The effect of different anesthetics on neurovascular coupling
Maria Angela Franceschini⁎, Harsha Radhakrishnan, Kiran Thakur, Weicheng Wu, Svetlana Ruvinskaya,
Stefan Carp, David A. Boas
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
a b s t r a c ta r t i c l e i n f o
Received 13 November 2009
Revised 1 March 2010
Accepted 20 March 2010
Available online 27 March 2010
To date, the majority of neurovascular coupling studies focused on the thalamic afferents' activity in layer IV
and the corresponding large spiking activity as responsible for functional hyperemia. This paper highlights
the role of the secondary and late cortico-cortical transmission in neurovascular coupling. Simultaneous
scalp electroencephalography (EEG) and diffuse optical imaging (DOI) measurements were obtained during
multiple conditions of event-related electrical forepaw stimulation in 33 male Sprague–Dawley rats divided
into 6 groups depending on the maintaining anesthetic — alpha-chloralose, pentobarbital, ketamine–
xylazine, fentanyl–droperidol, isoflurane, or propofol. The somatosensory evoked potentials (SEP) were
decomposed into four components and the question of which best predicts the hemodynamic responses was
investigated. Results of the linear regression analysis show that the hemodynamic response is best correlated
with the secondary and late cortico-cortical transmissions and not with the initial thalamic input activity in
layer IV. Baseline cerebral blood flow (CBF) interacts with neural activity and influences the evoked
hemodynamic responses. Finally, neurovascular coupling appears to be the same across all anesthetics used.
© 2010 Elsevier Inc. All rights reserved.
Since its introduction in 1991 the role of functional magnetic
resonance imaging (fMRI) in basic and clinical neuroscience has
grown rapidly, with 2500 papers published in 2005 alone (Bandettini,
2007). Although fMRI is now widely used for non-invasive investiga-
tions of human brain function, we still do not have a clear
understanding of how accurately these images based on vascular
changes reflect neural activity (Bandettini, 2007; Iadecola, 2004;
Shibasaki, 2008). Because of the growing number of functional studies
with fMRI, quantification of the relationship between the hemoglobin
signal and the underlying neural activity is becoming increasingly
Recently, significant effort has been devoted to invasive animal
responses in different ways. While the effects of different anesthetics
on electrical, metabolic, or vascular responses alone have been
reported, only a few studies have investigated the effect of anesthetics
on the relationship between the electrical and vascular functional
Maandag et al., 2007; Martin et al., 2006; Masamoto et al., 2009; Ueki
et al., 1992). During event-related parametric electrical forepaw
stimulation in rats, we investigated the neurovascular coupling using
six different anesthetics (alpha-chloralose, isoflurane, pentobarbital,
propofol, ketamine–xylazine, and fentanyl–droperidol).
Different anesthetics act differently on neurotransmitters and
neuronal membrane polarization thresholds (Hyder et al., 2002;
Maandag et al., 2007; Sibson et al., 1998; Sicard et al., 2003), and as a
result modulate the measured EEG evoked signals differently
(Antunes et al., 2003a,b). The main properties/characteristics of the
anesthetics we chose for this study are briefly listed here.
Alpha-chloralose, pentobarbital, isoflurane, and propofol are
mainly GABAergic anesthetics and prolong the evoked inhibitory
postsynaptic currents mediated by γ-aminobutyric acid A (GABAA) by
increasing channel conductance or channel open time (Belelli et al.,
1999; Franks and Lieb, 1994). Of these four GABAergic anesthetics,
alpha-chloralose is the most commonly used for functional hemody-
namic studies because of its weaker effects on cardiovascular,
respiratory, and reflex functions (Nakao et al., 2001), and more
importantly, the evoked hemodynamic responses are larger with
alpha-chloralose than with other anesthetics (Austin et al., 2005).
Isoflurane is commonly used in electrophysiology studies due to its
ease of use, even though it partially reduces neuronal excitation and
cerebral metabolism like most volatile anesthetics. At doses higher
than 1.6% isoflurane increases cerebral blood flow (CBF) (Eger, 1984)
and for this reason it is not commonly used for hemodynamic
functional studies. Pentobarbital and propofol are usually not used in
functional studies as they depress the central nervous system as well
as cortical and subcortical structures, and produce large decreases in
NeuroImage 51 (2010) 1367–1377
⁎ Corresponding author. Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, 13th Street Bldg. 149 (rm 2301), Charlestown, MA
02129, USA. Fax: +1 617 726 7422.
E-mail address: firstname.lastname@example.org (M.A. Franceschini).
1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved.
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EEG responses (Antognini et al., 2006; Crosby et al., 1983).
Pentobarbital, a barbiturate, in contrast with other anesthetics,
potentiates not only inhibitory but also excitatory postsynaptic
receptors (Franks and Lieb, 1994). Propofol is the intravenous
anesthetic of choice in surgery because of its favorable operating
conditions and associated rapid recovery. It reduces heart rate (Wang
et al., 2004) and baseline cerebral blood flow (Cenic et al., 2000;
Veselis et al., 2005). With propofol, a dose-dependent gradual
reduction of SEP amplitude and prolonged latency has been measured
in rats (Logginidou et al., 2003).
The other two combinations of anesthetics we used are not
GABAergic. Ketamine, in particular, does not interact with GABA
receptors (Franks and Lieb, 1994) but mainly inhibits excitatory
glutamatergic neurotransmission, blocking N-methyl-D-aspartate
(NMDA) receptors. Ketamine also acts on opioid, monoaminergic
and muscarinic receptors (Hirota and Lambert, 1996). Ketamine is
often used in functional electrophysiology studies because it does not
suppress neural activity (Kochs and Bischoff, 1994), and also because
animals do not need to be intubated for its use. In fact, with ketamine,
there is minimal cardio-respiratory depression. Ketamine causes both
increases and decreases in cerebral metabolism (glucose utilization)
depending on the brain region. Decreases in metabolism occur in the
somatosensory and auditory cortices (Crosby et al., 1982). It produces
a dose-related clinical state of dissociative anesthesia in combination
with analgesic properties, and is commonly used in conjunction with
xylazine.Xylazineis a sedative and a muscle relaxant.It minimizesthe
side effects produced by ketamine alone, such as tremor, muscle
rigidity, and excitement during recovery (Wright, 1982). Xylazine
inhibits noradrenergic neurotransmission by activating presynaptic
adrenergic alpha-2 receptors (Oria et al., 2008). Because it is an
agonist for alpha-2-adrenoreceptors, xylazine decreases the heart
rate, causes hypotension, decreases venous cerebral blood volume
and intracranial pressure, and depresses the central nervous system
(Greene and Thurmon, 1988). In addition, xylazine induces a
reduction in CBF (Lei et al., 2001).
Fentanyl is a synthetic opiate analgesic used routinely in
anesthesia procedures in humans. Fentanyl binds with high affinity
to the morphine μ-opioid receptors (Villiger et al., 1983). Fentanyl
produces a dose-related decrease in both CBF and cerebral metabolic
rate of oxygen (CMRO2) (Carlsson et al., 1982). It is commonly used in
conjunction with droperidol to induce neuroleptanesthesia (Bissonn-
ette et al., 1999). Droperidol is a neuroleptic drug and belongs to the
butyrophenones; it has antipsychotic effects, which are produced by
blocking dopamine receptors (Bissonnette et al., 1999). One of the
advantages of droperidol is its lack of EEG effects, and although it
reduces cerebral blood flow by vasoconstricting the cerebral vessels,
the cerebral metabolic rate of oxygen remains unchanged. Clinical
doses of droperidol decrease systemic blood pressure and cause
reductions in tidal volume, airway resistance and functional residual
We used scalp electroencephalography (EEG) and diffuse optical
imaging(DOI) for our functional measurements. Thesetwo modalities
allow for non-invasive scalp measurements, interrogate large
volumes of tissue, and can be easily integrated together for
simultaneous measurements (Franceschini et al., 2008). While EEG
offers only an indirect measure of the cascade of neuronal events
during neural activity, it is the only modality other than MEG that can
be used to monitor neural activity non-invasively in humans, and
facilitatesthetranslationof resultsobtainedin animalsusing thesame
techniques to human studies. Much effort over the past 30 years has
focused on determining the relationship between somatosensory
evoked potentials (SEP), local field potentials (LFP), and neural
activity. Anatomical data from the sites of specific thalamic inputs
(White, 1979; White and Hersch, 1982) and the projection patterns of
pyramidal axons between cortical layers (Thomson and Bannister,
2003) have confirmed that activation of the primary somatosensory
cortex (SI) starts with the thalamic input in layers IV and VI and is
followed by activation in layers III and II and then layer V. Response
latencies increase in a systematic fashion from middle to superficial to
deep cortical layers (Simons, 1978). In addition, when synaptic
activity reaches the superficial layers, it propagates horizontally with
a large amount of synapses between layers I and III. Evoked potential
responses across primary cortices in rats, primates and humans have a
similar structure, consisting first of a large and narrow positive
component, P1, followedwithin10 msbya large negativecomponent,
N1, and then by two slow components, P2 and N2, tens of ms after N1
(Allison et al., 1989; Arezzo et al., 1981; Di and Barth, 1991; Kulics and
Cauller, 1986). Current source density (CSD) analysis of laminar
profiles of LFP link P1 to the largest and earliest current sinks in layers
IV and VI. P1 is the primary evoked potential directly originated from
SI-specific thalamocortical inputs (Mitzdorf, 1985), and reflects the
initial depolarization of layer II and V pyramidal cells. Following this
initial depolarization, population spikes are generated in layer Vb
infragranular cells. Axon-collaterals of layer Vb pyramidal cells
produce an enhanced activation of the supragranular pyramidal
cells in layer I–II, which generates the secondary evoked potential N1
(Agmon and Connors, 1991; Jellema et al., 2004; Kulics and Cauller,
1986). P2 and N2 arise from activation of cortico-cortical connections
originating in thesuperficiallayers ofthe centralcolumn(Kublik etal.,
2001; Wrobel et al., 1998), and derive from a combination of both
inhibitory and repolarization processes (Steriade, 1984). While the
neural origin of P1 and N1 is well established, the inconsistent source-
sink patternsand absenceof multi-unit activity (MUA)at longertimes
make the functional significance of P2 and N2 unclear (Kulics and
While most invasive animal studies have attempted to correlate
the hemodynamic responses to the primary evoked potential, we
believe that a substantial contribution of the hemodynamic response
is derived from secondary and late cortical transmission. In fact, in a
previous study using the same measurement modalities (EEG and
DOI) and parametric electrical forepaw stimulation (Franceschini
et al., 2008), we have found that the primary SEP component (P1)
exhibits a weaker correlation with the hemodynamic response than
the secondary (N1) and late (P2) SEP components. Similarly in
humans, using DOI and MEG during median nerve stimulation
experiments, we have obtained better hemoglobin response predic-
tions using late (N30 ms) neural components (Ou et al., 2009).
Parametric stimulation alone does not produce enough differences in
the SEP components to distinguish the individual contributions of N1
and P2. Here, we tested whether the pharmacological manipulation
produced by the different anesthetics on the SEP is sufficient to
disentangle individual contributions of the secondary and late
components to the hemodynamic response.
Determining whether the hemodynamic response from the SEP is
driven by secondary or late cortico-cortico transmission rather than
by afferent inputs to layer IV would have a profound effect on the
clinical role of BOLD fMRI and DOI. In fact, the presence of a
hemodynamic response to a sensory stimulus itself would confirm the
integrity of the sensory system to the level of the cortico-cortical
responses and indicate that sensory information had arrived and was
processed to the point that it could pass to other areas of the cortex.
However, if the presence of a hemodynamic response only confirms
the integrity of the system to the level of the thalamic inputs, the
hemodynamic response would only confirm that sensory information
had reached the cortex; it would provide no information as to the
potential for further processing. Therefore, if the hemodynamic
response is a marker not just of information arrival from non-cortical
areas but of local information processing, the hemodynamic response
As discussed above, anesthetics also act on baseline cerebral blood
flow, cerebral blood volume (CBV) and vascular reactivity (Hyder et al.,
2002; Maandag et al., 2007; Sibson et al., 1998; Sicard et al., 2003). To
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
account for the resulting effects on neurovascular coupling, in our
experiments,in additiontofunctional electrical and vascular responses,
we measured baseline blood flow and response to a hypercapnia
challenge with each anesthetic. These measures allow us to determine
the influence of baseline blood flow on the neurovascular coupling.
Materials and methods
Sixgroups ofmaleSprague–Dawleyrats (5–6 ratsin eachanesthetic
group), a total of 33 animals (298±18 g), were included in this study.
During all surgical procedures the animals were anesthetized with
isoflurane(2–2.5%)administered via a facemaskin a gasmixture of 80%
air and 20% oxygen. After tracheotomy and cannulation of the femoral
arteryand vein,animals were mountedon a stereotactic frame. Heating
blankets maintained core temperature at 36.5–37.5 °C. Six different
anesthetics were used, in different animals, to maintain anesthesia
during the functional experiments: alpha-chloralose (40 mg/kg/h,
intravenous (i.v.)) (Devor et al., 2008), pentobarbital (25 mg/kg,
intra-peritoneal (i.p.)) (Oria et al., 2008), ketamine–xylazine (20 mg/
kg/h — 2 mg/kg/h, i.v.) (Oria et al., 2008), fentanyl–droperidol (90 μg/
kg/h–4.5 mg/kg/h, i.v.) (Safo et al., 1985), isoflurane (1.2% gas)
(Masamoto et al., 2007), and propofol (50 mg/kg/h i.v.) (Oria et al.,
2008). Anesthetic doses were chosen based on what is typically used
during functional studies, maximizing functional responses, maintain-
ing blood gasses within normal ranges, and ensuring that the animal
was not alert enough to respond to a tail pinch. We waited at least
45 min before beginning the measurements, to allow anesthetic
transition. During the measurements, animals were mechanically
ventilated with 80% air and 20% O2. Continuous monitoring of arterial
blood pressure (MBP), arterial oxygen saturation (SaO2) and heart rate,
and blood sample recording of arterial pH, PaO2, and PaCO2at the
beginning, middle and end of the experiment, assured maintenance of
basic physiological parameters. Table 1 shows the doses of anesthetics
used and the systemic physiological parameters measured in each
group. All procedures were approved by the Massachusetts General
Hospital Subcommittee on Research Animal Care.
In all animals we performed parametric forepaw electrical
stimulation using train durations of 1, 3, 5, 7, 9, 11, 13 s (7 conditions).
The stimuli were applied using hypodermic needles inserted into the
left and right forepaws of the animals. The stimuli were delivered in
trains of 200-μs pulses at a 3-Hz repetition rate. The amplitude of the
stimuli was adjusted just above the motor threshold by increasing the
stimulation current until forepaw movement was visible (stimulation
current 1.3±0.3 mA, never below 1 mA or above 2 mA). The stimuli
were presented pseudo-randomly with an average inter-stimulus
interval (ISI) of 12 s (the ISI ranged from 2 to 20 s) between the trains.
In each animal we alternated 12-minute runs between the left and
right forepaw, for a total of 5 runs in each paw. In the data analysis, we
treated the results of each paw independently. At the end of the
functional experiments, we performed a 5% hypercapnia challenge in
all animals, and measured baseline CBF to evaluate vascular reactivity
under different anesthetics. The hypercapnia challenge consisted of
10 min of data acquisition with an inspired mixture of 20% O2and 80%
air, followed by 10 min of adding 5% CO2to the inspired gas mixture.
The EEG data were obtained using 9 channels in a 40-channel
monopolar digital amplifier system (NuAmps, NeuroScan, USA). Four
Ag/Agcl disk-type EEG electrodes (4-mm diameter, Warner Instru-
ments, Hamden CT, USA) were used to record the neuronal activity
and were placed around the optical probe under the animal skin as in
Franceschini et al. (2008). Five additional electrodes (8-mm diameter,
Warner Instruments, Hamden CT, USA) were positioned using paste
as follows: a ground electrode was positioned above the nose of the
animal, a reference electrode on the neck, 2 ECG electrodes on the left
and right sides of the torso, and an additional ground electrode more
posterior in the torso. We verified that the impedance of the
electrodes was smaller than 5 KΩ and that the cross talk between
the stimulation and recording systems did not affect the features
calculated from the EEG signals. The EEG and ECG measurements
were acquired at a sampling frequency of 1 kHz.
The EEG data were processed off-line using software designed in-
house and implemented in the MATLAB environment (Mathworks
Inc., Natick, MA). Specifically, for each animal, the EEG data from the
electrode in the contralateral SI cortex was high-pass filtered at a
−3 dB cutoff frequency of 3.5 Hz. A notch filter was applied to
suppress 60 Hz interference and the ECG signal was used to reduce
the arterial pulsation with a linear regression model. For each
duration condition we calculated the average SEP responses,
averaging across stimuli in the same run and across all runs. We
isolated the SEP components (P1, N1, P2, N2) (see Fig. 1b) by
evaluating the zero crossings of the SEP signal. We then determined
the integrated SEP responses (ΣSEP) for each condition by calculating
the area under each component and summing the response areas to
each stimulus in the train. Finally, for each anesthetic group, we
averaged the SEP responses across animals without any normalization
to preserve the amplitude of the responses with each anesthetic.
Animal's anesthesia and physiological variables.
Alpha-chloralosePentobarbital Ketamine–xylazineFentanyl–droperidol IsofluranePropofol
# of animals
# of measurements included
Anesthesia maintaining dose
Respiration rate (breaths per min)
Heart rate (Hz)
MBP (mm Hg)
20 mg/kg/h–2 mg/kg/h
90 μg/kg/h–4.5 mg/kg/h
# of animal is the number of animals measured for each anesthetic. # of measurements indicates the number of measurements included in the data analysis. For each animal we
stimulated left and right sensory cortices alternately and in a few cases had to discard one side because of low SNR or no functional response, either SEP or hemodynamic.
Pentobarbital was administrated via intra-peritoneal injections (i.p.) every 30–45 min, isoflurane was administrated with the inspired mixture of 80% air and 20% oxygen and the
other 4 anesthetics were infused i.v. pH, PaCO2, and PaO2are the averages across animals and all three measurements during the functional experiments.
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
Diffuse optical imaging
DOI is a non-invasive technique that has been used for more than a
decade to measure brain activity in humans (Hoshi et al., 1993;
Villringer et al., 1993). The technique quantifies local cortical
hemodynamic changes spectroscopically by measuring light absor-
bance changes at different wavelengths, and has been validated
extensively against fMRI (Huppert et al., 2006a,b; Kleinschmidt et al.,
1996; Strangman et al., 2002; Toronov et al., 2001). For our animal
experiments, we used a continuous-wave imaging system (CW4,
TechEn Inc., Milford, MA), as described in Franceschini et al. (2008)
and Siegel et al. (2003). In this system, 18 laser diodes (nine emitting
light at 690 nm, and nine at 830 nm) are frequency-encoded and their
signals acquired simultaneously by 16 parallel detectors. Each
detector's output is digitized at 40 kHz. The individual source signals
are filtered off-line using an infinite-impulse-response filter with a
20-Hz band-pass frequency, which allows for a 10-Hz acquisition rate
per image. The optical probe is comprised of a 4×4 grid of 16
detectors interleaved with a 3×3 grid of 9 sources (with each source
including a 690- and a 830-nm laser), and covers a flat region of the
rat head extending 7.5 mm to either side of the midline, and from
4 mm anterior to 11 mm posterior of the Bregma.
Thirty-six source-detector pairs at a 3.5-mm separation (nearest
neighbors) were considered for the data analysis. The optical probe
was secured in contact with the head and supported by metal posts.
The CW4 system used here is the same as we typically use in human
experiments but the source-detector separations used in the animal
experiments were 10 times smaller (Franceschini et al., 2008).
Previously, we verified that the hemoglobin response measured
with this system and probe in rats is co-localized and temporally
equivalent to the BOLD response measured with fMRI (Culver et al.,
2003b; Siegel et al., 2003).
As a pre-analysis, for each animal, the DOI raw data was band-pass
filtered between 0.02 and 0.5 Hz. Oxy- and deoxy-hemoglobin
concentrations (HbO and HbR, respectively) were calculated using
the modified Beer–Lambert law (Delpy et al., 1988) without any path-
length correction. To obtain the average hemodynamic response for
the seven conditions, the data were deconvolved with the stimulation
onsets. In each rat and on each side, we identified the source-detector
pair with the most statistically significant oxy-hemoglobin activation
in the contralateral SI cortex (p-valueb0.05) and used it for the
further data analysis. For each animal and each condition we
calculated the area under the curve of the hemoglobin responses
(ΣHbO and ΣHbR), and for each anesthetic group we calculated the
grand average of ΣHbO and ΣHbR responses across animals without
applying any normalization, as with the SEP responses.
Diffuse correlation spectroscopy
To measure baseline cerebral blood flow non-invasively through
the scalp, at the end of the functional experiment and during the
hypercapnia challenge, we used a relatively new optical technique:
diffuse correlation spectroscopy (DCS) (Boas et al., 1995; Cheung et
al., 2001). The instrument we used in our experiments is similar to the
system developed by Dr. Arjun Yodh at the University of Pennsylvania
(Durduran et al., 2004b) and tested in several animal (Cheung et al.,
2001; Culver et al., 2003a; Durduran et al., 2004a; Zhou et al., 2009)
and human experiments (Durduran et al., 2004b; Durduran et al.,
2009). Our DCS system employs a solid-state long coherence length
laser (785 nm, ∼70 mW of power) to illuminate the surface of the
head and four photon-counting avalanche photodiodes connected to
four single-mode optical fibers to collect the diffusely reflected light
(Roche-Labarbe et al., 2010). The intensity auto-correlation function
of each channel is computed by a digital correlator. We co-localized
the source and four detector DCS fibers with the DOI fibers in the left
SI. This four-channel system acquires data at 2 Hz and the blood flow
index (BFi) is obtained off-line by fitting the measured electric field
auto-correlation functions with a model of dynamic light scattering in
deep tissues (Cheung et al., 2001). In a human study we have
demonstrated that the blood flow index is proportional to CBF as
measured with Doppler ultrasound and can be compared across
subjects (Roche-Labarbe et al., 2010).
Fig. 1. Time traces of SEP responses under different anesthetics. (a) Average train
responses. (b) Average of all stimuli. For each anesthetic, here the average is calculated
as the average of responses to all conditions and all rats. Error bars in Fig. 1b represent
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
Neurovascular coupling data analysis
We estimated linear regression models for predicting the area
under the curve of the hemoglobin responses (ΣHbO and ΣHbR,
average across animals for each anesthetic and each condition), given
measured components of the neural responses (ΣSEP, average across
animals for each anesthetic and each condition). Our linear regression
model allows for the possibility of considering any number of the
components of the neural response, as well as the interaction with
baseline blood flow. It assumes that the vascular response is related to
the components of the neural response by:
∑Hb = ∑
where Hb indicates either HbO or HbR, the index i is the number of
included SEP components, BF is the baseline blood flow index
(average across animals), and hHbiand hBF,Hbiare the regression
coefficients for the ith component. The regression coefficients are
estimated simultaneously for all stimulus conditions and all anes-
thetics. The goodness-of-fit was estimated by calculating the
correlation coefficient, R, the coefficient of determination, R2, and to
control for the degrees of freedom we also calculated the F test
where k is the number of parameters and n is the number of
measurements. Statistical significance of predictions using less regres-
the F cumulative distribution function (p-valueb0.05).
Baseline CBF and CBF changes during hypercapnia for different
As part of the protocol, we measured cerebral blood flow using the
DCS system to evaluate the effects of the different anesthetics on the
baseline vascular state, as well as the possible effects of different
baseline vascular states on neurovascular coupling. As expected,
isoflurane provided the largest baseline BFi, and ketamine–xylazine
the lowest because of the action of xylazine on systemic blood flow
(see Table 2). In all rats, in addition to baseline BFi, we measured BFi
changes during hypercapnia at the end of the functional experiments,
and evaluated CO2reactivity (change in BFi/ΔPaCO2) under different
anesthetics to ensure vascular reactivity is preserved and as expected
CO2 reactivity was lower for intravenous anesthetics and larger for
volatile anesthetics (Eng et al., 1992; Smith and Wollman, 1972). We
observed increases in BFiin response to hypercapnia with all of the
anesthetics, with the largest CO2-induced BFi changes measured
under isoflurane and the smallest under ketamine–xylazine and
propofol (see Table 2). The enhanced CO2reactivity under isoflurane
is in agreement with previous studies (Drummond and Todd, 1985;
Young et al., 1991).
SEP and hemodynamic responses with different anesthetics
Fig. 1 shows the average SEP responses for each anesthetic. In
Fig. 1a we show the average train responses. Responses under
isoflurane show long-term habituation, with responses to initial
stimuli being much larger than responses to later stimuli in the train.
Responses under alpha-chloralose show inter-stimuli habituation,
with smaller responses for every other stimulus in the train. Fig. 1b
shows the SEP response for all the stimuli averaged together.
Differences between the amplitudes and latencies of the responses
for the various SEP components are evident. For instance, the SEP
responses under pentobarbital and propofol show a large P1 but very
small N1 and P2. Responses under alpha-chloralose show the largest
P1 and delayed N1 and P2 components with respect to the other
anesthetics. Under ketamine–xylazine and fentanyl–droperidol, the
SEP responses show a small P1 and a very pronounced P2 and in
addition, these are the only anesthetics that show a pronounced N2
component. In a different group of animals (not reported here), we
observed that with anesthetic dose within a 30–40% range of what is
used here, the N2 component was always present with ketamine–
xylazine and fentanyl–droperidol and never present with pentobar-
bital and propofol. For concentrations of isoflurane b1% and alpha-
chloralose b30 mg/kg/h we observed a small N2 component, but for
such low doses animals were starting to respond to tail pinch. For
higher doses of isoflurane or alpha-chloralose, N2 was negligible.
Similar differences in the amplitudes and latencies of the SEP
components with anesthetics have been previously reported, and are
due to the differences in action of the various anesthetics on neural
conduction and synaptic transmission (Sloan, 1998).
The hemodynamic responses also differed across anesthetics (see
Fig. 2). The largest HbO (right panel) and HbR (left panel) responses
were observed under alpha-chloralose and isoflurane anesthesia; the
smallest, under pentobarbital and propofol. Stronger habituation with
train duration was observed under ketamine–xylazine and fentanyl–
SEP responses (ΣSEP) vs. stimulus train duration were all fairly
linear with R2N0.90 in the cases of P1 for ketamine–xylazine
(R2=0.82) and P2 for pentobarbital and propofol (R20.86 and 0.82,
respectively). Oxy- and deoxy-hemoglobin responses (ΣHbO and
ΣHbR) vs. train duration had R2b0.90 for ketamine–xylazine (R20.83
and 0.85), fentanyl–droperidol (R20.84 and 0.78), and propofol (R2
0.72 and 0.88).
SEP and DOI coupling
Fig. 3 shows the scatter-plots between HbR and SEP responses to
the 7 conditions for the 6 anesthetics (figures for HbO vs. SEP are very
similar and are shown in the online supplemental material). By
performing a linear regression analysis we evaluated how well each of
the SEP component predicts the hemodynamic response indepen-
dently for each anesthetic. The resulting correlation coefficients (R),
the F test statistics, and the regression coefficients for HbR are
reported in Table 3 (results for HbO are reported in an online
supplemental table). As expected, based on our previous work
(Franceschini et al., 2008), by changing stimulus train duration,
even if P1 has a slightly higher correlation coefficient on average (but
Baseline blood flow and changes with hypercapnia.
%BFichange 5% CO2
For each anesthetic, grand average (±standard error) of blood flow index measured with the DCS system at the beginning, middle and the end of the functional measurements in all
rats, blood flow changes with hypercapnia, and %BFi/ΔPaCO2ratio with hypercapnia.
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
a lower F), we did not find any statistically significant difference
between the R and the F of the three SEP components across
anesthetics (paired T test analysis, p-valuesN0.13). Also by
performing the analysis on single animals, we verified that there are
no statistically significant differences on predictions of single SEP
components when considering animals from single anesthetic groups
(paired T test analysis, p-valuesN0.05). This is because the hemody-
namic and SEP responses are all quite linear with respect to stimulus
train duration. By looking at the scatter-plots in Fig. 3, and at the
regression coefficients in Table 3, we observed that hemoglobin
changes vs. ΣP1 responses are quite scattered and have very different
regression coefficients for different anesthetics, and that ΣHbR vs.
ΣN1 and ΣP2 responses for the four GABAergic anesthetics (alpha-
chloralose, isoflurane, pentobarbital and propofol) are grouped
together and have very similar regression coefficients, while they
deviate for ketamine–xylazine and fentanyl–droperidol. Interestingly,
the two anesthetics with deviant responses exhibit a strong N2
component. In these cases by using both P2 and N2 as regressors, the
regression coefficients for P2 (last column in Table 3) are closer to the
P2 from the GABAergic drugs.
In Table 3 we also report results of the linear regression analysis
when combining all anesthetics together, or combining only the four
component when we consider all anesthetics combined, as expected
basedon Fig. 3, and very good for P2and N1when we consideronly the
Since none of the SEP components alone can predict the
hemodynamic responses of all anesthetics combined, we consider
the effect of multiple SEP components and their interaction with
baseline cerebral blood flow, which as shown in Table 2 changes with
anesthetics. Table4 reportstheresultsof thelinear regressionanalysis
fitting all anesthetics simultaneously for all possible combinations of
SEP components with or without baseline CBF interaction. Without
CBF interaction, the largest R and F were obtained using all of the SEP
components as regressors (Table 4 last row). This prediction using all
of the SEP components was statistically significantly better than those
using any other combination of SEP components except for the
By considering the interaction between baseline blood flow and
SEP, we were able to better predict the hemodynamic responses for all
anesthetics (in general, higher R and F values than without the
baseline CBF interaction terms), and the best predictions were
obtained with P2–N2. Fig. 4 shows the predictions of ΣHbR vs.
measured ΣHbR for some of the possible SEP combinations with
baseline CBF interactions (figures of predictions without baseline CBF
interaction are reported in the online supplemental material).
Interesting results from Table 4 are as follows: predictions using P1
and P2 alone or combined did not improve by adding the blood flow
interaction. Instead, predictions using N1 alone substantially im-
proved by adding the interaction with blood flow. Including
additional regressors did not improve the F test statistic for N1, and
only slightly increased the correlation coefficients. The use of all
regressors (P1, N1, P2, N2 and CBF interaction) did not produce
statistically better predictions (p-valueN0.05) than P2–N2 and N1–
P2–N2 without CBF interaction and P2–N2, P1–N1–P2, P1–N1–N2,
P1–P2–N2 and N1–P2–N2 with CBF interaction.
Discussion and conclusions
These results suggest that the hemodynamic response is not solely
driven by thalamic afferent inputs (P1) but it is largely controlled by
secondary and late cortical transmission and influenced by baseline
blood flow. In fact, when using a linear regression model, the coupling
between the thalamic afferent component P1 and the hemodynamic
responses changes across anesthetics. In order to maintain the same
neurovascular coupling relationship across different anesthetics we
need to add secondary and late SEP components and their interaction
with baseline CBF. In particular, the late SEP components P2–N2 alone
(plus CBF interaction) are sufficient to predict the hemodynamic
responses of all anesthetics simultaneously.
For any single anesthetic used, by changing stimulus train
duration, predictions of the hemodynamic responses are good using
any SEP component (Table 3). This is because all SEP components and
Fig. 2. Time traces of ΔHbO (left panels) and ΔHbR (right panels) responses under
different anesthetics. For each anesthetic, the average is calculated as the average of
responses to all rats. Different colors and/or symbols represent different conditions.
Error bars indicate standard error.
Fig. 3. Scatter plot of ΣHbR vs. ΣSEP components for the 7 conditions (different points)
and 6 anesthetics (different curves).
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
hemodynamic responses increase linearly when varying stimulus
train duration and there is insufficient variation to reach statistically
significant differences, as we showed in Franceschini et al. (2008). It is
important to notice that by limiting the analysis to single SEP
components, the regression coefficients change considerably across
anesthetics (differences from 45% for N1, to 60% for P1). This result is
consistent with previous studies using LFP and invasive microscopic
imaging techniques. Huttunen et al. (2008), during electrical forepaw
stimulation at different frequencies, found linear relationships
(R2∼0.85–0.87) between LFP and BOLD under alpha-chloralose and
urethane anesthesia, but very different regression coefficients. Martin
et al. (2006) compared LFP and hemoglobin responses in awake and
urethane-anesthetized rats over a large range of forepaw stimulation
frequencies. While limiting the discussion to the principal LFP
component, they found a very different coupling relationship
between awake and anesthetize animals.
ForthefourGABAergic anesthetics,hemoglobin predictionsusingP2
0.04×10−7for HbO and =0.29±0.05×10−7for HbR) while P1
regression coefficients are very different (N60%). Ketamine–xylazine
andfentanyl–droperidol haveanadditionalSEP component N2which is
missing, or negligible, with GABAergic drugs within the concentrations
obtain the same P2 regression coefficients (within a 35% range) for each
of the 6 anesthetics. N1, P2 and N2 are generated by excitatory and
inhibitory cortical events (Cauller and Kulics, 1991; Steriade, 1984) and
the couple P2–N2 in particular describe cortico-cortical transmissions
andthus it makes sense to consider them together. Several electrophys-
iology studies have shown that cortical transmissions are modulated by
al., 1981; Cauller and Kulics, 1988). The fact that P2 for different
GABAergic agents has the same regression coefficient, and again the
same coefficient when we add N2 as a regressor to predict hemoglobin
responses under fentanyl–droperidol, suggests to us that its contribu-
tion, and the contribution of cortico-cortical transmission in general,
should be considered when studying neurovascular coupling.
A more rigorous analysis is performed by combining all anesthetic
agents together and testing for all possible combinations of SEP
components with or without baseline blood flow interaction. We used
theFtest statistic totakeintoaccountthedifferentdegreesoffreedom
when different numbers of regressors are used in the linear regression
analysisto predict hemoglobinresponses. Using all regressors (P1, N1,
P2 and N2 with baseline CBF interaction) did not give statistically
significant better predictions of hemoglobin responses than using
P2–N2 (either with or without CBF interaction). The best F test statistic
and R were obtained using P2–N2 and CBF interactions, confirming the
Result of F test statistics and correlation coefficients for hemoglobin predictions.
HbO fitHbR fit
No BFBF No BF BFNo BF BFNo BF BF
P1 N1 P2
P1 N1 N2
P1 P2 N2
N1 P2 N2
P1 N1 P2 N2
F test statistics and R of HbO and HbR predictions. Bold text indicate best predictors.
Fig. 4. Measured and predicted ΣHbR using as regressors different SEP components and
baseline blood flow interaction and fitting for all anesthetic simultaneously. Results for
ΣHbO predictions are very similar and a corresponding figure is reported in the online
Correlation coefficients R, F test statistics and regression coefficients hHbR(10−7) of P1, N1, and P2 to predict HbR for each anesthetic and for combined anesthetics.
P1 N1P2 P1N1 P2 P1N1P2 (P2,N2)
Regression all Anesthetics Combined
Regression GABAergic anesthetics combined
We do not report results for N2 in this table because N2 is 0 for four of the six anesthetics. Last column report P2 and N2 regression coefficients when both P2 and N2 are used as
regressors. Results for HbO are similar and shown in the online supplemental material.
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
above observation of the predominant involvement of P2–N2 in the
While alpha-chloralose and isoflurane produce the largest hemo-
dynamic responses, we verified that they do not solely drive our
results. In fact by excluding either one or both of those anesthetics
from the combined analysis, P2–N2 still produces better hemoglobin
predictions than P1 and N1.
These results suggest that the hemodynamic response is primarily
driven by cortico-cortical transmissions and not by thalamic inputs in
layer IV. This finding is in agreement with our previous results using
parametricstimulation(Franceschini et al.,2008), butin contrast with
the common belief that functional hyperemia is driven by the
thalamic afferents' activity in layer IV. A retrograde vasodilation
mechanism (Iadecola et al., 1997) controlled by layer IV thalamic
afferent synaptic activity is necessary to support this common belief,
since pial arteries regulate the local increase of blood flow into
downstream branches (Iadecola, 2004). If the hemodynamic response
is driven by synaptic activity in general, as our data suggest, and not
just by the primary synaptic activity in layer IV, then synaptic activity
in more superficial layers will initiate superficial vascular responses
before any retrograde contribution from layer IV (∼100–400 ms delay
vs. ∼1 s delay to propagate ∼600 μm (Iadecola et al., 1997)). With
diffuse optical imaging, while we detect hemodynamic changes in
both superficial and deeper cortical layers with good temporal
resolution, we do not have the spatial resolution to differentiate
between cortical layers and cannot determine the origin of the
hemodynamic responses. Several groups have tried to resolve laminar
differences in the onset of the hemodynamic responses. Using high-
resolution fMRI in rats, Silva and Koretsky (2002) found BOLD signal
onset starting in layer IV 0.5 s before starting in layers II and III. Jin and
Kim (2008), in a recent fMRI study of the cat visual cortex, found that
cerebralbloodvolumeresponse(CBV)in superficial cortical layershas
a faster time to peak than CBV in middle cortical layers, suggesting
that arterial volume increase in the surface of the cortex precedes
dilation of microvessels in deeper cortical layers. Using Laser Doppler
and electrical stimulation of the trigeminal nerve of rats, Norup Nielsen
and Lauritzen (2001) found earlier CBF onset times in superficial layers
thanin layer IV. Recently, using optical coherencetomography(OCT) to
measure blood volume changes in the rat forepaw cortex, Chen et al.
600 μm (layers II–IV), but significantly earlier with respect to the brain
surface. The observation of no onset delays from depths of 200 to
600 μm supports the role of secondary and late synaptic activity in
initiating vasodilation. We believe that technological advances of
techniques such as OCT and two-photon microscopy may soon allow
researchers to resolve the issue.
A few other papers indirectly support our finding that the
hemodynamic response is driven by cortico-cortical transmissions and
not by thalamic inputs. Anna Devor et al. (2005) have shown that the
hemodynamic response in one cortical column cannot be explained
solely by the neuronal activity in that column; rather, neuronal activity
in the neighboring columns needs to be included. In the cerebellum,
Mathiesen et al. (1998) have shown that the hemodynamic response is
mostlysensitivetothe level of synapticactivity. Iffunctional hyperemia
is controlled by the amount of synaptic activity, the larger number of
activated synapses occurs not during the layer IV volley, but from the
later cortico-cortical transmissions (Szentagothai, 1978).
Still, we do not rule out a smaller contribution of P1 to the
pentobarbital or propofol anesthesia, there is a large P1 response and
smaller N1 and P2 responses. Yet we do observe a small change in the
hemodynamic signals. This small hemodynamic response under
pentobarbital and propofol may be driven by P1. Correlation
coefficients between ΣSEP and ΣHb under these two anesthetics are
higher for P1 than for N1 and P2 when we average all animals in the
same anesthetic group together (Table 3). By carrying the analysis to
single rats and single anesthetics under pentobarbital or propofol, the
N1 or P2 (p-valuesN0.05). To isolate and evaluate the P1 contribution
to the hemodynamic response, either more invasive methods with
drug application like MK801 (Hoffmeyer et al., 2007), one may be able
and estimate individual contributions to the hemodynamic response.
While our results, when fitting for all anesthetics combined, show
that P1 does not correlate as well as the subsequent SEP components
with the hemodynamic response, we are cautious in disentangling the
roles ofN1and P2–N2inthehemodynamic response.In general,P2–N2
better predicted the hemoglobin responses than N1 (note that using all
regressors is statistically significantly better than using only N1 while
using all regressors is not significantly better than using only P2–N2).
However, the fact that P2 strongly covaries with N1 (Kulics, 1982;
Wrobel et al., 1998) suggests a close relationship between N1 and
P2–N2. This issue needs to be further investigated.
In these experiments, we modulated the stimulation by changing
of animals using six different anesthetics. Previously (Franceschiniet al.,
2008) using alpha-chloralose anesthesia and changing stimulus fre-
quency or amplitude, we found that N1 and P2 better predicted the
did not find statistically significant differences between the three SEP
six individual anesthetics. While confirming this negative result, we did
observethat the variation acrossanesthetics providedpredictive power.
It is possible that our results are specific to the duration stimulation
paradigm. Experiments across anesthetics, by changing frequency, or
visual cortex need to be carried out before making our results more
We found that baseline cerebral blood flow has a significant
interaction with neurovascular coupling. Specifically, adding baseline
BFi to the SEP regressors substantially improves hemodynamic
predictions (increased F and R values). The positive interaction
between SEP and baseline cerebral blood flow indicates that, under a
condition of constant neural activity, a higher baseline blood flow
correlates with a higher hemodynamic evoked response (ΔHb).
Apparently in contrast, Sicard and Duong (2005) have shown that
modulating baseline blood flow by changing inspired O2or CO2does
not change the absolute magnitude of evoked hemoglobin changes
(ΔBOLD and ΔCBF), but does decrease the relative changes. In Sicard
and Duong (2005), neural activity under different gas concentration
wasnot measured, but assumed constant.This assumptionmaynot be
hypercapnia. In their experiments they found that while increasing
and evoked hemoglobin responses. Reduction of neural activity with
moderate hypercapnia has also been shown in nonhuman primates
(Zappe et al., 2008). Sicard and Duong's (2005) finding of constant
evoked hemodynamic changes for different baseline blood flow may
be masked by changes of neural activity with CO2.
As expected, of the six anesthetics used, alpha-chloralose produced
the largest hemodynamic responses. Hemodynamic responses under
isoflurane, at the concentration used here (∼1.2%), were only slightly
smallerthanresponsesunder alpha-chloralose. This is because, atthese
concentrations of isoflurane, baseline CBF and CBV are not strongly
affected. At higher concentrations (N1.6%), isoflurane becomes a strong
vasodilator (Eger, 1984) and by either preventing further vasodilation
changes in response to stimulation.
We measured only small functional hemoglobin changes in animals
under pentobarbital and propofol. Pentobarbital waspreviously used in
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
a study by Ueki et al. (1992) and its effect on neural and metabolic
activity was compared to that of alpha-chloralose and two other
anesthetics. Similar to our findings, while they measured principal
evoked responses under both pentobarbital and alpha-chloralose, they
found an increase in the metabolic rate of glucose under alpha-
chloralose anesthesia, but not under pentobarbital anesthesia. Our
hemoglobin responses with pentobarbital are small but not zero. The
difference may be due to the fact that they used a higher dose of
pentobarbital than we did. In general, differences in dose of anesthetics
used makes it difficult to compare hemodynamic responses reported in
the literature, since hemodynamic responses (as well as electrical
responses) depend on the amount of anesthesia used (Dueck et al.,
2005; Purdon et al., 2009). The advantage of our approach is that we
compare the electrical and vascular responses simultaneously and
measure their correlation, which, within limits, should not be affected
by anesthetic dosages.
The N2 SEPcomponentwas measured only withketamine–xylazine
and fentanyl–droperidol, and was not present with GABAergic
anesthetics. In the regression model, the N2 contribution to the
hemodynamic response was always negative (see Table 3 last column),
suggesting a vasoconstrictive role for this component. More measure-
ments are needed to link N2 activity (likely related to inhibitory
interneurons since it disappears with GABAergic anesthetics) with
We used scalp electroencephalography (EEG) and diffuse optical
have poor spatial resolution and are sensitive to large tissue volumes
and multiple cortical layers. The inferior spatial resolution with
respect to invasive microscopic studies may be a disadvantage of our
method, and parallel invasive studies need to be performed. But there
are important advantages that justify our study. NIRS and scalp EEG
allow for non-invasive measurements and can be directly translated
to human studies (Mackert et al., 2008; Obrig and Villringer, 2003; Ou
et al., 2009; Shibasaki, 2008). We believe that controlled animal
studies with the same methodologies that are applicable in humans
are necessary precursors to neurovascular studies in humans. Also,
several invasive neurovascular coupling studies have suggested that
the uncoupling between hemodynamic responses and electrophysi-
ology may be an artifact arising from the limited field of view of
microelectrodes with respect to the more extensive field of view of
standard microscopic hemodynamic measures (Devor et al., 2005;
Ureshi et al., 2005). We believe that experiments with both
macroscopic and microscopic methodologies should be carried out
to better understand neurovascular coupling.
Neural input affects secondary and late activity and different
anesthetics differently affect neural input and secondary-late activity.
With these experiments we found that the hemodynamic response
better correlates with secondary-late activity than with the neural
input. In conclusion, these results indicate that the magnitude of the
hemodynamic response is proportional to the secondary and late SEP
components, that baseline blood flow positively affects hemodynamic
evoked responses, and that neurovascular coupling is constant across
anesthetics. The cause of the effect of different anesthetics on the late
SEP requires further investigation.
We would like to thank Anna Devor, Ellen Grant and John Marota
for valuable discussions and Gary Boas for careful editing of the
manuscript. This research is supported by the US National Institutes of
Health (NIH) grants R01-EB001954 and R01-EB006385.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.neuroimage.2010.03.060.
Adrian, E.D., 1941. Afferent discharges to the cerebral cortex from peripheral sense
organs. J. Physiol. 100, 159–191.
Agmon, A., Connors, B.W., 1991. Thalamocortical responses of mouse somatosensory
(barrel) cortex in vitro. Neuroscience 41, 365–379.
Allison, T., McCarthy, G., Wood, C.C., Williamson, P.D., Spencer, D.D., 1989. Human
cortical potentials evoked by stimulation of the median nerve. II. Cytoarchitectonic
areas generating long-latency activity. J. Neurophysiol. 62, 711–722.
Antognini, J.F., Bravo, E., Atherley, R., Carstens, E., 2006. Propofol, more than halothane,
depresses electroencephalographic activation resulting from electrical stimulation
in reticular formation. Acta Anaesthesiol. Scand. 50, 993–998.
Antunes, L.M., Golledge, H.D., Roughan, J.V., Flecknell, P.A., 2003a. Comparison of
electroencephalogram activity and auditory evoked responses during isoflurane
and halothane anaesthesia in the rat. Vet. Anaesth. Analg. 30, 15–23.
Antunes, L.M., Roughan, J.V., Flecknell, P.A., 2003b. Effects of different propofol infusion
rates on EEG activity and AEP responses in rats. J. Vet. Pharmacol. Ther. 26, 369–376.
Arezzo, J.C., Vaughan Jr., H.G., Legatt, A.D., 1981. Topography and intracranial sources of
somatosensory evoked potentials in the monkey. II. Cortical components.
Electroencephalogr. Clin. Neurophysiol. 51, 1–18.
Austin, V.C., Blamire, A.M., Allers, K.A., Sharp, T., Styles, P., Matthews, P.M., Sibson, N.R.,
2005. Confounding effects of anesthesia on functional activation in rodent brain: a
study of halothane and alpha-chloralose anesthesia. NeuroImage 24, 92–100.
Bandettini, P., 2007. Functional MRI today. Int. J. Psychophysiol. 63, 138–145.
Belelli, D., Pistis, M., Peters, J.A., Lambert, J.J., 1999. General anaesthetic action at
transmitter-gated inhibitory amino acid receptors. Trends Pharmacol. Sci. 20,
Berwick, J., Johnston, D., Jones, M., Martindale, J., Martin, C., Kennerley, A.J., Redgrave, P.,
Mayhew, J.E., 2008. Fine detail of neurovascular coupling revealed by spatiotem-
poral analysis of the hemodynamic response to single whisker stimulation in rat
barrel cortex. J. Neurophysiol. 99, 787–798.
Bissonnette, B., Swan, H., Ravussin, P., Un, V., 1999. Neuroleptanesthesia: current status.
Can. J. Anaesth. 46, 154–168.
Boas, D.A., Campbell, L.E., Yodh, A.G., 1995. Scattering and imaging with diffusing
temporal field correlations. Phys. Rev. Lett. 75, 1855–1858.
Carlsson, C., Smith, D.S., Keykhah, M.M., Englebach, I., Harp, J.R., 1982. The effects of
high-dose fentanyl on cerebral circulation and metabolism in rats. Anesthesiology
Cauller, L.J., Kulics, A.T., 1988. A comparison of awake and sleeping cortical states by
analysis of the somatosensory-evoked response of postcentral area 1 in rhesus
monkey. Exp. Brain Res. 72, 584–592.
Cauller, L.J., Kulics, A.T., 1991. The neural basis of the behaviorally relevant N1
component of the somatosensory-evoked potential in SI cortex of awake monkeys:
evidence that backward cortical projections signal conscious touch sensation. Exp.
Brain Res. 84, 607–619.
Cenic, A., Craen, R.A., Howard-Lech, V.L., Lee, T.Y., Gelb, A.W., 2000. Cerebral blood
volume and blood flow at varying arterial carbon dioxide tension levels in rabbits
during propofol anesthesia. Anesth. Analg. 90, 1376–1383.
Chen, Y., Aguirre, A.D., Ruvinskaya, L., Devor, A., Boas, D.A., Fujimoto, J.G., 2009. Optical
coherence tomography (OCT) reveals depth-resolved dynamics during functional
brain activation. J. Neurosci. Methods 178, 162–173.
Cheung, C., Culver, J.P., Takahashi, K., Greenberg, J.H., Yodh, A.G., 2001. In vivo
cerebrovascular measurement combining diffuse near-infrared absorption and
correlation spectroscopies. Phys. Med. Biol. 46, 2053–2065.
Crosby, G., Crane, A.M., Sokoloff, L., 1982. Local changes in cerebral glucose utilization
during ketamine anesthesia. Anesthesiology 56, 437–443.
Crosby, G., Crane, A.M., Jehle, J., Sokoloff, L., 1983. The local metabolic effects of
somatosensory stimulation in the central nervous system of rats given pentobar-
bital or nitrous oxide. Anesthesiology 58, 38–43.
Culver, J.P., Durduran, T., Furuya, D., Cheung, C., Greenberg, J.H., Yodh, A.G., 2003a.
Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in
rat during focal ischemia. J. Cereb. Blood Flow Metab. 23, 911–924.
Culver, J.P., Siegel, A.M., Stott, J.J., Boas, D.A., 2003b. Volumetric diffuse optical
tomography of brain activity. Opt. Lett. 28, 2061–2063.
Delpy, D.T., Cope, M., van der Zee, P., Arridge, S., Wray, S., Wyatt, J., 1988. Estimation of
optical pathlength through tissue from direct time of flight measurement. Phys.
Med. Biol. 33, 1433–1442.
Devor, A., Ulbert, I., Dunn, A.K., Narayanan, S.N., Jones, S.R., Andermann, M.L., Boas, D.A.,
Dale, A.M., 2005. Coupling of the cortical hemodynamic response to cortical and
thalamic neuronal activity. Proc. Natl. Acad. Sci. U. S. A. 102, 3822–3827.
Devor, A., Hillman, E.M., Tian, P., Waeber, C., Teng, I.C., Ruvinskaya, L., Shalinsky, M.H.,
Zhu, H., Haslinger, R.H., Narayanan, S.N., Ulbert, I., Dunn, A.K., Lo, E.H., Rosen, B.R.,
Dale, A.M., Kleinfeld, D., Boas, D.A., 2008. Stimulus-induced changes in blood flow
and2-deoxyglucose uptake dissociateinipsilateralsomatosensorycortex.J.Neurosci.
Di, S., Barth, D.S., 1991. Topographic analysis of field potentials in rat vibrissa/barrel
cortex. Brain Res. 546, 106–112.
Drummond, J.C., Todd, M.M., 1985. The response of the feline cerebral circulation to
PaCO2 during anesthesia with isoflurane and halothane and during sedation with
nitrous oxide. Anesthesiology 62, 268–273.
Dueck, M.H., Petzke, F., Gerbershagen, H.J., Paul, M., Hesselmann, V., Girnus, R., Krug, B.,
Sorger, B., Goebel, R., Lehrke, R., Sturm, V., Boerner, U., 2005. Propofol attenuates
responses of the auditory cortex to acoustic stimulation in a dose-dependent
manner: a FMRI study. Acta Anaesthesiol. Scand. 49, 784–791.
Durduran, T., Burnett, M.G., Yu, G., Zhou, C., Furuya, D., Yodh, A.G., Detre, J.A., Greenberg,
J.H., 2004a. Spatiotemporal quantification of cerebral blood flow during functional
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
activation in rat somatosensory cortex using laser-speckle flowmetry. J. Cereb.
Blood Flow Metab. 24, 518–525.
2004b. Diffuse optical measurement of blood flow, blood oxygenation, and
metabolism in a human brain during sensorimotor cortex activation. Opt. Lett. 29,
Durduran, T., Zhou, C., Edlow, B.L., Yu, G., Choe, R., Kim, M.N., Cucchiara, B.L., Putt, M.E.,
Shah, Q., Kasner, S.E., Greenberg, J.H., Yodh, A.G., Detre, J.A., 2009. Transcranial
optical monitoring of cerebrovascular hemodynamics in acute stroke patients. Opt.
Express 17, 3884–3902.
Eger II, E.I., 1984. The pharmacology of isoflurane. Br. J. Anaesth. 56 (Suppl 1), 71S–99S.
Eng, C., Lam, A.M., Mayberg, T.S., Lee, C., Mathisen, T., 1992. The influence of propofol
with and without nitrous oxide on cerebral blood flow velocity and CO2 reactivity
in humans. Anesthesiology 77, 872–879.
Franceschini, M.A., Nissila, I., Wu, W., Diamond, S.G., Bonmassar, G., Boas, D.A., 2008.
Coupling between somatosensory evoked potentials and hemodynamic response
in the rat. NeuroImage 41, 189–203.
Franks, N.P., Lieb, W.R., 1994. Molecular and cellular mechanisms of general
anaesthesia. Nature 367, 607–614.
Greene, S.A., Thurmon, J.C., 1988. Xylazine—a review of its pharmacology and use in
veterinary medicine. J. Vet. Pharmacol. Ther. 11, 295–313.
Hirota, K., Lambert, D.G., 1996. Ketamine: its mechanism(s) of action and unusual
clinical uses. Br. J. Anaesth. 77, 441–444.
Hoffmeyer, H.W., Enager, P., Thomsen, K.J., Lauritzen, M.J., 2007. Nonlinear neurovas-
cular coupling in rat sensory cortex by activation of transcallosal fibers. J. Cereb.
Blood Flow Metab. 27, 575–587.
Hoshi, Y., Hazeki, O., Tamura, M., 1993. Oxygen dependence of redox state of copper in
cytochrome oxidase in vitro. J. Appl. Physiol. 74, 1622–1627.
Huppert, T.J., Hoge, R.D., Dale, A.M., Franceschini, M.A., Boas, D.A., 2006a. Quantitative
spatial comparison of diffuse optical imaging with blood oxygen level-dependent
and arterial spin labeling-based functional magnetic resonance imaging. J. Biomed.
Opt. 11, 064018.
Huppert, T.J., Hoge, R.D., Diamond, S.G., Franceschini, M.A., Boas, D.A., 2006b. A
temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor
stimuli in adult humans. NeuroImage 29, 368–382.
Huttunen, J.K., Grohn, O., Penttonen, M., 2008. Coupling between simultaneously
recorded BOLD response and neuronal activity in the rat somatosensory cortex.
NeuroImage 39, 775–785.
Hyder, F., Rothman, D.L., Shulman, R.G., 2002. Total neuroenergetics support
localized brain activity: implications for the interpretation of fMRI. Proc. Natl.
Acad. Sci. U. S. A. 99, 10771–10776.
Iadecola, C., 2004. Neurovascular regulation in the normal brain and in Alzheimer's
disease. Nat. Rev. Neurosci. 5, 347–360.
Iadecola, C., Yang, G., Ebner, T.J., Chen, G., 1997. Local and propagated vascular responses
evoked by focal synaptic activity in cerebellar cortex. J. Neurophysiol. 78, 651–659.
Jellema, T., Brunia, C.H., Wadman, W.J., 2004. Sequential activation of microcircuits
underlying somatosensory-evoked potentials in rat neocortex. Neuroscience 129,
Jin, T., Kim, S.G., 2008. Cortical layer-dependent dynamic blood oxygenation, cerebral
blood flow and cerebral blood volume responses during visual stimulation. Neuro-
Image 43, 1–9.
Jones, M., Berwick, J., Hewson-Stoate, N., Gias, C., Mayhew, J., 2005. The effect of
hypercapnia on the neural and hemodynamic responses to somatosensory
stimulation. NeuroImage 27, 609–623.
Kleinschmidt, A., Obrig, H., Requardt, M., Merboldt, K.D., Dirnagl, U., Villringer, A.,
Frahm, J., 1996. Simultaneous recording of cerebral blood oxygenation changes
during human brain activation by magnetic resonance imaging and near-infrared
spectroscopy. J. Cereb. Blood Flow Metab. 16, 817–826.
Kochs, E., Bischoff, P., 1994. Ketamine and evoked potentials. Anaesthesist 43 (Suppl 2),
Kublik, E., Musial, P., Wrobel, A., 2001. Identification of principal components in cortical
evoked potentials by brief surface cooling. Clin. Neurophysiol. 112, 1720–1725.
the rhesus monkey. Electroencephalogr. Clin. Neurophysiol. 53, 78–93.
Kulics, A.T., Cauller, L.J., 1986. Cerebral cortical somatosensory evoked responses,
multiple unit activity and current source-densities: their interrelationships and
significance to somatic sensation as revealed by stimulation of the awake monkey's
hand. Exp. Brain Res. 62, 46–60.
Lei, H., Grinberg, O., Nwaigwe, C.I., Hou, H.G., Williams, H., Swartz, H.M., Dunn, J.
F., 2001. The effects of ketamine–xylazine anesthesia on cerebral blood flow
and oxygenation observed using nuclear magnetic resonance perfusion
imaging and electron paramagnetic resonance oximetry. Brain Res. 913,
Logginidou, H.G., Li, B.H., Li, D.P., Lohmann, J.S., Schuler, H.G., DiVittore, N.A., Kreiser, S.,
Cronin, A.J., 2003. Propofol suppresses the cortical somatosensory evoked potential
in rats. Anesth. Analg. 97, 1784–1788.
Maandag, N.J., Coman, D., Sanganahalli, B.G., Herman, P., Smith, A.J., Blumenfeld, H.,
Shulman, R.G., Hyder, F., 2007. Energetics of neuronal signaling and fMRI activity.
Proc. Natl. Acad. Sci. U. S. A. 104, 20546–20551.
Mackert, B.M., Leistner, S., Sander, T., Liebert, A., Wabnitz, H., Burghoff, M., Trahms, L.,
Macdonald, R., Curio, G., 2008. Dynamics of cortical neurovascular coupling
analyzed by simultaneous DC-magnetoencephalography and time-resolved near-
infrared spectroscopy. NeuroImage 39, 979–986.
Martin, C., Martindale, J., Berwick, J., Mayhew, J., 2006. Investigating neural-
hemodynamic coupling and the hemodynamic response function in the awake
rat. NeuroImage 32, 33–48.
Masamoto, K., Kim, T., Fukuda, M., Wang, P., Kim, S.G., 2007. Relationship between
neural, vascular, and BOLD signals in isoflurane-anesthetized rat somatosensory
cortex. Cereb. Cortex 17, 942–950.
on neurovascular coupling in rat cerebral cortex. Eur. J. NeuroSci. 30, 242–250.
Mathiesen, C., Caesar, K., Akgoren, N., Lauritzen, M., 1998. Modification of activity-
dependent increases of cerebral blood flow by excitatory synaptic activity and
spikes in rat cerebellar cortex. J. Physiol. 512 (Pt 2), 555–566.
Mitzdorf, U., 1985. Current source-density method and application in cat cerebral
cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev. 65,
Nakao, Y., Itoh, Y., Kuang, T.Y., Cook, M., Jehle, J., Sokoloff, L., 2001. Effects of anesthesia
on functional activation of cerebral blood flow and metabolism. Proc. Natl. Acad.
Sci. U. S. A. 98, 7593–7598.
Norup Nielsen, A., Lauritzen, M., 2001. Coupling and uncoupling of activity-dependent
increases of neuronal activity and blood flow inrat somatosensory cortex. J.Physiol.
Obrig, H., Villringer, A., 2003. Beyond the visible—imaging the human brain with light.
J. Cereb. Blood Flow Metab. 23, 1–18.
Oria, M., Chatauret, N., Raguer, N., Cordoba, J., 2008. A new method for measuring
motor evoked potentials in the awake rat: effects of anesthetics. J. Neurotrauma
Study of neurovascular coupling in humans via simultaneous magnetoencephalo-
graphy and diffuse optical imaging acquisition. NeuroImage 46, 624–632.
Purdon, P.L., Pierce, E.T., Bonmassar, G., Walsh, J., Harrell, P.G., Kwo, J., Deschler, D.,
Barlow, M., Merhar, R.C., Lamus, C., Mullaly, C.M., Sullivan, M., Maginnis, S.,
Skoniecki, D., Higgins, H.A., Brown, E.N., 2009. Simultaneous electroencephalogra-
phy and functional magnetic resonance imaging of general anesthesia. Ann. N. Y.
Acad. Sci. 1157, 61–70.
2010. Noninvasive optical measures of CBV, StO(2), CBF index, and rCMRO(2) in
human premature neonates' brains in the first six weeks of life. Hum Brain Mapp 31,
Safo, Y., Young, M.L., Smith, D.S., Greenberg, J., Carlsson, C., Reivich, M., Keykhah, M.,
Harp, J.R., 1985. Effects of fentanyl on local cerebral blood flow in the rat. Acta
Anaesthesiol. Scand. 29, 594–598.
Sheth, S.A., Nemoto, M., Guiou, M., Walker, M., Pouratian, N., Toga, A.W., 2004. Linear
and nonlinear relationships between neuronal activity, oxygen metabolism, and
hemodynamic responses. Neuron 42, 347–355.
Shibasaki, H., 2008. Human brain mapping: hemodynamic response and electrophys-
iology. Clin. Neurophysiol. 119, 731–743.
Sibson, N.R., Dhankhar, A., Mason, G.F., Rothman, D.L., Behar, K.L., Shulman, R.G., 1998.
Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal
activity. Proc. Natl. Acad. Sci. U. S. A. 95, 316–321.
Sicard, K.M., Duong, T.Q., 2005. Effects of hypoxia, hyperoxia, and hypercapnia on
baseline and stimulus-evoked BOLD, CBF, and CMRO2 in spontaneously breathing
animals. NeuroImage 25, 850–858.
Sicard, K., Shen, Q., Brevard, M.E., Sullivan, R., Ferris, C.F., King, J.A., Duong, T.Q., 2003.
Regional cerebral blood flow and BOLD responses in conscious and anesthetized
rats under basal and hypercapnic conditions: implications for functional MRI
studies. J. Cereb. Blood Flow Metab. 23, 472–481.
Siegel, A.M., Culver, J.P., Mandeville, J.B., Boas, D.A., 2003. Temporal comparison of
functional brain imaging with diffuse optical tomography and fMRI during rat
forepaw stimulation. Phys. Med. Biol. 48, 1391–1403.
Silva, A.C., Koretsky, A.P., 2002. Laminar specificity of functional MRI onset times during
somatosensory stimulation in rat. Proc. Natl. Acad. Sci. U. S. A. 99, 15182–15187.
Simons, D.J., 1978. Response properties of vibrissa units in rat SI somatosensory
neocortex. J. Neurophysiol. 41, 798–820.
Sloan, T.B., 1998. Anesthetic effects on electrophysiologic recordings. J. Clin.
Neurophysiol. 15, 217–226.
Smith, A.L., Wollman, H., 1972. Cerebral blood flow and metabolism: effects of
anesthetic drugs and techniques. Anesthesiology 36, 378–400.
Steriade, M., 1984. The excitatory-inhibitory response sequence in thalamic and
neocortical cells: state-related changes and regulatory systems. In: Edelman, G.M.,
G.W., Cohen, W.M. (Eds.), Dynamic Aspects of Neocortical Function. John Wiley &
Sons, New York, pp. 107–158.
Strangman, G., Culver, J.P., Thompson, J.H., Boas, D.A., 2002. A quantitative comparison
of simultaneous BOLD fMRI and NIRS recordings during functional brain activation.
NeuroImage 17, 719–731.
Szentagothai, J., 1978. The Ferrier Lecture, 1977. The neuron network of the cerebral
cortex: a functional interpretation. Proc. R. Soc. Lond. B Biol. Sci. 201, 219–248.
Thomson, A.M., Bannister, A.P., 2003. Interlaminar connections in the neocortex. Cereb.
Cortex 13, 5–14.
Toronov, V., Webb, A., Choi, J.H., Wolf, M., Michalos, A., Gratton, E., Hueber, D., 2001.
Investigation of human brain hemodynamics by simultaneous near-infrared
spectroscopy and functional magnetic resonance imaging. Med. Phys. 28, 521–527.
Ueki, M., Mies, G., Hossmann, K.A., 1992. Effect of alpha-chloralose, halothane,
pentobarbital and nitrous oxide anesthesia on metabolic coupling in somatosen-
sory cortex of rat. Acta Anaesthesiol. Scand. 36, 318–322.
Ureshi, M., Kershaw, J., Kanno, I., 2005. Nonlinear correlation between field potential
and local cerebral blood flow in rat somatosensory cortex evoked by changing the
stimulus current. Neurosci. Res. 51, 139–145.
Veselis, R.A., Feshchenko, V.A., Reinsel, R.A., Beattie, B., Akhurst, T.J., 2005. Propofol and
thiopental do not interfere with regional cerebral blood flow response at sedative
concentrations. Anesthesiology 102, 26–34.
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377
Villiger, J.W., Ray, L.J., Taylor, K.M., 1983. Characteristics of [3H]fentanyl binding to the Download full-text
opiate receptor. Neuropharmacology 22, 447–452.
Villringer, A., Planck, J., Hock, C., Schleinkofer, L., Dirnagl, U., 1993. Near infrared
spectroscopy (NIRS): a new tool to study hemodynamic changes during activation
of brain function in human adults. Neurosci. Lett. 154, 101–104.
Wang, X.,Huang, Z.G., Gold, A.,Bouairi, E., Evans, C., Andresen, M.C., Mendelowitz, D., 2004.
to cardiac vagal neurons in the nucleus ambiguus. Anesthesiology 100, 1198–1205.
White, E.L., 1979. Thalamocortical synaptic relations: a review with emphasis on the
projections of specific thalamic nuclei to the primary sensory areas of the neocortex.
Brain Res. 180, 275–311.
White, E.L., Hersch, S.M., 1982. A quantitative study of thalamocortical and other
synapses involving the apical dendrites of corticothalamicprojection cells in mouse
SmI cortex. J. Neurocytol. 11, 137–157.
Wright, M., 1982. Pharmacologic effects of ketamine and its use in veterinary medicine.
J. Am. Vet. Med. Assoc. 180, 1462–1471.
Wrobel, A., Kublik, E., Musial, P., 1998. Gating of the sensory activity within barrel
cortex of the awake rat. Exp. Brain Res. 123, 117–123.
Young, W.L., Prohovnik, I., Correll, J.W., Ostapkovich, N., Ornstein, E., Quest, D.O.,
1991. A comparison of cerebral blood flow reactivity to CO2 during halothane
versus isoflurane anesthesia for carotid endarterectomy. Anesth. Analg. 73,
Zappe, A.C., Uludag, K., Oeltermann, A., Ugurbil, K., Logothetis, N.K., 2008. The influence
of moderate hypercapnia on neural activity inthe anesthetized nonhuman primate.
Cereb. Cortex 18, 2666–2673.
Zhou, C., Eucker, S.A., Durduran, T., Yu, G., Ralston, J., Friess, S.H., Ichord, R.N., Margulies,
S.S., Yodh, A.G., 2009. Diffuse optical monitoring of hemodynamic changes in piglet
brain with closed head injury. J. Biomed. Opt. 14, 034015.
M.A. Franceschini et al. / NeuroImage 51 (2010) 1367–1377