normal physiological conditions, whenthe brain is enclosed inside the skull, brain tissue is approximatelytwo orders of magnitude stiffer (Pattison et al., 2010). For this model,we will only address the dilation amplitudes of surface and deepintracortical vessels (those deeper than the inflection point in Fig. 3B),(Fig. 6). We did not model the dilation amplitude of the transitionzone between the surface and inflection point, as this would require de-tailed reconstruction of the Virchow–Robin space, and a substantiallymore complicated biomechanical model.

he surface. A) Population average of locomotion-triggered arteriole diameter changes insiden in the vascular tree and depth below the surface (50 μm: 0 b depth ≤ 50 μm, 100 μm:ilation of each arteriole versus the cortical depth along the arteriole that the measurementswere included for depth 0 μm. Lines are piecewise linearfits. The insets show the locations of. HL: hindlimb region, FL: forelimb region, Vi: vibrissa region. C) Comparison of the peak di-the FL/HL region and the other regions (*: p b 0.05, **: p b 0.01, ***: p b 0.001). At all locationsther regions (penetrating branch: 24.0%±7.5% (FL/HL region), 17.0%±7.1% (other regions),intracortical arterioles: 10.1%±6.3% (FL/HL region), 7.7%±3.7% (other regions), p=0.029,arger than the dilation of intracortical arterioles (FL/HL: p = 1.8 × 10−14, other regions:n C. Surface main branches in both regions had significantly delayed onset compared to themain branch), p = 0.043; other regions: 0.30 ± 0.52 s (penetrating branch), 0.73 ± 0.39 sace penetrating and intracortical vessels (intracortical arterioles: 0.34±0.61 s (FL/HL region),plotted versus its depth below the pia. Venules inside the FL/HL region are purple (left), thosey cortex relative to the FL/HL representation. Scale bar 1 mm.

A

60µm

80µm

30µm

100µm

GFAP phalloidin B

Fig. 5. Visualizing arterioles and their interaction with the surrounding brain tissue. A) 2PLSM image of a penetrating arteriole (red, actin labeled with rhodamine-conjugated phalloidin)and surrounding astrocytes (green, labeled with anti-GFAP antibody) in an ex vivo brain. The pial funnel, a hole in the brain into which the penetrating vessel enters, is marked with thewhite arrow. B) Four images of the penetrating arteriole in A taken at increasing depths below the pia. The perivascular space between astrocytes and vessel wall below the surface wasvery small. Any dilation of the intracortical vessel would compress the surrounding brain tissue. Scale bars 20 μm.

CSF

Brain

Pial funnel

Arteriole

Fig. 6.Modeling the mechanics of surface and intracortical vessel dilation. Schematic of thebrain–vessel interactions showing that the surface vessel is surrounded by CSF, while theintracortical vessel is enclosed by brain tissue. The mechanical environment of the surfacevessel was modeled as a single spring whose elastic modulus was given by the pressure–strain modulus of the vessel (Evessel). For the intracortical portion of the vessel, the mechan-ical environment was equivalent to two springs in parallel, with an elastic modulus as thesum of the pressure–strain modulus of the vessel and the Young's modulus of brain tissue(Eintracortical = Evessel + Ebrain). The pressure drop within a short segment of vessel will besmall, so both segments were assumed to experience equivalent pressures.


We assumed that the vessel wall experienced a pressure that wasthe difference between blood pressure and intracranial pressure, andwas either surrounded by CSF (in the case of a surface vessel), or bybrain tissue (intracortical vessel) (Fig. 6). The forces exerted on theblood vessel wall were assumed to be isotropic. Because blood vesselsare roughly cylindrical (Gao and Drew, 2014; Tsai et al., 2009), theywill experience circularly symmetric forces. Within the physiologicalrange, blood vessel walls can be considered linear elastic solids thatobey Hooke's law (Peterson et al., 1960; Bergel, 1961). Using these as-sumptions, we made an equilibrium 1-D mechanical model where thevessel wall wasmade of isotropic linear elastic solid. Wemade the sim-plifying assumptions that any changes in intracranial pressure causedby vessel dilation were negligible, and only the mechanical propertiesof the vessel and brain tissue came into play. For a cylinder with elasticwalls, such as a blood vessel, the change in diameter is given by:

ΔP ¼ EpΔDD0

ð5Þ

where ΔP is the change in pressure,ΔD and D0 are the changes in vesseldiameter and initial diameter respectively, and Ep is the pressure–strainmodulus (Peterson et al., 1960; Bergel, 1961), and can be thought of asthe “spring constant” of the vessel wall in response to an isotropic force.We assumed that the pressure drop and vessel diameter change werenegligible in the short distance (b250 μm) over which the vessels aremeasured, so both the intracortical and surface portions of the vesselsexperienced the same pressure change, ΔP, and had the same initialdiameter, D0. From our arteriole data where the surface dilations werelarger than the intracortical vessels, we can write:

αΔDintracortical ¼ ΔDsurface ð6Þ

where α is the ratio of surface vessel diameter change to intracorticalvessel diameter change (Figs. 3B & E). Using this model, we then couldexpress the ratio of dilations of the surface vessels as a ratio betweenthe effective Young's modulus of the surface and intracortical vessels:

EintracorticalEsurface

¼ ΔDsurface

ΔDintracorticalð7Þ

For the surface arteriole, the effective Young's modulus was taken tobe the pressure–strain modulus of the vessel alone, Evessel. Wemodeledthe mechanical effects of the brain tissue surrounding the vessel as aspring in parallel with the vascular wall (Fig. 6), so the effective Young'smodulus, Eintracortical, of the portion of vessel in the brainwould be givenby the sum of the Young's modulus of the brain tissue surrounding the

vessel Ebrain, and the pressure–strain modulus of the vessel, yieldingEintracortical = Evessel + Ebrain. From Eqs. (6) and (7), we determinedEintracortical = αEsurface and therefore:

Ebrain ¼ α−1ð ÞEvessel ð8Þ

In the pressure ranges that cerebral arterioles experience (Lipowsky,2005), cerebral arterioles have effective elastic moduli in the range of~7 kPa (Hajdu and Baumbach, 1994). Elastography measurements ofbrain tissue indicate that Ebrain is ~20 kPa (Pattison et al., 2010). Venuleshave similar elastic properties as arterioles at the physiologicallyrelevant pressures (Baird and Abbott, 1977), so brain tissue should re-strict the dilation of venules by a similar factor as arterioles, which isconsistent with what was seen experimentally (Fig. 3E). Putting theseempirically measured quantities into Eq. (8), our mathematical modelpredicted that surface vessel dilations would be approximately 3×larger than the intracortical vessels. We calculated average α for theset of vessel segments for which we had both surface (penetratingandmain branch) and deep (N100 μm)measurements of vessel dilation


by taking the ratio of the average surface vessel peak dilation to averagedeep intracortical vessel peak dilation. From our data we calculate α tobe 3.10 ± 1.36 (Fig. 3), very close to the value of 2.9 predicted fromthe known mechanical properties of brain tissue and blood vessels(Hajdu and Baumbach, 1994; Pattison et al., 2010). The intermediateamplitude dilations observed in vessel segments near the surfacecould be due to extra space afforded by the pial funnel. Both the Young'smodulus of brain tissue and the pressure–strain modulus of the bloodvessel are in a range where brain tissue will have appreciable effectson the amplitude of vessel dilation.

Discussion

Spatial specificity of surface and intracortical vascular responses

In the present study, we imaged neural and vascular dynamics in thesomatosensory cortex during voluntary locomotion in mice, and foundthat locomotion drove large increases in neural activity followed bylocal blood vessel dilation in the limb representations (Fig. 1). The sig-nificant difference between the amplitudes of arteriole dilation insideand outside the FL/HL representation was surprising, given the closeproximity of the two populations of measured vessels to each other,and given the relative length constants of arterioles. The differences inarterial dilations between adjacent areas that we observed in the so-matosensory cortex support the idea that hemodynamic signals cancarry information about activity on a columnar level (Moon et al., 2013).

Our data can also address the issue of spatial and temporal specificityof the vascular response, which has been a contentious issue in fMRI(Kriegeskorte et al., 2010; Gardner, 2010). The voxel size used in fMRIimaging experiments will encompass many vessels, and it is notknown if these vesselswill respond in a temporally and spatially homog-enous manner, or if individual vessels within the voxel exhibit differentresponse dynamics for different stimuli, or reflect non-local activation.If the hemodynamic response is not spatiotemporally separable(Bießmann et al., 2012), that is, the hemodynamic response is a waveof dilation (Drysdale et al., 2010; Aquino et al., 2012; Aquino et al.,2014a) then the non-separable hemodynamic response should betaken into account when decoding hemodynamic signals (Bießmannet al., 2012; Aquino et al., 2014b). Surface arteries outside the FL/HL rep-resentation had delayed (b0.25 s) and smaller dilations to locomotionthan surface arteries inside the FL/HL representation. This delay in thedilation corresponds to an effective velocity across the cortical surfaceof N2 mm/s, in agreement with other measures of hemodynamicwaves (Aquino et al., 2012). The propagation velocity of the hemody-namic wave across the cortex will be slower than the propagation ofthe dilation within the arteries, as the arteries do not (usually) formstraight-line connections between regions. Thus, our results supportthe existence of hemodynamics waves (Drysdale et al., 2010; Aquinoet al., 2012) during natural behavior, which implies a more detailedhemodynamic response (Kriegeskorte et al., 2010).

Laminar pattern of dilation cannot be accounted for by peripheralinnervation of pial vessels

An alternative explanation for the difference between surface andintracortical arteriole dilation seen here is that vasodilatory signals, viaperipheral innervation of the large vessels (Hamel, 2006) are targetedon surface vessels and conducted down into the intracortical vessels.We think that this is highly unlikely for several reasons. First, targetedoptogenetic stimulation of cortical neurons induces increases in bloodvolume (reflecting vasodilation) that are indistinguishable from sensorystimulation (Vazquez et al., 2013), indicating that the vasodilatory sig-nals are under the local control of cortical neurons, not the peripheralnerves that innervate the large vessels. Second, the known physiologyand anatomy of the innervations of pial vessels is inconsistent withvasodilatory signals targeted to the surface vessels (Hamel, 2006). Pial

vessels receive large sympathetic and parasympathetic innervation,but no direct input from the central nervous system (Hamel, 2006).During exercise, such as the voluntary locomotion paradigm studiedhere (Huo et al., 2014; Huo et al., 2015), sympathetic tone increases(reviewed in (Christensen and Galbo, 1983)). Since electrical stimula-tion of the sympathetic nerves that innervate the pial vasculature causesdecreases in cerebral blood flow (Tuor, 1990), by constricting the largepial arteries (Baumbach and Heistad, 1983), the increased sympathetictone during locomotion would cause vasoconstriction. Parasympathetictone decreases during exercise (Arai et al., 1989), so any vasodilatoryinput from the parasympathetic nerves will be decreased duringexercise. Thus, the actions of the peripheral nervous system wouldoppose any vasodilation in pial arterioles during exercise and tend tocause vasoconstriction, the opposite of what was observed here. Third,a surface-originating vasodilatory mechanism could not explain thedifferences between surface and intracortical venules, because veinspassively dilate in response to changes in pressure (Edvinsson et al.,1983). Since a surface origin of the vasodilatory input is inconsistentwith physiological and anatomical evidence, and does not explain thedepth dependent responses of venules, themost parsimonious explana-tion dilation differences between surface and intracortical vessels is thatthe mechanical properties of brain limit intracortical vessel dilation.

Alternative origins of reduced intracortical vessel dilations

What explanations other than mechanical interactions couldaccount for the smaller dilations of intracortical arterioles and venulesrelative to those on the surface? Below, we consider several alternativehypotheses.

Given what is known of the anatomy and physiology of cerebralblood vessels, we do not think it is possible that the pial vessels are ex-posed to higher levels of vasodilators. The intracortical arterioles areensheathed by astrocyte endfeet, and receive direct projections fromneurons that release vasoactive NO, ACh, and peptides (Attwell et al.,2010). In contrast, the pial vessels receive no direct innervation fromthe nerves of the brain, and are not ensheathed by astrocytes. Thiswould require that vasoactive transmitters diffuse tens to hundreds ofmicrons to the pial vessels. Ngai and Winn (2002) directly tested thehypothesis that surface vessels respond to spillover of vasodilators byflushing the pial vessels with ACSF during sensory stimulation. Theyfound that flushing the pial surface with ACSF had no effect on sensoryevoked dilation of pial arterioles, but did reduce CO2 evoked dilation,supporting the idea that CO2 dilates vessels via a different pathwaythan neural activity. This result supports the hypothesis that the pialvessel dilation was due to a hyperpolarization originating in the brainand conducted through the vessel to the pial surface. Lastly, differentlevels of vasodilatory substances would not explain the differencebetween intracortical and surface venule dilations.

A second possibility might be that we are unable to detect the dila-tion of the intracortical vessels due to a resolution or signal-to-noiseissue. We think neither of these possibilities is plausible for severalreasons. First, under isoflurane, wewere able tomeasure the same dila-tion in surface and intracortical vessels (Fig. 4), which was a positivecontrol for detection of vessel dilation. Second, we can clearly detectthe difference between the dilation amplitudes of surface vessels andintracortical vessels within 100 μm of the cortical surface. Within thisdepth range, single spines can be resolved through a PoRTS window(Drew et al., 2010), so any dilations should be clearly detected.We were also able to clearly visualize microvessels and capillaries atthe depths that we measured intracortical arterioles and venules(Supplementary Fig. 6), and there was no differences in the averagediameters of themicrovessels within the imaged portion of cortex, indi-cating that our point spread functionwas not degraded (SupplementaryTable 1). Lastly, the TiRS algorithm that we used to quantify vesselcross-sectional area has been validated to work well down the signal-to-noise ratios (S/N) of 1 or less (Gao and Drew, 2014), and our signal


to noise ratio was calculated to be N4 for the imaged vessels(Supplementary Table 1). All of these factors suggest that the smalldilation of intracortical vessels during locomotion cannot be accountedfor by our inability to detect the dilations.

Given that the surface component of the vessels was less than100 μm away from the intracortical portion and that electrical signalspropagate passively through arterioles over millimeters due to theirlarge electrotonic length constants (Wölfle et al., 2011; Segal andDuling, 1986; Segal, 2005), it is unlikely that the smooth muscles ofthe surface arterioles and intracortical arterioles that we measuredfrom have different intracellular potentials. It is possible that largerdilation of surface vessels could be due to pressure waves travelingupstream (Kim, Khan et al. 2013b). One possibility that we cannotrule out is that the surface and intracortical vessels have different con-tractile properties, specifically that intracortical arterioles require amore hyperpolarized voltage to completely relax relative to the surfacearterioles, even if the maximal relaxation of both types of arterioles isthe same (Fig. 4). This seems very unlikely for several reasons. First, toour knowledge, no abrupt spatial changes in excitation–contractioncoupling have ever been observed in vascular smooth muscle, despitethe many studies studying the spatial propagation of vasodilation(Segal, 2005). Second, an abrupt change in the arterioles' contractileproperties as it entered the brain would serve no physiological purpose,as it would reduce bloodflow and the specificity of the blood flow to thecolumn-sized area supplied by a single penetrating arteriole (Blinderet al., 2013; Shih et al., 2012a, 2012b). Lastly, this explanation doesnot explain the differences in dilation of surface and intracortical ve-nules, which have little to no contractile tissue (Edvinsson et al., 1983).

Mechanical limitation of intracortical vessel dilation

Our results showed an unexpected relationship between the posi-tion of blood vessels and their dilation during functional activation.During voluntary locomotion, intracortical vessels dilated substantiallyless than surface vessels, and the depth dependent profile was similaracross all vessel types and somatosensory locations (Fig. 3). This differ-ence in dilation amplitude was not due to saturation of dilation, asisoflurane was able to dilate intracortical vessels more than locomotionand to the same degree as surface vessels (Fig. 4), or due to diameter-related reactivity differences (Supplementary Fig. 5), or to a lack ofsmooth muscle present on intracortical arterioles (SupplementaryFig. 7). The most straightforward explanation of the larger dilation ofsurface arterioles and venules, was that intracortical arterioles and ve-nules are mechanical restricted by brain tissue (Fig. 5B). Importantly,mechanical restriction of vessel dilation would be consistent with anintracortical origin of vasodilation. It is likely that the vasodilation isconducted throughout the vascular tree, but the dilation is only fullyexpressed at the surface (Fig. 3C).

Previous studies using two-photon microscopy to investigate singlevessel dynamics in response to sensory stimulation have used anesthe-tized animals (Takano et al., 2006; Tian et al., 2010; Nizar et al., 2013),which attenuates surface arterial and venous dilations by 70 and100%, respectively (Drew et al., 2011). These studies also used the full-width at half-max to quantify penetrating vessel diameter, which cangive erroneously large results for penetrating vessels due to changesof shape during dilation and constriction (Gao and Drew, 2014). Thesestudies also used craniotomies for optical access, and craniotomiescause a decrease in the stiffness of brain tissue by a factor of two(Hatashita and Hoff, 1987), whichwould reduce themechanical restric-tion effects present in intact skull preparations. Prior studies of the he-modynamic response in awake animals restricted their measurementsto surface vessels (Drew et al., 2011; Huo et al., 2015), or used intrinsicoptical signal imaging (Martin et al., 2006a, 2006b; Pisauro et al., 2013;Huo et al., 2014), which could not resolve single vessel dilations.Previous fMRI studies (Zhao et al., 2006; Jin and Kim, 2010) haveobserved the largest fractional cerebral blood volume (CBV) increases

in the middle of the cortex. The differences between these previous re-sults and ours are likely due to several factors. First, we only imaged~250 μm below the surface of the cortex, meaning that the entiredepth of cortex we imaged over would be lumped together into a single‘upper layer’ in an fMRI experiment, in which the contribution fromintracortical vessels in the upper levels will likely overwhelm anyblood volume changes due to pial vessels. In addition, the vasculardensity on the surface of the brain is ~3× higher than inside the brain,and the pial vasculature is almost entirely composed of large vessels(Tsai et al., 2009), which could cause greatly reduced sensitivity to pialvessel dilation when using supermagnetic particle fMRI techniques,which are sensitive to vessel size (Mandeville, 2012; S.-G. Kim et al.,2013a; J.H. Kim et al., 2013b). Therefore, the CBV signals measuredfrom the pial vessels based on these techniques might be dominatedby the signal from intracortical vessels. Additionally, arterioles in themiddle layers of cortex have substantially more collateral branches(Blinder et al., 2013), and these branches, as well as capillaries, canthemselves dilate (Tian et al., 2010; Hall et al., 2014). It could be thatthe larger increase in CBV observed in the middle layers of the cortexis due to small dilations of many small vessels. It is also possible thatthe mechanical properties of the layers differ, with the middle layers,having a lower Young's modulus and thus restrict dilation less thanupper layers. Elastography measurements have found that the whitematter has a higher Young's modulus than gray matter (Pattison et al.,2010), and atomic force microscopy measurements have observedthat the outer layers of cortex are stiffer than the middle layers(Franze, 2013). We might expect the upper layers, which have moreneuronal processes and fewer cell bodies, to be stiffer.

As brain tissue resists the dilation of intracortical vessels, whythen do surface and intracortical arterioles dilate the same amountunder isoflurane? Isoflurane causes large decreases in cerebrovascularresistance via dilation of large cerebral arteries, with little change inthe cardiac output (Gelman et al., 1984; Janssen et al, 2004), whichwill greatly increase the pressure in thedownstreamvesselswe imaged.Isoflurane relaxes smooth muscle beyond the normal physiologicalrange, dilating the arterioles by N40%. Both arterioles and venules ex-hibit strain-stiffening (Baird and Abbott, 1977; Hajdu and Baumbach,1994; Coulson et al., 2004), meaning the vessel become substantiallystiffer as it dilates. For arterioles, the pressure–strain modulus rises rap-idly for dilations beyond 30%, by a factor of greater than ten between25% and 35% (Coulson et al., 2004). This means that under the concen-trations of isoflurane used here, arterioles are approximately 10× stifferthan in the unanesthetized animal, making the effective stiffness of theartery several times higher than that of the surrounding brain tissue.Under isoflurane, or any other anesthetic agents that would dilate cere-bral blood vessels, the mechanical properties of the vessel, rather thanits surroundings, determine the extent of the dilation, which would ex-plain why the intracortical and surface vessel dilations under isofluranewere of the same size.

We anticipate that under anesthetics that have minimal effect onvascular tone, a similar restriction of intracortical vessel dilation shouldtake place as in awake animals, as brain tissue elasticity measurementsmade under isoflurane (Pattison et al., 2010) are similar to those inawake humans (Mousavi et al., 2014). We think that the restriction ofintracortical vessel dilation that we see here will not just occur duringlocomotion, but also generalize in response to other stimuli, since thedynamics and amplitude of surface vessel dilation in response to volun-tary locomotion (Huo et al., 2015) are nearly identical to those evokedby stimulation of the vibrissae (Drew et al., 2011). It should be kept inmind that dilation of blood vessels will displace CSF. Since CSF is essen-tiallywater, it is incompressible, but is easily displaced. It is thought thatthe CSF either is displaced to remote locations within the brain and spi-nal cord (Jin and Kim, 2010), and/or enters into the vasculature (Kriegeret al., 2012; Iliff et al., 2012) during vasodilation. The pattern of vasculardilation observed here may play a role in the driving the circulation ofthe CSF (Iliff et al., 2013).


Mechanical interactions between tissues in biological systems

If the brain tissue restricts intracortical vessel dilation, changes in thesize of the perivascular space may be an important factor in alterationsof cerebral blood flow associated with aging and disease (D'espositoet al., 2003). The Virchow–Robin space increases in size with age (Zhuet al., 2010), and can become enlarged or altered in certain neurologicaldiseases (Esiri and Gay, 1990; Etemadifar et al., 2011). The perivascularspace can befilledwithmacrophages andother cells, potentially imped-ing the circulation of CSF (Iliff et al., 2012; Xie et al., 2013) andintracortical vessel dilation. Increases in the size of the perivascularspace should permit greater dilation of intracortical vessels, suggestingthese changes might be compensatory mechanisms. Interestingly, themechanical restriction of blood flow by tissue may be a ubiquitousphenomenon that affects blood flow in many organs. The healthy liverhas similar mechanical properties to the brain (Glaser et al., 2012),and the stiffness of the liver is dramatically increased by cirrhosis(Sandrin et al., 2003). Consistent with the hypothesis that the mechan-ical properties of the tissue can reduce vasodilation, postprandialincreases in blood flow in cirrhotic patients are substantially reducedrelative to healthy controls (Ludwig et al., 1998).

The mechanical properties of cells, tissues and their surroundingenvironment are important for normal physiological function andplay a role in many pathologies (Janmey and Miller, 2011), includingcancer (Suresh, 2007). In the brain, mechanical factors have beenpostulated to shape cortical folding (Van Essen, 1997; Hilgetag andBarbas, 2006), to guide neural development (Siechen et al., 2009;Franze, 2013), and to influence the spatiotemporal dynamics of theBOLD signals (Aquino et al., 2012). Our results suggest that the spa-tial profile of hemodynamic signals depends not only upon neuralactivity, but it is also sculpted by the mechanical properties of thebrain.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2015.04.054.

Conflict of interest statement

The authors declare no competing financial interests.

Author Contributions

Y.-R. G and P.J.D. conceived and designed the experiments, analyzedthe data and wrote the manuscript. Y.-R. G and S.E.G. acquired data.

Acknowledgments

This work was supported by a National Scientist Development grantfrom the AHA (12SDG9130022), a Scholar Award from theMcKnight En-dowment Fund for Neuroscience, NS078168 and NS079737 from the NIHto PJD, and ARRA stimulus funds through NS070701. We thank K. PurdyDrew, C. Drapaca, B. Gluckman, F. Constanza, andN. Zhang for helpful dis-cussions, C. Mateo, P. Tsai, and A. Shih for comments on the manuscript,and M.M. Rolls for use of her confocal microscope.

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Mechanical restriction of intracortical vessel …pjd17/Drew_Lab/Publications_files/Gao...Mechanical restriction of intracortical vessel dilation by brain tissue ... to the cortex

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