Intracortically Distributed NeurovascularCoupling Relationships within and betweenHuman Somatosensory Cortices

O.J. Arthurs1, T. Donovan1, D.J. Spiegelhalter2, J.D. Pickard1 and

S.J. Boniface1

1Wolfson Brain Imaging Centre, University of Cambridge,

Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK and2Medical Research Council Biostatistics Unit, Institute of Public

Health, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK

The coupling of neuronal cellular activity to its blood supply is ofcritical importance to the physiology of the human brain and hasbeen under discussion for more than a century. Linearity in thisrelationship has been demonstrated in some animal studies, butevidence is lacking in humans. In this study, we compared scalpevoked potentials and the functional magnetic resonance imaging(fMRI) blood oxygen level--dependent (BOLD) signal from healthyhuman volunteers with changes in the intensity of a somatosensorystimulus. By weighting the fMRI images according to the evokedpotential amplitude at corresponding intensities, we tested forpositive and negative covariation between these 2 data sets andthe extent to which these were linear. Hemodynamic changes inprimary somatosensory cortex covaried positively with neuronalactivity in a predominantly linear manner, with a small quadraticcontribution. Simultaneously, other cortical areas corresponding tothe nonstimulated limbs were found to covary negatively andlinearly in the hemispheres ipsilateral and contralateral to thestimulus. These concurrent and bilateral cortical dynamics, as wellas the intraregional features of this neurovascular coupling, areboth more complex than had been considered to date, withconsiderable implications.

Keywords: fMRI, intracortical, neurovascular coupling, SEP

Introduction

The coupling of the brain’s neural activity to its blood supply,

termed neurovascular coupling, and its mechanisms are a fun-

damental feature of brain physiology that have been under

discussion for more than a century and are still not fully

understood. Recent studies have identified a linear relationship

between measures of hemodynamic change and neuronal

activity in rats (Mathiesen and others 1998; Ngai and others

1999) and primates (Mathiesen and others 1998; Heeger and

others 2000; Rees and others 2000; Logothetis and others 2001)

and between functional magnetic resonance imaging (fMRI)

blood oxygen level--dependent (BOLD) responses in humans

and primate neuronal activity (Mathiesen and others 1998;

Heeger and others 2000; Rees and others 2000; Logothetis and

others 2001). Some nonlinearity in this relationship has also

been identified in relation to animal experiments (Ances and

others 2000; Jones and others 2004; Hewson-Stoate and others

2005). The relationship is less well characterized in humans, but

modern neuroimaging methods combined with more tradi-

tional electrophysiological techniques now allow for a definition

of the neurovascular relationship in normal human subjects.

A further physiological question relates to whether the brain

increases blood flow to functionally active areas at the expense

of other nonfunctioning areas. Neuroimaging has thrown some

light on this: some studies have identified decreases in blood

flow responses in functionally related (but not adjacent)

cortical areas in both sensory (Drevets and others 1995; Peyron

and others 1999) and motor areas (Allison and others 2000)

during activation. However, whether these findings are related

to decreased neuronal activity causing reductions in hemody-

namic change (i.e., an underlying negative neurovascular

coupling mechanism), or are a purely vascular phenomenon,

remains unresolved.

The experiments presented here investigate the direction

and linearity of the neurovascular coupling relationship in

normal human subjects. We compared changes in cerebral

blood flow (CBF) (using blocked design fMRI BOLD) and scalp

electrophysiology (using somatosensory evoked potentials

[SEPs]) in parallel experiments with changes in the intensity

of a median nerve electrical stimulus. By weighting the fMRI

images according to the evoked potential amplitudes at

corresponding intensities, we sought to test the hypothesis

that these 2 data sets covaried with each other, either positively

or negatively, in a linear or nonlinear manner. We thus identified

whether these relationships might vary within the somatosen-

sory cortex itself or between hemispheres.

Methods

StimulationSix healthy adults participated (4 males; mean age 24.33 years, range 22--

29 years), recruited from local university members. All studies were

performed under Local Ethics Committee Approval guidelines, with full

informed consent obtained. Stimuli were 0.2-ms square-wave electrical

pulses delivered to the median nerve at the wrist for 30-s blocks.

Stimulation intensity values were chosen to span a range from just above

sensory threshold to the highest level bearable for 30 s but did not

exceed 30 mA or individual pain thresholds. Values were normalized to

individual motor thresholds to enable comparisons between data sets

and across the group. During fMRI scanning, stimuli were delivered at

100 Hz to ensure that a detectable BOLD response could be recorded

(Kampe and others 2000). During SEPs recording, stimuli were de-

livered at 20 Hz to allow accurate identification of cortical SEP com-

ponents in transient mode. Current limiting resistors were placed in the

stimulating cables during fMRI as a safety precaution (Lemieux and

others 1997). The null hypothesis in this experiment was that there

would be no significant covariance between fMRI BOLD responses in

somatosensory cortex and SEP amplitudes during changes in stimulus

intensity.

Somatosensory Evoked PotentialsSEPs were recorded using Ag/AgCl 10-mm disc electrodes from

contralateral parietal cortex, 3 cm posterior and 7 cm lateral to the

vertex (Cz) referenced to Fz, and from the mixed nerve at the elbow of

the stimulated arm. Electrode impedances were maintained at less than

8 kX. Over 450 averages were made of 50 ms bin width and stored for

subsequent off-line analysis. Scalp potentials were amplified using

a band-pass filter of 3--3000 Hz. An automatic artifact rejection system

Cerebral Cortex

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excluded from the averages all runs containing transients exceeding ±50 lV at any recording channel, commonly due to muscular or pulsatile

artifacts. Stimulation intensity was pseudorandomized between succes-

sive recordings. The analysis was concentrated on the initial cortical

component of the short-latency SEP, the N20--P25 component (termed

‘‘SEP amplitude’’). The N20--P25 waveform of the human SEP is gen-

erated by a well-circumscribed population in the bank of the postcentral

sulcus, that is, primary somatosensory cortex (Broughton and others

1969; Goff and others 1977; Allison and others 1980; Grimm Schreiber

and others 1998). Pearson’s rank test was used for correlation analysis

(e.g., between SEP amplitudes and intensity level) using the Statistical

Package for the Social Sciences. Statistical significance was set at P <

0.05. Statistical significance threshold was set at P < 0.05 throughout.

fMRI AcquisitionImaging was performed on a Bruker Medical S300 scanner (Bruker

Medical, Ettlingen, Germany), acquiring gradient echo-planar imaging

(EPI) fMRI BOLD images of 25 contiguous 5-mm oblique axial slices at

3 T. The matrix size was 128 3 64 (an in-plane resolution of 2 3 4 mm),

repetition time of 4000 ms, echo time of 27 ms, and 90� flip angle.

Subjects lay supine in the scanner and wore earplugs and ear defenders

for noise attenuation throughout, according to our local protocols. A

functional imaging series comprised 72 sequential images of each slice

during stimulus presentation in a blocked design, alternating 8 scans

(32 s) on, 8 scans off, that is, a total of 4 min and 48 s. The first 8 scans

were discarded to allow for T1 saturation effects. Stimulation intensities

were unchanged during a 72-scan sequence but were pseudorandomized

between sequences. Scanning also included the once-only acquisition of

a fast gradient echo T1-weighted anatomical reference image of the

whole brain. No neurological abnormalities were identified in any of the

subjects studied.

fMRI PreprocessingAll image preprocessing and statistical analysis were done using sta-

tistical parametric mapping (SPM99;WellcomeDepartment of Cognitive

Neurology, http://www.fil.ion.ucl.ac.uk/spm/) on Matlab (Mathworks

Inc., MA) under Linux. Each image volume was reoriented, adjusted for

acquisition slice timing, and realigned to the first of each sequence. The

images were further spatially normalized into a standardized stereo-

tactical space (Montreal Neurological Institute [MNI] space; the EPI

template provided in SPM99) before being smoothed using a Gaussian

filter of 4 mm full-width half maximum. Coordinates are therefore given

in MNI space; areas were anatomically defined by transforming these

coordinates into ‘‘Talairach space’’ (Talairach and Tournoux 1988).

fMRI BOLD AnalysisBlocks of stimuli were modeled using a boxcar function, incorporating

a delay appropriate to the hemodynamic response. The size of the

hemodynamic response was measured by calculating signal minus

average baseline response across all scans. The modulation of hemody-

namic responses by task-related activation was further characterized

using both the (mean corrected) intensity and SEP amplitudes as linear

regressors. This identified brain regions in which the size of task versus

baseline hemodynamic response covaried linearly with these measures

on a voxel-by-voxel basis (either positively or negatively).

We used SEP amplitudes as independent regressors in the fMRI

analysis in order to show which voxels would best covary with SEP

amplitudes. This approach does not require an a priori hypothesis

regarding fMRI activation. This approach therefore eliminates the

inherent assumption that the fMRI locus that correlates best with

stimulus intensity will be that which correlates best with SEP amplitude.

Nonlinear covariances of the fMRI with the SEP data were similarly

modeled using a second-order (quadratic) derivative of SEP amplitudes

(Robson 1958). Results were averaged across the group in a fixed-effects

analysis to form a group-mean image, as it is widely recognized that at

least 12 subjects are required for reasonable population effects to be

seen in a random-effects model, due to intersubject variability (Holmes

and Friston 1998). The images are shown in the standard radiological

convention. All statistical maps were thresholded at P < 0.05, after

correcting for multiple comparisons. Where clusters of activations were

identified as significant, the coordinates of the voxel with highest t-

score are given.

Results

Effects of Stimulus Intensity

Effects of Stimulus Intensity on SEP Amplitude

N20--P25 amplitudes of the cortical SEP correlated linearly with

stimulus intensity in all subjects examined (P < 0.01 for each

subject, group data P < 0.001, Fig. 1A, an example from one

subject is given in Fig. 1C). Largest amplitudes were reached at

125% of motor threshold, which showed marked individual

variability in absolute amplitude and intensity.

fMRI BOLD Activity

Irrespective of intensity, the fMRI BOLD voxel of maximal

stimulus-induced activation was found in contralateral somato-

sensory cortex in each subject. Gradient echo fMRI BOLD voxel

Z score and percent signal change at this peak voxel increased

significantly with increasing stimulus intensity (P < 0.05, P <

0.05, respectively; Fig. 1B).

Testing for Linear Covariation with Stimulus Intensity

Positive Linear fMRI BOLD Covariation with

Stimulus Intensity

The fMRI BOLD areas in contralateral somatosensory cortex

(peak voxel coordinates: 38, –24, 62; Z score 10.23; cluster size

626; P < 0.001), contralateral thalamus (peak voxel coordinates:

16, –22, 02; Z score 6.81; cluster size 30; P < 0.001), and

ipsilateral cerebellum (peak voxel coordinates: –20, –52, –34; Z

score 5.38; cluster size 17; P < 0.001) showed significant linear

covariation with stimulus intensity (Table 1; Fig. 2A).

Negative Linear fMRI BOLD Covariation with

Stimulus Intensity

Areas that covaried negatively with increasing stimulus intensity

were contralateral somatosensory cortex near the midline

(peak voxel coordinates: 2, –26, 58; Z score 4.80; cluster size

1; P < 0.05) and ipsilateral somatosensory cortex (peak voxel

coordinates: –40, –34, 64; Z score 5.09; cluster size 5; P < 0.05;

Table 1; Fig. 2B).

Testing for Linear Covariation with SEP Amplitude

Positive Linear fMRI BOLD Covariation with

SEP Amplitude

Contralateral somatosensory cortex and ipsilateral cerebellum

covaried significantly in a positive linear fashion with SEP am-

plitudes in each subject (Table 2, Fig. 3). The group analysis also

showed most significant linear covariation with SEP amplitudes

in contralateral somatosensory cortex (peak voxel coordinates:

40, –26, 64; Z score 10.17; cluster size 458; P < 0.001), con-

tralateral thalamus (peak voxel coordinates: 16, –22, –02; Z score

6.08; cluster size 15; P < 0.05), and ipsilateral cerebellum (peak

voxel coordinates: –12, –56, –32; Z score 5.21; cluster size 15; P <

0.05), across subjects (Table 3; Fig. 4A).

Negative Linear fMRI BOLD Covariation with

SEP Amplitude

Two areas which covaried negatively with SEP amplitudes were

found: the first in ipsilateral primary somatosensory cortex, in

a similar location to that which covaried positively with SEP

amplitudes above but in the opposite hemisphere (upper limb

Page 2 of 8 Intracortically Distributed Neurovascular Coupling d Arthurs and others

hand areas; peak voxel coordinates: –42, –32, 62; Z score 6.04;

cluster size 15; P < 0.05; Table 3; Fig. 4B); and the second in

contralateral primary somatosensory cortex areas, close to the

midline (leg and/or foot areas; peak voxel coordinates: 4, –44,

58; Z score 6.28; cluster size 127; P < 0.001; Table 3; Fig. 4B).

Testing for Nonlinear Covariation with SEP Amplitude

Positive Quadratic fMRI BOLD Covariation with

SEP Amplitude

Nonlinear contributions were also modeled, and a small area

(peak voxel coordinates: 34, –34, 58; Z score 4.85; cluster size 2;

P < 0.05; Table 3; Fig. 4C) of fMRI BOLD activity in contralateral

primary somatosensory cortex was found to covary significantly

with SEP amplitudes. This cluster fell within the boundaries of

the larger cluster of 458 voxels that covaried linearly with SEP

amplitudes (Fig. 4A).

Negative Quadratic fMRI BOLD Covariation with

SEP Amplitude

No clusters reached significance thresholding for negative,

nonlinear covariations with SEP amplitudes (results not shown).

No other types of nonlinearity reached significance in pre-

liminary data analysis, neither positively nor negatively.

Discussion

SEP amplitudes and fMRI BOLD responses correlated linearly

with stimulus intensity in all subjects, and across subjects. Both

modalities therefore exhibited the same qualitative pattern of

experimental effects in primary somatosensory cortex when

measured in parallel. Furthermore, the covariation of fMRI

BOLD responses with SEP amplitudes (indicative of neuro-

vascular coupling) was found to be strongly linear in this area.

Short-latency SEPs are attributed mainly to synchronized

extracellular currents from summated postsynaptic potentials

of pyramidal cells in primary somatosensory cortex (Eccles

1951; Creutzfeldt and others 1966; Lopes da Silva and Storm van

Leeuwan 1978; Nunez 1981; Lopes da Silva 1991). fMRI BOLD

responses predominantly measure the CBF response from

cortical vessels due to the inherent magnetic changes in

hemoglobin during activation (a transient drop in the deoxy:

oxy-hemoglobin ratio [Fox and Raichle 1986; Ogawa and others

1990; Kwong and others 1992; Malonek and others 1997]).

Although the correlation found in these experiments does not

prove causation, these findings together imply that the synaptic

activity of a population of somatosensory cortical neurons play

a major role in signaling the needs of the neuron to the

vasculature. This is consistent with previous findings in pri-

mates (Logothetis, Pauls and others 2001) and the empirical

Figure 1. The correlation of SEP N20--P25 amplitudes with stimulus intensity (A) is linear across subjects (mean ± standard error [SE] of mean; P < 0.001). An example from onesubject is given in (C). Largest amplitudes were reached at 125% of motor threshold. The correlation of fMRI BOLD percent signal intensity change with stimulus intensity (B) islinear across subjects (mean ± SE mean; P < 0.05). Group data of n = 6 are shown.

Cerebral Cortex Page 3 of 8

evidence that cortical action potentials may contribute rela-

tively little to the metabolic demand of the brain (Creutzfeldt

1995).

However, electrophysiological responses have inherent dif-

ferences in signal to noise between different techniques, such

that electrical signals that can be measured by extracellular

electrodes are preferentially biased toward the slow oscillations

of membrane potential (local field potential [LFP]) rather than

action potentials or spikes. SEP measurements are too coarse to

detect spiking activity, and therefore their contribution to the

fMRI BOLD signals observed cannot be meaningfully analyzed

using these techniques. Given evidence that human fMRI BOLD

activity may be proportional to primate single-cell firing activity,

this suggests that action potentials may be important in the

neurovascular coupling relationship (Heeger and others 2000;

Rees and others 2000). The hemodynamic response may

therefore be better related to the underlying ensemble elec-

trical activity, including both LFP and spiking activity. The

importance of this possibility and several other factors that may

affect the lack of a perfect relation between BOLD and SEPs

have been discussed elsewhere at length (Arthurs and Boniface

2002; Heeger and Rees 2002; Rees and others 2002; Logothetis

and Pfeuffer 2004). Our results suggest that the cortical activity

measured using the early component of the SEP makes a strong

linear contribution to the vascular changes that dominate the

human BOLD response.

The exact metabolic nature of the neurovascular signal

currently remains unknown, although there are many possibil-

ities, including the astrocytic recycling of glutamate (the

‘‘astrocyte-neuron lactate shuttle’’ hypothesis) (Pellerin and

others 1998), increased potassium levels causing vasodilatation

(Paulson and Newman 1987), and/or increases in nitric oxide

Figure 2. The fMRI BOLD areas that covary linearly, (A) positively, and (B) negativelywith stimulus intensity. These are contralateral somatosensory cortex, contralateralthalamus, and ipsilateral cerebellum (covary positively; A) and contralateralsomatosensory cortex near the midline and ipsilateral somatosensory cortex (covarynegatively; B). A fixed-effects group analysis of n = 6 is shown. These images arethresholded at P < 0.05 corrected for multiple comparisons. The corresponding dataare given in Table 1.

Table 2fMRI BOLD areas that covary positively and linearly with SEP amplitudes in individual subjects

Figure Subject Brain region Coordinates x, y, z Z score Cluster size

3A 1 Contralateral SI 50, �22, 52 10.88** 466**1 Ipsilateral Cerebellum �16, �62, �30 6.34** 27**

3B 2 Contralateral SI 40, �22, 62 9.22** 150**3C 3 Contralateral SI 38, �24, 58 7.34** 106**

3 Ipsilateral Cerebellum �20, �50, �38 5.75** 10**3D 4 Contralateral SI 44, �28, 50 8.14** 113**3E 5 Contralateral SI 52, �20, 52 11.74** 237**

5 Ipsilateral Cerebellum �24, �62, �30 7.49** 102**3F 6 Contralateral SI 46, �36, 58 5.85** 29**

Note: Fixed-effects group analysis of n 5 6 shown. **P\ 0.001, *P\ 0.05; SI 5 primary

somatosensory cortex.

Figure 3. The fMRI BOLD areas that covary linearly and positively with SEP N20--P25amplitudes in individual subjects. These are contralateral somatosensory cortex andipsilateral cerebellum. These images are thresholded at P < 0.05 corrected for multiplecomparisons. The corresponding data are given in Table 2.

Table 1fMRI BOLD areas that covary linearly with stimulus intensity

Figure Covariation Brain region Coordinates x, y, z Z score Cluster size

2A Positive Contralateral SI 38, �24, 62 10.23** 636**50, �22, 56 10.16**44, �16, 54 10.04**

Positive Contralateral thalamus 16, �22, 2 6.81** 30**2B Negative Contralateral SI 2, �26, 58 4.80** 1*

Negative Ipsilateral SI �40, �34, 64 5.09* 5*

Note: Fixed-effects group analysis of n 5 6 shown. **P\ 0.001, *P\ 0.05; SI 5 primary

somatosensory cortex.

Page 4 of 8 Intracortically Distributed Neurovascular Coupling d Arthurs and others

and adenosine (Dirnagl and others 1994). All these metabolic

candidates fail to demonstrate the necessary temporal and

precise spatial relationship between accumulations and flow

increase and have been previously discussed in detail with other

possibilities (Lou and others 1987; Villringer and Dirnagl 1995;

Kuschinsky 1997).

Nonlinearity

In this study, a much smaller area of fMRI BOLD activity in

primary somatosensory cortex was also found to covary with

SEP amplitudes in a quadratic fashion (Fig. 4C). This area fell

within the larger cluster in somatosensory cortex that corre-

lated linearly (Fig. 4A). Given the small nature of this response,

any inference made must be speculative. However, a small

nonlinear component to the overwhelming linear neurovascu-

lar coupling relationship (2 nonlinear/458 linearly covarying

voxels, i.e., 0.44%) is consistent with most (Mathiesen and

others 1998; Brinker and others 1999; Heeger and others 2000;

Ogawa and others 2000; Rees and others 2000; Logothetis and

others 2001) but not all recent studies (Ances and others 2000).

Previous studies have found nonlinearities at the extreme ends

of this relationship, such that the relationship is well approx-

imated by a linear function over the midrange of stimuli

(Hewson-Stoate and others 2005), as seen here.

There is currently a debate as to whether this nonlinearity

may itself be attributable to a nonlinear neuronal response,

including the contribution of transiently high neuronal-spiking

activity to a hemodynamic response that is primarily synapti-

cally driven (Bandettini and Ungerleider 2001; Logothetis and

others 2001; Birn and Bandettini 2005), or alternatively due to

the inherent nonlinear relationship between metabolic demand

and the BOLD signal. The BOLD response is linearly related

neither to CBF nor to the cerebral metabolic rate of oxygen

consumption (CMRO2) (Rees and others 1997; Hoge and others

1999; Mandeville and others 1999). Changes in cerebral oxygen

consumption have been shown to increase linearly with

synaptic activity but demonstrate a threshold effect, also

contributing to nonlinearities (Sheth and others 2004). Alter-

natively, the nonlinearity may arise from slight differences in

sensitivity of our acquisition techniques, as discussed later in

this section.

Negative Coupling

Two areas of negative linear neurovascular coupling were also

found in this study: in ipsilateral somatosensory cortex corre-

sponding to the upper limb including the hand area (mirroring

the area of positive linear coupling in the opposite hemisphere)

and in contralateral somatosensory cortex corresponding to the

sensory representation of the leg and foot (Fig. 4B). This

suggests an efficient suppression of blood flow to relatively

‘‘inactive’’ limb cortical areas. These findings were accessible

only by virtue of this type of voxel-by-voxel covariance analysis,

and there are a number of possible interpretations.

We use the term ‘‘negative’’ BOLD to mean a reduction in

BOLD activity relative to baseline which corresponds to the

timing of the stimulus, also referred to as deactivation. There are

2 such types of negative BOLD signal identified in the current

literature. The first is a transient initial negative dip in BOLD

signal to stimulus activation characterizing the hemodynamic

response function. This is thought either to be a hemodynamic

steal phenomenon or to be caused by oxygen consumption in

the absence of a hemodynamic response (Rother and others

2002). The other, demonstrated in this experiment, is a sus-

tained negative BOLD response usually seen at distances of

centimeters away from stimulated areas but in physiologically

correlated areas, such as opposite motor (Hamzei and others

2002), sensory (Drevets and others 1995), and visual areas

(Smith and others 2004). Initially thought to be a hemodynamic

steal phenomenon caused by a redistribution of blood flow to

adjacent areas of cortex (Harel and others 2002), it now appears

much more likely to represent a neuronally driven inhibitory

phenomenon, when observed at sites distant to, but functionally

related to, active brain areas. No areas of blood flow decreases

were observed in the penumbral region of the activated area in

somatosensory cortex in our study.

The fMRI BOLD signal decreases seen in ipsilateral motor

cortex during unilateral hand movements are proportional to

the task-related increases in contralateral M1 (in parallel with

duration of movement) (Newton and others 2005). Studies have

also shown that the reductions in BOLD signal in this area

Figure 4. The fMRI BOLD areas that covary positively and linearly with SEP N20--P25amplitudes (A), negatively and linearly with SEP N20--P25 amplitudes (B), andpositively and nonlinearly with SEP N20--P25 amplitudes (C). These are contralateralsomatosensory cortex and contralateral thalamus (covary positively and linearly, A),contralateral somatosensory cortex near the midline and ipsilateral somatosensorycortex (covary negatively and linearly, B), and contralateral somatosensory cortex(covary positively and nonlinearly, C). A fixed-effects group analysis of n = 6 is shown.These images are thresholded at P < 0.05 corrected for multiple comparisons. Thecorresponding data are given in Table 3.

Table 3fMRI BOLD areas that covary with SEP amplitude

Figure Covariation Brain region Coordinates x, y, z Z score Cluster size

4A Positive, linear Contralateral SI40, �26, 64 10.17**

458**50, �20, 56 10.14**

Positive, linear Contralateralthalamus

16, �22, �2 6.08** 15*

4C Positive, nonlinear Contralateral SI 34, �34, 58 4.85* 2*4B Negative, linear Contralateral SI 4, �44, 58 6.28** 127**

Negative, linear Ipsilateral SI �42, �32, 62 6.04** 15*— Negative, nonlinear None

Note: Fixed-effects group analysis of n 5 6 shown. **P\ 0.001, *P\ 0.05; SI 5 primary

somatosensory cortex.

Cerebral Cortex Page 5 of 8

during contralateral activations are linearly related to the

metabolic down-regulation, that is, CBF and cerebral metabolic

rate of oxygen consumption (CMRO2) changes, suggesting an

inhibitory neuronal signal underlying this negative BOLD re-

sponse (Stefanovic and others 2004). Negative BOLD has also

been reported during electroencephalography (EEG) spiking

activity and found to occur at sites that are distant from

anatomical areas related to spikes (Kobayashi and others

2005), suggesting neuronal inhibition.

Decreases in activation have also previously been attributed

to higher level cortical function changes, such as the ‘‘anticipa-

tion’’ of expected stimuli elsewhere: CBF decreases in hand and

face zones of ipsilateral somatosensory cortex (while attending

to toe stimulation) have been correlated with anxiety levels

during anticipation of stimuli (Drevets and others 1995). This

suggests the suppression of ipsilateral responses in order to

‘‘focus on,’’ or ‘‘attend to,’’ contralateral responses where stimuli

are expected in direct proportion to the anxiety level. fMRI

BOLD responses in our experiment might therefore covary neg-

atively with SEP amplitudes because they, in turn, covary with

increasing stimulus intensity. However, although decreases in

fMRI BOLD signal from ipsilateral somatosensory cortex do cor-

relate with stimulus intensities, P values were lower and voxel

t-statistics less significant than the equivalent analyses with SEP

amplitudes (Tables 1 and 3, respectively). Ipsilateral cortical

responses may therefore be more closely (albeit negatively)

related to contralateral responses rather than to the stimulus.

An alternative consideration is the changes in ongoing event-

related desynchronization or synchronization at particular

frequency bands (Pfurtscheller and Lopes da Silva 1999;

Pfurtscheller 2001), which may represent increased cortical

activation and deactivation, respectively, where ‘‘activation’’

represents increased resonance-like behavior of connected

subnetworks. These types of changes are time locked to the

event but not phase locked and therefore cannot be extracted

using conventional linear methods such as averaging (as in

SEP recordings), but require frequency analysis. They have been

demonstrated in somatosensory and visual cortices (Neuper and

Pfurtscheller 2001; Pfurtscheller and others 2001; Singh and

others 2002; Moosmann and others 2003). The amplitude of

negative fMRI BOLD responses to acoustic stimulation has been

shown to correlate positively with measures of EEG synchroni-

zation during sleep (Czisch and others 2004), suggesting a re-

lationship between cortical deactivation and negative BOLD

signals. Further, analysis of these network synchronization

changes may give a greater understanding of the underlying

signal causing negative BOLD changes.

What we have shown is that the negative fMRI BOLD activity

seen in contralateral somatosensory cortex correlates with neu-

ronal activity (as indexed by the SEP) and fMRI BOLD changes in

the ‘‘active’’ cortical area, that is, they are directly related to

markers of neuronal activity elsewhere. If these findings reflect

a neuronally mediated corticocortical inhibition, such that

ipsilateral cortical activity is inhibited in proportion to increases

in contralateral cortical activity, then it is possible that lesions of

the corpus callosum might disrupt these neuronal connections.

Experimental Confounds

We must acknowledge some differences in the implementation

of the fMRI BOLD and SEP protocols used in this experiment,

although these are unlikely to create substantial experimental

confounds. First, electrical stimulation of the medial nerve was

applied at 100 Hz during fMRI recording and at 20 Hz during SEP

recording, as in a previous study (Arthurs and others 2000). The

lower frequency allows reliable identification of early compo-

nents in the SEP recording, whereas the higher stimulation

frequency is more efficient for determining the fMRI BOLD

responses (Kampe and others 2000). SEPs at 100 Hz are

inherently difficult to record due to the stimulus artifact, and

the standing waveform generated is difficult to interpret at this

frequency in the absence of more sophisticated analysis

methodology. Short-latency SEP intensity--dependent stimulus

response curves have not been found to vary significantly

between 20 and 100 Hz (O. Arthurs and S. Boniface, un-

published data). However, 2 different frequencies are required

to optimize each signal (fMRI and SEP), and this suggests that

the different frequencies may generate subtly different re-

sponses. The use of multichannel EEG or magnetoencephalog-

raphy recording may be required to accurately identify SEP

component generators at high stimulation frequencies to

resolve these issues.

Second, we also only modeled quadratic, second-order non-

linearities in the data and did not further investigate third order

or other types of nonlinearity. However, no other types of

nonlinearity reached significance in preliminary data analysis.

Further, detailed modeling of this relationship may reveal more

subtle nonlinearities.

Third, we used block design fMRI recording and event-related

SEP recordings, although both SEPs and the fMRI BOLD re-

sponse were summarized over a 30-s block, in order to eliminate

and minimize any initial adaptive responses. Given the long

periods over which these data sets were recorded in parallel,

this is unlikely to make a significant difference. However, we

acknowledge that these practical constraints and differences in

techniques could account for small changes, such as the

nonlinear covariations observed.

Summary

In conclusion, the simultaneous activation and suppression of

functionally related cortical areas, as well as the intraregional

features of the neurovascular coupling response, appear con-

siderably more complex than has been considered to date and

require further investigation.

Notes

This project was supported by Technology Foresight, UK and by

Oxford Instruments, UK. Merck Sharp and Dohme supported OJA on an

MB/PhD program. Conflict of Interest: None declared.

Address correspondence to O.J. Arthurs, Wolfson Brain Imaging

Centre, University of Cambridge, Box 65, Addenbrooke’s Hospital, Hills

Road, Cambridge, CB2 2QQ, UK. Email: fMRI@owenarthurs.co.uk.

References

Allison JD, Meador KJ, Loring DW, Figueroa RE, Wright JC. 2000.

Functional MRI cerebral activation and deactivation during finger

movement. Neurology 54(1):135--142.

Allison T, Goff W, Williamson PD, Van Gilder JC. 1980. On the neural

origin of early components of the human somatosensory evoked

potential. In: Desmedt JE, editor. Progress in clinical neurophysiol-

ogy: clincal uses of cerebral, brainstem and spinal somatosensory

evoked potentials. Basel, Switzerland: Karger. 7:p 51--68.

Ances BM, Zarahn E, Greenberg JH, Detre JA. 2000. Coupling of

neural activation to blood flow in the somatosensory cortex of rats

is time-intensity separable, but not linear. J Cereb Blood Flow Metab

20(6):921--930.

Page 6 of 8 Intracortically Distributed Neurovascular Coupling d Arthurs and others

Arthurs OJ, Boniface SJ. 2002. How well do we understand the neural

origins of the fMRI BOLD signal? Trends Neurosci 25(1):27--31.

Arthurs OJ, Williams EJ, Carpenter TA, Pickard JD, Boniface SJ. 2000.

Linear coupling between functional magnetic resonance imaging

and evoked potential amplitude in human somatosensory cortex.

Neuroscience 101(4):803--806.

Bandettini PA, Ungerleider LG. 2001. From neuron to BOLD: new

connections. Nat Neurosci 4(9):864--866.

Birn RM, Bandettini PA. 2005. The effect of stimulus duty cycle and ‘‘off’’

duration on BOLD response linearity. Neuroimage 27(1):70--82.

Brinker G, Bock C, Busch E, Kerp H, Hossmann KA, Hoehn-Berlage M.

1999. Simultaneous recording of evoked potentials and T2*-weighted MR images during somatosensory stimulation of rat.

Magn Reson Med 41(3):469--473.

Broughton R, Meier-Ewert KH, Ebe M. 1969. Evoked visual, somato-

sensory and retinal potentials in photosensitive epilepsy. Electro-

encephalogr Clin Neurophysiol 27(4):373--386.

Creutzfeldt OD. 1995. Cortex cerebri: performance, structural and func-

tional organisation of the cortex. New York: Oxford University Press.

Creutzfeldt OD, Watanabe S, Lux HD. 1966. Relations between EEG

phenomena and potentials of single cortical cells. I. Evoked

responses after thalamic and erpicortical stimulation. Electroence-

phalogr Clin Neurophysiol 20(1):1--18.

Czisch M, Wehrle R, Kaufmann C, Wetter TC, Holsboer F, Pollmacher T,

Auer DP. 2004. Functional MRI during sleep: BOLD signal decreases

and their electrophysiological correlates. Eur J Neurosci

20(2):566--574.

Dirnagl U, Niwa K, Lindauer U, Villringer A. 1994. Coupling of cerebral

blood flow to neuronal activation: role of adenosine and nitric oxide.

Am J Physiol 267(1 Pt 2):H296--H301.

Drevets WC, Burton H, Videen TO, Snyder AZ, Simpson JR, Jr, Raichle

ME. 1995. Blood flow changes in human somatosensory cortex

during anticipated stimulation. Nature 373(6511):249--252.

Eccles JC. 1951. Interpretation of action potentials evoked in the

cerebral cortex. Electroencephalogr and Clin Neurophysiol

3:449--464.

Fox PT, Raichle ME. 1986. Focal physiological uncoupling of

cerebral blood flow and oxidative metabolism during somatosensory

stimulation in human subjects. Proc Natl Acad Sci USA 83(4):

1140--1144.

Goff GD, Matsumiya Y, Allison T, Goff WR. 1977. The scalp topography of

human somatosensory and auditory evoked potentials. Electroence-

phalogr Clin Neurophysiol 42(1):57--76.

Grimm C, Schreiber A, Kristeva-Feige R, Mergner T, Hennig J, Lucking

CH. 1998. A comparison between electric source localisation and

fMRI during somatosensory stimulation. Electroencephalogr Clin

Neurophysiol 106(1):22--29.

Hamzei F, Dettmers C, Rzanny R, Liepert J, Buchel C, Weiller C. 2002.

Reduction of excitability (‘‘inhibition’’) in the ipsilateral primary

motor cortex is mirrored by fMRI signal decreases. Neuroimage

17(1):490--496.

Harel N, Lee SP, Nagaoka T, Kim DS, Kim SG. 2002. Origin of negative

blood oxygenation level-dependent fMRI signals. J Cereb Blood Flow

Metab 22(8):908--917.

Heeger DJ, Huk AC, Geisler WS, Albrecht DG. 2000. Spikes versus BOLD:

what does neuroimaging tell us about neuronal activity? Nat Neuro-

sci 3(7):631--633.

Heeger DJ, Rees D. 2002. What does fMRI tell us about neuronal activity?

Nat Rev Neurosci 3(2):142--151.

Hewson-Stoate N, Jones M, Martindale J, Berwick J, Mayhew J. 2005.

Further nonlinearities in neurovascular coupling in rodent barrel

cortex. Neuroimage 24(2):565--574.

Hoge RD, Atkinson J, Gill B, Crelier GR, Marrett S, Pike GB. 1999. Linear

coupling between cerebral blood flow and oxygen consumption

in activated human cortex. Proc Natl Acad Sci USA

96(16):9403--9408.

Holmes AP, Friston KJ. 1998. Generalisability, random effects and

population inference. Neuroimage 7:S754.

Jones M, Hewson-Stoate N, Martindale J, Redgrave P, Mayhew J. 2004.

Nonlinear coupling of neural activity and CBF in rodent barrel

cortex. Neuroimage 22(2):956--965.

Kampe KK, Jones RA, Auer DP. 2000. Frequency dependence of the

functional MRI response after electrical median nerve stimulation.

Hum Brain Mapp 9(2):106--114.

Kobayashi E, Bagshaw AP, Grova C, Dubeau F, Gotman J. 2005. Negative

BOLD responses to epileptic spikes. Hum Brain Mapp [Epub ahead of

print].

Kuschinsky W. 1997. Neuronal-vascular coupling: a unifying hypothesis.

Adv Exp Med Biol 413:167--176.

Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM,

Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R. 1992.

Dynamic magnetic resonance imaging of human brain activity during

primary sensory stimulation. Proc Natl Acad Sci USA

89(12):5675--5679.

Lemieux L, Allen PJ, Franconi F, Symms MR, Fish DR. 1997. Recording

of EEG during fMRI experiments: patient safety. Magn Reson Med

38(6):943--952.

Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. 2001.

Neurophysiological investigation of the basis of the fMRI signal.

Nature 412(6843):150--157.

Logothetis NK, Pfeuffer J. 2004. On the nature of the BOLD fMRI

contrast mechanism. Magn Reson Imaging 22(10):1517--1531.

Lopes da Silva F. 1991. Neural mechanisms underlying brain waves: from

neural membranes to networks. Electroencephalogr Clin Neuro-

physiol 79:81--93.

Lopes da Silva F, Storm van Leeuwan W. 1978. The cortical alpha rhythm

in dog: the depth and surface profile of phase. In: Brazier MAB,

Petsche H, editors. Architectonics of the cerebral cortex. New York:

Raven Press. p 319--333.

Lou HC, Edvinsson L, MacKenzie ET. 1987. The concept of coupling

blood flow to brain function: revision required? Ann Neurol 22(3):

289--297.

Malonek D, Dirnagl U, Lindauer U, Yamada K, Kanno I, Grinvald A.

1997. Vascular imprints of neuronal activity: relationships between

the dynamics of cortical blood flow, oxygenation, and volume

changes following sensory stimulation. Proc Natl Acad Sci USA

94(26):14826--14831.

Mandeville JB, Marota JJ, Ayata C, Moskowitz MA, Weisskoff RM, Rosen

BR. 1999. MRI measurement of the temporal evolution of relative

CMRO(2) during rat forepaw stimulation. Magn Reson Med

42(5):944--951.

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.

Moosmann M, Ritter P, Krastel I, Brink A, Thees S, Blankenburg F, Taskin

B, Obrig H, Villringer A. 2003. Correlates of alpha rhythm in

functional magnetic resonance imaging and near infrared spectros-

copy. Neuroimage 20(1):145--158.

Neuper C, Pfurtscheller G. 2001. Evidence for distinct beta resonance

frequencies in human EEG related to specific sensorimotor cortical

areas. Clin Neurophysiol 112(11):2084--2097.

Newton JM, Sunderland A, Gowland PA. 2005. fMRI signal decreases in

ipsilateral primary motor cortex during unilateral hand movements

are related to duration and side of movement. Neuroimage

24(2):1080--1087.

Ngai AC, Jolley MA, D’Ambrosio R, Meno JR, Winn HR. 1999. Frequency-

dependent changes in cerebral blood flow and evoked potentials

during somatosensory stimulation in the rat. Brain Res 837(1--

2):221--228.

Nunez PL. 1981. Electric fields of the brain. New York: Oxford University

Press.

Ogawa S, Lee TM, Kay AR, Tank DW. 1990. Brain magnetic resonance

imaging with contrast dependent on blood oxygenation. Proc Natl

Acad Sci USA 87(24):9868--9872.

Ogawa S, Lee TM, Stepnoski R, Chen W, Zhu XH, Ugurbil K. 2000. An

approach to probe some neural systems interaction by functional

MRI at neural time scale down to milliseconds. Proc Natl Acad Sci

USA 97(20):11026--11031.

Paulson OB, Newman EA. 1987. Does the release of potassium from

astrocyte endfeet regulate cerebral blood flow? Science

237(4817):896--898.

Cerebral Cortex Page 7 of 8

Pellerin L, Pellegri G, Bittar PG, Charnay Y, Bouras C, Martin JL, Stella N,

Magistretti PJ. 1998. Evidence supporting the existence of an

activity-dependent astrocyte-neuron lactate shuttle. Dev Neurosci

20(4--5):291--299.

Peyron R, Garcia-Larrea L, Gregoire MC, Costes N, Convers P, Lavenne F,

Mauguiere F, Michel D, Laurent B. 1999. Haemodynamic brain

responses to acute pain in humans: sensory and attentional net-

works. Brain 122(Pt 9):1765--1780.

Pfurtscheller G. 2001. Functional brain imaging based on ERD/ERS.

Vision Res 41(10--11):1257--1260.

Pfurtscheller G, Krausz G, Neuper C. 2001. Mechanical stimulation of

the fingertip can induce bursts of beta oscillations in sensorimotor

areas. J Clin Neurophysiol 18(6):559--564.

Pfurtscheller G, Lopes da Silva FH. 1999. Event-related EEG/MEG

synchronization and desynchronization: basic principles. Clin Neu-

rophysiol 110(11):1842--1857.

Rees G, Friston K, Koch C. 2000. A direct quantitative relationship

between the functional properties of human and macaque V5. Nat

Neurosci 3(7):716--723.

Rees G, Howseman A, Josephs O, Frith CD, Friston KJ, Frackowiak

RS, Turner R. 1997. Characterizing the relationship between

BOLD contrast and regional cerebral blood flow measurements

by varying the stimulus presentation rate. Neuroimage 6(4):

270--278.

Rees G Kreiman G, Koch C. 2002. Neural correlates of consciousness in

humans. Nat Rev Neurosci 3(4):261--270.

Robson DS. 1958. A simple method for constructing orthogonal

polynomials when the independent variable is unequally spaced.

Biometrics 15:187--191.

Rother J, Knab R, Hamzei F, Fiehler J, Reichenbach JR, Buchel C, Weiller

C. 2002. Negative dip in BOLD fMRI is caused by blood flow--oxygen

consumption uncoupling in humans. Neuroimage 15(1):98--102.

Sheth SA, Nemoto M, Guiou M, Walker M, Pouratian N, Toga AW.

2004. Linear and nonlinear relationships between neuronal activity,

oxygen metabolism, and hemodynamic responses. Neuron

42(2):347--355.

Singh KD, Barnes GR, Hillebrand A, Forde EM, Williams AL. 2002. Task-

related changes in cortical synchronization are spatially coincident

with the hemodynamic response. Neuroimage 16(1):103--114.

Smith AT, Williams AL, Singh KD. 2004. Negative BOLD in the visual

cortex: evidence against blood stealing. Hum Brain Mapp

21(4):213--220.

Stefanovic B, Warnking JM, Pike GB. 2004. Hemodynamic and meta-

bolic responses to neuronal inhibition. Neuroimage 22(2):771--778.

Talairach T, Tournoux P. 1988. Co-planar stereotaxic atlas of the human

brain. Stuttgart, Germany: Thieme.

Villringer A, Dirnagl U. 1995. Coupling of brain activity and cerebral

blood flow: basis of functional neuroimaging. Cerebrovasc Brain

Metab Rev 7(3):240--276.

Page 8 of 8 Intracortically Distributed Neurovascular Coupling d Arthurs and others