A mouse model for studying large-scale neuronal networks using EEG mapping techniques

Final article published in NeuroImage 2008 Aug 15;42(2):591-602. http://dx.doi.org/10.1016/j.neuroimage.2008.05.016.

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A mouse model for studying large-scale neuronal networks using EEG mapping

techniques

Pierre Mégevand1,2, Charles Quairiaux1, Agustina M. Lascano1,2, Jozsef Z. Kiss1, Christoph

M. Michel1,2

1 Fundamental Neuroscience Department, Geneva University Medical School, Rue Michel-

Servet 1, 1211 Geneva 14, Switzerland

2 Functional Brain Mapping Laboratory, Neurology Clinics, Clinical Neuroscience

Department, Geneva University Hospital and Medical School, Rue Micheli-du-Crest 24, 1211

Geneva 14, Switzerland

Corresponding authors:

Pierre Mégevand

Christoph M. Michel

Fundamental Neuroscience Department

Geneva University Medical School

Rue Michel-Servet 1

1211 Geneva 14, Switzerland

phone: +41 22 379 5457

fax: +41 22 379 5452

e-mail: [email protected]

Mégevand et al., Neuroimage 2008

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ABSTRACT

Human functional imaging studies are increasingly focusing on the identification of large-

scale neuronal networks, their temporal properties, their development, and their plasticity and

recovery after brain lesions. A method targeting such large-scale networks in rodents would

open the possibility to investigate their neuronal and molecular basis in detail. We here

present a method to study such networks in mice with minimal invasiveness, based on the

simultaneous recording of epicranial EEG from 32 electrodes regularly distributed over the

head surface. Spatiotemporal analysis of the electrical potential maps similar to human EEG

imaging studies allows quantifying the dynamics of the global neuronal activation with sub-

millisecond resolution. We tested the feasibility, stability and reproducibility of the method by

recording the electrical activity evoked by mechanical stimulation of the mystacial vibrissae.

We found a series of potential maps with different spatial configurations that suggested the

activation of a large-scale network with generators in several somatosensory and motor areas

of both hemispheres. The spatiotemporal activation pattern was stable both across mice and in

the same mouse across time. We also performed 16-channel intracortical recordings of the

local field potential across cortical layers in different brain areas and found tight

spatiotemporal concordance with the generators estimated from the epicranial maps.

Epicranial EEG mapping thus allows assessing sensory processing by large-scale neuronal

networks in living mice with minimal invasiveness, complementing existing approaches to

study the neurophysiological mechanisms of interaction within the network in detail and to

characterize their developmental, experience-dependent and lesion-induced plasticity in

normal and transgenic animals.


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INTRODUCTION

The complex sensory, motor and cognitive functions of the cerebral cortex are mediated by

large-scale networks linking groups of neurons in separate cortical areas into functional

entities (Bressler, 1995; Fuster, 2006; Mesulam, 1998). Plasticity of these networks is thought

to be crucial during development, learning, and functional recovery following brain lesions

(Callan et al., 2003; Price and Friston, 2002; Sigman et al., 2005; Tombari et al., 2004).

Structural and functional neuroimaging in humans has greatly added to the current

understanding of large-scale network anatomy and physiology. However, the precise neuronal

and molecular structure of large-scale brain networks, the mechanisms of communication

between the network modules, and the exact temporal structure of information flow in these

networks are still poorly understood (Bressler and Tognoli, 2006; Fingelkurts and Fingelkurts,

2006; McIntosh, 2000; Mesulam, 1990; Schnitzler and Gross, 2005). One way to study

information processing in large-scale brain networks in humans are the evoked or event-

related potentials (Shah et al., 2004), which are characterized by a series of components that

reflect different stages or steps of information processing performed by the network (see e.g.

Linden, 2007 for a recent review). By recording these evoked responses simultaneously from

multiple sensors distributed over the whole scalp and applying source localization algorithms

to these multichannel data, the putative brain areas involved in the processing of the stimuli

can be identified and the temporal dynamics of the network can be studied (Michel et al.,

1999, 2001, 2004). However, the spatial resolution of these recordings is limited and

systematic studies on the neuronal and molecular basis of network functioning, on early post-

natal network development, lesion-induced plasticity or pharmacological effects are not

possible.


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A mouse model of large-scale network function would open the possibility to investigate

these questions. Existing electrophysiological (Benison et al., 2007) and optical (Ferezou et

al., 2006) approaches assess the function of local cortical areas in the living animal with high

spatial and temporal resolution. However, their invasive nature makes them impractical for

studying network function repeatedly in the same animal, a prerequisite for development and

plasticity studies. Implanted chronic electrodes also have high local spatial and temporal

resolution (Buzsaki, 2004; Hollenberg et al., 2006), but research using these methods often

focuses on the detailed description of a limited cortical area, overlooking the large scale of

global brain networks. On the other hand, the temporal resolution of functional magnetic

resonance imaging (fMRI; de Zwart et al., 2005) and other methods that measure vascular or

metabolic correlates of neuronal function is insufficient to describe the fast temporal

dynamics – on the order of milliseconds – that characterize network activity. It would

therefore be of interest to develop a minimally invasive method for repeatedly assessing large-

scale network function in mice with millisecond temporal resolution.

We previously showed that epicranial recording of the electroencephalogram (EEG) is a

minimally invasive approach for repeatedly assessing somatosensory evoked potentials (SEP)

in anesthetized mice (Troncoso et al., 2000, 2004). Here, in order to describe more completely

the large-scale somatosensory cortical network, we simultaneously recorded the response of

the brain to mechanical stimulation of the mystacial vibrissae using 32 electrodes regularly

distributed over the skull bones. We considered the data as topographic representations of the

surface-recorded potential field and analyzed the spatiotemporal dynamics of these potential

maps using quantitative, reference-free methods originally developed in the human EEG,

MEG and event-related potential studies mentioned above (Michel et al., 1999, 2001, 2004).

In this paper we present the results of our studies on the feasibility, stability, and


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reproducibility of this mapping approach by comparing the data between animals and within

the same animal measured twice.

Because of the electromagnetic inverse problem, the location of the sources that generated the

surface maps cannot be determined unambiguously (Fender, 1987). Even if the lissencephalic

cortex of the mouse produces simpler extracranial field potentials than those observed in

humans, it is impossible to resolve whether the positive and negative potentials of a given

map reflect the activity of two separate brain regions or volume-conducted activity from one

region. Therefore, we complemented the epicranial SEP mapping with 16-channel

intracortical recordings of the local field potential (LFP) in several cortical areas, applying

current source-density (CSD) analysis to describe the local profile of whisker-evoked activity

across cortical layers (Mitzdorf, 1985).

METHODS

Fifteen male C57BL/6J mice aged 3-6 months and housed in individual cages were used for

the epicranial SEP recordings and twelve for the intracortical LFP recordings. All procedures

were in accordance with Swiss laws and were approved by the Ethics Committee on Animal

Experimentation of Geneva University Medical School and by the Veterinary Office of

Geneva.

Recording setup and procedure


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Mice were anesthetized with isoflurane in 20% oxygen/80% air and placed in a stereotaxic

frame. Light anesthesia during recordings was maintained with 0.5-0.8% isoflurane, so as to

not completely suppress the hindlimb withdrawal and corneal reflexes. In 13 mice, deep

anesthesia (1.5% isoflurane), suppressing hindlimb withdrawal and corneal reflexes

completely, was also used to test the sensitivity of our method to experimental manipulation.

Body temperature was maintained at 37° C by a heating blanket connected to a rectal probe.

The skin overlying the skull was anesthetized with bupivacaine, incised and retracted. The

skull surface was cleaned and dried.

Whisker stimulation: A custom-made computer-controlled electromechanical device was used

for stimulating whiskers (Troncoso et al., 2000). Stimuli consisted of 500-micrometer back-

and-forth deflections (initial direction downwards and backwards) with 1-ms rise time,

applied to all whiskers on each side of the snout, 1 cm away from the face. Two hundred

stimuli were administered with a 2011-ms inter-stimulus interval.

Epicranial SEP recordings: An array of 32 stainless steel electrodes (500-µm diameter) held

by a stereotaxic manipulator was lowered into contact with the skull surface. The electrodes

were kept in position in the horizontal plane by a perforated Plexiglas grid and were freely

movable in the vertical axis. The tip of the electrodes was immersed in EEG paste (EC2,

Grass Technologies, West Warwick, RI) before making contact with the skull surface.

Electrode impedance was around 50 kOhms. Electrodes were not aligned in a rectangular

grid; rows were offset so that the electrodes formed equilateral triangles in the horizontal

plane (distance between electrode rows 1.33 mm), giving a constant 1.54-mm distance

between an electrode and all its immediate neighbors (see Fig. 1A). Electrode coordinates

were (in mm, anteroposterior/lateral with respect to bregma): +2.67/1.54, +2.67/0, +1.33/2.31,


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+1.33/0.77, 0/3.08, 0/1.54, 0/0, -1.33/3.85, -1.33/2.31, -1.33/0.77, -2.67/4.62, -2.67/3.08, -

2.67/1.54, -4/3.85, -4/2.31, -4/0.77, -2.67/0 (reference electrode), +4/0 (ground electrode).

Signals were amplified (1000-x gain), filtered (0.5-Hz high-pass, 1500-Hz low-pass),

digitized (16-bit resolution, 5-kHz sampling rate), displayed online and stored on hard drive

using a 64-channel conventional human EEG system (hardware: EAAS-111.64, M&I, Prague,

Czech Republic; software: EASYS2, Neuroscience Technology Research, Prague, Czech

Republic). At the end of the first recording session, the skull surface was cleaned, the skin

was sutured and mice were returned to their cage. SEP were recorded again two weeks later in

the same mice under identical conditions.

Intracortical LFP recordings: One or two craniotomies were performed in the parietal (6

mice) or the parietal and frontal bones (6 mice). The dura mater was left intact and covered

with warm NaCl 0.9% in water. A linear 16-electrode probe with 50-µm inter-electrode

spacing (NeuroNexus Technologies, Ann Arbor, MI) was inserted inthrough the dura mater

into the cortex perpendicular to its surface. The 14 uppermost electrodes were used to record

the LFP, referenced to an electrode attached to the scalp. Signals were amplified (5000-x

gain) and filtered (1-Hz high pass, 500-Hz low-pass) using a custom-built amplifier

(Troncoso et al., 2000), digitized (16-bit resolution, 2-kHz sampling rate; DT3004, Data

Translation, Marlboro, MA) and displayed online or stored on hard drive for post-hoc analysis

using custom-made programs designed in VEE PRO 6 (Agilent Technologies, Santa Clara,

CA). At the end of the recording session, a lesion was made at the sites of electrode

penetration by inserting a 500-µm-diameter solid needle 1 mm deep into the cortex, mice

were killed by an overdose of pentobarbital and the brains were processed for histology as

described (Troncoso et al., 2004).


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Data display and analysis

Data analysis was performed using the Cartool software (D. Brunet, Geneva University

Hospital and Medical School, Geneva, Switzerland;

http://brainmapping.unige.ch/Cartool.php) and custom-made programs designed in MATLAB

(The MathWorks, Natick, MA). Epicranial SEP signals were digitally filtered between 1 and

500 Hz and computed against the average reference before subsequent analyses. Responses to

200 stimuli were averaged to obtain the SEP/LFP in individual mice. Responses to individual

stimuli were visually inspected offline and responses contaminated by electromagnetic noise

artifacts were excluded from the average. Baseline correction was applied using the 50-ms

pre-stimulus period as baseline. The grand average SEP/LFP was averaged from the SEP/LFP

of individual mice. The period of analysis lasted from 5 to 60 ms post-stimulus.

Epicranial SEP maps: For visualizing the spatial distribution of the surface potentials, two-

dimensional color-coded voltage maps were constructed by interpolating values between

electrodes using Delaunay triangulation. These maps were superimposed on an anatomical

C57BL/6J mouse brain MRI image (MacKenzie-Graham et al., 2004). In order to localize the

electrode array with respect to the MRI space, MRI slices were examined for neuroanatomical

landmarks and compared to an atlas of the C57BL/6J mouse brain (Franklin and Paxinos,

1997).

Topographical mapping of event-related potentials has advantages over conventional

waveform analysis when assessing large-scale network function because surface topography

reflects the global configuration of the underlying neuronal activity, and different surface

topographies are necessarily generated by different neuronal populations (Srebro, 1996;


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Vaughan, 1982). In addition, topographical mapping is a reference-independent measure that

is not influenced by the choice of the reference electrode (Geselowitz, 1998; Lehmann, 1987)

and does not require the arbitrary selection of waveform features for analysis.

Identification of component maps: To characterize the spatiotemporal dynamics of the

whisker-evoked epicranial SEP, we used a modified k-means cluster analysis to identify the

most dominant maps in the grand average SEP in terms of the spatial distribution of the

surface potential, i.e. in terms of map topography. This method and the subsequent fitting

procedure described below have proven to be powerful tools for identifying the dominant

component maps in event-related potentials (Arzy et al., 2006, 2007; Murray et al., 2006;

Ortigue et al., 2004; Pascual-Marqui et al., 1995; Thierry et al., 2007; for detailed descriptions

and reviews, see Michel et al., 2001, 2004; Murray et al., 2008). Since different topographies

of the surface potential necessarily reflect the activity of different underlying neuronal

sources, the cluster analysis provides a means of defining the different steps in the pattern of

cerebral activity evoked by the stimulus, i.e. the different SEP components. The cluster

analysis is exclusively based on the spatial correlation between strength-normalized potential

maps. The number of clusters that optimally described the grand average SEP was determined

using a modified Krzanowski-Lai criterion (Krzanowski and Lai, 1988). These cluster maps

were then fitted back to the original grand average SEP by means of the spatial correlation.

Each momentary map was labeled with the cluster map it best correlated with, thus

identifying successive time points or time periods represented by the different cluster maps.

Periods shorter than 2 ms were excluded and allocated to the preceding or following segment

depending on which they correlated better with. Once the different segments were

determined, the average map during each segment was calculated, representing the different

component maps of the grand average SEP.


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Evaluation of the stability of component maps: In order to statistically evaluate the

spatiotemporal stability of the component maps identified by the cluster analysis, each of

these maps was compared to the SEP map series of individual mice at each time point by

computing the spatial correlation, a measure of the topographical similarity between two

maps:

∑∑

∑

==

=

⋅

⋅=

n

ii

n

ii

n

iii

vu

vuSC

1

2

1

2

1

)(

where ui and vi are the voltages (vs. the average reference) at each electrode for the two maps

(Brandeis et al., 1992; Khateb et al., 2003). Each time point of the response of individual mice

was attributed to the component map with which its spatial correlation was highest and the

time point of maximal correlation with each component map was defined as the latency of this

component map in each individual evoked potential. In addition, the global explained

variance for each component map and the number of time frames where each component map

was present in each individual evoked potential were determined. These parameters were

compared between groups or in pairs of successive component maps by two-tailed paired t-

tests. In order to maintain the experiment-wise significance level at 0.05, the significance

level for each test repetition was adapted using Bonferroni correction.

Intracortical LFP analysis: Since the length of the multi-electrode probe did not span the

whole cortical thickness, separate recordings were made with the probe inserted superficially

so that the uppermost recording electrode was at the level of the cortical surface and with the

probe inserted deeper so that the lowermost recording electrode was 1 mm below the cortical

surface. Individual averaged superficial and deep LFP were then combined; for depths


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between 350 and 650 µm, where 2 recordings were obtained, a linearly weighted average of

both recordings was computed:

1

)1(

+⋅+⋅−+

=n

vhvhnv deeperfsup

where n is the number of depths where two recordings were obtained, h represents the depth

and varies from 1 to n, and vsuperf and vdeep are the voltages at depth h obtained with the probe

inserted superficially and deeper, respectively.

Intracortical CSD analysis: CSD analysis enhances the spatial resolution of intracortical LFP

recordings by revealing the location of current sinks and sources – the generators of the field

potentials – in cortical laminae, while suppressing far-field, volume-conducted potentials

originating from distant neuronal structures (Mitzdorf, 1985; Nicholson and Freeman, 1975).

One-dimensional CSD is calculated as the product of the second spatial derivative of the

electric potential in this dimension with the conductivity tensor. Since the relatively small

conductivity variations of different cortical depths only slightly affect CSD estimates, the

conductivity tensor is often assumed to be constant (Mitzdorf and Singer, 1980). CSD was

therefore estimated by calculating the finite-difference second spatial derivative:

2

2

h

vvvCSD hhhhh

∆+⋅−

∝ ∆+∆−

where vh is the voltage at depth h and ∆h is the distance between electrodes (Freeman and

Nicholson, 1975; Quairiaux et al., 2007). The intracortical LFP were spatially smoothed

before estimating the CSD (Freeman and Nicholson, 1975). To compute the smoothing and

CSD at the extremities of the electrode, virtual voltage values were extrapolated by assuming

no voltage decay above the uppermost and below the lowermost electrodes (Vaknin et al.,

1988). To better visualize the CSD profiles, color-coded plots were computed using linear

interpolation. Absolute onset latencies were determined for each penetration as the first time


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point where any post-stimulus CSD trace was greater than 4 times the standard deviation of

its pre-stimulus 50-ms baseline for at least 2 ms consecutively, starting 5 ms post-stimulus.

Onset latencies were compared between areas using one-way ANOVA followed by post-hoc

Games-Howell tests, performed with the SPSS 14 software (SPSS Inc., Chicago, IL). Since

the variances of latencies were not equal across cortical areas, results of the ANOVA were

only considered as indicative of statistical significance. However, the Games-Howell test

accommodates unequal variance between groups (Chen et al., 2007). The significance level

for the post-hoc tests was set at 0.05.

RESULTS

Epicranial SEP mapping

Whisker stimulation evoked a complex pattern of brain activity (Fig. 1A, B) that was

summarized into six epicranial SEP component maps by the cluster analysis (Fig. 1C, D).

Importantly, fitting these maps back to the data revealed that each map is present during a

certain consecutive time period, i.e. that the evoked potentials are characterized by a

progression of quasi-stable processing states (see Fig. 1E). The SEP began with a focal

voltage-positive response over the parietal cortex contralateral to stimulation. This map

configuration (map 1) lasted from 5 to 8 ms post-stimulus. This positivity then spread towards

electrodes overlying the frontal cortex, while a focal and strong negative potential appeared

over central parietal sites (map 2, 8-13.5 ms). The next component (map 3, 13.5-18 ms) was

characterized by a frontal positivity on the hemisphere contralateral to stimulation and the

appearance of a second focal positivity over the parietal cortex ipsilateral to stimulation, as


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well as a central parietal and contralateral occipital diffuse negativity of low intensity. The

positivity became less intense and more diffuse during the next component (map 4, 18-27

ms), involving contralateral frontal and parietal as well as central regions, and was then again

more focused over the contralateral parietal cortex during component maps 5 (27-50 ms) and

6 (50-60 ms), while the negativity was located over the most posterior electrodes. The SEP

maps in response to stimulation of right-sided whiskers were essentially mirror images of that

to left-sided stimulation (see Fig. 5). These data suggest that whisker stimulation evoked a

stereotypical spatiotemporal pattern of brain activity, reflecting the activation of a distributed

neuronal network including parietal and frontal areas and involving both hemispheres.

Laminar pattern of cortical responses to whisker stimulation

Intracortical LFP recordings and CSD analysis were performed in those cortical areas that

were suggested as possible sources of the recorded maps, i.e. in areas located underneath focal

maxima or minima of the epicranial SEP component maps: S1 (Woolsey and Van der Loos,

1970), the frontal vibrissa motor cortex (corresponding to the rostral part of cytoarchitectonic

field AGm in rats; Brecht et al., 2004), and a cortical area situated medial to the recordings

made in S1 and probably corresponding to the caudal vibrissa motor cortex (caudal part of

AGm in rats; Brecht et al., 2004; Franklin and Paxinos, 1997). In addition, recordings were

performed in S2, which lies mostly beyond our epicranial electrode array, as it is known to

respond to whisker stimulation (Benison et al., 2007; Carvell and Simons, 1986). As a

control, intracortical LFP recordings and CSD analysis were performed in the primary visual

cortex.


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Local whisker-evoked activity in S1 began on average 5.3 (standard deviation, SD 0.3) ms

post-stimulus with two sinks, one at the layer III-IV border that shifted to layer II-III after

approximately 3 ms and one in deep layer V and superficial layer VI (Figs. 2, 3). These sinks

were flanked by 3 sources in layer I, deep layer IV-superficial layer V, and deep layer VI.

This configuration inverted between 25 and 30 ms post-stimulus to supragranular and

infragranular sources flanked by sinks. In S2, the response began 6.1 (SD 0.6) ms post-

stimulus with 2 sinks at the layer III-IV and layer V-VI borders, flanked by a superficial and a

deep source. The 2 sinks merged together after approximately 4 ms to form a large sink

occupying layers II to V. This pattern was gradually replaced by the inverse configuration

between 20 and 30 ms post-stimulus. The onset of local activity in S1 and S2, as determined

by CSD analysis, coincided with the onset of the epicranial response to whisker stimulation,

characterized by a focal parietal positivity contralateral to stimulation (Fig. 1D, map 1, Fig.

2). Local activity in S2 might have influenced the most lateral epicranial electrodes through

volume conduction. CSD analysis did not show local activity in other cortical areas during

map 1 (Fig. 2). Similarly, during maps 5 and 6, which were topographically similar to map 1

(Fig. 1), local activity was recorded mostly in S1 and S2, other cortical areas appearing

largely inactive.

Whisker-evoked activity in the vibrissa motor cortex (rostral AGm) started 7.5 (SD 0.7) ms

post-stimulus with a sink at the layer III-IV border, which shifted to layer II-III after

approximately 4 ms, a superficial source, and a much fainter infragranular sink and deep

source. Activity in the rostral AGm started significantly later than in S1 and S2 (Fig. 3).

Between 25 and 30 ms post-stimulus, the supragranular source-sink complex inverted to a

fainter sink-source configuration. In the caudal part of AGm, whisker-evoked activity started

6.4 (SD 1.4) ms post-stimulus and consisted of a complex pattern of faint, brief sinks and


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sources involving all cortical layers, with a slightly stronger superficial sink and infragranular

source appearing approximately 9-10 ms post-stimulus. Between 15 and 20 ms post-stimulus,

the configuration changed to a faint infragranular sink and superficial source. The onset of

intracortical activity in the rostral AGm cortex co-occurred with the extension of the surface

positivity towards the frontal cortex contralateral to stimulation (Fig. 1, map 2, Fig. 2). Map 2

was also characterized by a focal central parietal negativity. The temporal extent of the

superficial sink-infragranular source in the caudal AGm contralateral to stimulation was

related to that of the surface-recorded negativity.

Activity in S1 ipsilateral to whisker stimulation appeared 12.9 (SD 2.3) ms post-stimulus with

faint supragranular and infragranular sinks flanked by 3 sources. This configuration lasted

until approximately 30 ms post-stimulus. In the ipsilateral S2, activity appeared 8.4 (SD 2.4)

ms post-stimulus, became larger after about 4 ms, and consisted of a layer IV-V sink, a fainter

supragranular sink, and superficial and deep sources, lasting until approximately 30 ms post-

stimulus. The onset of activity in ipsilateral S2 was slightly earlier than in S1, but this

difference was not found to be statistically significant. Otherwise, activity in ipsilateral S1

was significantly later than in all other cortical areas investigated (Fig. 3). The onset of local

activity in the ipsilateral S1 coincided with the appearance of a small focal surface positivity

over the parietal cortex ipsilateral to whisker stimulation (Fig. 1, map 3). Local activity in

ipsilateral S2 began earlier, but grew more intense at approximately the time of onset of

activity in ipsilateral S1 (Fig. 2). The very lateral position of the surface positivity might

reflect a contribution of S2 through volume conduction.

In the caudal AGm ipsilateral to stimulation, whisker-evoked activity appeared 7.1 (SD 1.2)

ms post-stimulus with several sinks and sources involving all cortical layers. The response


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lasted until approximately 15 ms post-stimulus. Response to whisker stimulation in the

ipsilateral rostral AGm cortex was extremely faint and was not observed in all mice. The

additional intracortical SEP recordings and CSD analyses in the primary visual cortex (V1)

revealed only small contralateral responses evoked by whisker stimulation in only 3 out of 6

mice. These responses were very variable between individual animals but generally involved

the infragranular layers (data not shown). Virtually no response to ipsilateral whisker

stimulation was observed in V1.

Interindividual stability of epicranial SEP maps

The stability of the whisker-evoked brain response across individual mice was assessed by

comparing the grand average component maps to the complete map series of each animal by

means of the spatial correlation. The average latencies of highest spatial correlation between

each component map and the SEP maps of individual mice were very similar across mice,

especially for the shorter-latency components (Fig. 1E). The differences in latency of best

correlation between pairs of successive component maps were all significant (all p values <

10-4 for each comparison of successive map latencies, 5 two-tailed paired t-tests, significance

level for each test = 0.01), indicating that the spatiotemporal pattern of neuronal network

activity evoked by whisker stimulation was similar in each mouse.

Intraindividual stability of epicranial SEP maps

The stability of whisker-evoked cerebral activity in individual mice was studied by recording

the SEP twice in the same animals with a 2-week interval. The two recordings yielded a

highly similar spatiotemporal pattern of SEP maps (Fig. 4). The cluster analysis showed


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nearly identical component maps for both grand averages, with almost no difference in the

latency of best correlation of each map (data not shown). There was no significant difference

in global explained variance when comparing the grand average component maps of the first

recording with the individual map series of the first and the second recordings; the same was

true for the grand average component maps of the second recording. This shows that the

intraindividual variability of the SEP maps over time was also limited.

Effect of anesthetic depth on epicranial SEP maps

The effect of varying the depth of anesthesia on epicranial SEP mapping was studied in 13

mice during the first recording session with deep (1.5% isoflurane) anesthesia. The amplitude

of the whisker-evoked brain responses was lower under deep anesthesia, as illustrated by the

amplitude of the global field power (Fig. 5). The clustering analysis summarized the response

into 3 maps only. Activity appeared to be restricted to the parietal cortex contralateral to

stimulation; no focused activity was seen in either the frontal cortex or the hemisphere

ipsilateral to stimulation (Fig. 5B). The average time spent in map 1 was longer under deep

than light anesthesia (18 vs. 4 ms, p < 0.001, two-tailed paired t-test). These results suggest

that deep anesthesia altered the propagation of whisker-evoked potentials beyond the

somatosensory cortices contralateral to stimulation.

DISCUSSION

We developed a multichannel epicranial EEG recording and analysis technique to describe

large-scale neuronal networks in mice. We used the well-studied whisker-evoked potentials to


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probe our method (for recent reviews on the whisker somatosensory cortex, see Brecht, 2007;

Petersen, 2007). The result of the spatiotemporal analysis of the evoked potential maps after

whisker stimulation is in agreement with previous reports using local recordings and shows

that somatosensory stimulation activates a neuronal network involving somatosensory and

motor cortical areas of both hemispheres, both serially and in parallel. Our method allows

detecting these different activation areas in one single recording and unravelling the temporal

dynamics of activation of these areas. Intracranial recordings under the areas of potential

maxima and minima confirm the interpretation of the maps with respect to the location of the

generators in the brain. Our results are similar to recently reported work using voltage-

sensitive dye imaging to map the response of the sensorimotor cortical network to whisker

stimulation (Ferezou et al., 2007). They also confirm earlier findings regarding whisker-

evoked activity in the primary (e.g. Petersen et al., 2003; Rojas et al., 2006) and secondary

somatosensory cortex (e.g. Benison et al., 2007) and the vibrissa motor cortex (Ahrens and

Kleinfeld, 2004; Farkas et al., 1999) contralateral to stimulation as well as the primary

(Pidoux and Verley, 1979; Shuler et al., 2001) and secondary somatosensory cortex (Carvell

and Simons, 1986) ipsilateral to stimulation. In addition, our CSD profiles give (to our

knowledge) hitherto unreported insight into the laminar pattern of response to whisker

stimulation in the mouse cortex in S2 and the rostral vibrissa motor cortex contralateral to

stimulation, as well as in the hemisphere ipsilateral to stimulation. We propose that epicranial

SEP mapping usefully complements existing approaches to investigate with minimal

invasiveness and repeatedly the functionality, integrity, vulnerability, and plasticity of large-

scale cortical networks in an animal model.

Interpretation of epicranial SEP maps


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The potential field generated by synchronous synaptic activity in neurons of a given cortical

area can be modeled by an equivalent dipole whose direction is perpendicular to the cortical

surface (Lopes da Silva and Van Rotterdam, 2005). In the mouse cerebral cortex, the absolute

amplitude (regardless of polarity) of the potential field generated by activity in a cortical area

is expected to be maximal at the region of skull surface overlying this area, and focal maximal

or minimal potentials with steep gradients recorded by epicranial SEP mapping should reflect

activity in cortical areas lying close to the electrode that recorded the voltage peak. Not every

deflection in voltage should be taken to mean local cortical activity, especially if it is spatially

diffuse rather than focal. For example, the focal positivity in the parietal cortex contralateral

to whisker stimulation in map 1 (Fig. 1D) likely reflects an underlying dipole caused by

intracortical activity in S1, whereas the diffuse, low-intensity minimum recorded over the

opposite hemisphere in the same map probably corresponds to the volume-conducted negative

pole of the same dipole. Our intracortical recordings concurred with the proposed

interpretation of the surface maps. Sites where epicranial focal maxima or minima were

recorded lay over cortical areas that showed local intracortical activity as determined by the

CSD analysis. In particular, there was good temporal correspondence between the onset of

appearance of a surface-recorded focus and the onset of intracortical activity (Fig. 2).

Conversely, areas such as the frontal or occipital cortex ipsilateral to stimulation, where no

clear surface focus appeared for the whole analysis period, showed no evidence of local

intracortical activity.

The large-scale cortical network activated by whisker stimulation

Our data suggest that cortical activity evoked by whisker stimulation initially involves the

primary somatosensory cortex contralateral to stimulation. A single potential maximum with


20

very steep gradient dominates the map during this early period (map 1 in Fig. 1D). The

intracranial recordings support this interpretation (Fig. 2). The electrode in the contralateral

S1 underneath the map maximum is the first to show significant activation during this early

period. The CSD configuration shows two sinks at the layer III-IV and V-VI borders flanked

by 3 sources, the largest of which spans the deepest cortical layers. This profile is similar to

previously reported findings (see e.g. Agmon and Connors, 1991; Jellema et al., 2004; Lecas,

2004). These findings are consistent with previous observations that thalamic input from the

ventroposteromedial nucleus to S1 terminates mainly in layer IV and lower layer III, with a

smaller projection to the layer V-VI border (Bernardo and Woolsey, 1987). TheseNeurons in

these layers are the first ones to be activated following somatosensory stimulation

(Armstrong-James et al., 1992). Physiological and modeling studies in monkey primary

sensory cortices have established that the earliest sensory-evoked CSD configurations are

generated by depolarization of both thalamocortical axon terminals and their main targets in

layer IV, spiny stellate cells. This combination of pre- and postsynaptic events in turn

contributes to the generation of the initial surface sensory-evoked potentials (Peterson et al.,

1995; Schroeder et al., 1991; Steinschneider et al., 1992; Tenke et al., 1993). Similarly, we

suggest that the initial current sinks evoked by whisker stimulation in the mouse S1

correspond to the depolarization of thalamocortical axon terminals and of cortical neurons at

the layer III-IV and (to a lesser extent) V-VI borders, and that this depolarization is reflected

in the focal positivity of epicranial map 1.

The second map identified in our epicranial recordings (map 2 in Fig. 1D, 2, 3) shows a

propagation of the potential maximum to more anterior electrodes. In addition, a strong focal

minimum on parietal midline electrodes is noted. The intracranial CSD profiles show, besides

the ongoingappearance of large supra- and infragranular current sinks in S1 that likely reflect


21

activation of neurons in S1these layers (Armstrong-James et al., 1992), a strong activation in

S2, consisting of two sinks at the layer III-IV and V-VI borders. This S2 activation is

probably due to direct projections from the ventroposteromedial nucleus of the thalamus that

terminate in the same layers as those directed to S1 (Pierret et al., 2000; Wise and Jones,

1978). Unfortunately, S2 lies mostly beyond our epicranial electrode array and probably

contributes relatively little to the epicranial SEP maps through volume conduction.

On the other hand, the intracranial CSD profiles also show activation in the frontal vibrissa

motor cortex (rostral AGm) that involves mostly supragranular layers. This CSD profile is

similar to previously reported findings in the rat (Ahrens and Kleinfeld, 2004) and is likely to

be due to connections between the somatosensory and the motor cortices (Farkas et al., 1999;

Izraeli and Porter, 1995; Welker et al., 1988). These connections might play an important role

in informing sensory areas about motor planning and in modulating exploratory motor

behavior as a function of sensory input (Kleinfeld et al., 2006).

Map 2 is also characterized by a strong focal negativity over central parietal sites. It seems to

at least partly reflect activity of the caudal part of area AGm, as indicated by the onset of

activation in the intracranial electrode positioned in this area. Sensory-evoked potentials were

previously reported using epidural electrodes placed close to the parietal midline in rats

(Miyazato et al., 1995; Stienen et al., 2003). The physiological implication of whisker-related

activity in the medial parietal cortex is unclear. Lesions of the caudal AGm and neighboring

areas in rats were reported to cause a behavioral syndrome comparable in some aspects to

multimodal sensory neglect (King and Corwin, 1990), suggesting that this cortical area might

be implicated in building multimodal spatial representations and in orienting behavior

accordingly. It must be noted that the low intensity of intracortical activity compared to the


22

strong surface negativity suggests that caudal AGm might not be the sole generator. Further

research is needed to clarify the role of medial parietal cortical areas in sensory processing.

Map 3 shows, in addition to a contralateral frontal positive potential, a focal small surface

positivity over the parietal cortex of the hemisphere ipsilateral to stimulation. The CSD

profile of the intracranial recordings indeed indicates onset of activity in the ipsilateral

hemisphere at this latency. However, it involves S2 more strongly than S1. As already

mentioned above, our epicranial electrode array does not extend to S2. This is probably the

reason why this activity that is clearly seen in the intracranial electrode is only seen relatively

feebly by the most lateral electrodes. Nevertheless, the intracranial CSD profiles show that the

ipsilateral S1 cortex is also (though weakly) activated at this time period. In both areas,

supragranular and infragranular layers are involved; in S2, the lower part of layer IV also

appears to be involved.

Map 4 is characterized by a diffuse positivity over the central and contralateral frontal and

parietal areas, without any steep and focused voltage gradient. Since several cortical areas

show a change in their CSD configuration during this time period, it might be that no single

area is sufficiently consistently activated to generate an equivalent dipole strong enough to

show on the surface recordings. The ongoing CSD activity in ipsilateral S2 during map 4 is

largely unseen by the epicranial electrodes, probably due to the lateral location of S2.

Maps 5 and 6 show a focused positivity over the parietal cortex contralateral to stimulation.

The CSD profiles confirm that activity is confined to contralateral S1 and S2 during this

period. The positive wave over the somatosensory cortex, consistently reported in previous

SEP studies (Di and Barth, 1991; Rojas et al., 2006), is of the same polarity as that during


23

map 1, whereas the CSD configuration in S1 and S2 during maps 5 and 6 reverses as

compared to their initial configuration. This illustrates the complexity underlying the

generation of the EEG signal, which represents only the equivalent dipolar component of the

multipolar sources inside the cortex (Tenke et al., 1993). The fact that most of the anatomical

connections between the somatosensory cortices and the other cortical areas involved in the

somatosensory network are reciprocal suggests that the activity in S1 and S2 during maps 5

and 6 might be influenced by feedback from these other areas.

Dynamics of large-scale neuronal networks

The cluster analysis and the subsequent fitting of the cluster maps in the individual data

revealed that the evoked responses were characterized by a series of distinct map

configurations, each one remaining stable for a given period of time and then quickly

changing into a new configuration in which it remains stable again. This characteristic has

been repeatedly described for human event-related potentials and human spontaneous EEG

and it has been postulated that human large-scale neuronal networks evolve through a

sequence of quasi-stable states, the so-called microstates (Koenig et al., 2002; Lehmann and

Skrandies, 1980; Lehmann et al., 1998; Wackermann et al., 1993). It has been proposed that

these microstates represent the basic building blocks of cognition, the different steps in the

stream of information processing. Each of these microstates represent a stable pattern of the

large-scale network activity (for a discussion of this concept see Bressler and Tognoli, 2006;

Changeux and Michel, 2006; Fingelkurts and Fingelkurts, 2006; Lehmann, 1987; Michel et

al., 1999). The fact that these metastable states are also observed in the SEP of an anesthetized

mouse supports the hypothesis that information processing occurs through a stream of discrete

units or epochs rather than in a continuous flow of neuronal activity (for a comprehensive


24

discussion of this dichotomy, see Fingelkurts and Fingelkurts, 2006). Our data suggests that

these discrete blocks of microstates do not only appear in cognitive processing in humans, but

may represent a fundamental property of large-scale neuronal network functioning in the

mammalian cerebral cortex. This is further supported by our intracranial recordings that

confirm these periods of stable sink-source patterns across the different cortical layers,

corresponding to the different periods of stable surface maps.

Interindividual and intraindividual stability of the whisker-evoked cortical response

In order to evaluate the capacity of epicranial mapping to assess large-scale network function

reliably and repeatedly, we looked at the stability of SEP maps across and within individual

mice. The temporal sequence of epicranial maps evoked by whisker stimulation was very

stable and similar across mice (Fig. 1E). Furthermore, repeating epicranial SEP mapping in

the same mice after two weeks yielded almost identical maps (Fig. 4). Thus, both the

interindividual and the intraindividual variability of SEP are limited. This extends our

previous findings about the temporal stability of epicranial SEP waveforms (Troncoso et al.,

2000) and suggests that the large-scale cortical network activated by whisker stimulation is a

fundamental component underlying sensorimotor processing in mice (Kleinfeld et al., 2006).

This low intra- and interindividual variance is an important prerequisite for using the method

as an animal model for studying the development and plasticity of large-scale neuronal

networks.

Effects of anesthesia on the activity of the large-scale somatosensory network


25

As a first approach to test the sensitivity of the method to detect changes of the large-scale

somatosensory network, we evaluated the effect of varying the level of anesthesia on the

whisker-evoked brain response (Fig. 5). Our epicranial waveforms over S1 under deep

isoflurane anesthesia are similar to those recorded with epidural electrodes in similarly

anesthetized rats (Rojas et al., 2006). SEP mapping showed that whisker-evoked activity was

now mostly restricted to the parietal cortex contralateral to stimulation, suggesting an

alteration in the propagation of activity from somatosensory areas to the other regions

involved in the network. It was recently found that whisker-evoked neuronal firing in S1 of

isoflurane-anesthetized mice was more strongly inhibited than subthreshold activity as

isoflurane concentration was increased (Berger et al., 2007). It is tempting to suggest that this

disproportional reduction in firing is reflected in our data by the relative preservation of the

response in the somatosensory cortex contralateral to stimulation (Fig. 5, map 1) and by the

absence of propagation of whisker-evoked activity to the other cortical areas of the large-scale

somatosensory network. An alternative interpretation of our findings stems from recent

evidence that evoked potentials may be generated by phase resetting of ongoing brain

oscillations in addition to stimulus-evoked neuronal activity (Fell et al., 2004; Makeig et al.,

2002). In particular, EEG phase resetting might be a relatively greater contributor to event-

related responses in higher-order cortical areas compared to primary sensory cortices (Shah et

al., 2004). Since deep isoflurane anesthesia markedly reduces and alters the spontaneous EEG

(our own observations; Rojas et al., 2006), its potential impact on the phase-resetting

component of evoked potential generation might interfere relatively more with the SEP in

cortical areas downstream of the somatosensory cortex, as observed here. Although the

mechanisms underlying the effects of varying anesthetic depth on SEP maps are yet

incompletely understood, epicranial SEP mapping is able to resolve differences in the

spatiotemporal pattern of sensory-evoked responses across experimental conditions.


26

The effects of anesthetic depth on SEP maps shown here raise the issue of comparing

somatosensory processing in the waking versus lightly anesthetized state. Multichannel

epicranial SEP recordings in the waking mouse would clearly be of great interest to address

arousal- and behavioral-state-dependent cortical function. However, the propagation of

whisker-evoked activity to several cortical areas in both hemispheres that we observed under

light isoflurane anesthesia is similar to that reported following passive whisker stimulation in

awake, head-fixed mice using voltage-sensitive dye imaging (Ferezou et al., 2007). This

suggests that light isoflurane anesthesia does not cause major disturbances of whisker-evoked

activity in cortical networks.

Mouse epicranial SEP mapping and intracortical CSD analysis as a model approach to

cortical network function

The stability of whisker-evoked responses suggests that epicranial SEP mapping is adequate

for repeated, minimally invasive functional assessment of the cortical somatosensory network.

Most importantly, CSD analysis in cortical areas selected from the surface recordings brings

further detail about the local processing of somatosensory input. Fig. 6 shows surface SEP

waveforms and maps in mice (A) and healthy human subjects (B) as well as intracranial SEP

recordings from subdural electrodes in an epileptic patient (C). Human data are consistent

with previously published surface and subdural SEP recordings (Allison et al., 1989a, 1989b;

Urbano et al., 1997; Valeriani et al., 1998; van de Wassenberg et al., 2008). In both species,

surface waveforms and topographic mapping show two successive positivities overlying the

frontoparietal cortex. Furthermore, human subdural SEP recordings display the same polarity

reversal across the central sulcus at both latencies, suggesting that activity in the cortex


27

surrounding the sulcus is similar at both these moments. However, these data do not allow

concluding unambiguously whether or not two components with similar polarity and

topography but separated in time are generated by the same neuronal events. Indeed, our CSD

analysis in mouse S1 (Fig. 6D) indicates that this is not the case, at least in mice. Thus, mouse

epicranial EEG mapping coupled to intracortical CSD analysis reveals crucial information

about the genesis of the surface SEP that would have been impossible to uncover from the

results of human scalp recordings or invasive subdural recordings. Of course, the point here is

not to establish direct analogies between mouse and human SEP components, but rather to

illustrate how similar surface potentials may be generated by different neuronal events. In any

case, care is needed when generalizing from results obtained in a given species and sensory

modality. For instance, although our CSD configurations in S1 are in good agreement with

recently published profiles in rats (Jellema et al., 2004; Lecas, 2004), they differ somewhat

from those observed in monkey S1 (Lipton et al., 2006; Schroeder et al., 1995). Some degree

of difference is also apparent with respect to other sensory modalities in rodents (Barth and

Di, 1990; Heynen and Bear, 2001) and monkeys (Schroeder et al., 1991; Steinschneider et al.,

1992). These differences likely reflect the adaptation of cortical sensory processing to species-

and modality-specific demands (Hirsch and Martinez, 2006).

Some technical limitations must also be kept in mind, however. The spatial extent of the

electrode array is restricted by the insertion on the skull of temporal and neck muscles, so that

the array covers mostly the frontal, parietal and occipital cortices. Thus, some somatosensory

areas and most of the auditory cortex lie beyond the array. The spatial resolution is limited by

the number of electrodes that can be placed over the skull and by the blurring of electrical

potentials generated by the brain as they traverse the cerebrospinal fluid, meninges and skull

(Nunez and Srinivasan, 2006). This technique is therefore most useful as a first step in


28

approaching cortical function at the global network level, particularly with respect to temporal

characteristics of network activities. If more local details are of interest, the method needs to

be complemented by other, more spatially precise techniques, such as the multichannel

intracranial recordings in areas of interest as demonstrated here. The major advantage of

epicranial SEP mapping (besides its spatial extension to the global network level) is that it can

be repeated several times in the same animal and thus allows studying how network function

changes over time. This approach will therefore be suitable for studying large-scale network

plasticity during the early postnatal development of the somatosensory system, as well as after

changes in sensory experience and localized ischemic lesions to the cerebral cortex.

Combining this approach with transgenic mouse strains will give insight into the role played

by specific proteins in network plasticity.


29

ACKNOWLEDGMENTS

We thank Cynthia Saadi for technical assistance with the histological preparations. The

Cartool software (http://brainmapping.unige.ch/Cartool.php) is developed by Denis Brunet,

from the Functional Brain Mapping Laboratory, Geneva, supported by the Center for

Biomedical Imaging (CIBM), Geneva and Lausanne, Switzerland. This work was supported

by the Swiss Academy of Medical Sciences grant 323600-111505 (MD-PhD Program of the

Swiss Universities) to P.M., the Swiss National Science Foundation grant 31-64030.00, the

Eagle Foundation and the European Community Grant Promemoria No. 512012-2005 to

J.Z.K., and the Swiss National Science Foundation grant 320000-111783 to C.M.M.


30

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FIGURES

Fig. 1. Epicranial somatosensory evoked potential mapping in response to whisker

stimulation. A. The grand average waveforms of the SEP recorded from multiple epicranial

electrodes in response to left-sided whisker stimulation during the first recording session are

superimposed on a template MRI brain surface. Interval: 0 to 60 ms post-stimulus. Average of

15 mice. Average number of responses to individual stimuli removed due to artifact


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contamination: 4.3 out of 200 (range 0-28). B. Superimposed grand average waveforms. C.

The temporal extent of the 6 component maps identified by the cluster analysis as optimally

summarizing the grand average map series appears as colored segments on the global field

power trace. The global field power is the spatial standard deviation of all voltage values at

each time point and represents the strength of the electrical field. D. The topography of the 6

component maps is color-coded (red, positive voltage; blue, negative voltage) over a MRI

brain surface. E. Latencies of best correlation of each component map with the map series of

individual mice (mean and SD). Latency differences between pairs of successive maps were

all highly significant (all p values < 10-4).


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Fig. 2. Current source-density analysis of intracortical responses to whisker stimulation.

Color-coded plots of the averaged second spatial derivatives of LFP recordings are shown.

Sinks are coded in yellow and red, sources in blue. The same color scale applies for all CSD

plots. Each CSD is averaged from 6 mice. The approximate extent of cortical layers (labeled

in Roman numerals) is indicated on the left-most part of the CSD plots. The times of onset of

the 6 component maps of the epicranial SEP are indicated as dotted lines. AGm: vibrissa

motor cortex.


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Fig. 3. Absolute onset latencies of intracortical activity in response to whisker stimulation.

The mean and SD of absolute onset latency of intracortical activity are plotted for each

cortical area. Latencies are averaged from 6 mice except for S1 in the ipsilateral hemisphere,

where one outlier was removed. ANOVA showed a significant effect of the cortical area on

the latency (F6,34 = 15.92; p < 0.001). The asterisks indicate significant post-hoc Games-

Howell tests (*, p < 0.05; **, p < 0.01).


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Fig. 4. The temporal extent and topography of the component maps of the grand average

(n=15) epicranial SEP are shown for the first (A) and second (B) recordings, performed two

weeks apart. There were only minor, non-significant differences in the latency of best

correlation of each component map between both recording sessions. Average number of

responses to individual stimuli removed due to artifact contamination: first recording, 4.3/200

(range 0-28); second recording, 2/200 (range 0-6); no significant difference between

recordings.


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Fig. 5. The effect of anesthetic depth on epicranial SEP mapping. The temporal extent and

topography of the component maps of the grand average (n=13) epicranial SEP in response to

stimulation of right-sided whiskers are shown under light (0.8% isoflurane, A) and deep

(1.5% isoflurane, B) anesthesia. Responses under deep anesthesia were of lesser amplitude

and complexity; the 3 component maps that summarized the data showed that focused activity

was restricted to the parietal cortex contralateral to stimulation. The average time spent in

component map 1 was significantly longer under deep than light anesthesia (18 vs. 4 ms, p <

0.001, two-tailed paired t-test). Onset latency differences for maps 5 and 6 between groups

were non-significant (two-tailed paired t-tests). Average number of responses to individual

stimuli removed due to artifact contamination: light anesthesia, 1.9/200 (range 0-6); second

recording, 1.8/200 (range 0-18); no significant difference between recordings.


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Fig. 6. A. Grand average epicranial waveform and instant maps of left whisker-evoked SEP in

mice (same dataset as in Fig. 1). B. Grand average scalp waveform and instant maps of SEP

to electrical median nerve stimulation in 44 healthy human subjects. 256-channel EEG was

continuously acquired while square current pulses (2000 repetitions, 200-µs duration, 267-ms

inter-stimulus interval, amplitude just sufficient to elicit slight thumb adduction) were

administered to the left median nerve. Black dots in A and B illustrate the location of the

electrode whose waveform is shown. C. Average waveforms and maps of SEP to electrical

stimulation of the left median nerve (stimulation parameters identical to those in B) in an

epileptic patient implanted with subdural electrode strips spanning the central sulcus

(indicated by an arrow). D. Grand average whisker-evoked CSD profile in mouse S1 (same

dataset as in Fig. 2). Waveforms: positive voltages upwards; vertical bar in A: 50 µV in A, 2

µV in B, 30 µV in C; horizontal bar in A: 5 ms; red color: positive voltages in A-C, current

sinks in D; blue color: negative voltages in A-C, current sources in D. Note that despite

similar polarities and topographies of SEP maps at the selected time points in both mice and


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humans, the CSD profile in the mouse suggests that these two temporally separated evoked

potential components are not generated by the same neuronal structures.

A mouse model for studying large-scale neuronal networks using EEG mapping techniques

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