Reliable detection of bilateral activation in human primarysomatosensory cortex by unilateral median nerve stimulation
Matthew T. Sutherlanda and Akaysha C. Tanga,b,!
aDepartment of Psychology, University of New Mexico, Logan Hall, Albuquerque, NM 87131, USAbDepartment of Neuroscience, University of New Mexico, Albuquerque, NM, USA
Received 12 January 2006; revised 25 July 2006; accepted 13 August 2006Available online 25 September 2006
The discovery of neurons with bilateral receptive fields in thehand region of primary somatosensory (SI) cortex has firmlyestablished in non-human primates the bilateral representation ofsomatosensory information presented to the hands at this lowest
stage of cortical processing (Iwamura et al., 1994; Iwamura, 2000).In regard to the human brain, diverse approaches have been takenin search of a parallel bilateral representation of somatosensoryinformation at the level of SI cortex. Such approaches includemeasuring evoked ipsilateral SI responses to electrical mediannerve stimulation (MNS) using EEG or MEG (Tamura, 1972;Salamy, 1978; Allison et al., 1989; Korvenoja et al., 1995;Noachtar et al., 1997; Korvenoja et al., 1999; Kanno et al., 2003,2004), measuring ipsilateral event-related changes in the ongoingmu-rhythm following somatosensory stimulation (Nikouline et al.,2000; Stancak et al., 2003), measuring hemodynamic responsesfrom ipsilateral SI with fMRI during median nerve (Boakye et al.,2000; Nihashi et al., 2005) or tactile stimulation (Hansson andBrismar, 1999; Hlushchuk and Hari, 2006), and indirectlymeasuring interference effects due to ipsilateral tactile stimulation(Schnitzler et al., 1995).
Previous studies, particularly evoked response studies, indi-cated that the detection of ipsilateral SI responses in humans washighly variable, from as low as 3% (Kanno et al., 2003) up to100% (Korvenoja et al., 1999). Furthermore, even when ipsilateralactivations were detected in a given subject, such responses werenot reliably found in both the left and right hemispheres (asdiscussed in Kanno et al., 2003; Nihashi et al., 2005). For example,Korvenoja et al. (1995), using MEG, were able to detect ipsilateralresponses to MNS only in the right hemisphere of 5 out of 10subjects and did not detect such responses in the left hemisphere.Thus, in humans, it remains controversial whether somatosensoryinformation carried by each of the median nerves reaches the SI ofboth the ipsi- and contralateral hemisphere.
Because both EEG and MEG sensors record mixtures of signalsfrom many different brain regions as well as noise sources, wehypothesized that the previously reported low detection rates inevoked response studies reflect the challenge of detecting a muchweaker ipsilateral response in the presence of stronger neuronalactivations and noise sources. If ipsilateral responses exist at all,such responses are expected to have relatively small amplitudes,which could be easily “masked” by spatially and temporallyoverlapping neuronal signals, such as contralateral SI, contra- andipsilateral secondary somatosensory (SII) cortex, or noise sources
recorded by the EEG/MEG sensors. To extract relatively smallsignals from mixtures recorded by the EEG, we used second-orderblind identification (SOBI: Belouchrani et al., 1997), a blind sourceseparation (BSS) algorithm that has been used by a number oflaboratories (Tang et al., 2002b; Joyce et al., 2004; Muller et al.,2004) to increase the effective signal-to-noise ratio of EEG andMEG data (Tang et al., 2005b).
SOBI takes the continuous time course of the recorded EEG/MEG signals as inputs and recovers a set of components, some ofwhich correspond to neuronal activations associated with sensory(Tang et al., 2000, 2002a,b; Tang et al., 2005b) andmotor processing(Wubbeler et al., 2000; Mackert et al., 2001). The ability of SOBI torecover signals originating from the spatially focal left- and right-SIcortices during MNS has been validated and specific criteria foridentifying left- and right-SI components have been established(Tang et al., 2005b). Additionally, the advantage that SOBI offers indetecting event-related potential (ERP) differences across experi-mental conditions has recently been demonstrated (Tang et al.,2006). Specifically, using ERPs from the SOBI-recovered left- andright-SI components, one is able to better discriminate between left,right, and bilateral stimulation conditions than when using ERPsrecorded from the two EEG electrodes with maximum peak ERPamplitude over the hand region of sensorimotor cortex.
In the present study, we show that by decomposing the recordedmixture of EEG source signals into individual components, thusisolating left- and right-SI activity from other neuronal and extra-cranial noise components, ipsilateral responses from SI can bereliably detected in both the left and right hemisphere across all 10subjects studied. It should be noted that the term reliability in thecurrent report refers to the reliable detection of ipsilateral SIactivations across subjects and across both cerebral hemispheres andnot to the consistency of the ipsilateral waveforms themselves.Additionally, although SOBI recovers approximately a dozenpotential neuronal components of interest, that most likelycorrespond to the extensive cortical network activated by MNS(Mauguiere et al., 1997; Korvenoja et al., 1999; Boakye et al., 2000),the scope of the present study was limited to the detection ofipsilateral responses from only the left- and right-SI components thathave been previously characterized in detail (Tang et al., 2005b).
Methods
Subjects
EEG data were collected from 10 healthy right-handedvolunteers who reported no history of neurologic or psychiatricdisorders (6 females, 4 males; 21 to 32 years old). All experimentalprocedures were conducted in accordance with the Human ResearchReview Committee at the University of New Mexico, and writteninformed consent was obtained from each subject prior toinvestigation. During the recording sessions, subjects were seatedcomfortably with their eyes closed in an electrically shielded room.
Stimuli
Although widely used, electrical stimulation of the mediannerve may be regarded as un-physiological due to the bypassing ofmechanoreceptors and the resulting large afferent volley. However,it was the choice for the present study for the following reasons: (1)previously most studies addressing the issue of ipsilateral SIactivation in humans have employed this form of stimulation; and
(2) the precision in stimulus timing, duration, and amplitudeafforded by MNS was expected to reduce variability in evokedresponses both within and across subjects. This reduction invariability was intended to improve signal-to-noise ratios, whichmay increase the chance of detecting the relatively weakeripsilateral SI response against a background of stronger signalsfrom other brain regions and noise sources.
The left and right median nerves were stimulated transcuta-neously at the wrist with 0.25 ms constant current square-wavepulses using a pulse generator (S88) and a photoelectric stimulusisolation unit (SIU7) from Grass Instrument (Astro-Med, Inc. WestWarwich, RI). To locate the median nerves of each subject, theintensity of stimulation and the location of the electrodes were al-tered until an observable twitch of the thumb was produced. Stimu-lus intensity was then adjusted to just below the motor threshold andto produce approximately equal sensations in both hands (3.5–10.0 mA: mean±SEM=6.4±0.4 mA). Subjects were asked toremain attentive to the stimuli throughout the recording session andno overt behavioral responses were required. No perceivablechanges in stimulus intensity during the experiments were reported.
A blocked stimulation protocol was used for eight of the tensubjects. Consecutive blocks of stimulation (1 left, 1 right) weredelivered over a period of ~10 min. The order of block presentationswas counterbalanced and randomly assigned to subjects. Each blockconsisted of 200 trials with inter-trial intervals (ITIs) uniformlydistributed between 1.0 and 1.5 s by increments of 0.1 s. To ensurethat the recovery of ipsilateral SI activations was not dependent uponone particular stimulation paradigm, an intermixed stimulationprotocol was used for the remaining two subjects. A pseudorandomsequence of intermixed left and right stimulations (150 each) waspresented with ITIs ranging from 1.5 to 2.0 s by increments of 0.1 s.The sequence contained nomore than three consecutive stimulationson one side. As the goal of the present study was to determinewhether SI responses to ipsilateral stimulation could be reliablydetected across subjects, how the ipsilateral SI activity can bemodulated by alterations in various stimulation parameters such asITIs, stimulus duration, and stimulus intensity is not assessed here.
EEG data
Scalp signals were recorded using a 128-channel SynAmpsEEG system (NeuroScan, El Paso, TX) with tin electrodesmounted in a custom made cap (ElectroCap International, Eaton,OH). Prior to data collection, three fiduciary points (nasion, left,and right pre-auriculars) and electrode positions were recordedwith a 3-dimensional digitizer (Fastrak, Polhemus Inc., Colchester,VT) and subsequently used for source localization. All channelswere referenced to the nose and impedances were maintainedbelow 10 k!. The EEG was continuously recorded throughout theduration of the experiments at 2000 Hz (blocked stimulationprotocol) or 1000 Hz (all other sessions) with a bandpass filter of0.1–200 Hz.
SOBI aided extraction of signals from left- and right-SI
Details on SOBI (Belouchrani et al., 1993, 1997) as well as itsapplication to EEG/MEG data have been described elsewhere(Tang et al., 2002a,b; Joyce et al., 2004; Muller et al., 2004; Tanget al., 2005b). A brief step-by-step description is provided here as aflow chart (Fig. 1). SOBI was applied to the continuous EEG datato decompose the n-channel EEG data into n components (Step 1,
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Fig. 1), each of which corresponds to a recovered putative source1
that contributes to the scalp recorded EEG signals.While some researchers have chosen to apply their BSS
algorithms to small windows of data (single-trial epochs) surround-ing events of interest (e.g., Onton et al., 2005) or even to theaveraged ERP itself (e.g., Makeig et al., 1999), we have consistentlychosen to apply SOBI to continuous data which include not onlyEEG signals surrounding events of interest but also ongoing back-ground EEG signals as well. Note that major differences between thechoice of single-trial data, averaged ERP, and the continuous EEGare: (1) using a set of single-trials entails discarding a large portion ofthe data outside of the time windows selected that could containinformation potentially useful for decomposition; and (2) the choiceof using averaged ERPs entails further discarding of informationcontained in the ongoing-rhythmic background activity that may nolonger be present after signal averaging. By choosing the entirecontinuous EEG data, we maximally utilize the information avail-able for source separation.
Each SOBI component has an associated time course ofactivation and a sensor-weight (scalp) map that specifies the effectof that component, in isolation, on each of the n electrodes. Let x(t)represent the n continuous time series from the n EEG channels,where xi(t) corresponds to the ith EEG channel. Because variousunderlying sources are summed via volume conduction to give riseto the scalp EEG, each of the xi(t) is assumed to be an instantaneouslinear mixture of n unknown components or sources si(t), via anunknown n!n mixing matrix A,2
x!t" # As!t":
The putative sources, !(t), are given by
!!t" # Wx!t";
where, W=A!1. SOBI finds the unmixing matrix W through aniterative process that minimizes the sum squared cross-correlationsbetween one recovered component at time t and another at time t+!,across a set of time delays (Cardoso and Souloumiac, 1996: http://sig.enst.fr/~cardoso/).
The following set of delays, "s (in ms), were chosen to cover areasonably wide interval without extending beyond the support ofthe autocorrelation function:
Similar sets of !s have been used to effectively isolate artifactsfrom EEG and MEG data as well as various task-related neuronalcomponents (Tang et al., 2002a,b; Tang et al., 2005b). For detailson how the choice of time delay parameters affect the extraction ofSI signals, see Tang et al., 2005b.
The continuous time course of activation for the ith componentis given by !i(t), from which stimulation-triggered averages to leftand right MNS can be generated (Step 2, Fig. 1). Componentsshowing any somatosensory evoked potential (SEP) were con-
sidered potential neuronal sources and those displaying activationin the 20–60 ms time window following contralateral MNS wereconservatively identified as potential SI candidates. Stimulation-triggered averages were generated for both left and rightstimulation and candidate SI components were expected to showselectivity for one side of stimulation, with large response ampli-tude to contralateral stimulation and smaller response amplitude toipsilateral stimulation. From the total set of potential neuronalcomponents recovered (~12), a subset of ~4 components wereconsidered SI candidates for each subject.
The spatial location of the ith component is determined by theith column of  (referred to as the component’s sensor weights;Step 3, Fig. 1), where Â=W!1. By assessing the sensor weights ofa component, candidates corresponding to left- and right-SIactivity can be easily identified. Candidate SI components typicallydisplayed large weights for the sensors overlying the hand regionof the respective left- or right-SI cortices. Typically, no more thanhalf a dozen components were selected.
From the two independently constructed candidate lists above,an overlapping set of components were further evaluated (Step 4,Fig. 1). The signal strength of the ith component, measured acrossall sensors at time t in the original units of measure (here "V), canbe estimated from its sensor space projection x(i)(t),
x!i"!t" # si!t"a!i";
where â(i) is the ith column of  (i.e., the component’s sensorweights), and !i(t) is a scalar which in this case is the average SEPsto either ipsi- or contralateral stimulation. A candidate’s averageSEP following left and right stimulation can then be viewed as 3-Dscalp current source density (CSD) maps, which are the secondspatial derivative of the voltage distribution and are consideredbetter at visually revealing the underlying source generators thanthe potential maps themselves (Lagerlund, 1999). Note that thespatial location of a component is given by its sensor weights, â(i),which do not change in time. However, the scalar, !i(t) is time-varying. Consequently, the dipole strength will differ between theipsi- and contralateral stimulation conditions. By comparing theipsi- and contralateral stimulation-triggered averages of a neuronalcomponent, we can assess how a given brain region, captured bythat component, differently responds to ipsi- and contralateralstimulation. Most relevant to the current topic of ipsilateral SIactivation, the sensor space projection of the component’s SEP inresponse to contralateral stimulation was first used to identify thelocation and strength of SI and the component’s SEP in response toipsilateral stimulation was then used to determine whether thesame underlying SI area also responded to ipsilateral stimulation.
The generators underlying the sensor space projections can bedetermined using a range of existing source modeling methods,including both point-source modeling algorithms (e.g., Scherg,1990; Hamalainen et al., 1993) and distributed-source modelingmethods (e.g., Sarvas, 1987; Ioannides et al., 1990). It has beenshown that the sensor space projection of some neuronalcomponents can be modeled surprisingly well by a single or, insome instances, a few dipolar sources (Vigario et al., 2000; Tanget al., 2002b; Makeig et al., 2004). SI component activations toMNS, as well as to touch, have been successfully modeled byequivalent current dipoles (Tang et al., 2002b; Tang et al., 2005b).Here, we estimated the SI components’ spatial origins and signalstrengths (Step 5, Fig. 1) using a point-source modeling methodprovided by BESA (Brain Electrical Source Analysis 5.0; MEGIS
1 In the present report, the terms recovered putative source andcomponent are used interchangeably.2 The general BSS problem requires A to be an n!m matrix, with n!m
(n: number of mixtures; m: number of sources). In most algorithmicderivations, an equal number of sources and sensors are assumed (Vigarioand Oja, 2000).
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Software, Munich, Germany). Equivalent current dipoles (ECDs)were fit for the small set of candidate SI components within anellipsoidal four-shell head model created for each subject using theirdigitized electrode positions. ECD locations (mm) were found usinga least-squares algorithm and are reported in a Talairach coordinatesystem within the BESA standard brain.
The resulting dipole positions were used to determine whetherthe components corresponded to left- or right-SI activations. Twosymmetrically positioned dipoles were first fit at the peak time (t*) ofthe component’s SEP in response to contralateral stimulation.Although candidate SI components have an asymmetric sensor pro-jection pattern, we chose to fit the sensor projection with a two-symmetric dipole model as opposed to making a stronger assump-tion that the projection was solely generated by a single dipolelocated in one cerebral hemisphere. Bymaking a weaker assumptionin dipole modeling, we allow both the possibility of 100% unilateralactivation as well as the possibility that a dominant SI activation inone hemisphere is also accompanied by synchronized activation inthe other hemisphere.3 Consequently, the estimated contralaterallocalization is considered less error prone. A candidate SI com-ponent was considered to reflect SI activation if the dominant dipolewas at the expected location activated by MNS determined fromconverging imaging techniques (see, Tang et al., 2005b for briefreview) and if the dipole’s peak response amplitude, measured by thedipole strength (nAm) at the peak time (t*) of the SEP, was greaterfollowing contralateral than ipsilateral stimulation.
After this source localization procedure (Step 5), typically nomore than two pairs of components (left and right) were localized tolocations considered to correspond to the hand region of SI. Of thetwo pairs of SI components, one has (Tang et al., 2005b) and theother has not been previously characterized. Because a structuralMR image would be needed to discern and characterize the precisespatial relations between these two pairs of SI components, thepresent analysis of ipsilateral responses was confined to thepreviously characterized pair of SI components (see Discussionfor consideration of the second set of SI components).
In contrast to typically howBESA is used for sourcemodeling, inthe present study the input data to BESAwere the SEP sensor spaceprojection of a single SOBI-recovered SI component, as opposed tothe original EEG sensor readings. The former is viewed ascontaining signals from a functionally distinct neuronal populationwhile the latter containing electrical signals from many neuronal aswell as noise sources. Because a SOBI component has fixed sensorweights, â(i), the estimated dipole locations do not change withrespect to time. In contrast, when fitting multiple dipole sourcesfrom the mixture of EEG signals, one must consider the fact thateach underlying source can have a different time course ofactivation, thus their mixing changes over time. Consequently, theestimated dipole locations may also change when fit at differenttimes and somewhat arbitrary decisions would need to be made.Choosing which of these multiple alternatives to report requiressignificant subjectivity. Here, by taking a SOBI component as thestarting point for source localization, complications regarding thetime of model fitting were circumvented.
Determination of significant ipsilateral SI responses
A three-step process was used to determine whether the left- andright-SI components of each subject displayed significant activationsfollowing ipsilateral stimulation. First, background variation in theaverage SEPs during the pre-stimulus interval was quantified bycomputing a standard deviation using all data points in a 200 ms pre-stimulus window. Second, within the 30–90 ms window followingstimulation, a window in which ipsilateral SEPs were expected tooccur (Allison et al., 1989;Korvenoja et al., 1999; Kanno et al., 2003),each data point was assessed to determine whether it exceeded 3standard deviations beyond the mean of the pre-stimulus baselineperiod (which is zero due to baseline correction). Finally, ipsilateralactivationswere defined as significant if 5 consecutivemilliseconds ofthe ipsilateral SEP following stimulation were beyond the 3 standarddeviation threshold. Onset times were determined for each significantipsilateral response andwere defined as the first time points exceeding3 standard deviations beyond the baseline mean.
Finding ipsilateral SI responses via conventional use of BESA
To rule out the possibility that the detection of ipsilateral SIactivation was somehow the result of SOBI incorrectly attributingsignals from different sources to the SI components, data from onesubject were assessed using a commonly adopted source modelingprocedure independent from SOBI. Multiple dipoles were fittedsequentially according to the following three steps. (1) Several dipoleswere fit during the pre-stimulus baseline period and immediatelyfollowing stimulus presentation (!200 to 5ms in reference to stimulusonset) to capture stimulus artifact, noise from bad sensors, residualocular artifacts (despite the fact that eyes were closed), and ongoingrhythmic activity in left and right occipitoparietal cortices. Dipoleswere added until at least 80% of the variance in the observed dataduring this pre-stimulus time window could be accounted for by themodel. The 80% criterion was adopted for the partial models thatconstitute the final model followingMauguiere et al. (1997). Once the80% goodness-of-fit criterion was met, the locations and orientationsof the noise dipoles were fixed. (2) Following practices described inthe literature (see below), unconstrained dipoles were sequentiallyadded to the model to account for activity in the following brainregions and fit at time points when they were expected to show thelargest responses: contralateral SI (~35 ms: Allison et al., 1991),contra- and ipsilateral SII (~90 ms: Hari and Forss, 1999),contralateral posterior parietal cortex (~95 ms: Forss et al., 1994),and mesial cortex (~140 ms: Forss et al., 1996). After a dipole was fitat the corresponding time point, its location and orientation were fixedand the next unconstrained dipole was then added to the model. Onceall of the abovementioned sources were included, the ipsilateral SI(~50 ms: Allison et al., 1989; Kanno et al., 2003) dipole was added tothe model. (3) Finally, all dipoles were kept fixed in location but wereallowed to vary in orientation and amplitude over the considered timeinterval (!200 to 250ms).An acceptable finalmodel was identified asone with a goodness-of-fit value over the whole time interval of atleast 90%. In comparison to the SOBI-aided extraction of ipsilateral SIactivations, this spatiotemporal multiple dipole modeling process istime consuming and contains numerous arbitrary decisions.
Statistical analyses
ECD locations (xyz coordinates) and goodness-of-fit (g) valuesof the SOBI-recovered left- and right-SI components were compared
3 Occasionally, such synchronized activation exists, albeit much weakerthan the dominant dipole. Its precise origin is not well understood and is notdiscussed further. Instead, the focus is on whether the brain region withstrong activation to contralateral MNS, also responds to ipsilateral MNS.Only the dominant dipole is discussed further.
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using the Wilcoxon test. Additionally, onset times and peak dipolemoments of the contra- versus ipsilateral responses were alsocompared using the Wilcoxon test. Statistical significance wasassumed when p<0.05. Data are reported as mean±SEM.
Results
SOBI-recovered SI components
Spatial and temporal aspects of the SOBI-recovered left- andright-SI components from a typical subject are illustrated (Fig. 2).Spatially, the scalp CSD maps for the left- and right-SI components(Fig. 2AB, top) showed focal and confined activity at sensorlocations over the respective primary somatosensory cortices. Thecorresponding ECD locations (Fig. 2AB, bottom), shown super-imposed on the structural MR images of a standard brain, were inthe vicinity of the presumed location of the left-(x=!35, y=!26,z=57) and right-SI hand regions (x=37, y=!24, z=61) andyielded goodness-of-fit values above 95% (left-SI: g=97.5%;right-SI: g=96.8%). Temporally, the SEPs of these componentsdisplayed characteristic SI responses following contralateralstimulation (Figs. 2C and D), and both components showedsignificant evoked responses following ipsilateral stimulation(Figs. 2E and F). In other words, the left-SI component displayeda relatively large amplitude contralateral response to rightstimulation (Fig. 2C) and a much smaller and slightly delayedipsilateral response to left stimulation (Fig. 2E), similarly, the right-SI component showed a large amplitude contralateral response toleft stimulation (Fig. 2D) and an ipsilateral response to rightstimulation (Fig. 2F).
SOBI simultaneously recovered slightly fewer than a dozenneuronal components in addition to the above characterized SIcomponents. Due to the scope of the present study, these additionalcomponents are not documented in detail here. However, it isimportant to point out that we interpret the activity captured by theabove SI components as reflecting activity from only SI and not amixture of activity from other neuronal (e.g., ipsilateral SII) or noisesources for the following reasons. First, dipoles fit to SI locationsaccounted for over 95% of the variance in the components’ sensor
Fig. 1. SOBI-aided extraction of SI responses to contra- and ipsilateral MNS:A schematic illustration. (1) The continuous EEG sensor data, x(t), wassupplied as input to the SOBI algorithm. The output of SOBI is an unmixingmatrix, W. (2) Identifying the candidate SI components using temporalinformation: The continuous time course of the ith component, !i(t), is givenby the ith row of W*x(t). From !(t), left and right stimulation-triggeredaverages, can be computed for each SOBI component. Components thatcorrespond to left- and right-SI, !Left-SI(t) and !Right-SI(t), can be easilyidentified by responses in the 20–60 ms time window followingcontralateral stimulation. (3) Identifying the candidate SI componentsusing spatial information: The sensor-weight map of the ith component isdetermined by â(i), the ith column of W"1; â(Left-SI) and â(Right-SI) are twoeasily identifiable weight maps that correspond to the estimated left- andright-SI sources. (4) The sensor space projection of a candidate SIcomponent's average SEP is computed from x(i)(t)=!i(t)*â(i); the peakdipole moment is determined at t* (peak time). (5) Location and strength ofthe candidate left- and right-SI components can be determined with BESAusing the sensor space projection of contra- and ipsilateral SEPs as inputs.Note that dipole fitting results should not be interpreted as a source model forthe ipsilateral SEP per se. Instead, the dipole locations of the same SOBIcomponent following both contra- and ipsilateral stimulation are the same,the difference is in the relative strength of the fitted dipoles during ipsi- andcontralateral stimulations as indicated by the size of the dipoles.
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space projections. Hence, activity from other sources, such as SII,being captured within these components was considered unlikely.Second, the residual activity not accounted for by the SI dipoleswhen viewed as a scalp map did not reveal any dipolar field patternsthat would indicate the presence of SII or any other neuronal source(data not shown). For these reasons, we are confident that theobserved SI components are not a mixture of SI and other signals, ormore precisely, the contribution of sources other than SI to thedetected ipsilateral responses appeared negligible.
Reliable detection of ipsilateral SI responses across subjects andacross the left and right hemispheres
Left- and right-SI components similar to those detailed abovewere observed for all 10 subjects. As indicated by the highgoodness-of-fit values (range: 94–99%), the sensor space projec-tions of these components were well explained by ECDs whoselocations corresponded to the left- (x=!35.4±0.7, y=!18.4±1.9,z=53.9±0.6, g=96.9±0.6%) and right-SI cortices (x=38.1±0.8,y=!15.6±1.4, z=55.9±0.9, g=96.3±0.5%). The estimated dipole
locations showed considerable consistency from subject to subjectas indicated by the rather small variations in estimated locations(SEM<2 mm). Consistent with previous reports of interhemi-spheric differences in SI source locations (Wikstrom et al., 1997;Reite et al., 2003), the estimated right-SI mean dipole location waslocated more lateral (2.7±0.7 mm, p<0.01)4 and more anterior(2.7±1.1 mm, p<0.05) than the estimated left-SI location.
Average SEPs in response to ipsilateral stimulation are shown foreach of the 10 subjects with the initial periods of significant acti-vations indicated in shaded regions (Fig. 3). Significant ipsilateralresponses were detected from both the left- and right-SI componentsfor all subjects although variations exist in the waveforms and theprecise onset latencies of the ipsilateral responses.
Ipsilateral activity was observed in blocked and intermixed designs
Fig. 3 illustrates that ipsilateral responses can beobserved during both blocked (Subjects 1–8) and intermixed
4 For the x-coordinates, absolute values were compared.
Fig. 2. Typical example of ipsilateral responses to MNS observed in the SOBI-recovered left- and right-SI components (Subject 1). AB: CSD maps (top) and thecorresponding ECDs (bottom) of the left- (A) and right-(B) SI components. The CSD maps are shown at the time of peak response where the box indicates thelocation of the sensor with maximum amplitude. ECDs are superimposed on the structural MRI of a standard brain and only the dominate dipole is shown. (C–F)SEPs from the left-(CE) and right-(DF) SI components in response to contralateral (CD) and ipsilateral (EF) stimulation. Initial periods in the ipsilateralwaveforms meeting the criteria for statistical significance are shown shaded. All SEPs shown in the subsequent figures were similarly measured.
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protocols (Subjects 9–10). Significant ipsilateral activationswere detected when either protocol was used. This findingsuggests that ipsilateral activity in the recovered SI sourceswas not contingent on the particular stimulation protocolemployed.
Ipsilateral SEPs were observed regardless of the segment of dataused for source separation
To rule out the possibility that the ipsilateral responses observedin the SOBI-recovered SI sources were the result of erroneous
Fig. 3. Reliable detection of ipsilateral SEPs from the left- and right-SI components across 10 subjects. Subjects 1–8 blocked stimulation; Subjects 9–10intermixed stimulation.
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mixing of source signals originating from left- and right-SI, weevaluated whether both ipsilateral SEPs from the left- and right-SIcomponents could be recovered from a segment of EEG data con-taining only left or right stimulations. Similar to results obtainedwhenusing all data collected during the left and right stimulation blocks forsource separation (Fig. 4A), both left- and right-SI components wererecovered when only segments of data from either the right or leftstimulation blocks were used for separation (Fig. 4B). In both cases,the recovered left- (Fig. 4B, lower left) and right-SI (Fig. 4B, upperright) components displayed significant responses to ipsilateralstimulation that were similar to those observed when all data wereused for separation (Fig. 4A, lower left and upper right, respectively).Similar observations were made when analyzing different segmentsof the available EEG data from all subjects (data not shown).Therefore, the detected ipsilateral responses cannot be explained bySOBI incorrectly mixing signals from the left- and right-SI.
Ipsilateral activations can be observed independent of SOBIsource separation
When spatiotemporal multiple dipole models were used toexplain the scalp recorded EEG mixture, a 10-dipole solution wasable to account for more than 90% of the total variance over thewhole time interval of the SEPs. From these multiple dipolemodels, the estimated left contra- and ipsilateral SI dipole locationswere x=!33, y=!25, z=59 and x=!35, y=!26, z=56,respectively, and the estimated right locations were x=35, y=
!26, z=59, and x=36, y=!27, z=58. The contra- and ipsilateralsource waveforms from the SI dipoles extracted with these modelsare shown in Fig. 5. This finding indicates that an ipsilateral SIresponse could be detected independent from SOBI processing.
Comparison of ipsilateral and contralateral SI responses
Across each of the 10 subjects studied, the mean onset latencyof the ipsilateral response was 44.1±2.1 ms, significantly slowerthan the initial contralateral response (19.9±0.2 ms, p<0.001).Ipsilateral SI onset latencies similar to those reported here havealso been described elsewhere (Allison et al., 1989; Korvenoja etal., 1999; Kanno et al., 2003). The mean ipsilateral peak dipolemoment (9.4±0.4 nAm) was significantly smaller than the peakcontralateral moment (25.7±1.7 nAm, p<0.001). Similar ipsilat-eral SI dipole moments following MNS have also been reported(Korvenoja et al., 1995; Kanno et al., 2003). No significant left–right differences in onset latency or dipole moment were detectedfollowing either contra- or ipsilateral stimulation.
Discussion
SOBI-aided isolation of ipsilateral SI response to median nervestimulation
SOBI is a blind source separation algorithm that differs fromseveral well known independent component analysis methods
Fig. 4. Successful extraction of ipsilateral responses independent of the stimulation condition under which the EEG data were collected. (A) Data containing bothright and left stimulation blocks; (B) data containing only right (upper row) or left (lower row) stimulation blocks.
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(e.g., InfoMax ICA and fICA) in that it can separate correlatedsources (Belouchrani et al., 1997). This unique property of SOBImakes it particularly suitable for the problem of separating signalsassociated with the activation of ipsi- and contralateral SI that arecorrelated due to shared stimulation. This ability to separatecorrelated neuronal activations has been previously demonstratedby the recovery of separate components for left- and right-SI fromEEG data collected during a mixture of unilateral and bilateralMNS (Tang et al., 2005a, 2006) where bilateral stimulationgenerated correlated activation between the left- and right-SI. Here,following previously established criteria and procedures (Tang etal., 2005a,b, 2006), we applied SOBI to high-density EEG datacollected from 10 subjects during MNS and identified componentsthat corresponded to the left- and right-SI cortices. In each of the10 subjects, we found that the SEPs of these SI componentsshowed statistically significant responses to both ipsilateral andcontralateral stimulations.
This finding was critically evaluated by several experimentalmanipulations. First, to rule out any possible dependency ofdetecting ipsilateral responses on a particular stimulation protocolor stimulation condition, we found that SI responses to ipsilateralstimulation could be detected (1) when either a blocked or anintermixed stimulation sequence was used, and (2) when the inputEEG data to SOBI were obtained during only the left or rightstimulation blocks. The latter finding eliminates the possibility thatthe ipsilateral responses might be the result of an incorrect linearcombination of signals associated with left- and right-SI activationscreated by the SOBI process. Second, we showed that SI responsesto ipsilateral stimulation can be detected independently from theSOBI process by constructing spatiotemporal multiple dipolesource models using commercially available source localizationsoftware. Finally, we compared the amplitude of the ipsi- andcontralateral source waveforms. Consistent with an ipsilateralorigin, the activation from ipsilateral SI showed weaker dipolemoments and longer onset latencies than activity from contralateralSI. Together, these findings suggest that, in humans, under thecondition of MNS, unilaterally presented somatosensory informa-tion can reach both the left and right hand regions of SI cortex.
In addition to the two SI components described here, otherSOBI components showing event-related activity have yet to befully characterized. These include components that appeared tocorrespond to SII, motor cortex, and frontal midline activity as well
as a second pair of SI components that differed from the first intheir dipole orientation. SII components were only sometimesrecovered possibly due to the fact that the EEG data were collectedwhen the subjects were instructed to be passive during stimulation.When they were recovered, the location of such SII componentsappeared to be in the Sylvian fissure. In some cases, componentscorresponding to motor cortex activity could also be localizeddirectly anterior to the SI sources. Frontal midline componentspossibly corresponding to anterior cingulate activity were con-sistently observed.
The reliable detection of ipsilateral responses from left- andright-SI was only examined for one of the two pairs of SIcomponents—the pair that has been previously characterized (Tanget al., 2005b). In the absence of structural MR images, wespeculate that these two pairs of SI sources may reflect signalsarising from two anatomically distinct subdivisions of SI.Intracranial recordings (Allison et al., 1991) indicate two spatiallydistinct and partially temporally overlapping sources of activity inpostcentral gyrus following MNS, a tangential source in theposterior wall of the central sulcus (Brodmann’s Area 3b) and aradial source in the crown of the postcentral gyrus (potentially area2 and/or 5). The SI components analyzed here suggest a radiallyoriented underlying source generator. While it is possible thatregional differences in ipsilateral responses exist within subdivi-sions of SI, such differences should not change the conclusion thatipsilateral SI responses can be reliably detected across subjects, atleast for one of the subdivisions.
It is important to emphasize that the left- and right-SI SOBIcomponents have not only a sensor space projection that allows forpotential localization of its corresponding signal generator but alsoa continuous time course containing three types of signals: (1)event-related activity to contralateral stimulation; (2) event-relatedactivity to ipsilateral stimulation; and (3) the remaining continuousongoing activity outside of the ERP windows. Therefore, weconsider recovering the entire continuous time course of eachSOBI-component as more complete than recovering only single-trial or averaged ERPs because the single-trials are nothing but asubset of the continuous EEG signals and the averaged ERPs arecomputed from such single-trials. In other words, by recovering theentire length of the continuous time course of a SOBI component,we can obtain the single-trial as well as the averaged ERPs inresponse to either contra- or ipsilateral stimulation. Hence, SOBI
Fig. 5. Detection of ipsilateral SI responses independent of the SOBI blind source separation process. SEPs from the estimated left- and right-SI dipoles obtainedwith a conventional spatiotemporal multiple dipole model in response to contralateral (top) and ipsilateral (bottom) MNS. Data are from the same subject(Subject 1) shown in Fig. 2.
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does not recover ipsilateral or contralateral SEPs per se. Instead,SOBI recovers the time course of an SI component in its entiretyand this time course is then used for the evaluation of ipsilateral aswell as contralateral SEPs to MNS.
We have performed preliminary analysis to explore whether thepresence of event-related activity in the continuous EEG data isnecessary for isolating the left- and right-SI components describedhere. By applying SOBI to continuous EEG data collected whilesubjects were at “rest” (i.e., receiving no stimulation and performingno task) immediately following the present MNS experiment, wefound that, in some but not all cases, left- and right-SI componentscould be recovered. This preliminary finding suggests that thepresence of event-related responses to sensory stimulation is notnecessary for SOBI to recover left- and right-SI activity. Whilerelatively high power in the 8–13 Hz frequency band (mu-rhythm) isexpected from somatosensory cortex, it cannot be logically deducedfrom this fact alone that the mu-rhythm is the “cause” of SOBIisolation of left- and right-SI because the involvement of signalsfrom other frequency bandsmust be ruled out in order tomake such aclaim. Regardless of the signals used by SOBI to isolate these left-and right-SI components, what is of most interest here is that SOBIcan isolate components that localize to the presumed location of theSI hand region along with other neuro-anatomically meaningfulcomponents.
Origin of variability in the detection rate of human ipsilateral SIresponses
The reliable detection of ipsilateral responses in human SI fromboth the left and right hemispheres is in variance with previousstudies, which report a wide range of detection rates. At one extreme,in a large MEG study involving 482 subjects, SI evoked response toipsilateral MNS was detected in only 14 subjects (Kanno et al.,2003). At the other extreme, in a significantly smaller MEG study of5 subjects, SI responses to ipsilateral stimulation were detected in allsubjects assessed after seeding dipoles using additional informationgained from separately acquired fMRI data (Korvenoja et al., 1999).A range of detection rates in-between these extremes has beenreported by studies utilizing different recording techniques,including EEG recorded at the scalp (Tamura, 1972; Salamy,1978) or from implanted electrodes (Allison et al., 1989; Noachtar etal., 1997), as well as MEG (Korvenoja et al., 1995; Schnitzler et al.,1995), and fMRI (Hansson and Brismar, 1999; Boakye et al., 2000;Nihashi et al., 2005).
A second inconsistency in the literature involves whether SIresponse to ipsilateral stimulation can be detected in both the leftand right hemispheres of the same subject. Korvenoja et al. (1995)were able to detect ipsilateral responses only in the righthemisphere of 5 out of 10 subjects and did not detect suchresponses in the left hemisphere. In another study by the samegroup (Korvenoja et al., 1999), only cortical responses to rightMNS were assessed. Using tactile stimulation and fMRI, Hanssonand Brismar (1999) found ipsilateral responses in both the left andright hemisphere in 7 out of 12 subjects. Most recently, Nihashi etal. (2005), using fMRI, found ipsilateral SI responses in the righthemisphere among 6 out of 8 subjects and in the left among only 3out of 8 subjects. Such variations across studies and across the twocerebral hemispheres make it controversial whether an ipsilateralSI response is universal (Noachtar et al., 1997; Kanno et al., 2003).
A unifying explanation is needed to reconcile the reliabledetection of ipsilateral SI responses reported in the present study
with these inconsistencies in the literature, particularly in regard tothe evoked response studies. To provide such an account, weconsider classifying previous evoked response studies attempting todetect ipsilateral SI activations into two general categories; thosemeasuring evoked responses using sensor signals that are directlymeasured by the EEG/MEG sensors, and those measuring evokedresponses using source signals derived from a source modelingprocess. Because multiple neuronal populations can affect manysensors through volume conduction, evoked responses in thedirectly recorded sensor signals cannot be used as an accuratemeasure of the underlying source activity. Therefore, the formerclass of studies is intrinsically incapable of offering unambiguousevidence for ipsilateral SI activations. Unfortunately, many earlyEEG studies (Tamura, 1972; Salamy, 1978; Kakigi, 1986) fall intothis first category. Although providing much higher sensitivity andspatial resolution than signals recorded at the scalp, signals recordedfrom electrodes placed directly at the cortical surface (Lueders et al.,1983; Allison et al., 1989; Noachtar et al., 1997) potentially share thesimilar problem of recording a mixture of signals from differentbrain regions and noise sources.
The second category of studies takes this issue into considerationwhen attempting to detect ipsilateral SI responses by constructingsource models to account for the recorded data. We consider thisshift from sensor-based to source-based analyses a conceptualadvance in the search for bilateral SI activation followingunilaterally presented somatosensory stimulation. Studies usingconverging imaging methods have revealed that a large number ofbrain regions, including contralateral SI (Allison et al., 1991; Kakigiet al., 2000), contra- and ipsilateral secondary somatosensory (SII)areas (Hari et al., 1984; Hari and Forss, 1999), contralateral posteriorparietal cortex (Forss et al., 1994; Boakye et al., 2000), prefrontalareas (Mauguiere et al., 1997), and mesial cortex (Forss et al., 1996),are involved in the processing of afferent somatosensory input.Because the signals recorded by scalp EEG/MEG sensors duringMNS are mixtures of all these source signals as well as other noisesources, it is unlikely that accurate estimates of the true underlyingactivations can be obtained from an incomplete source model.
For example, using a two-dipole (contra- and ipsilateral SI)model, Kanno et al. (2003) found SI responses to ipsilateralstimulation in less than 3% (14 out of 482) of subjects. Using a morecomplete model consisting of 5 dipoles (contra- and ipsilateral SI,contra- and ipsilateral SII, and contralateral posterior parietalcortex), Korvenoja et al. (1995) achieved a higher detection rate of50% (5 out of 10 subjects). Finally, when the most complete modelwas constructed, by seeding a total of eight dipoles according toseparately obtained fMRI data (contra- and ipsilateral SI, contra- andipsilateral SII, contra- and ipsilateral frontal areas, contralateralposterior parietal cortex and supplementary motor area), the highestdetection rate of 100% (5 out of 5 subjects) was obtained from theright hemisphere (Korvenoja et al., 1999). These findings areconsistent with the interpretation that the wide range of detectionrates reported in previous EEG/MEG reports may partly arise fromvariable accuracy and completeness in the source models used toestimate the ipsilateral SI signals. In the present study, the reliabledetection5 of ipsilateral SI responses indicates that SOBI, although
5 Reliable detection refers to the 100% detection rate across multiplesubjects. It does not imply consistency of the SEP waveforms acrosssubjects nor does it imply reliability of single-trial SEP detection within asubject. Reliability in the latter senses is not addressed in the presentmanuscript.
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not necessary, may serve as an effective preprocessing tool to parsethe mixture of recorded signals into components, thus allowing forconsistent detection of such weak signals across multiple subjects.
Origins of ipsilateral SI activations
The exact pathway mediating the ipsilateral response remainsdebatable and can only be speculated upon here. Three possiblepathways to ipsilateral SI have been considered: (1) callosal inputsfrom contralateral SI; (2) uncrossed ipsilateral input; and (3) top-down inputs from SII.
Most evidence is consistent with the idea that ipsilateral SIresponses are generated by transcallosal input from the contralateralhemisphere. First, ipsilateral responses appear to be dependent oncontralateral inputs via the callosum. In non-human primates, thebilateral or ipsilateral receptive fields of neurons in somatosensorycortex are contingent on the integrity of homologous regions in thecontralateral hemisphere (Iwamura, 2000). In humans, hemody-namic responses in the posterior parietal cortex of the hemisphereipsilateral to tactile stimulation are abolished by resection of thecorpus callosum (Fabri et al., 1999, 2001). Second, characteristics ofthe ipsilateral SEPs appear to be mediated by the maturation and thesize of the corpus callosum. Specifically, the latency of ipsilateralSEPs has been suggested to decrease with maturation, which istypically accompanied by the myelogenic development of thecorpus callosum (Salamy, 1978), and the amplitude of ipsilateralevent-related synchronization in the beta frequency band followingtactile stimulation has been shown to be positively correlated withthe size of the corpus callosum (Stancak et al., 2003). Finally, theestimated latency of ipsilateral SI responses in the present study(~45 ms) was delayed relative to the contralateral response(~20 ms), thus consistent with the interpretation that such ipsilateralresponses are mediated via callosal input from contralateral SI.
In terms of precise cytoarchitectonic areas within SI, the origin ofthe presently observed ipsilateral SI SEPs remains unclear. Thescarcity of interhemispheric inputs to Brodmann’s Area (BA) 3b innon-human primates (Jones and Hendry, 1980; Killackey et al.,1983) suggests that more caudal portions of SI cortex such as areas 2and 5/7 (Iwamura et al., 1994), where callosal connections are moreprevalent, are the most likely candidates for ipsilateral SI origins. Arecent fMRI study using MNS offers further support for thispossibility. Nihashi et al. (2005) demonstrated that the posteriorportion of the postcentral gyrus, roughly corresponding to BA 2 and/or 5, was activated by ipsilateral stimulation. In the present study, noindication of the precise loci of ipsilateral activity in terms ofcytoarchitectonic areas can be provided as the structuralMRI of eachsubject required for such precise localization was not obtained.
The second possibility, the presence of an uncrossed ipsilateralafferent pathway to SI, although unspecified, cannot be quicklydismissed. Noachtar et al. (1997) observed very fast ipsilateralresponses (1–17 ms following contralateral response onset) atimplanted electrode sites immediately above SI. Such shortinterhermispheric latencies between the ipsi- and contralateralresponses suggest that these ipsilateral responses are unlikely to begenerated by callosal input. However, because these responseswere measured by sensor signals containing potential mixtures ofactivity, we consider these ipsilateral responses to be of a differentnature or origin than the ipsilateral SI responses extracted usingSOBI. Using MEG, Kanno et al. (2003; 2004) have providedevidence for an ipsilateral SI response corresponding to BA 3b.Based on the relatively sparse callosal connections in area 3b
observed in non-human primates, Kanno et al. (2003) suggestedthat uncrossed afferent fibers, as opposed to transcallosalconnections, may be responsible for ipsilateral SI responses.However, it cannot be ruled out that such sparse callosalconnections may nevertheless be sufficient to mediate the smallsignals associated with ipsilateral SI activation. In support for anon-callosally mediated ipsilateral response, Kanno et al. (2004)provided further evidence in a study of two patients with severe lefthemispheric damage. In that study, ipsilateral SI responses in thenon-damaged right hemisphere were detected in the absence ofevoked responses from the damaged contralateral hemisphere. Thisobservation clearly indicated that the observed ipsilateral SIresponses were not dependent on the integrity of contralateral SI.However, this evidence does not preclude the possibility that insubjects without SI damage, contralateral SI inputs via a callosalconnection may be the mediator of ipsilateral SI responses. In fact,because one subject was studied several years after the initial injuryand the other a few moths after the insult, the ipsilateral SIresponses reported by Kanno et al. (2004) may reflect an ipsilateralresponse resulting from subcortical or cortical reorganization (e.g.,Holloway et al., 2000; Olausson et al., 2001).
The third possibility, that ipsilateral SI responses may be due tofeedback signals arising from higher-level processing areas such asSII can be ruled out with relatively greater confidence. Given that SIIdisplays initial activation at ~60–70 ms after stimulation (Hari andForss, 1999), such a late activation is unlikely to be the source of anearlier ipsilateral SI response with a latency of ~45ms. In view of theabove evidence, although an uncrossed ipsilateral pathway cannotbe entirely ruled out, a majority of evidence supports theinterpretation that the ipsilateral SI responses to MNS observed inthe present study originate from contralateral SI via the callosum.
Acknowledgments
This work was funded by a grant to ACT from the DARPAAugmented Cognition Program (ONR: N00014-02-1-0348) andthe MIND institute (#2021). We thank B.C. Reeb and C.J.McKinney for assistance during data collection and the anonymousreviewers for their comments on the manuscript.
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