Low-frequency calcium oscillations accompanydeoxyhemoglobin oscillations in ratsomatosensory cortexCongwu Dua,1, Nora D. Volkowb, Alan P. Koretskyc, and Yingtian Pana

aDepartment of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794; bNational Institute on Alcohol Abuse and Alcoholism, NationalInstitutes of Health, Bethesda, MD 20892; and cLaboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders andStroke, National Institutes of Health, Bethesda, MD 20892

Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved September 16, 2014 (received for review June 12, 2014)

Spontaneous low-frequency oscillations (LFOs) of blood-oxygen-level-dependent (BOLD) signals are used to map brain functionalconnectivity with functional MRI, but their source is not wellunderstood. Here we used optical imaging to assess whether LFOsfrom vascular signals covary with oscillatory intracellular calcium(Ca2+i) and with local field potentials in the rat’s somatosensorycortex. We observed that the frequency of Ca2+i oscillations intissue (∼0.07 Hz) was similar to the LFOs of deoxyhemoglobin(HbR) and oxyhemoglobin (HbO2) in both large blood vesselsand capillaries. The HbR and HbO2 fluctuations within tissuecorrelated with Ca2+i oscillations with a lag time of ∼5–6 s. TheCa2+i and hemoglobin oscillations were insensitive to hypercapnia.In contrast, cerebral-blood-flow velocity (CBFv) in arteries andveins fluctuated at a higher frequency (∼0.12 Hz) and was sensi-tive to hypercapnia. However, in parenchymal tissue, CBFv oscil-lated with peaks at both ∼0.06 Hz and ∼0.12 Hz. Although thehigher-frequency CBFv oscillation (∼0.12 Hz) was decreased byhypercapnia, its lower-frequency component (∼0.06 Hz) was not.The sensitivity of the higher CBFV oscillations to hypercapnia,which triggers blood vessel vasodilation, suggests its dependenceon vascular effects that are distinct from the LFOs detected in HbR,HbO2, Ca

2+i, and the lower-frequency tissue CBFv, which were in-

sensitive to hypercapnia. Hemodynamic LFOs correlated both withCa2+i and neuronal firing (local field potentials), indicating thatthey directly reflect neuronal activity (perhaps also glial). Thesefindings show that HbR fluctuations (basis of BOLD oscillations)are linked to oscillatory cellular activity and detectable throughoutthe vascular tree (arteries, capillaries, and veins).

spontaneous low-frequency brain oscillations | resting-state functionalconnectivity | neuronal calcium | cerebral hemodynamic | neuroimaging

Measures of resting-state functional connectivity with func-tional MRI (fMRI) are based on spontaneous low-frequency

blood-oxygen-level-dependent (BOLD) oscillations that occurthroughout the brain with the assumption that regions withcorrelated oscillations are functionally connected (1, 2). Thenetworks that emerge from resting-state functional connec-tivity correspond roughly with neuroanatomical connectivity(3, 4) and are modified by brain diseases (5–7). BOLD signalsin fMRI reflect the interplay between hemodynamics (includingblood volume and velocity of blood flowing in the vessels) andcellular (neuronal and glial) metabolism, which affect the amountof deoxygenated hemoglobin (HbR) in brain tissue that leads tochanges in BOLD fMRI (8, 9). Human studies using near-infraredspectroscopy (NIRS) (10) have reported low-frequency oscillations(LFOs) of ∼0.04–0.1 Hz for oxygenated hemoglobin (HbO2) andHbR in the brain consistent with those measured by BOLD (11).However, there is still no quantitative understanding of the relativedirect contribution of spontaneous oscillations in cellular activity(neuronal and glial) vs. oscillations that reflect hemodynamiccoupling (velocity and vessel diameter) (12) to the resting-statesignal. It is also unclear how fluctuations in HbR progress through

the vascular tree (13); whereas BOLD signals are believed to pre-dominantly reflect postcapillary and venous compartments, recentevidence suggests that capillaries and arteries also contribute (14).Here we test the hypothesis that slow BOLD oscillations re-

flect neuronal oscillatory activity that drives the hemodynamicchanges detected with fMRI. For this purpose we use a multi-modal optical imaging platform whose high spatiotemporal res-olution allowed us to measure spontaneous LFOs in cerebralblood flow velocity (CBFv), HbO2, and HbR in different vascularcompartments (veins vs. arteries) and in parenchymal tissue inthe rat’s somatosensory cortex both under normocapnia (base-line) and hypercapnia (5% CO2). In parallel we measured sponta-neous LFOs in intracellular calcium fluorescence (Ca2+i) using thefluorescent indicator Rhod2-AM (Molecular Probes), which servesas a marker of cellular activity (15). In addition, local field potentials(LFPs) from neurons were measured to assess their correlationswith the hemodynamic LFOs. Hypercapnia dilates cerebral bloodvessels, increasing blood flow, but has minimal effects on neuronalactivity (16, 17) and neurovascular coupling (18–20). Thus, we usedhypercapnia as a strategy to differentiate oscillatory componentsthat are due to neurovascular coupling as opposed to other mech-anisms that affect vascular tone. Fluorescence histochemistryexperiments of Rhod2-Ca

2+i revealed that the Ca2+i signal reflected

cellular activity (neuronal and perhaps also glial activity). Theresults indicate that HbR fluctuations occur throughout the

Significance

Spontaneous low-frequency oscillations (LFOs) of blood-oxy-gen-level-dependent (BOLD) signals in brain constitute thebasis for mapping resting functional connectivity with func-tional MRI (fMRI). However the origin of these LFOs is not wellunderstood. Using optical imaging we provide evidence that (i)LFOs in calcium (marker of cellular oscillations) show frequen-cies similar to those of deoxyhemoglobin (main contributor tothe BOLD signal) and precede them by 5–6 s; (ii) hemodynamicslow oscillations (including LFOs in deoxyhemoglobin) alsocorrelate with spontaneous neuronal firing activity (as assessedwith slow local field potentials); and (iii ) LFOs of deoxy-hemoglobin (HbR) are observed in arteries, veins, and capil-laries. These findings therefore corroborate the cellular basisunderlying resting-state fMRI and indicate that oscillating HbRsignals are detectable across the vascular tree.

Author contributions: C.D., N.D.V., A.P.K., and Y.P. designed research; C.D. and Y.P. car-ried out the experiments and data analysis; and C.D., N.D.V., A.P.K., and Y.P. contributedsignificantly to discussing the results and writing the manuscript.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.1To whom correspondence should be addressed. Email: Congwu.Du@stonybrook.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1410800111/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1410800111 PNAS | Published online October 13, 2014 | E4677–E4686

NEU

ROSC

IENCE

ENGINEE

RING

PNASPL

US

Dow

nloa

ded

by g

uest

on

July

27,

202

0

vascular tree (arteries, veins, and capillaries) and that Ca2+i oscil-lations are strongly correlated but occur before HbR fluctuations.Interestingly, we uncovered that CBFv fluctuations have two distinctcomponents, one at the frequency of HbR that was insensitive tohypercapnia and one at a higher frequency that was reduced byhypercapnia, suggesting its dependence on vascular effects. Parallelstudies of LFP signals of neurons showed oscillations correlatedwith those of CBFv, HbR, and HbO2 that indicate they directlyreflect neuronal oscillatory activity. Thus, the HbR fluctuations thatare the basis of resting-state fMRI are linked to cellular oscillationsand observed in arteries, veins, and capillaries.

ResultsRats were anesthetized with α-chloralose to minimize anesthesia-induced depression of neuronal activity (21–23), after whicha cranial window was created over the somatosensory cortex for invivo assessment using an optical/fluorescence imaging (OFI)platform (SI Appendix, Fig. S1). Simultaneous dynamic images ofCBFv, HbO2, and HbR from relatively large cerebrovascularvessels (>ϕ30 μm) and from parenchymal cortical tissue (in-cluding vessels <ϕ30 μm), and of Ca2+ i fluorescence fluctu-ations in the resting state were acquired with OFI at ∼30-μmspatial and 16-Hz temporal resolution over a 5 × 6 mm2 field ofview. OFI (Fig. 1A) integrates laser speckle imaging with four-wavelength spectral imaging (SI Appendix, Fig. S1) to permitconcurrent dynamic measures of CBFv (λ4 = 830 nm, Fig. 1D);total hemoglobin (tHb) concentration from λ2 = 570 nm, which isan isosbestic point at which the absorption of HbO2 and HbR isequal (Fig. 1B); and HbR from λ3 = 630 nm at which the ab-sorption of HbR predominates (Fig. 1C), allowing separation ofHbR from HbO2 (24). The spontaneous LFOs were retrieved bytime-frequency analysis of the acquired image sequences (Fig.1H, Top and SI Appendix, Fig. S2). To empirically analyze therelationship of the hemodynamic LFOs with the oscillatory cellularactivity (neurons and glia), a fluorescence imaging technique wasapplied to measure intracellular calcium concentration ([Ca2+]i)using Rhod2-AM with excitation at λ1 = 530 nm and emission atλem = 590 ± 10 nm (Fig. 1E). The integration of OFI with opticalcoherence Doppler tomography (ODT) allowed us to obtain 3DCBFv images of the neurovascular network (Fig. 1 F and G) andto separately assess the spontaneous LFOs of CBFv in arteriesfrom those in veins (AF and VF in Fig. 1H). SI Appendix, Fig. S2illustrates the retrieval of spontaneous LFOs in CBFv from CBF

images obtained from OFI and ODT. A similar approach wasused to extract the LFOs in HbO2, HbR, and Ca2+i pixel by pixelfrom the time-lapse images of OFI and averaged over the regionof interest (ROI) (e.g., within a vessel or within parenchymaltissue with vessels <ϕ30 μm) to enhance signal-to-noise ratio.For example, Fig. 1H, Middle shows a vascular LFO in CBFv(unit: %), and Fig. 1H, Bottom shows its spectrogram obtained bya short-time Fourier transform (STFT), which illustrates that theLFO frequency of CBFv peaked at ∼0.12 Hz.

The Global Neurovascular Network Fluctuates at Low Frequency, andCBFv Oscillates Faster Than HbO2 and HbR and Dominates the CBFvSignal Within Blood Vessels. Fig. 2 represents the low-frequencyfluctuations in vessels (artery and vein) and in parenchymal tissueobserved from the different OFI channels and ODT, includingCBFv (by λ4 and λODT), tHb (i.e., cerebral blood volume by λ2),and raw HbR (by λ3) and Rhod2 fluorescence (excited at λ1,emitted at λem) before calculation or correction. Whereas bothOFI and ODT provided LFO signals of CBFv in vessels, OFIadditionally detects CBFv fluctuations in tissue, which includedsignals from very small vessels (<ϕ30 μm) and from perfused bloodflow in the cerebral parenchyma (SI Appendix, Fig. S2). This indi-cates that the neurovascular network fluctuates at low frequency.Figs. 3–5 illustrate LFOs of CBFv, HbO2, and HbR in arteries

(Fig. 3), veins (Fig. 4), and parenchymal tissue (includes vessels<ϕ30 μm) (Fig. 5) during normocapnia (Left) and hypercapnia(Center). In each panel, a spectrogram is inserted to show the fre-quency-time characteristics. During normocapnia, LFOs oscillatingat <0.15 Hz were observed for CBFv, HbO2, and HbR in arteries(Fig. 3, A0–C0) and veins (Fig. 4, A0–C0), and the full half bandwidthof the oscillation spectrum was defined as its oscillation frequencyband as illustrated in Fig. 7, Inset, which was in the range of{0.08 Hz, 0.15 Hz} for CBFv and of {0.03 Hz, 0.1 Hz} for HbO2and HbR and Ca2+fluoresence, as illustrated in Figs. 3–5 (graystrips). The CBFv in arteries and veins oscillated at higherfrequencies (arteries: 0.125 ± 0.013 Hz; veins: 0.13 ± 0.01 Hz,P < 0.006) than those of HbO2 (arteries: 0.06 ± 0.016 Hz; veins:0.07 ± 0.012 Hz) and HbR (arteries: 0.063 ± 0.017 Hz; veins:0.069 ± 0.014 Hz) (Table 1, n = 10). Specifically, CBFv oscillated atapproximately twice the frequency of the LFOs of HbO2 and HbRin arteries (Fig. 3D) and veins (Fig. 4D). However, in tissue, CBFvoscillated with two frequency bands (a lower band at CBFv-L =0.061 ± 0.009 Hz and a higher band at CBFv-H = 0.113 ± 0.016 Hz).

A

3D ODT3D ODT

HbO2

HbR

(570nm)

LFO (CBFv)

(530nm)

Mol

ar e

xtin

ctio

n (c

m

/M)

( m)

1 2

(630nm)3

(830nm)4

5

450 550 650 750 850

40

0

-40

24020016012080400

0.5

0.3

0.1

(3) LFO (Hz)

(1) CBFv

(2) CBFv (%)

t (s)

AF

VF

AFVF

10

410

310

210

-1

LSILSI630nm630nm Ca2+

AF-VFAF-VF

570nm570nm

B C D E

F G H

Fig. 1. OFI for imaging of LFOs in CBFv, HbO2, andHbR from cerebrovascular vessels and cortical tissue.(A) Absorption spectra of HbO2 and HbR to illustratethe principle of OFI. (B and C) Spectral images at λ2and λ3. (D) LSI at λ4 to image relative CBFv. (E) Ca2+

fluorescence with excitation at λ1. (F and G) Quan-titative 3D ODT of CBFv and separation of AF (redarrows) and VF (blue arrows). (H) Extraction of LFOsin an AF and a VF highlighted in F. (Top) time-lapseODT images to show CBFv LFOs. (Middle) LFO withDC removed. (Bottom) Time-varying LFO by STFT.

E4678 | www.pnas.org/cgi/doi/10.1073/pnas.1410800111 Du et al.

Dow

nloa

ded

by g

uest

on

July

27,

202

0

CBFv-L resembles the frequency of HbO2 (0.069 ± 0.012 Hz) andHbR (0.074 ± 0.019 Hz), whereas CBFv-H in tissue resembles thatof CBFv in arteries (0.125 ± 0.013 Hz) and veins (0.13 ± 0.01 Hz).Table 2 summarizes the computed LFO amplitudes of CBFv

and HbO2 and HbR, defined as the mean value of the powerspectrum within the frequency band (i.e., {0.08 Hz, 0.15 Hz} forCBFv and {0.03 Hz, 0.1 Hz} for HbO2 and HbR) against theirbackground mean values. Results show that in the arteries theamplitude of CBFv LFO (75 ± 22%) is higher than that of HbO2(41.2 ± 4.4%, P = 0.037) or HbR (38.4 ± 10%, P = 0.04), in-dicating that in the arteries under normocapnia CBFv dominatesthe signal intensity from LFOs (Fig. 3E). In veins (Fig. 4E), therewere no differences in LFO amplitudes between CBFv (81.5 ±26.9%) and HbO2 (68.8 ± 16.7%, P = 0.34) or HbR (66.2 ±21.2%, P = 0.32). In contrast, in the parenchymal tissue, the LFOamplitudes of HbO2 (78.8 ± 18.3%, P = 0.03) and HbR (76.1 ±10.3%, P = 0.02) were significantly higher than those of CBFv

(37.1 ± 5.0%), indicating that in parenchymal tissue HbO2 andHbR dominate the signal intensity from LFOs.To rule out a potential aliasing effect of physiological artifacts

related to the cardiac and respiratory cycles on the measured LFOsignals, a high sampling acquisition of 12–16 Hz was appliedduring the experiments. As expected, the power spectrum exhibitsoscillations at the respiratory (1.0 Hz, ventilated) and cardiac (4.3Hz) cycles in CBFv and HbO2 in arteries (SI Appendix, Fig. S3 B, 1and 2) but not in veins (SI Appendix, Fig. S3D, 1 and 2). However,the LFOs in CBFv, HbO2, and HbR are clearly observed in bothvessels (SI Appendix, Fig. S3, C1–C3 and E1–E3), indicating thatthe LFO signals were not due to respiration or heart rate.

CO2 Reduces the Higher-Frequency Oscillations in CBFv in Arteries andVeins But Does Not Affect the LFOs in HbO2, HbR, and Lower-FrequencyCBFv in Tissue. To investigate the contribution of vasomotion tothe LFOs in CBFv, HbO2, and HbR, we measured the effects of

0

4

8

12

16

24

VT

0

1

2

3

4

5

0

2

4

6

8

10

0

10

20

30

40

50

0

2

4

6

8

10

0

0.4

0.8

1.2

1.6

2.0

0

1

2

3

4

5

0

10

20

30

40

50

0

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5t (min)

0

0.3

0.6

0.9

1.2

1.5

f (Hz)0 0.2 0.4 0.6 0.8 1

A

CBFv (830nm/1.3 m) HbR (630nm/570nm)CBV (570nm)

Rhod - Ca (590nm)2

3D ODT(a.u.)

(a.u.)

(a.u.)(a.u.) (a.u.)

(a.u.)(a.u.) (a.u.)

(a.u.) (a.u.)

0.1

0.3

0.5

f (H

z)

Art

ery

Vein

Tiss

ue

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

f (Hz)0 0.2 0.4 0.6 0.8 1

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0.1

0.3

0.5f (

Hz)

0.1

0.3

0.5

f (H

z)

0.1

0.3

0.5

f (H

z)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

f (H

z)

0.1

0.3

0.5

f (H

z)

0.1

0.3

0.5

f (H

z)

0.1

0.3

0.5

f (H

z)

0.1

0.3

0.5f (

Hz)

0.1

0.3

0.5

f (H

z)

BA C

D 2+

μ

Fig. 2. LFO signals observed from multichannels of OFIand ODT modalities. Power spectra and spectrogramsof cortical artery, vein, and parenchymal tissue of (A)CBFv (observed by ODT, λ = 1,300 nm or LSI, λ4 = 830nm). (B) Total hemoglobin (tHb) or cerebral blood vol-ume (CBV) (observed by OFI, λ2 = 570 nm). (C) Raw HbRby λ3 = 630 nm. With λ2 it can be used to separate HbO2

from HbR. (D) Rhod2-Ca2+

i fluorescence (emitted at590 nm while excited at λ1 = 530 nm). Location of thevessels is demonstrated in 3D ODT.

After CO2Baseline

Arterial CBFv

Arterial HbO2

Arterial HbR

CBFv (%)

0

25

50

100

HbO2 HbR (%)

Baseline

Freq Comparison

0 00

0 03

0 06

0 09

0 12

CBF HbO2 HbR

Baseline

w CO2

f(H

z)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

*

*****

***

***

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

w CO2

CBF HbO2 HbR

LFO Amplitude Comparison

75

0

25

50

100

75*

0 15

****

****

Arterial HbR

CBF

(%)

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)

0

20

40

60

80

100

0

20

40

60

80

100

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

Arterial CBFv

0.1

0.3

0.5

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

)zH(f

)zH( f 0.1

0.3

0.5

)zH(f

Arterial HbO2

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)C

BF(%

)

0

20

40

60

80

100

0

20

40

60

80

100

A0

B0

C0

A1

B1

C1

D

E Fig. 3. Characteristics of arteriolar LFOs in ratcortex. (Left) LFO power spectra of CBFv (A0), HbO2

(B0) and HbR (C0) and their spectrograms (Insets)under normocapnia. (Center) LFO power spectra ofCBFv (A1), HbO2 (B1), and HbR (C1) and their spec-trograms (Insets) under hypercapnia. Dashed curvesare traces from selected ROIs (m = 6–8) for each rat.Bold curves are averaged traces from the rat. (D andE) Comparison of mean LFO peak frequency andmean amplitude within its oscillation band be-tween normocapnia and hypercapnia across theanimals (n = 10). Asterisks indicate statistical sig-nificance (P < 0.05).

Du et al. PNAS | Published online October 13, 2014 | E4679

NEU

ROSC

IENCE

ENGINEE

RING

PNASPL

US

Dow

nloa

ded

by g

uest

on

July

27,

202

0

hypercapnia (5% CO2 added to the respiratory gases), whichdilates vessels (25), thus reducing vasomotion (26). The valuesfor arterial blood gases, mean arterial blood pressure (MABP)(obtained from a femoral artery), and heart rate (HR) corre-sponded to PCO2 = 35.7 ± 0.5 mmHg, MABP = 98.7 ±7.0 mmHg, and HR = 284.0 ± 3.5 beats per minute for normo-capnia and PCO2 = 60.0 ± 2.9 mmHg, MABP = 94.6 ± 6.9 mmHg,and HR = 289.3 ± 4.3 beats per minute for hypercapnia.The LFOs in CBFv that oscillated at ∼0.12 Hz (red arrows)

were greatly reduced by hypercapnia in arteries (Fig. 3A1), veins(Fig. 4A1), and parenchymal tissue (Fig. 5A1). Within the pa-renchymal tissue, CO2 reduced the higher-frequency LFOs ofCBFv (i.e., CBFv-H at 0.113 ± 0.016 Hz) but did not affect thelower-frequency LFOs of CBFv (CBFv-L = 0.061 ± 0.009 Hzbefore CO2 vs. CBFv-L = 0.058 ± 0.012 Hz after CO2, P = 0.2)(Table 1). Also, CO2 did not change either the LFOs of HbO2 inarteries (e.g., 0.06 ± 0.016 Hz vs. 0.065 ± 0.019 Hz, P = 0.19) orthose of HbR in veins (e.g., 0.069 ± 0.014 Hz vs. 0.072 ±0.009 Hz, P = 0.22), thus indicating that LFOs of CBFv-L, HbO2,and HbR are distinct from LFOs from CBFv-H.In the arteries, CO2 significantly reduced the LFO amplitudes

in CBFv about 41% (from 75 ± 22% to 34 ± 23%, P = 0.04),presumably from arterial dilation, and increased LFO ampli-tudes in HbR (from 38.4 ± 10% to 79.7 ± 24%, P = 0.027) butdid not change LFO amplitudes in HbO2 (from 41.2 ± 4.4% to30.9 ± 10%, P = 0.09). In the veins, CO2 induced similar changesin LFO amplitudes: ∼42.6% decreases in CBFv (from 81.5 ±26.9% to 38.9 ± 8.7%, P = 0.03), ∼43.5% increases in HbR(from 66.2 ± 21.2% to 95 ± 2.1%, P = 0.045), and no changesin HbO2 (68.8 ± 16.7% before vs. 59.9 ± 33.4% after CO2,P = 0.36).In parenchymal tissue, CO2 reduced the LFO amplitudes in

CBFv about 10.9% (37.1 ± 5% to 26.2 ± 1.5%, P = 0.01) but didnot change those in HbO2 (78.8 ± 18.3% vs. 89.9 ± 7.8%, P =0.21) or HbR (76.1 ± 10.3% vs. 71.1 ± 15.5%, P = 0.33), whichindicates that in small vessels (<ϕ30 μm) CO2 influenced CBFvbut not HbO2 or HbR. Thus, hypercapnia could conceivably beused to differentiate the oscillatory components in CBFv LFOsthat are due to neurovascular coupling from other factors that

affect vascular oscillations. This might be particularly helpful forstudies that measure BOLD oscillations at rest with fMRI inregions with a relatively large contribution from blood vesselswith diameters >30 μm.To corroborate that hypercapnia resulted in blood vessel va-

sodilation we used angiographic optical coherence tomography(OCA) to image changes in blood vessel diameters induced byhypercapnia across an artery and a vein [SI Appendix, Fig. S4 A–C,time-lapse 2D OCA during normocapnia (PCO2 = 35.7 ±0.5 mmHg) and hypercapnia (PCO2 = 60.0 ± 2.9 mmHg)], whichshowed that CO2 induced vasodilatation in both the artery andthe vein (SI Appendix, Fig. S4D).A potential concern for spontaneous LFOs is that their

amplitudes may be too weak for reliable estimation. Thus, wecompared the amplitudes of the spontaneous LFOs in somato-sensory cortex with those of the activation signals under forepawelectrical stimulation. The signals we obtained with optical im-aging showed that the magnitudes of the spontaneous oscil-lations were robust albeit weaker than the hemodynamic signalsevoked by forepaw stimulation (18–25% vs. 40%; SI Appendix,Fig. S8).

Ca2+i Fluctuates Within a Frequency Band Similar to That of HbO2 andHbR in Parenchymal Tissue. To assess cellular oscillations, we mea-sured LFOs of Ca2+i fluorescence using the calcium indicatorRhod2-AM locally microinjected into the somatosensory cortex(27). The LFO power spectra of CBFv, HbO2, and HbR withinthe Rhod2-loaded tissue area were almost identical to those insurrounding areas without Rhod2 loading (SI Appendix, Fig. S5B, 1–3), thus documenting no effect of Rhod2 loading on localhemodynamics. There were also no differences in CBFv, HbO2,and HbR responses to CO2 between Rhod2-loaded and non-Rhod2-loaded tissue (SI Appendix, Fig. S5 C, 1–3), indicatingthat Ca2+i labeling did not affect the physiological responses oflocal tissue to CO2.Fig. 5D0 shows the power spectra of Ca2+i fluorescence and

its spectrogram during the baseline period. The LFOs of Ca2+iflorescence fluctuated at 0.082 ± 0.009 Hz (Table 1), similarto those in HbO2 and HbR (i.e., <0.1 Hz) but lower than the

Baseline

w CO2

****

*

**

***

Venous CBFv

**

CBF

(%)

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)C

BF(%

)

0

20

40

60

80

100

0

20

40

60

80

100

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0.1

0.3

0.5

)zH(f

0.1

0.3

0.5

)zH(f

0.1

0.3

0.5

)zH(f

0.1

0.3

0.5

)zH(f

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0 1 2 3 4 5t (min)

Venous HbR Venous HbR

Venous HbO2

After CO2Baseline

0 00

0 03

0 09

0 12

D

f(H

z)

0 15

0 06

CBF HbO2 HbR

Freq Comparison

Baseline

w CO2

0

25

50

100

75

0

25

50

100

75

CBF HbO2 HbR

CBFv (%) HbO2 HbR (%)

ELFO Amplitude ComparisonVenous HbO2

Venous CBFv

A0 A1

B0

C0 C1

B1

Fig. 4. Characteristics of venular LFOs in rat cortex. (Left) LFO power spectra of CBFv (A0), HbO2 (B0), and HbR (C0) and their spectrograms (Insets) undernormocapnia. (Center) LFO power spectra of CBFv (A1), HbO2 (B1), and HbR (C1) and their spectrograms (Insets) under hypercapnia. Dashed curves are tracesfrom selected ROIs (m = 6–8) for each rat. Bold curves are averaged traces from the rat. (D and E) Comparison of mean LFO peak frequency and meanamplitude within its oscillation band between normocapnia and hypercapnia averaged across animals (n = 10). Asterisks indicate statistical significance (P < 0.05).

E4680 | www.pnas.org/cgi/doi/10.1073/pnas.1410800111 Du et al.

Dow

nloa

ded

by g

uest

on

July

27,

202

0

higher-frequency CBFv component (i.e., >0.1 Hz). Although CO2slightly down-shifted the LFO frequency of cellular Ca2+i oscil-lations (from 0.082 ± 0.009 Hz to 0.074 ± 0.012 Hz, P = 0.04,Fig. 5E) it did not change their amplitude (i.e., from 28.2 ± 4%to 29.8 ± 7%, P = 0.39; Table 2).To ensure that the Rhod2-Ca

2+ oscillations were not the resultof “cross-talk” from CBFv fluctuations due to speckle noise, weused another fluorescence indicator, Sulforhodamine 101 (SR101),as a control. SR101 (28) has excitation (λ1) and emission (λem)wavelengths similar to those of Rhod2 (27) but provides informationon cell morphology instead of cellular activity, as is the case for theCa2+i changes with Rhod2.SI Appendix, Fig. S6(1) compares the fluorescence signals from

SR101 and Rhod2 when loaded to the somatosensory cortex andmeasured in vivo and ex vivo (SI Appendix, Fig. S7). Theseresults show (i) no evidence of changes in local CBFv afterloading with either SR101 or Rhod2 [SI Appendix, Figs. S6(1) Aand S7A], (ii) similar brightness of fluorescence [SI Appendix,Figs. S6(1) B and S7B] and mean intensity [SI Appendix, Fig. S6(1) D] with SR101 and Rhod2, and (iii) similar cortical pene-tration and cellular uptake with SR101 and Rhod2 [SI Appendix,Figs. S6(1) C and S7C]. SI Appendix, Fig. S6(2) shows the LFOpower spectra of CBFv and fluorescence within SR101-loadedtissue under normocapnia [Fig. S6(2), B0 and C0] and hypercap-nia [SI Appendix, Fig. S6(2), B1 and C1]. The CBFv oscillationappeared in the SR101-loaded tissue and also responded to hy-percapnia. However, there were no oscillations in the SR101fluorescence [SI Appendix, Fig. S6(2), C0 and C1], in contrast tothe oscillations in the Rhod2 fluorescence (Fig. 5,D0 andD1). Thisindicates that LFOs observed with Rhod2-Ca

2+i fluorescence are

not due to cross-talk from CBFv or hemoglobin fluctuations.

To further evaluate the effects of hypercapnia on oscillatoryneuronal activity, we also measured LFP, which predominantlyreflects neuronal signals (29), in contrast to Ca2+i LFOs, whichreflect both neuronal and glial activity. SI Appendix, Fig. S4Eshows the density of LFP spikes as we progressively increasedPCO2 from 32 mmHg to 66 mmHg. The average LFP spikes didnot change (from 92.6 ± 4.3 spikes per minute to 92.3 ± 3.3spikes per minute) with PCO2 (from 33 mmHg to 66 mmHg), in-dicating that neuronal LFPs are not sensitive to PCO2 changes.This is consistent with the interpretation that the minimaleffects of hypercapnia on Ca2+i LFOs reflect the widely heldview that CO2 in the range used here does not have large effectson neuronal activity or on neurovascular coupling (18–20).To analyze the temporal relationship of Ca2+i LFOs with those

of HbO2 and HbR, their time traces were processed by low-passfiltering (fcutoff ≤0.1 Hz). Their typical time courses are plotted inFig. 6A and the parameterized numbers of the oscillating wavesin Rhod2-Ca

2+i (1, black trace), HbR (2, blue trace) and HbO2

(3, red trace) signals within ∼25 s are shown in Fig. 6B. Fig. 6Cshows the normalized oscillating waves from Fig. 6B, indicatingthat the Rhod2-Ca

2+i waves tend to peak before HbR or HbO2

signals. Fig. 6D shows the temporal lags in spontaneous HbO2,HbR, and Rhod2-Ca

2+i fluctuations in Fig. 6C obtained by

cross-correlation and the positive lags of ΔtCa-HbR and ΔtCa-HbOpresent Rhod2-Ca

2+i oscillation preceding HbO2 and HbR oscil-

lations. Table 3 summarizes the time lags of HbR and HbO2 toCa2+i signals, indicating that ΔtCa-HbR and ΔtCa-HbO are 5.59 ±0.97 s and 5.7 ± 0.65 s, respectively.Fig. 6E shows the correlation of Ca2+i signaling with HbO2 and

HbR within tissue statistically across the experiments. Withoutshifting of the oscillation waves (t = 0 s as demonstrated in

Tissue (w/Rhod ) HbR2

Baseline

w CO2

2

w CO2

******

*

*****

[R

hod

-Ca

] (%

)2+

Tissue (w/Rhod ) Ca22+

Tissue (w/Rhod ) CBFv2

Ca2+

F

2Tissue (w/Rhod ) HbO2

CBF

(%)

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

[H

bO ]

(%)

2[

HbR

] (%

)C

BF(%

)

0

20

40

60

80

100

0

20

40

60

80

100

After CO2Baseline

0

20

40

60

80

100

2[

Rho

d -C

a] (

%)

2+

0

20

40

60

80

100

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

0.0 0.1 0.2 0.3 0.4 0.5f (Hz)

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

0 1 2 3 4 5t (min)

0.1

0.3

0.5

)zH(f

Tissue (w/Rhod ) CBFv2

2Tissue (w/Rhod ) HbO2

Tissue (w/Rhod ) HbR2

Tissue (w/Rhod ) Ca22+

0 00

0 03

0 09

0 12

f(H

z)

0 15

0 06

CBF HbO2 HbR

Freq Comparison

Baseline

CBFv (%) HbO2 HbR, (%)

LFO Amplitude Comparison

Ca2+CBF HbO2 HbR

Ca2+

0

25

50

100

75

0

25

50

100

75

A0

B0 B1

C1C0

D0 D1

A1

E

F

Fig. 5. Characteristics of tissue LFOs in rat cortex. (Left) LFO power spectra of CBFv (A0), HbO2 (B0), HbR (C0), and Ca2+i (D0) and their spectrograms (Insets)under normocapnia. Mid (Center) LFO power spectra of CBFv (A1), HbO2 (B1), HbR (C1), and Ca2+i (D1) and their spectrograms (Insets) under hypercapnia.Dashed curves are traces from selected ROIs (m = 6–8) for each rat. Bold curves are averaged traces from the rat. (E and F) Comparison of mean LFO peakfrequency and mean amplitude within its oscillation band between normocapnia and hypercapnia averaged across animals (n = 10). Asterisks indicatestatistical significance (P < 0.05).

Du et al. PNAS | Published online October 13, 2014 | E4681

NEU

ROSC

IENCE

ENGINEE

RING

PNASPL

US

Dow

nloa

ded

by g

uest

on

July

27,

202

0

Fig. 6D) there is a negative correlation between Ca2+i and HbR(k = −0.5 ± 0.11) or Ca2+i and HbO2 (k = −0.23 ± 0.19).However, after shifting a few seconds (i.e., ΔtCa-HbR and ΔtCa-HbO as specified in Table 3), positive correlations between Ca2+iand HbR (k = 0.42 ± 0.09) and HbO2 (k = 0.31 ± 0.12) wereobserved, thus confirming a 5- to 6-s time lag of HbR and HbO2to Ca2+i slow fluctuations. This could be caused by the delays inthe metabolic and vascular responses to the Ca2+ increase or thetransient time of hemoglobin through the vasculature, which ison the order of a few seconds (30). Indeed, in rodent modelspeak BOLD response occurs 4–6 s after the onset of stimulation(18, 31).To determine whether the Ca2+ signals measured with Rhod2-

AM (Rhod2-[Ca2+]i) arise from neurons or glia, or both, we used

immunohistochemistry to assess in which cell types the uptake ofRhod2 occurred. GFAP antibody was used to stain glial fibrillaryacidic protein to identify astrocytes, NeuN antibody was used toidentify neurons, and DAPI was used to label the nucleus of allcells. SI Appendix, Fig. S7 D–H show the confocal images ofcortical tissue with fluorescent markers for nuclei (D), astrocytes(E), Rhod2 (F), neurons (G), and an overlay of all four channels(H), which indicates that Rhod2 uptake occurs in both astrocytes(in E, and light blue arrows in H) and neurons (in G, and yellowarrows in H). Because we used Ca2+ to assess cellular oscillationsin the cortex, this indicates that our signals not only reflectoscillations in neurons but might also reflect oscillations in glia.

Spontaneous LFOs Correlate with Neuronal Activity. The LFP is adirect measurement of neuronal activity in brain (32–36). Toassess whether the slow hemodynamic oscillation was associatedwith neuronal activity, two additional studies were performedto simultaneously record CBFv and LFP using a laser Dopplerprobe and an LFP probe side by side on the somatosensorycortex. Because the laser Doppler probe was ∼ϕ1 mm it providesintegrated information of CBFv signals from vessels and paren-chymal tissue. CBFv fluctuation cycles and LFP firing frequencywere analyzed during the resting state (>30 min, SI Appendix,Fig. S8).Fig. 7 A and B shows the distribution of CBFv oscillation

cycles and spontaneous neuronal firing frequency, respectively.It indicates that (i) CBFv mostly oscillated at ∼0.1 Hz with aFWHM bandwidth of {0.08 Hz, 0.15 Hz} and (ii) the peak LFPfiring frequency of ∼0.1 Hz corresponded to that of CBFv os-cillation. Fig. 7C illustrates the fitting process for the CBFv andLFP activity profiles. Interestingly, the CBFv oscillation followeda linear increase along with the LFP activity up to 0.15 Hz (r =0.93), then both signals decreased rapidly as a function of activityfrequency with the different offsets. This indicates that thespontaneous hemodynamic LFOs were associated with neuronalactivity. A linear correlation across animals shown in Fig. 7D

further confirms that the slow hemodynamic oscillations corre-late with neuronal firing frequency.

DiscussionSpontaneous LFOs have been widely used in fMRI to map brainfunctional connectivity at resting state and are believed to reflectongoing intrinsic activity of the brain (37). However, becauseBOLD reflects vascular and hemodynamic processes linked toneuronal and glial activity it is not possible to differentiate therelative contribution of these processes to the BOLD signals withfMRI (38). Here we used optical imaging, which allowed us tosimultaneously measure both oxy- and deoxyhemoglobin andCa2+i signals at high spatiotemporal resolutions to distinguishthe contribution of hemodynamic and cellular oscillations toLFOs in vascular and tissue compartments.In our study we show that, at the resting state, (i) LFOs in

CBFv, HbO2, and HbR were observed both in large vessels andcapillary networks; (ii) in arteries and veins CBFv oscillated ata higher frequency band (centered at ∼0.12 Hz) than HbO2 andHbR (centered at ∼0.06–0.07 Hz), whereas in the parenchymaltissue (composed of capillaries and small arterioles and venules<ϕ30 μm) CBFv oscillated at both the lower HbO2 and HbRfrequency band and the higher vascular CBFv frequency band;(iii) Ca2+i signal oscillated at a frequency of 0.082 ± 0.009 Hz,closer to HbR (0.074 ± 0.019 Hz) and HbO2 (0.07 ± 0.012 Hz)than to CBFv-H (0.113 ± 0.016 Hz), and the Ca2+i oscillationpreceded that of HbR and HbO2 and a shift of 5–6 s in themeasured Ca2+i oscillations correlates with the HbR and HbO2oscillations; (iv) hypercapnia reduced the CBFv-H LFOs in bothvessels and tissue, whereas it had minimal effects on the LFOs ofCa2+i, HbO2, HbR, and CBFv-L and on LFPs; and (v) LFOscorrelated with the neuronal spiking frequencies. Care was takento remove the artifacts from respiratory and heart rate varia-

Table 1. Mean peak frequencies of LFOs in cortical CBFv, HbO2, and HbR of animals undernormocapnia and hypercapnia (n = 10)

State Artery, Hz Vein, Hz Tissue, Hz

Normocapniafb(CBFv) 0.125 ± 0.013 0.130 ± 0.010 fb(CBFv-H): 0.113 ± 0.016 fb(CBFv-L): 0.061 ± 0.009fb(HbO2) 0.060 ± 0.016 0.07 ± 0.012 0.069 ± 0.012fb(HbR) 0.063 ± 0.017 0.069 ± 0.014 0.074 ± 0.019fb(Ca) — — 0.082 ± 0.009

Hypercapniafh(CBFv) 0.05 ± 0.018 0.057 ± 0.014 fh(CBFv-H): 0.047 ± 0.016 fh(CBFv-L): 0.058 ± 0.012fh(HbO2) 0.065 ± 0.019 0.074 ± 0.011 0.069 ± 0.0117fh(HbR) 0.06 ± 0.014 0.072 ± 0.009 0.057 ± 0.012fh(Ca) — — 0.075 ± 0.012

Table 2. Mean power spectral amplitudes of LFOs in CBFv,HbO2, and HbR oscillation band under normocapnia andhypercapnia (n = 10)

State Artery, % Vein, % Tissue, %

NormocapniaAb(CBFv) 75.0 ± 22.0 81.5 ± 26.9 37.1 ± 5.0Ab(HbO2) 41.2 ± 4.4 68.8 ± 16.7 78.8 ± 18.3Ab(HbR) 38.4 ± 10.0 66.2 ± 21.2 76.1 ± 10.3Ab(Ca) — — 28.2 ± 4.0

HypercapniaAh(CBFv) 34.0 ± 23.0 38.9 ± 8.7 26.2 ± 1.5Ah(HbO2) 30.9 ± 10.0 59.9 ± 33.4 89.9 ± 7.79Ah(HbR) 79.7 ± 24.0 95.0 ± 2.16 71.1 ± 15.5Ah(Ca) — — 29.8 ± 5.0

E4682 | www.pnas.org/cgi/doi/10.1073/pnas.1410800111 Du et al.

Dow

nloa

ded

by g

uest

on

July

27,

202

0

tions, which could be directly sampled at the high acquisitionrate used.The LFOs for HbO2 and HbR were preserved throughout the

arterial and venous vessels and in parenchymal tissue (composedof capillaries and vessels <ϕ30 μm) regardless of whether theCBFv-H oscillations (centered at ∼0.12 Hz) were present (nor-mocapnia) or attenuated (hypercapnia). In contrast, hypercap-nia, which induces vasodilation, reduced the CBFv-H oscillations,thus indicating the contribution of factors that affect vasculartone distinct from those of neurovascular control. Hypercapniadid not affect LFPs (SI Appendix, Fig. S4), which reflect neuronalactivity, and had minimal effects on Ca2+i oscillations, whichmost likely arise from neuronal and glial activity. This is con-sistent with the idea that hypercapnia at the level used here doesnot have large effects on neuronal function or neurovascularcoupling (18–20).Similar to hemoglobin and Ca2+i oscillations, the CBFv-L

oscillations, which were only present in tissue, were also in-sensitive to hypercapnia, consistent with capillary oscillationsreflecting direct influence by neuronal and glial oscillatory ac-tivity. Because capillaries are the blood vessels with closest in-teraction to neuronal and glial activity this could explain why intissue, which comprised capillary vessels and small arterioles andvenules, the CBFv components included not only the fasteroscillations that predominated in the larger vessels (>ϕ30 μm)but also the slower oscillations that had frequencies corre-lated to those of HbR, HbO2, and Ca2+i oscillations.The lack of an effect of hypercapnia on LFOs in HbR and

CBFv-L is consistent with their being directly driven by oscil-lations in cellular activity. Moreover, the good correspondencebetween the frequencies of the oscillations in HbO2 and HbRand those in cellular Ca2+ provides evidence that the HbRoscillations are driven by neurovascular coupling. The minimaleffects of hypercapnia on Ca2+i signals or on LFPs are consistentwith studies showing no large changes in neuronal metabolismduring hypercapnia in rodents (39) and in awake humans (40).Our results are also consistent with prior electrophysiologicalfindings revealing a correlation between oscillations in neuronalactivity as measured with LFP and resting BOLD signals (35, 41),further supporting the evidence that the resting BOLD oscil-lations are predominantly driven by neuronal activity. Our resultsextend these findings to show for the first time to our knowledgethat Ca2+i signal also oscillates at a frequency similar to HbR/HbO2 and precedes HbR and HbO2 changes by 5–6 s. The 5- to 6-stime lag between HbR, HbO2, and Ca2+i signals is in agreement

with that of BOLD to neuronal activity (35, 41), consistent withthe model that HbR fluctuations (basis of spontaneous BOLDoscillations) are linked to oscillatory Ca2+i signals and thus in-dicating that they reflect oscillatory neuronal (perhaps alsooscillatory glial) activity, which further clarifies the neuronalbasis underling resting-state BOLD oscillations with fMRI.The vascular oscillations show a slow CBFv-L oscillation that

follows the HbR and Ca2+i LFOs and also a faster CBFv-H os-cillation that was sensitive to hypercapnia and thus probablyreflects factors that affect vascular tone differently from neuro-vascular coupling mechanisms. However, because neuronal ac-tivity might also influence vascular tone (42) one has to reconcilethe possibility that these CBFv-H frequencies might also belinked with neuronal activity and the processes that mediatehemodynamic changes. This contrasts with the CBFv-L oscil-lations in the tissue, which predominantly reflect oscillations incapillary networks where signals are proximal to the cellularmetabolic changes that influence the content of HbR and HbO2in capillaries, whereas in the larger vessels oscillations reflectalso the regulatory mechanisms that modulate vasodilation ofblood vessels to perfuse larger tissue areas. Indeed, increases inflow with activation occur in areas that are larger than the acti-vated region, which results in the positive BOLD signals detectedby fMRI.There is no evidence that increases in CBF and oxygenation

owing to mild hypercapnia influence spontaneous neuronal os-cillatory activity and because oscillations in HbR, CBFv-L, andCa2+i reflect neuronal activity this explains the lack of an effectwith hypercapnia. Whereas hypercapnia did not affect the fre-quencies of HbR, HbO2, and CBFv-L LFOs and had a minimaleffect on Ca2+i LFOs, it significantly influenced the amplitudesof CBFv, HbR, and HbO2 in the vessels. The relative strength ofthe hemodynamic signals might be expected to change with in-crease in blood vessel diameter triggered by hypercapnia, whichwe observed both for CBFv and HbR in arterioles (CBFv: from

0 20 40 60 80 100 120 140-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

0 5 10 15 20 25-1.0

-0.5

0.0

0.5

1.0

Lag time t (s)0 2 4 6 8 10

Corr. coeff.

-1.0

-0.5

0.0

0.5

1.0

-2

no shifting (t=0)

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Corr. coeff.

after shifting (t= t)

Ca HbR2+-

Ca HbO2+- 2

time (s)LF

Os

(a.u

.)

HbRCa2+HbO2

A

B

C

D ECa2+

Ca2+

HbO2

HbO2

HbR

HbR

0 5 10 15 20 25

Ca HbR2+-

Ca HbO2+- 2

tCa-HbR

tCa-HbO

).u.a(s

OFL). u.a(

sOFL

1

2

31

21 2

1

21 21

32

1

2

1 2

132

2

Fig. 6. Cross-correlation between Ca2+i and HbR/HbO2 LFOs. (A) Time traces of Ca2+i (black), HbR (blue), and HbO2 (red) LFOs acquired from a rat. (B and C) Aclose view of LFOs in A and the normalized traces of B. (D) Cross-correlation between the time traces of Ca2+i-HbR (blue) and Ca2+i–HbO2 (red). (E) Statisticalresults (n = 10) of correlation coefficients of Ca2+i-HbR and Ca2+i-HbO2 LFOs without shifting (Δt = 0) and after shifting (time lag − Δt).

Table 3. Time lags of HbR and HbO2 to Ca2+i signals from theexperiments (n = 10)

Experimental ROIs

Time lag, s 1 2 3 4 5 6 7 8 9 10 Mean, s

ΔtCa-HbO 8.6 7.8 7.7 5.4 5.9 6.9 4.7 2.5 4.3 3.1 5.70 ± 0.65ΔtCa-HbR 8.8 8.7 8.6 5.4 6.9 7.8 4.3 0.3 2.8 2.3 5.59 ± 0.97

Du et al. PNAS | Published online October 13, 2014 | E4683

NEU

ROSC

IENCE

ENGINEE

RING

PNASPL

US

Dow

nloa

ded

by g

uest

on

July

27,

202

0

75 to 34%, HbR: from 38 to 79%) and venules (CBFv: from 81to 38%, HbR: from 66 to 95%) and to a lesser extent in tissue(CBFv: from 37 to 26%), which would also explain why BOLDactivation signals are influenced by hypercapnia (19). Here wemake the distinction between observing an effect of hypercapniain oscillatory frequencies (Table 1) vs. an effect of hypercapnia inamplitudes (Table 2). Nevertheless, a linear correlation of LFOsin CBFv with LFP firing rates in the low-frequency range(<0.15 Hz) further corroborates that spontaneous hemodynamicLFOs are neuronal in origin.There is growing interest in using optical imaging to study

resting-state fluctuations in human (38, 43) and rodent cortex(44–47). For example, a recent study (10) demonstrated LFOs inHbO2, HbR, and cytochrome oxidase in human visual cortexusing NIRS. The LFOs of HbO2 and HbR were centered be-tween 0.04 s−1 (i.e., 0.04 Hz) and 0.1 s−1 (i.e., 0.1 Hz) and theHbR changes preceded HbO2 oscillations, which is in agreementwith our findings. However, different from our report, attenua-tion was observed in HbO2 and HbR oscillations with hyper-capnia (5% CO2 in 21% O2, 74% N2). This discrepancy might bedue to differences in human vs. rodents or methodologies (e.g.,anesthetics and spatial resolution differences, which for NIRSrequired that they average signals from vessels of various diam-eters). As shown by our results, CO2 reduced the amplitude ofthe fluctuation in CBFv across the vascular network (i.e., artery,vein and capillary within the tissue, Table 2) and thus attenu-ations observed with NIRS in HbO2 and HbR could result fromthe corresponding decrease in CBFv.In this study we show that the LFO amplitudes of CBFv in

arteries and veins did not differ significantly (P = 0.23). A studywith multiphoton microscopy (46) also observed spontaneousoscillations in arterioles and venules but at a higher frequency(0.2–1 Hz) and weaker in venules than arterioles. The discrep-ancy of findings could reflect the different methodologies used,including different parameters measured [e.g., changes in CBFvelocity in current study vs. changes in vessel diameters by

multiphoton microscopy (46, 48)] and different dimensions ofthe vessels measured [e.g., >ϕ30 μm here vs. <ϕ20 μm (46)].A limitation for this study was that we required the use of

anesthesia, which affects neuronal responses. To minimizethis confound and make it relevant to fMRI studies we choseα-chloralose, which has been widely used for rodent fMRIstudies because (i) it preserves metabolic coupling for so-matosensory stimulation (21), (ii) it provides a normal CBFbaseline close to that measured in the awake state comparedwith other anesthetic agents such as isoflurane (22), and (iii) itpreserves cerebrovascular reactivity (23). In addition, becauseof the slow Ca2+i acquisition rate used, we cannot rule out thepossibility that the “slow” Ca2+i oscillations might be due tofolding over from higher-frequency Ca2+i signals (e.g., Ca2+

transients) or from slow variations in higher-frequency Ca2+ioscillations. Thus, we cannot precisely determine the neuronaland/or glial frequency oscillations that are contributing to theslow Ca2+ i oscillations that precede the slow HbR oscillations.The detection of CBFv at a higher frequency (∼0.12 Hz) than

HbR and HbO2 (∼0.06–0.07 Hz) is interesting. It is not clear whya fluctuation in CBFv is not reflected in a corresponding HbRand HbO2 frequency. Because HbR and HbO2 reflect effects ofboth oxygenation and velocity as well as metabolic factors owingto oxygen consumption, this result indicates that metabolic demandsof the tissue determine HbR and HbO2 oscillators in the restingstate. The measure of CBFv was based on the Doppler shift ofparticles in the vessel or speckle variation that reflects RBCvelocity (49). It may be that changes in RBC velocity are notreflecting changes in total blood flow through the capillary bed.The changes in CBFv oscillations in the veins follow the CBFvchanges in arteries, suggesting the possibility that the origin is inthe arterial compartment. A direct measure of bulk CBF wouldhelp distinguish whether flow is changing independent of RBCvelocity and whether this is occurring throughout the vasculartree. Recent work on MRI has demonstrated CBF fluctuations atfrequencies similar to those of BOLD fMRI oscillations (50, 51),but it is unclear that the higher frequencies reported in this studywould have been detected owing to technical issues. It will beinteresting to determine whether there is a higher-frequencycerebral blood flow (CBF) oscillation that correlates with thehigher-frequency red cell velocity (CBFv) measured here.No consensus has yet arisen about the detailed molecular

mechanisms of neurovascular coupling (37, 40). A mechanisticunderstanding that links specific neuromodulators to hemo-dynamic effects still needs to be developed. Release of mostneurovascular modulators requires an increase in Ca2+i, andhere we show that slow Ca2+i oscillates at a frequency similarto HbR, HbO2.In summary, we provide evidence of spontaneous slow oscil-

lations in cellular activity in cortical brain tissue that match andprecede the oscillations of HbR and HbO2 and the CBFv-L oscil-lations in neuronal tissue. Thus, BOLD oscillations most likely re-flect neuronal oscillations. This adds to the growing evidence thatBOLD oscillations reflect oscillations in neuronal activity.

MethodsAnimal Preparation. All experiments were carried out according to NationalInstitutes of Health guidelines andwere approved by the Institutional AnimalCare and Use Committee. A total of 13 Sprague–Dawley rats (250–300 g each)were used in this study: 10 for OFI/ODT imaging to retrieve the LFOs in CBFv,HbO2, HbR and Ca2+i fluorescence, 2 for simultaneous CBFv and LFP mea-surements, and 1 for microinfusion of SR101 fluorescence indicator tocompare with the studies obtained with Rhod2-loaded animals. Each rat wasanesthetized and ventilated with ∼2% isoflurane mixed in oxygen/air duringthe surgical procedures and its femoral artery was catheterized for contin-uous arterial blood pressure monitoring. An ∼6-mm cranial window wascreated on side of the parietal bone to expose the somatosensory motorcortex area. The dura was carefully removed and the exposed brain wasimmediately covered with 2% (vol/vol) agarose gel and affixed with a glass

f (Hz)0.0 0.2 0.4 0.6 0.8 1.0

0

5

10

15

20

25

f (Hz)0.0 0.1 0.2 0.3 0.4 0.50

5

10

15

20

25

f (FWHM)

f (Hz)0

5

10

15

20

25

f (Hz)0

5

10

15

20

25

LFP counts vs f(Hz)CBFv vs f(Hz)CBFv fittingLFP fittingCBFv and LFO fitting (low-f)

CBFv LFO (counts)

LFP LFO (counts)

LFOs (counts)

0 5 10 15 20 250

5

10

15

20

25

( fLFO<0.15 Hz)

CBFv LFO (counts)

LFP LFO (counts)

Rat 1Rat 2Ln fitting

A

B

C

D

0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.2 0.4 0.6 0.8 1.0

)stnuoc(OFL

FB

C

Fig. 7. (A) Frequency distribution of CBFv LFO mostly oscillated at ∼0.1 Hz.(Inset) LFO profile and its FWHM bandwidth of 0.08–0.15 Hz (red box). (B)Distribution of LFP spontaneous firing frequency, indicative of neuronalfiring rate also around 0.1 Hz. (C) Least-squares fitting of CBFv and LFP ac-tivity curves, indicating a corresponding linear increase (r = 0.93) in the lowfrequency range (<0.15 Hz) followed by rapidly decays with different offsets.(D) Correlation of LFP firing rates with CBFv LFOs (r = 0.83), implying that thespontaneous hemodynamic fluctuations are of neuronal origin.

E4684 | www.pnas.org/cgi/doi/10.1073/pnas.1410800111 Du et al.

Dow

nloa

ded

by g

uest

on

July

27,

202

0

coverslip using biocompatible cyanocrylic glue to avoid changes in cranialpressure. After the surgery, the anesthesia was switched to α-chloraloseusing an initial bolus of 50 mg/kg followed by continuous infusion of25 mg·kg−1·h−1 through a femoral vein. After the measurements undernormocapnia, mild hypercapnia was induced by switching the respiratory gasfrom O2/air mixture to one that contained 5% CO2 to repeat the measure-ments after 15 min. For in vivo detection of Ca2+i fluorescence shown in Fig. 1Eand Fig. 5, 6 of the 10 rats received microinfusion of the Ca2+i indicatorRhod2-AM (100μM, 3 μL/min; Molecular Probes) locally in the cortex (27) beforethe dura was removed, whereas other the 4 rats were used to assess effect ofPCO2 on vascular dilation and to measure local field potentials (LFP) forassessing changes in neuronal activity from normocapnia to hypercapnia.Hemodynamic parameters were compared between an ROI near the Rhod2injection spot (red circle and red curves) and a distant ROI (light blue circle andlight blue curves) as shown in SI Appendix, Fig. S5 to ensure that the injectiondid not affect the parameters measured.

Multimodal OFI.We used a custom-built OFI platform in this study (SI Appendix,Fig. S1), which integrates spectral/fluorescence imaging, laser speckle contrastimaging (LSI), and 3D ODT. For spectral imaging, two high-brightness light-emitting diodes (LEDs, 150 mW each) at the wavelengths of λ2 = 570 nm(sensitive to total hemoglobin absorption, i.e., tHb in Fig. 1C) and λ3 = 630 nm(sensitive to deoxygenated hemoglobin absorption, i.e., HbR in Fig. 1D) werecoupled into a ϕ3-mm fiber bundle (N.A. 0.25) for illumination of the cortex toimage the dynamic characteristics of total blood volume ([tHb]) and hemo-globin oxygenation ([HbO2] = [tHb] − [HbR]). A third 100-mW LED at λ1 = 530nmwas coupled into the fiber bundle to excite Rhod2(AM) for intracellular Ca2+

fluorescence imaging. In parallel, a pigtailed diode laser (60 mW) at λ4 = 830 nmwas delivered through a monomode fiber (N.A. 0.12) for LSI imaging. The fiber-guided illumination was incident on the cortical brain through a cranialwindow at an oblique angle to reduce surface specular reflection; all four-wavelength channels were pulse-modulated to sequentially illuminate corticalbrain synchronized by a time-base (time-sharing) interfaced with a workstationto permit “simultaneous” imaging at 12–16 Hz. The back-reflected light fromeach channel was collected through microscope optics (2×/0.22 N.A., a modifiedNikon AZ100 microscope), filtered by a long-pass barrier filter (λBP2 > 570 nm),and imaged by a 14-bit electron-multiplying CCD (iXon3 885; Andor). Changesin [HbO2] and [HbR] were calculated pixel by pixel directly through the time-lapse images at λ2 and λ3. Because hemoglobin absorption affects Ca2+i orSR101 fluorescence both at excitation and emission wavelengths betweennormocapnia and hypercapnia, spectral correction (SI Appendix, section 9-2)was implemented to minimize the artifacts using a frequency-domain approachto extract Ca2+i fluorescence images (Fig. 5, D0 and D1). For LSI flow imagereconstruction the dynamic speckle contrast was calculated based on a 5 × 5pixel moving window (spatial resolution of 5 × 6 ∼30 μm for LSI) across thewhole field of view (FOV, e.g., 5 × 6 mm2) to convert flow index (proportionalto flow rate) from the acquired raw photographic images (24, 51).

In parallel a fast 3D optical coherence tomography (OCT) system (illumi-nated by a λ = 1.3-μm broadband source with Δλ ≥90 nm spectral band-width) was integrated into the microscope via a custom dichroic mirror(DM1) reflecting λ >1 μm light for simultaneous 3D ODT (lower dashed box,SI Appendix, Fig. S1). Light exiting the sample arm of the fiberoptic Michelsoninterferometer was connected to a custom scan head (C1) mounted on themicroscope objective, in which light a ϕ5-mm collimated beam was trans-versely scanned by a pair of servo mirrors (VM500; General Scanning), fo-cused by an achromate (f40 mm/0.1 N.A.) and reflected by DM1 onto thecortex. The backscattered light from brain was recombined with the refer-ence light and detected by a high-speed spectrograph (a 1,024-pixel linearInGaAs array sampling rate up to 47 kHz; Goodrich). By synchronizing withsequential x–axis scanning (e.g., 500 pixels), 2D OCT image (z–x cross-section)was acquired at up to 94 frames per second and 3D OCT image was acquiredby additional y-axis scanning. The OCT dataset was transferred to a Raidhard disk array (300 MB/s, Raid 0 configured) on the workstation for parallelimage processing and display. An axial resolution (defined by the coherencelength Lc = 2(ln2)1/2/π·λ2/Δλ) of 8 μm and a transverse resolution of ∼ϕ12 μm(for the achromate f40 mm/0.1 N.A. used) were reached in brain tissue. Atypical FOV of 5 × 6 × 2 mm3 on cortex was imaged to register and comparewith LSI. Different from OCT for structural imaging, specific raster scanningschemes were implemented to optimize the flow detection sensitivity for 3DODT, by which the camera was configured to operate at 20,000 A-lines persecond with dense sampling (e.g., 0.05-μm pitch) along the x axis for fastflows and down-binned to 10,000 and 5,000 A-lines per second in postimageprocessing to enhance slow minute flows (52).

High Spatiotemporal Resolution Acquisition for LFO Analyses. OFI providesuniquely high spatial resolution (e.g., ∼6 μm for λ1–λ3 channels, ∼30 μm for λ4channel or LSI, and ∼10 μm for ODT) and large FOV (e.g., 5 × 6 mm2), whichallows the simultaneous study of hemodynamic, metabolic, and cellular charac-teristics in various vascular compartments [e.g., arteriolar flow (AF) and venularflow (VF)] and in brain tissue.. For brain functional studies in rats (e.g., LFOs), twomajor sources of artifacts include heartbeats at fsys ∼4–4.5 Hz (may vary slightlyfrom animal to animal and with its physiological conditions) and respiration rateat fres = 1 Hz (incubated, controllable). According to the Nyquist sampling the-orem, a sampling rate of fs/2 ≥max{fsys, fres}, i.e., fs >9 Hz, is needed to eliminatethe aliasing artifacts resulting from the folding back of fsys and fres componentsto the LFOs in the low-frequency range (53–57). Because LSI is a full-field ac-quisition modality, simultaneous images in CBFv, HbO2, and HbR from cerebro-vascular vessels and cortical tissue (including capillaries and vessels <ϕ30 μm)along with Ca2+i fluorescence fluctuations in the resting state can be acquiredwith OFI at ∼30 μm/16 Hz spatiotemporal resolutions and over FOV of 5 × 6mm2.However, for ODT to study the CBFv oscillation in vessels, a small FOV of 3 × 0.2 ×2 mm3 was imaged to increase the acquisition rate to 12 frames per second toavoid aliasing artifact (SI Appendix, Fig. S3).

Image Processing and LFO Analysis. We used a new image processing algo-rithm (52), phase intensity mapping, for quantifying 3D Doppler flowvelocity (i.e., vascular CBFv). In addition to significantly enhanced flowdetection (e.g., Fig. 1F), this method allowed for separation of AF and VFas highlighted in Fig. 1G.

For analysis, an ROI was selected (e.g., along a vein, an artery, and anavascular area for tissue perfusion) and its temporal dynamics was extractedfrom the time-lapse image traces. For instance, Fig. 1H, Top shows the time-lapse images of an AF and a VF highlighted in Fig. 1F (3D ODT). By applyingfast Fourier transform (FFT) to the time traces (e.g., Fig. 1G), their frequencycharacteristics (e.g., LFO) were analyzed. Specifically, two FFT analysis methodswere performed; one was to produce the power spectrum of each ROI (shownin Figs. 2–5) and the other was to generate the spectrogram (shown in theinsets in Figs. 2–5) by STFT (SI Appendix, section 9-1). For the power spectralanalysis of each ROI, ∼5 min of continuous data under a stable physiologicalstate (normocapnia or hypercapnia) were used, and the FWHM bandwidth ofthe “main” spectral peak in CBFv, HbO2, HbR, or Ca

2+i LFOs was calculated to

define their oscillation frequency band (as shown by gray shadows in Figs. 3–5). The LFO amplitude was calculated from its mean value of the poweragainst background within the frequency band. In each animal, six to eightROIs (i.e., m = 6∼8, dished traces) for each vessel type (e.g., artery or vein) ortissue area were manually selected (SI Appendix, Fig. S2), and their spectrawere averaged to produce its final power spectra (solid traces) of AF (Fig. 3), VF(Fig. 4), and tissue (Fig. 5). Further averaging across different animals (n = 10)was conducted to obtain the average peak frequency of CBFv, HbO2, HbR, andCa2+i (Figs. 3, 4D, and 5E) and the mean amplitude of LFOs in its frequencyband (Figs. 3, 4E, and 5F), as summarized in Tables 1 and 2.

LFP Acquisition and Laser Doppler Measurement. LFP signal traces of the cortexwere recorded (at 5 kHz sampling rate, 0.1–35 Hz band-pass-filtered) by apair of referenced electrodes (ϕ300 μm) with the corresponding multichan-nel electroencephalogram amplifier (EL450/MP150/EEG100C; Biopac). Thesignal electrode was affixed on the thinned bone of the cortex of interest,the reference electrode was placed on the symmetric side of the cortex, anda third electrode was inserted under the neck skin for grounding reference.Continuous acquisition of LFP was performed throughout the entire ex-periment (e.g., along with PCO2 from 33 mmHg to 66 mmHg), and a custompeak detection algorithm counted the LFP spikes per minute as shown in SIAppendix, Fig. S4. For simultaneous measurement of LFP and laser Dopplerflowmeter (Moor Instruments), the Doppler probe (ϕ1 mm) and LFP elec-trode were placed side by side on the rat somatosensory cortex. Continuousrecording of both LFP and CBFv was conducted throughout the experi-mental period during which electrical forepaw stimulation (2 mA, 1 Hz, 10 s)was induced periodically (2–3 min). Both CBFv fluctuation cycles and LFPfiring frequency were analyzed during the resting state.

All of the statistics were performed within a two-tail Student t test, unlessotherwise indicated, and all of the results are reported as mean ± SD.

ACKNOWLEDGMENTS. We thank Dr. H. Ren, W. Chen, and J. Li for partiallyassisting with data representation; K. Park for assisting with animal handling;Dr. Z. Luo for laser speckle contrast imaging; S. Sundaresh for assisting within vitro studies; and Dr. J.R. Walters [National Institute of Neurological Disordersand Stroke, National Institutes of Health (NIH)] for helpful discussions. This researchwas supported in part by NIH Grants K25-DA021200 (to C.D.), 1RC1DA028534(to C.D. and Y.P.), R21DA032228 (to Y.P. and C.D.), and R01DA029718 (to C.D.and Y.P.) and by the NIH intramural program (N.D.V. and A.P.K.).

Du et al. PNAS | Published online October 13, 2014 | E4685

NEU

ROSC

IENCE

ENGINEE

RING

PNASPL

US

Dow

nloa

ded

by g

uest

on

July

27,

202

0

1. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in themotor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541.

2. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed withfunctional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711.

3. Cordes D, et al. (2000) Mapping functionally related regions of brain with functionalconnectivity MR imaging. AJNR Am J Neuroradiol 21(9):1636–1644.

4. Vincent JL, et al. (2006) Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol 96(6):3517–3531.

5. Zeng LL, et al. (2012) Identifying major depression using whole-brain functionalconnectivity: A multivariate pattern analysis. Brain 135(Pt 5):1498–1507.

6. Mayer AR, Mannell MV, Ling J, Gasparovic C, Yeo RA (2011) Functional connectivity inmild traumatic brain injury. Hum Brain Mapp 32(11):1825–1835.

7. Gu H, Salmeron BJ, Ross TJ, Geng X, Zhan W, Stein EA, Yang Y (2010) Meso-corticolimbic circuits are impaired in chronic cocaine users as demonstrated by resting-state functional connectivity. Neuroimage 53(2):593–601.

8. Biswal BB, Van Kylen J, Hyde JS (1997) Simultaneous assessment of flow and BOLDsignals in resting-state functional connectivity maps. NMR Biomed 10(4–5):165–170.

9. Elwell CE, Springett R, Hillman E, Delpy DT (1999) Oscillations in cerebral haemody-namics. Implications for functional activation studies. Adv Exp Med Biol 471:57–65.

10. Obrig H, et al. (2000) Spontaneous low frequency oscillations of cerebral hemody-namics and metabolism in human adults. Neuroimage 12(6):623–639.

11. Tong Y, Frederick BD (2010) Time lag dependent multimodal processing of concurrentfMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory originfor low-frequency oscillation signals in human brain. Neuroimage 53(2):553–564.

12. Razavi M, et al. (2008) Source of low-frequency fluctuations in functional MRI signal.J Magn Reson Imaging 27(4):891–897.

13. Boas DA, Jones SR, Devor A, Huppert TJ, Dale AM (2008) A vascular anatomical net-work model of the spatio-temporal response to brain activation. Neuroimage 40(3):1116–1129.

14. Zheng Y, et al. (2005) A three-compartment model of the hemodynamic response andoxygen delivery to brain. Neuroimage 28(4):925–939.

15. Helmchen F, Waters J (2002) Ca2+ imaging in the mammalian brain in vivo. Eur JPharmacol 447(2-3):119–129.

16. Bandettini PA, Wong EC (1997) Magnetic resonance imaging of human brain func-tion. Principles, practicalities, and possibilities. Neurosurg Clin N Am 8(3):345–371.

17. Hoge RD, et al. (1999) Linear coupling between cerebral blood flow and oxygenconsumption in activated human cortex. Proc Natl Acad Sci USA 96(16):9403–9408.

18. Kennerley AJ, et al. (2012) Early and late stimulus-evoked cortical hemodynamic re-sponses provide insight into the neurogenic nature of neurovascular coupling. J CerebBlood Flow Metab 32(3):468–480.

19. Krieger SN, et al. (2014) Using carbogen for calibrated fMRI at 7Tesla: Comparison ofdirect and modelled estimation of the M parameter. Neuroimage 84:605–614.

20. Desjardins M, Berti R, Pouliot P, Dubeau S, Lesage F (2014) Multimodal study of thehemodynamic response to hypercapnia in anesthetized aged rats. Neurosci Lett 563:33–37.

21. Ueki M, Mies G, Hossmann KA (1992) Effect of alpha-chloralose, halothane, pento-barbital and nitrous oxide anesthesia on metabolic coupling in somatosensory cortexof rat. Acta Anaesthesiol Scand 36(4):318–322.

22. Masamoto K, Kim T, Fukuda M, Wang P, Kim SG (2007) Relationship between neural,vascular, and BOLD signals in isoflurane-anesthetized rat somatosensory cortex.Cereb Cortex 17(4):942–950.

23. Bonvento G, et al. (1994) Is alpha-chloralose plus halothane induction a suitable an-esthetic regimen for cerebrovascular research? Brain Res 665(2):213–221.

24. Yuan Z, Luo Z, Volkow ND, Pan Y, Du C (2011) Imaging separation of neuronal fromvascular effects of cocaine on rat cortical brain in vivo. Neuroimage 54(2):1130–1139.

25. Grubb RL, Jr, Raichle ME, Eichling JO, Ter-Pogossian MM (1974) The effects of changesin PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time.Stroke 5(5):630–639.

26. Morita-Tsuzuki Y, Bouskela E, Hardebo JE (1992) Vasomotion in the rat cerebral mi-crocirculation recorded by laser-Doppler flowmetry. Acta Physiol Scand 146(4):431–439.

27. Du C, et al. (2006) Cocaine increases the intracellular calcium concentration in brainindependently of its cerebrovascular effects. J Neurosci 26(45):11522–11531.

28. Masamoto K, et al. (2012) Repeated longitudinal in vivo imaging of neuro-glio-vas-cular unit at the peripheral boundary of ischemia in mouse cerebral cortex. Neuro-science 212(212):190–200.

29. Buzsáki G, Anastassiou CA, Koch C (2012) The origin of extracellular fields and cur-rents—EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13(6):407–420.

30. Hutchinson EB, Stefanovic B, Koretsky AP, Silva AC (2006) Spatial flow-volume dis-sociation of the cerebral microcirculatory response to mild hypercapnia. Neuroimage32(2):520–530.

31. Silva AC, Koretsky AP (2002) Laminar specificity of functional MRI onset times duringsomatosensory stimulation in rat. Proc Natl Acad Sci USA 99(23):15182–15187.

32. Nicolelis MA, Baccala LA, Lin RC, Chapin JK (1995) Sensorimotor encoding by syn-chronous neural ensemble activity at multiple levels of the somatosensory system.Science 268(5215):1353–1358.

33. von Stein A, Chiang C, König P (2000) Top-down processing mediated by interarealsynchronization. Proc Natl Acad Sci USA 97(26):14748–14753.

34. Kara P, Reinagel P, Reid RC (2000) Low response variability in simultaneously recordedretinal, thalamic, and cortical neurons. Neuron 27(3):635–646.

35. Shmuel A, Leopold DA (2008) Neuronal correlates of spontaneous fluctuations infMRI signals in monkey visual cortex: Implications for functional connectivity at rest.Hum Brain Mapp 29(7):751–761.

36. Buzsáki G, Anastassiou CA, Koch C (2012) The origin of extracellular fields and cur-rents—EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13(6):407–420.

37. Raichle ME (2010) Two views of brain function. Trends Cogn Sci 14(4):180–190.38. White BR, et al. (2009) Resting-state functional connectivity in the human brain re-

vealed with diffuse optical tomography. Neuroimage 47(1):148–156.39. Barzilay Z, Britten AG, Koehler RC, Dean JM, Traystman RJ (1985) Interaction of

CO2 and ammonia on cerebral blood flow and O2 consumption in dogs. Am J Physiol248(4 Pt 2):H500–H507.

40. Jain V, et al. (2011) Rapid magnetic resonance measurement of global cerebralmetabolic rate of oxygen consumption in humans during rest and hypercapnia.J Cereb Blood Flow Metab 31(7):1504–1512.

41. Schölvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA (2010) Neural basis of globalresting-state fMRI activity. Proc Natl Acad Sci USA 107(22):10238–10243.

42. Urban A, Rancillac A, Martinez L, Rossier J (2012) Deciphering the neuronal circuitrycontrolling local blood flow in the cerebral cortex with optogenetics in PV:Cretransgenic mice. Front Pharmacol 3:105.

43. Mesquita RC, Franceschini MA, Boas DA (2010) Resting state functional connectivityof the whole head with near-infrared spectroscopy. Biomed Opt Express 1(1):324–336.

44. Biswal BB, Hudetz AG (1996) Synchronous oscillations in cerebrocortical capillary redblood cell velocity after nitric oxide synthase inhibition. Microvasc Res 52(1):1–12.

45. Saka M, Berwick J, Jones M (2012) Linear superposition of sensory-evoked and on-going cortical hemodynamics. Front Neuroenergetics 2:23.

46. Drew PJ, Shih AY, Kleinfeld D (2011) Fluctuating and sensory-induced vasodynamics inrodent cortex extend arteriole capacity. Proc Natl Acad Sci USA 108(20):8473–8478.

47. Stein EW, Maslov K, Wang LV (2009) Noninvasive, in vivo imaging of blood-oxygen-ation dynamics within the mouse brain using photoacoustic microscopy. J Biomed Opt14(2):020502.

48. Blinder P, et al. (2013) The cortical angiome: An interconnected vascular network withnoncolumnar patterns of blood flow. Nat Neurosci 16(7):889–897.

49. Luo Z, Wang Z, Yuan Z, Du C, Pan Y (2008) Optical coherence Doppler tomographyquantifies laser speckle contrast imaging for blood flow imaging in the rat cerebralcortex. Opt Lett 33(10):1156–1158.

50. Chuang KH, et al. (2008) Mapping resting-state functional connectivity using perfu-sion MRI. Neuroimage 40(4):1595–1605.

51. Fukunaga M, et al. (2008) Metabolic origin of BOLD signal fluctuations in the absenceof stimuli. J Cereb Blood Flow Metab 28(7):1377–1387.

52. Luo Z, Yuan Z, Tully M, Pan Y, Du C (2009) Quantification of cocaine-induced corticalblood flow changes using laser speckle contrast imaging and Doppler optical co-herence tomography. Appl Opt 48(10):D247–D255.

53. Ren H, et al. (2012) Cocaine-induced cortical microischemia in the rodent brain:Clinical implications. Mol Psychiatry 17(10):1017–1025.

54. Auer DP (2008) Spontaneous low-frequency blood oxygenation level-dependentfluctuations and functional connectivity analysis of the ‘resting’ brain. Magn ResonImaging 26(7):1055–1064.

55. Anderson JS (2008) Origin of synchronized low-frequency blood oxygen level-dependent fluctuations in the primary visual cortex. AJNR Am J Neuroradiol29(9):1722–1729.

56. Magnuson M, Majeed W, Keilholz SD (2010) Functional connectivity in blood oxy-genation level-dependent and cerebral blood volume-weighted resting state func-tional magnetic resonance imaging in the rat brain. J Magn Reson Imaging 32(3):584–592.

57. Hutchison RM, Mirsattari SM, Jones CK, Gati JS, Leung LS (2010) Functional networksin the anesthetized rat brain revealed by independent component analysis of resting-state FMRI. J Neurophysiol 103(6):3398–3406.

E4686 | www.pnas.org/cgi/doi/10.1073/pnas.1410800111 Du et al.

Dow

nloa

ded

by g

uest

on

July

27,

202

0