This article was published in an Elsevier journal. The attached copyis furnished to the author for non-commercial research and

education use, including for instruction at the author’s institution,sharing with colleagues and providing to institution administration.

Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party

websites are prohibited.

In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information

regarding Elsevier’s archiving and manuscript policies areencouraged to visit:

http://www.elsevier.com/copyright

Author's personal copy

High-gamma band activity of primary hand cortical areas:A sensorimotor feedback efficiency index

Franca Tecchio,a,b,⁎ Filippo Zappasodi,a,c Camillo Porcaro,c,d Giulia Barbati,c,e

Giovanni Assenza,f Carlo Salustri,a and Paolo Maria Rossinic,e,g

aISTC-CNR, Unità MEG, Fatebenefratelli hospital Isola Tiberina, 00186 Rome, ItalybCasa di Cura San Raffaele Cassino e IRCCS San Raffaele Pisana, ItalycAFaR, Fatebenefratelli Hospital, Isola Tiberina, 00186 Rome, ItalydITAB, ITAB—Institute for Advanced Biomedical Technologies, “G. D’Annunzio” University, Chieti, ItalyeDepartment of Public Health and Microbiology, University of Torino, ItalyfDepartment of Neurology, ‘Campus Bio-Medico’ University, Rome, ItalygIRCCS “Centro S. Giovanni di Dio,” Brescia, Italy

Received 18 August 2007; revised 23 November 2007; accepted 28 November 2007Available online 4 December 2007

Sensory feedback in motor control is widely recognized to be the keylink between the activity of the primary motor cortex to the motorbehavior.

Through an ad-hoc developed procedure for source extraction(functional source separation), the primary sensory and motor cortexactivities (FSS1 and FSM1) were obtained from magnetoencephalo-graphic recordings during a sensorimotor task sequence, andsensorimotor interaction was assessed. Source activity spectral powerswere evaluated in the alpha (8–13 Hz), beta (14–32 Hz), gamma1 (33–60 Hz) and gamma2 (61–90 Hz) frequency bands.

FSS1 and FSM1had different spectral properties, with FSS1prevailing in alpha and FSM1 in gamma band. Both FSS1 and FSM1

were reactive in the different sensorimotor tasks with respect to rest inall frequency bands, except for gamma2. During an isometriccontraction, we searched for an index dependent on the performancelevel and with low variability in the healthy population. We found theseproperties satisfied within a relationship between FSS1 and FSM1 in thegamma2 band.

This sensorimotor feedback efficiency index quantitatively esti-mates the continuous functional balance between primary sensory andmotor areas devoted to hand control and seems promising for futuredevelopments, as it is easily assessable in patients.© 2007 Elsevier Inc. All rights reserved.

Keywords: Functional source separation (FSS); Magnetoencephalography(MEG); M1 and S1 source activity; Sensorimotor feedback

Introduction

Movement control requires continuous and reciprocal exchangeof information between the brain areas involved in the execution ofthe motor task and those elaborating proprioceptive sensoryinformation (Terao et al., 1999; Scott, 2004). Anatomically, thisexchange relies on the direct connectivity between the primarymotor areas (agranular frontal cortex F1 or BA4, called M1), theprimary sensory areas (S1), and the superior parietal lobule (PE;Rizzolatti and Luppino, 2001). Functional imaging studies haveshown that both a significant inhibition of the motor area’sexcitability and a motor map reshaping occur when the skinoverlying the target muscle is anesthetized, suggesting that thetonic input from the skin enveloping the muscle is essential forenergizing the corticospinal output toward that muscle (Brasil-Netoet al., 1992, 1993; Rossini et al., 1996; Rossi et al., 1998). Thevoluntary movement induces an inhibition of the sensory cortex’sexcitability (called sensory gating; Rossini et al., 1989; Saleniuset al., 1997; Turner et al., 2002; Chapman and Beauchamp, 2006),which is more evident in the component that includes acontribution from the motor areas (Tecchio et al., 2006).

MEG is a noninvasive technique which detects the magneticfields generated by postsynaptic currents of brain neurons firingsynchronously (Del Gratta et al., 2001). It displays an excellentcapability to describe the cerebral activity of the regions devoted tothe hand sensorimotor control (Tecchio et al., 1997, 2006, 2007b;Pizzella et al., 1999; Zappasodi et al., 2006). Recently, ourlaboratory has developed a new source extraction method(functional source separation [FSS]; Tecchio et al., 2007a), whichincludes functional constraints added to the statistical contrastfunction of a standard independent component analysis algorithm(ICA). FSS is capable to assess the source’s activity in a variety ofdifferent experimental conditions, while the functional constraint

www.elsevier.com/locate/ynimgNeuroImage 40 (2008) 256–264

⁎ Corresponding author. ISTC-CNR, Unità MEG, Dipartimento diNeuroscienze, Ospedale Fatebenefratelli, Isola Tiberina, 00186 Rome, Italy.

E-mail address: franca.tecchio@istc.cnr.it (F. Tecchio).Available online on ScienceDirect (www.sciencedirect.com).

1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.neuroimage.2007.11.038

Author's personal copy

can exploit a ‘fingerprint’ property of that source occurring in aspecific period of time, which can be very short.

The aim of our study was twofold. First, we used a previouslyintroduced paradigm (Tecchio et al., 2006) to characterize thefunctional involvement of the primary sensory (S1) and motor(M1) areas oscillatory activities in different frequency bands duringa sensorimotor task designed to assess sensory–motor interactions.The subject performs a simple isometric contraction either alone(motor condition) or in combination with median nerve galvanicstimulation, that is, when a non-physiological sensory inflow isadded to the physiological feedback (sensory–motor condition). Asa control condition, median nerve stimulation is also performed atrest (sensory condition). In this way, M1 activity changes due tothe sensory inflow and S1 activity modulation during voluntarycontraction are simultaneously assessed.

The S1 and M1 activities were estimated by the FSS procedure(Porcaro et al., 2008).

The second aim was to possibly identify an index of‘sensorimotor feedback efficiency’ in physiological conditions,which describes the M1–S1 functional relationship. In pursuing thissecond aim, the isometric contraction at a fixed force level wasfocused (motor condition), as this task requires the functionalbalance between M1 and S1. We searched for an index displayingdifferent values in correspondence of better and worse performancesand characterized by low variability in the healthy population.

Materials and methods

The study conforms with The Code of Ethics expressed in theDeclaration of Helsinki (WMA, 1964) and was approved by ourHospital Ethical Committee. All subjects signed an informedconsent.

Eleven healthy and drug-free volunteers (4 females, mean age39±14 years, age range [24, 66] years) were enrolled in the study.All subjects were right-handed with an average EdinburghInventory Test (Oldfield, 1971) score of 86±12.

Experimental procedures

A tone beep indicated to the subject to alternate ca. 20 s longperiods of rest to isometric contraction. An isometric opposition of

the thumb to the other fingers of the right hand while pressing awater sphygmomanometer was required (Fig. 1) under visual checkto facilitate subjects in maintaining a constant isometric contractionof about 25% of maximal strength. The electromyographic activityfrom the right opponens pollicis (OP, median nerve supplied) wasrecorded as an easily accessible high-quality marker of theisometric contraction, which, in addition to the OP, involved aslight contraction of other muscles for wrist holding and fingerflexion. Both during the rest and contraction periods, a 10-s-longtrain of electric pulses was delivered to the median nerve at thewrist (interstimulus interval 631 ms, intensity at 300% of sensorythreshold). In this way, data related to four different conditions(Fig. 1) were collected: rest (rest), median nerve stimulation(sensory), voluntary isometric contraction (motor), simultaneousisometric contraction and stimulation of the nerve controlling OPmuscle (sensory–motor). The procedure lasted about 12 min,resulting in 3 min for each condition.

Recordings

MEG measurements were performed using a 28-channel systemoperating in a magnetically shielded room (VacuumschmelzeGMBH), the active channels being regularly distributed on aspherical surface (13.5 cm of curvature radius) covering a total areaof about 180 cm2. Cerebral activity from left Rolandic areas wasrecorded after centering the recording apparatus on the C3 positionof the 10–20 International EEG system. Ag–AgCl cup electrodeswere used for recording electrooculogram and electrocardiogram inorder to control eye blinking and cardiac interferences. EMG fromthe right OP was acquired by a pair of electrodes, whose activeelectrode was placed on the skin over the belly of the OP muscle,referred to the tendon in correspondence of the thumb metacarpo-phalangeal joint at a distance of 2.5 cm (see also Fig. 1). MEG andEMG data were sampled at 1000 Hz (pre-sampling analogicalbandpass filtering 0.48–256 Hz), and collected for off-lineprocessing.

Data analysis

A semi-automatic ICA-based procedure (Barbati et al., 2004)was applied off-line to identify and remove cardiac and/or ocular

Fig. 1. Experimental paradigm. (Left) Subject position during MEG recordings from the peri-Rolandic area contralateral to the moved–stimulated hand. Duringmotor and sensory–motor conditions, the pump of the non-magnetic water sphygmomanometer (black arrows) is compressed, and the water level allowsmonitoring of the contraction force. (Right from top) Triggers of sound indicating starting/stopping isometric contraction (tone beep) and galvanic stimuli to themedian nerve at the wrist (electrical stimuli). Electromyographic signal (OP EMG), where rest and contraction periods are clearly noticeable, as well as thesensory stimuli artifact. Off-line generated signal code differentiating the four experimental conditions: Rest (R), Sensory (S), Motor (M), Sensory–Motor (SM).

257F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

disturbances thus avoiding exclusion of the contaminated trials.Saturated epochs were rejected by visual inspection.

S1 and M1 functional source separation (FSS)The functional source separation procedure developed by our

group (FSS; Tecchio et al., 2007b) was applied to identify thecortical neural networks devoted to the hand representation inprimary motor (FSM1) and primary sensory (FSS1) areas. In theFSS algorithm, additional information is added to the contrastfunction of a standard ICA model to bias the algorithm searchtoward a single solution that satisfies the physiological assump-tions (functional source, FS). Our functional requirements were (i)maximal cortico-muscular coherence for FSM1 extraction and (ii)maximal responsiveness to median nerve stimulation at around20 ms for FSS1. The validity of these extractions has beenpreviously demonstrated by Porcaro et al. (2008, Fig. 2), showingFSS1and FSM1 in all subjects positioned consistently with the SM1organization, namely, FSS1in the postcentral and FSM1in theprecentral walls of the omega-shaped region of the Rolandicsulcus.

S1 and M1 involvementThe sources’ involvement during each experimental condition

was estimated through their power band distributions. This issomewhat similar to the block (desynchronization) of occipitalbackground electroencephalographic (EEG) activity on eyesopening, one of the most clinically used indexes of brain reactivity(Gastaut, 1952, Niedermeyer, 1999). We disregarded the periodwhen transient desynchronizations and/or synchronizations occur(Hari and Salmelin, 1997; Pfurtscheller and Lopes da Silva, 1999)and focused on the average power modulation during the stablestate of tasks. The Power Spectral Density (PSD) was estimated forboth FSM1 and FSS1 by Welch procedure (Hanning window,512 ms duration, no overlap) in the classical (IFSECN, 1974)

frequency bands, characteristic of the primary hand representationcortical network responsiveness, namely, alpha (α [8, 13] Hz), beta(β [14, 32] Hz) gamma1 (γ1 [33, 60]Hz) and gamma2 (γ2 [61, 90]Hz) bands. The estimate was obtained in the four experimentalconditions: for rest and motor conditions the windows werecontiguous; for sensory and sensory–motor, the posttriggerintervals between 25 and 536 ms were used. In the gamma1 band,four frequency points were excluded to eliminate the line noisecontamination. The same number of time epochs was chosen toestimate the band power in the four conditions in all subjects. Wewill use the term ‘source reactivity’ whenever a source powerreduction will be found during the tasks (sensory, motor, sensory–motor) with respect to rest.

Motor performance estimateThe variation coefficient, that is, the ratio between the standard

deviation and the mean, of the rectified OP EMG area of the 512-ms-long epochs during the motor condition was considered as anestimate of the ability to maintain a constant isometric contraction.

Sensorimotor feedback efficiency indexTo identify an index describing the continuous balance between

S1’s and M1’s activities, we considered that the motor condition isthe one where the balance between M1 and S1 allows the betterperformance control. For this reason, we calculated separatepowers in dependence on the level of the performance in the motorcondition. In the four frequency bands, the powers were estimatedgrouping the 512-ms-long trials in three subgroups with corre-sponding good (b33° percentile of rectified OP EMG areavariation coefficient), medium (between 33° and 66° percentile)and bad (N66° percentile) performance levels. About 100 trialswere acquired for each performance level as needed for signalpower and coherence estimates, resulting in a total of about 300trials per subject in the motor condition.

Fig. 2. FSS1 and FSM1 behavior and spectra. In one representative subject: (Left) Position of extracted sources representing the median nerve connected sensoryareas (FSS1) and the primary motor area (FSM1) in the left hemisphere projected on a representative axial section. (Center) Dynamics of FSS1 and FSM1 activitiesduring contralateral median nerve stimulation, shown after averaging the source signal timed on the stimulus onset (t=0, vertical line) in the [−20, 100] ms timewindow. Note that the y-axis does not have the measure unit, as FS does not have a physical unit dimension, before retro-projection on the original signal space(Porcaro et al., 2008). (Right) Power spectra of the FSS1 and FSM1 cerebral sources in the four experimental conditions. The four frequency points near 50 Hz areomitted from the analysis to eliminate the 50-Hz line noise contamination.

258 F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

In addition to S1’s and M1’s band power modulations, thecoupling between FSS1 and FSM1 was estimated via their spectralcoherence, as another candidate for S1–M1 functional relationshipindex. The coherence function was calculated for a givenfrequency ω as the square modulus of the cross-spectrum ofFSS1 and FSM1 normalized by the PSD of the FSS1 or FSM1

sources. The 95% confidence limit is estimated according toHalliday et al. (1995) by 1� 0:05

1L�1, where L is the number of

averages (about 100). The FSS1−FSM1 coherence was obtained inthe four frequency bands in each experimental condition in thesame windows indicated for the PSDs and in dependence on thedifferent performances in the motor condition.

Statistical analysis

All band power values were log-transformed to better fitthe Gaussian distribution (Kolmogorov–Smirnoff test pN .700consistently).

In order to estimate the spectral properties of the two sourcesand their modulations in different experimental conditions,repeated measure analysis of variance (ANOVA) was applied toband powers with source (FSS1, FSM1), condition(rest , sensory,motor, sensory–motor), and band (alpha, beta, gamma1, gamma2)as within-subject factors. The factors of interest were theinteraction effects Condition×Source, revealing source selectivityin reacting during the sensorimotor tasks, and Source×Band,revealing spectral selectivity in the behavior of the two sources.

An effect was reported as a result only if it was statisticallysignificant. In the case of a significant effect, we considered thepost-hoc contrasts.

In pursuing the second aim, namely, to possibly identify anindex of ‘sensorimotor feedback efficiency’, we searched for aquantity including a contribution of FSS1 and a contribution ofFSM1, showing dependence on the performance level anddisplaying low variability in the healthy population. As the moststraightforward procedure, we checked either the FSS1–FSM1

coherence, or a band power of each FSS1 and FSM1 depending onperformance. The ANOVAs of FSS1 and FSM1 source powers andtheir coherence in the motor condition were applied withperformance (good, medium, bad) and band (alpha, beta, gamma1,gamma2) as within-subject factors. If measures with such propertywere found, an index was defined including both FSS1 and FSM1

contributions. Finally, the index dependence on the performancewas tested and its variability in our healthy population wascalculated. To estimate the index variability, we took in eachsubject the first 100 trials among the whole 300, that is, 100 trialsindependently of the performance level, in order to create a‘standard’ situation applicable in future studies.

If the index will display dependence on performance and lowvariability in our healthy cohort, it will be indicated as‘sensorimotor feedback efficiency’ index.

Results

S1 and M1 involvement

The triple interaction Source×Condition×Band [F(2.8, 24.9)=3.801, p=.025, Figs. 2 and 3)] revealed a different reactivity of theFSS1 and FSM1 sources in different frequency bands. To understandthe source specificity, FSS1 and FSM1 activities were studied at rest.In fact, the band-specific reactivity is expected to be as higher as

that band activity is more represented at rest, resulting in anevanishment of the Source×Band interaction when considering allfour conditions together. The significant Source×Band interactionfactor was found [F(1.5, 13.8)=10.894, p=.002, Figs. 2 and 3)]. Inparticular, FSM1 prevailed in the gamma2 band with respect toFSS1 (paired t-test, p= .014), while FSS1 prevailed in alpha(p=.036); no difference was found in beta band.

Each source was further separately estimated by the corre-sponding reduced model ANOVA, and a clear different modulation

Fig. 3. FSS1 and FSM1 band power: Statistical analysis. In the fourexperimental conditions, average and standard error across subjects in thealpha, beta, gamma1 and gamma2 bands of the FSS1and FSM1 sourceactivity powers. The statistically different contrasts are indicated (*b .012,**b .005), the comparisons with rest in the upper part, and the comparisonsbetween different sensorimotor tasks in the bottom. The y-axis does not havethe measure unit, as FSs do not have a physical unit dimension, unless retro-projection on the original signal space (Porcaro et al., 2008).

259F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

appeared in the four frequency bands when the subject wascontracting or stimulated with respect to rest [Condition×Bandinteraction factor: F(2.7, 24.6)=8.921, pb .001 for FSM1 and F(2.5,22.6)=9.372, p=.001 for FSS1, Fig. 3]. Both FSM1 and FSS1sources showed reduced power in alpha and beta bands withrespect to rest in all the three sensorimotor tasks (Table 1, Fig. 3).When comparing the three sensorimotor tasks, interestingly, notonly FSM1 but also FSS1 had lower power in the motor than in thesensory condition in the gamma1 band (Table 1, Fig. 3, left).

No interaction effect was present for FSS1–FSM1 coherence(Table 1).

Sensorimotor feedback efficiency index

The analysis of the FSM1 and FSS1 characteristics in dependenceon the performance level showed no main or interaction effect ofFSS1–FSM1 coherence. The reduced model ANOVAwas applied toband powers separately for FSM1 and FSS1 sources. Differentpowers in correspondence to different performance levels occurredonly in the gamma2 band for both FSM1 and FSS1. In fact, the twosources showed a significant Performance×Band interaction factor[F(1.7, 15.2)=5.370, p=.021 for FSM1 and F(3.2, 29.5)=4.900,p=.006 for FSS1] and the ANOVA model in each band separately,

Table 1Statistical results

M1 power S1 power S1–M1 coherence

α β γ1 γ2 α β γ1 γ2 α β γ1 γ2

Motor vs. Rest .002 .001 .004 n.s. .008 .002 .010 .065 n.s. n.s. n.s. n.s.Sensory vs. Rest .015 .010 .034 n.s. .030 .005 n.s. n.s. .026 .083 n.s. n.s.Sensory–motor vs. Rest .002 .002 .001 n.s. .004 b .001 .035 n.s. .036 .031 n.s. n.s.Motor vs. Sensory .086 n.s. .032 n.s. n.s. n.s. .002 n.s. n.s. n.s. n.s. n.s.Motor vs. Sensory–motor .031 .007 .054 n.s. n.s. .002 .053 .023 n.s. .029 n.s. .039Sensory vs. Sensory–motor .022 .020 .021 n.s. .015 .006 .031 n.s. n.s. .031 n.s. .093

For the two sources S1 and M1, and for the S1–M1 coherence, paired t-test significance (p values) comparing band powers in the indicated conditions. pb0.012(Bonferroni corrected) are in bold and n.s. indicates pN .100.

Fig. 4. Sensorimotor feedback efficiency index. (Top) FSM1 and FSS1 gamma2 band powers in dependence on the performance level, expressed by the Box-and-Whiskers plot. The thick horizontal segment corresponds to the median, the box corresponds to the interquartile range and the whiskers show the largest andsmallest observed values. Asterisks codes as in Fig. 3. The power of both sources was reduced during trials with poor performance, than during the medium andgood performance ones. (Bottom Left) Scatterplot across subjects of the index estimated for the third of the trials with better (good) and worse (bad)performances; note that in all subjects, index corresponding to good performances is higher than that with bad. (Bottom Right) Scatterplot across subjects of theSM1fe (average). Note the low variability.

260 F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

showed a strong performance factor in the sole gamma2 band [F(1.9, 17.0)=11.423, p=.001], while this factor was not significantin the other three bands (consistently pN .050). Post-hoc compar-isons showed that gamma2 band power of both FSM1 and FSS1 waslower during the poor performance trials than during both the good(p= .001, Bonferroni correction) and the medium-level trials(pb .001). The behavior was confirmed for each source separately(Fig. 4).

Because only in the gamma2 band did both FSM1 and FSS1powers (FSM1

γ2 and FSS1γ2) change in dependence on the performance

level, we defined the following index for sensorimotor feedbackefficiency:

SM1fe ¼ ½ðFSg2M1Þ�Motor � ðFSg2M1ÞRest� þ ½ðFSg2S1ÞMotor � ðFSg2S1ÞRest�:The interquartile range (IQR) indicated that the distribution was

more ‘concentrated’ with respect to a standard Gaussian (IQR=1.35), displaying a median equal to −0.021, first quartile=−0.042,third quartile=0.004 resulting in IQR equal to 0.046 (=0.004+0.042).

Discussion

An optimized somatosensory feedback, weighted on an indexof performance, represents a key link between the primary motorcortex activity and the limb physics subtending motor behavior(Scott, 2004). Although the role of a sensory feedback in motorcontrol is widely recognized, there is no quantification of theefficiency of the cortico-cortical M1–S1 bidirectional connectivity.In this paper, we evaluated an index quantifying the M1–S1interaction, which depends on the quality of the performanceduring a simple isometric voluntary contraction. Such indexresulted well defined in the gamma2 frequency band of thesources’ oscillatory activity. Intracortical single and multiunitrecordings from multiple areas, as well as extracephalic electro-physiological studies, have already documented the functionalrelevance of oscillations and synchronization phenomena in thegamma frequency band (see Kaiser and Lutzenberger, 2003). Anenhancement of these high-frequency component synchrony,resulting from intrinsic dynamic interactions in the involvedsensorimotor loops, has been seen during attention to stimuli(Womelsdorf et al., 2006), visual search (Tallon-Baudry et al.,1997), object recognition (Yuval-Greenberg and Deouell, 2007),and learning (Osipova and colleagues 2006; Van Wassenhove andNagarajan, 2007). Gamma band synchronization not only drivescognitive processing arising in widespread neural networks, buttakes place locally in somatosensory areas, clearly enhancingperception acuity (Crone et al., 1998b; Meador et al., 2002; Palvaet al., 2005). In particular, in the primary sensory handrepresentation, the dynamic gamma band phase-locking displaysa somatotopic organization and associates with single-digitdiscrimination, perhaps representing a new code for fingerdexterity in the cortical sensory districts (Tecchio et al., 2007b).The gamma2 band SM1fe, significantly higher in correspondenceof higher performances, is a good candidate for an indexquantifying the efficiency of the primary sensorimotor feedbackcircuit, expressing the functional relationship between the primarymotor and sensory cortical areas and able to sustain adequatevoluntary motor control.

We saw a clear S1 involvement during the periods of sustainedvoluntary isometric contraction, maintained through the visual

feedback. In fact, the FSS1 source power appeared strongly reducedwith respect to the periods of rest. Interestingly, selectively in thegamma1 band, not only the primary motor but also the primarysensory cortex was more involved during the voluntarily controlledmovement than during the passive median nerve stimulation. Thetwo sources showed a reduced power in the motor task with respectto rest, not only in alpha and beta but also in the gamma1 band.Previous studies with intracortical recordings (Crone et al., 1998a,b)showed, in parallel to alpha and beta band desynchronization, agamma1 band synchronization after movement onset with respect tothe movement preparation. The task used in the present study isincomparable to that one, where the subject was always somehowalerted, preparing for the next movement to be chosen. Resultsobtained by a paradigm more similar to ours are described in Grosset al. (2005) and almost agree with our picture. In particular, theydescribed SMA andM1 powers in a task with low sensory feedback,while we describe S1 and M1 powers in a task requiring the wholeproprioceptive and cutaneous sensory feedback in controlling themovement effort.

S1 activity was more represented in alpha and M1 in gammabands. Oscillatory brain activity with prevalent high-frequencyspectral components has been already observed in frontal motorareas with respect to the parietal sensory ones with prevalentcoding in the alpha band (Crone et al., 1998b; Ohara et al., 2000,2001; Schoffelen et al., 2005).

We identified FSM1 by requiring maximal cortico-muscularcoherence during the voluntary isometric contraction (Porcaro etal., 2008). In fact, in addition to data in monkeys (Murthy and Fetz,1992; Donoghue et al., 1998), a robust literature in humansindicates bursts of oscillatory brain activity synchronous with thesurface electromyographic activity originating from M1 (Gross etal., 2000; Schnitzler and Gross, 2005; Porcaro et al., 2008). Thecortico-muscular coherence occurs in dependence of the movementphase (Baker et al., 1997) and could transiently vanish-out whilepreserving the movement control (Tecchio et al., 2006). This lastevidence agrees with other studies on local field potentialsrecorded from M1, which expressed oscillations coherent withEMG activity in some movement phases, and the firing ratemodulations encoding for other motor parameters in othermovement periods (Donoghue et al., 1998). The FSM1 sourceactivity we have extracted shows this property. In fact, in the sameperiod when the cortico-muscular coherence vanishes-out (sen-sory–motor condition; Tecchio et al., 2006) the FSM1 power isstrongly modulated with respect to rest. This indicates that theFSM1 source, extracted requiring maximal cortico-muscularcoherence during isometric contraction, describes adequately theprimary motor area involvement also when it is mediated bymechanisms different from the cortico-muscular synchronicity.

The aim of obtaining indexes of sensorimotor feedbackefficiency originates from the crucial role played by feedback inthe learning processes (Todorov, 2004; Bastian, 2006). Allsupporting devices, from standard rehabilitative procedures torobotic tools, require the subject to learn, and the most importantelement of such learning is the presence of feedback (Scott andNorman, 2003), tactile/haptic feedback seeming to be the mostcompelling (Treisman and Kanwisher, 1998). For this reason,studying the properties of sensorimotor feedback, and in particular,to assess quantitatively its efficiency by a paradigm requiring amotor task both as simple and common as possible, could help indeveloping and monitoring compensatory interventions on neuro-logical patients.

261F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

In conclusion, we have defined an index of sensorimotorfeedback efficiency, based on the relationship between FSM1 andFSS1 oscillatory activities in the gamma2 band. This indexquantitatively estimates the continuous functional balance betweenprimary sensory and primary motor areas devoted to hand control,required to maintain good motor performances. In fact, it displaysdifferent values in correspondence to good and poor performancesand has low variability in a healthy population. It is easilyassessable in patients affected by movement disorders.

Acknowledgments

The authors thank Dr. Patrizio Pasqualetti, Prof. Gian LucaRomani and Vittorio Pizzella for continuous support, Dr. AntonioD’Amore and Prof. Eugenio Guglielmelli for technical assistance.This work was partially supported by the RBNE01AZ92, PRIN2005027850 and PRIN 2005063547 of the Italian Department ofUniversity and Research (MUR) and by the European IST/FETIntegrated Project NEUROBOTICS—The fusion of NEU-ROscience and roBOTICS, Project no. 001917 under the 6thFramework Programme.

Appendix A. Functional Source Separation (FSS) procedurefor identification of primary sensory and motor areas

In the last decade, the blind source separation (BSS) techniques,in particular, independent component analysis (ICA) algorithms,have been successfully applied to EEG and MEG data to estimatesignal of interest (for comprehensive reviews on BSS, seeHyvärinen et al., 2001; Cichocki and Amari, 2002). BSSalgorithms estimate complete source time courses on the basis ofthe statistical properties of the generated signal, without taking intoaccount the physical nature of the generating phenomenon. Thus,BSS procedures use only information contained in the waveform oforiginal signals, a very convenient property for electrophysiologi-cal techniques (EEG and MEG), which provide the most rich time–frequency signal from the intact human brain. The ICA assumptionis that a set of statistically independent sources s have been mixedlinearly in the recorded data x by means of a mixing matrix A. Theaim is to recover both s and A starting from the observation of thelinear mixture x=As without making any particular assumptionother than statistical independence of the sources. Based on theobservation that when we deal with real-world signals we are nevercompletely ‘blind’, in that we know (in a more or less detailed andquantitative way) some of their characteristic features, a newapproach, called functional source separation (FSS), has beenrecently proposed by our group (for a review, see Tecchio et al.,2007a). The aim of FSS is to enhance the separation of relevantsignals by exploiting some a priori knowledge without renouncingthe advantages of using only information contained in originalsignal waveforms. A modified (with respect to standard ICA)contrast function is defined: F=J+λR, where J is the statisticalindex normally used in ICA, while R accounts for the priorinformation of the sources. According to the weighting parameterλ, it is possible to adjust the relative weight of these two aspects.Moreover, because prior information on the sources may also bedescribed by a non-differentiable function, the new contrastfunction F is optimized by means of simulated annealing. Thisdoes not require the use of derivatives, and performs globaloptimization, while gradient-based algorithms usually employed in

ICA only guarantee local optimization. To separate contributionsrepresenting different sources, we took into account that relevantcomponents could be not always reasonably assumed independent/uncorrelated, and in the FSS procedure is applied each time to theoriginal data. Different constraints are applied each time to producedifferent sources.

Primary sensory hand area (FSS1)

To obtain the hand sensory representation area, we exploited thecortical area responsiveness to the stimuli of the correspondingbody segment at appropriate times. We identified in the primarycortex devoted to the districts innervated by the median nerve, afunctional source related to the sensory flow induced by mediannerve stimulation. Named FSS1, it describes the activity related tothe well-known marker of the stimulus arrival in the primarysensory cortex, known to be mainly generated by excitatorypostsynaptic potentials impinging on BA 3b pyramidal cells. As itis maximally recruited at around 20 ms from the stimuli at wrist,the functional constraint taking into account the ‘reactivity’ to thestimuli to identify FSS1 was defined as:

RS1 ¼Xt20þD2t20

t20�D1t20

jEAðtÞj �X

10

15

jEAðtÞj

with the evoked activity, EA, computed by averaging source signalepochs triggered on the median nerve stimulus at wrist (t=0); t20 isthe time point with the maximum magnetic-field power on themaximal original MEG channel around 20 ms (searched in the [16–24] ms window) after the stimulus arrival; D1t20ðD2t20Þ=time pointcorresponding to a field amplitude of 50% of the maximal power–by definition in t20–before (after) t20; the baseline (no response)was computed in the time interval from 10 to 15 ms. As only onecomponent is extracted at each time, it is possible to avoid theamplitude indeterminacy inherent to the general ICA method. Oncethe source which optimizes the contrast function F has beenobtained, the estimated solution is multiplied by the Eucli-dean norm of its weight vector aS1 (aS1 such as aS1=aS1âS1,with |âS1|=1), allowing amplitude comparisons among sources in afixed position.

Primary motor hand area (FSM1)

To identify the source in the primary motor area devoted to thecontrol of the hand movements—named FSM1, the coupling ofcortical and muscular rhythmic oscillations in the beta band wastaken into account. In fact, it has been demonstrated that thecomponent of the synchronized cortical activity, coupled tosynchronous rhythmic motor-unit firing–assessed by surfaceEMG–within this band, characterizes aspects of cortical controlon voluntary movement, and is generated in the primary motorcortex. The corresponding functional constraint to obtain the FSM1

source was:

RM1 ¼XxmaxþD2xmax

xmax�D1xmax

CohðxÞ

where Coh is a function of frequency ω, obtained for each ω as theamplitude of the cross-spectrum between the FSM1 source signaland the rectified EMG, normalized by the root mean square of thepower spectral densities of these two signals; Δ1ωmax(Δ2ωmax)=

262 F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

frequency point corresponding to a coherence amplitude of 50% ofthe maximal value between [13.5–33] Hz–called ωmax–before(after) ωmax. As for FSS1, FSM1 was obtained by multiplying it bythe Euclidean norm of its weight vector aM1 (aM1 such asaM1=aM1âM1, with |âM1|=1).

FSS1 and FSM1 evaluation

Although the FSS constraints allow identifying sources only onthe basis of their function behavior, the field distribution theygenerate could be obtained by retro-projecting the source activityin the sensor space and could be used as the input for inverse-problem solution algorithms. We used the model of a single dipoleor multi-dipoles within an isotropic spherical head and the sourceswere, in all cases, positioned in agreement with well-establishedanatomical knowledge (i.e., FSS1 in the postcentral and FSM1 inthe precentral walls of the omega-shaped region of the Rolandicsulcus; Tecchio et al., 2007a).

Moreover, the two sources extracted requiring a characteristicfunctional property in a specific time period were obtained alongthe whole experimental session, including all the plannedexperimental conditions. As a consequence, both FSS1 andFSM1 activity band powers and their coherence were studiedduring the four experimental sensorimotor tasks.

References

Baker, S.N., Olivier, E., Lemon, R.N., 1997. Coherent oscillations inmonkey motor cortex and hand muscle EMG show task-dependentmodulation. J. Physiol. 501, 225–241.

Barbati, G., Porcaro, C., Zappasodi, F., Rossini, P.M., Tecchio, F., 2004.Optimization of ICA approach for artifact identification and removal inMEG signals. Clin. Neurophys. 115, 1220–1232.

Bastian, A.J., 2006. Learning to predict the future: the cerebellum adaptsfeedforward movement control. Curr. Opin. Neurobiol. 16, 645–649.

Brasil-Neto, J.P., Cohen, L.G., Pascual-Leone, A., Jabir, F.K., Wall, R.T.,Hallett, M., 1992. Rapid reversible modulation of human motor outputsafter transient deafferentation of the forearm: a study with transcranialmagnetic stimulation. Neurology 42, 1302–1306.

Brasil-Neto, J.P., Valls-Sole, J., Pascual-Leone, A., Cammarota, A.,Amassian, V.E., Cracco, R., Maccabee, P., Cracco, J., Hallett, M.,Cohen, L.G., 1993. Rapid modulation of human cortical motor outputsfollowing ischaemic nerve block. Brain 116, 511–525.

Chapman, C.E., Beauchamp, E., 2006. Differential controls over tactiledetection in humans by motor commands and peripheral reafference.J. Neurophysiol. 96, 1664–1675.

Cichocki, A., Amari, S.I., 2002. Adaptive Blind Signal and ImageProcessing. John Wiley & Sons.

Crone, N.E., Miglioretti, D.L., Gordon, B., Sieracki, J.M., Wilson, M.T.,Uematsu, S., Lesser, R.P., 1998a. Functional mapping of humansensorimotor cortex with electrocorticographic spectral analysis: I.Alpha and beta event-related desynchronization. Brain 121, 2271–2299.

Crone, N.E., Miglioretti, D.L., Gordon, B., Lesser, R.P., 1998b. Functionalmapping of human sensorimotor cortex with electrocorticographicspectral analysis: II. Event-related synchronization in the gamma band.Brain 121, 2301–2315.

Del Gratta, C., Pizzella, V., Tecchio, F., Romani, G.L., 2001. Magnetoence-phalography—a non invasive brain imaging method with 1 ms timeresolution. Rep. Prog. Phys. 64, 1759–1814.

Donoghue, J.P., Sanes, J.N., Hatsopoulos, N.G., Gaal, G., 1998. Neuraldischarge and local field potential oscillations in primate motor cortexduring voluntary movements. J. Neurophysiol. 79, 159–173.

Gastaut, H., 1952. Etude electrocorticographique de la reactivite desrhythms rolandiques. Rev. Neurol. 87, 176–182.

Gross, J., Tass, P.A., Salenius, S., Hari, R., Freund, H.J., Schnitzler, A.,2000. Cortico-muscular synchronization during isometric musclecontraction in humans as revealed by magnetoencephalography.J. Physiol. 527, 623–631.

Gross, J., Pollok, B., Dirks, M., Timmermann, L., Butz, M., Schnitzler, A.,2005. Task-dependent oscillations during unimanual and bimanualmovements in the human primary motor cortex and SMA studied withmagnetoencephalography. NeuroImage 26 (1), 91–98.

Halliday, D.M., Rosenberg, J.R., Amjad, A.M., Breeze, P., Conway, B.A.,Farmer, S.F., 1995. A framework for the analysis of mixed time series/point process data—theory and application to the study of physiologicaltremor, single motor unit discharges and electromyograms. Prog.Biophys. Mol. Biol. 64, 237–278.

Hari, R., Salmelin, R., 1997. Human cortical oscillations: a neuromagneticview through the skull. Trends Neurosci. 20, 44–49.

Hyvärinen, A., Karhunen, J., Oja, E., 2001. Independent ComponentAnalysis. John Wiley & Sons.

IFSECN, 1974. A glossary of terms commonly used by clinical electro-encephalographers. Electroencephalogr. Clin. Neurophysiol. 37, 538–548.

Kaiser, J., Lutzenberger, W., 2003. Induced gamma-band activity and humanbrain function. Neuroscientist 9, 475–484.

Meador, K.J., Ray, P.G., Echauz, J.R., Loring, D.W., Vachtsevanos, G.J.,2002. Gamma coherence and conscious perception. Neurology 59,847–854.

Murthy, V.N., Fetz, E.E., 1992. Coherent 25- to 35-Hz oscillations in thesensorimotor cortex of awake behaving monkeys. Proc. Natl. Acad. Sci.U. S. A. 89, 5670–5674.

Niedermeyer, E., 1999. The normal EEG of the waking adult, In:Niedermeyer, Ernst, Da Silva, Fernando Lopez (Eds.), Electroencepha-lography: Basic Principles, Clinical Applications and Related Fields, 4thed. Lippincott Williams & Wilkins, Baltimore, pp. 149–173.

Ohara, S., Ikeda, A., Kunieda, T., Yazawa, S., Baba, K., Nagamine, T., Taki,W., Hashimoto, N., Mihara, T., Shibasaki, H., 2000. Movement-relatedchange of electrocorticographic activity in human supplementary motorarea proper. Brain 123, 1203–1215.

Ohara, S., Mima, T., Baba, K., Ikeda, A., Kunieda, T., Matsumoto, R.,Yamamoto, J., Matsuhashi, M., Nagamine, T., Hirasawa, K., Hori, T.,Mihara, T., Hashimoto, N., Salenius, S., Shibasaki, H., 2001. Increasedsynchronization of cortical oscillatory activities between humansupplementary motor and primary sensorimotor areas during voluntarymovements. J. Neurosci. 21, 9377–9386.

Oldfield, R.C., 1971. The assessment and analysis of handedness: theEdinburgh inventory. Neuropsychologia 9, 97–113.

Osipova, D., Takashima, A., Oostenveld, R., Fernandez, G., Maris, E.,Jensen, O., 2006. Theta and gamma oscillations predict encoding andretrieval of declarative memory. J. Neurosci. 26, 7523–7531.

Palva, S., Linkenkaer-Hansen, K., Naatanen, R., Palva, J.M., 2005. Earlyneural correlates of conscious somatosensory perception. J. Neurosci.25, 5248–5258.

Pfurtscheller, G., Lopes da Silva, F.H., 1999. Event-related EEG/MEGsynchronization and desynchronization: basic principles. Clin. Neuro-physiol. 110, 1842–1857.

Pizzella, V., Tecchio, F., Romani, G.L., Rossini, P.M., 1999. Functionallocalization of the sensory hand area with respect to the motor centralgyrus knob. NeuroReport 10, 3809–3814.

Porcaro, C., Barbati, G., Zappasodi, F., Rossini, P.M., Tecchio, F., 2008.Hand sensory–motor cortical network assessed by functional sourceseparation. Hum. Brain Mapp. 29, 70–81.

Rizzolatti, G., Luppino, G., 2001. The cortical motor system. Neuron 31,889–901.

Rossi, S., Pasqualetti, P., Tecchio, F., Sabato, A., Rossini, P.M., 1998.Modulation of corticospinal output to human hand muscles followingdeprivation of sensory feedback. NeuroImage 8, 163–175.

Rossini, P.M., Narici, L., Romani, G.L., Peresson, M., Torrioni, G.,Traversa, R., 1989. Simultaneous motor output and sensory input:cortical interference site resolved in humans via neuromagneticmeasurements. Neurosci. Lett. 30, 300–305.

263F. Tecchio et al. / NeuroImage 40 (2008) 256–264

Author's personal copy

Rossini, P.M., Rossi, S., Tecchio, F., Pasqualetti, P., Finazzi-Agrò, A.,Sabato, A., 1996. Focal brain stimulation in healthy humans: motormaps changes following partial hand sensory deprivation. Neurosci.Lett. 214, 191–195.

Salenius, S., Schnitzler, A., Salmelin, R., Jousmäki, V., Hari, R., 1997.Modulation of human cortical rolandic rhythms during naturalsensorimotor tasks. NeuroImage 5, 221–228.

Schnitzler, A., Gross, J., 2005. Normal and pathological oscillatorycommunication in the brain. Nat. Rev., Neurosci. 6, 285–296.

Schoffelen, J.M., Oostenveld, R., Fries, P., 2005. Neuronal coherence as amechanism of effective corticospinal interaction. Science 308, 111–113.

Scott, S.H., 2004. Optimal feedback control and the neural basis ofvolitional motor control. Nat. Rev., Neurosci. 5, 532–546.

Scott, S.H., Norman, K.E., 2003. Computational approaches to motorcontrol and their potential role for interpreting motor dysfunction. Curr.Opin. Neurol. 16, 693–698.

Tallon-Baudry, C., Bertrand, O., Peronnet, F., Pernier, J., 1997. Oscillatoryγ-band (30–70 Hz) activity induced by a visual search task in humans.J. Neurosci. 17, 722–734.

Tecchio, F., Rossini, P.M., Pizzella, V., Cassetta, E., Romani, G.L., 1997.Spatial properties and interhemispheric differences of the sensory handcortical representation: a neuromagnetic study. Brain Res. 767, 100–108.

Tecchio, F., Zappasodi, F., Melgari, J.M., Porcaro, C., Cassetta, E., Rossini,P.M., 2006. Sensory–motor interaction in primary hand cortical areas: aMEG assessment. Neuroscience 141, 533–542.

Tecchio, F., Porcaro, C., Barbati, G., Zappasodi, F., 2007a. Functionalsource separation and hand cortical representation for BCI featureextraction. J. Physiol. 580, 703–721.

Tecchio, F., Graziadio, S., Barbati, G., Sigismondi, R., Zappasodi, F.,Porcaro, C., Valente, G., Balsi, M., Rossini, P.M., 2007b. Somatosensory

dynamic gamma-band synchrony: a neural code of sensorimotordexterity. NeuroImage 35, 185–193.

Terao, Y., Ugawa, Y., Hanajima, R., Furubayashi, T., Maschii, K., Enomoto,H., Shiio, Y., Mochizuki, H., Uesugi, H., Uesaka, Y., Kanazawa, I.,1999. Air-puff-induced facilitation of motor cortical excitability studiedin patients with discrete brain lesions. Brain 122, 2259–2277.

Todorov, E., 2004. Optimality principles in sensorimotor control. Nat.Neurosci. 7, 907–915.

Treisman, A.M., Kanwisher, N.G., 1998. Perceiving visually presentedobjects: recognition, awareness, and modularity. Curr. Opin. Neurobiol.8, 218–226.

Turner, L.C., Harrison, L.M., Stephens, J.A., 2002. Finger movement isassociated with attenuated cutaneous reflexes recorded from human firstdorsal interosseous muscle. J. Physiol. 542, 559–566.

Van Wassenhove, V., Nagarajan, S.S., 2007. Auditory cortical plasticityin learning to discriminate modulation rate. J. Neurosci. 27,2663–2672.

Womelsdorf, T., Anton-Erxleben, K., Pieper, F., Treue, S., 2006. Dynamicshifts of visual receptive fields in cortical area MT by spatial attention.Nat. Neurosci. 9, 1156–1160.

WMA—World Medical Association Declaration of Helsinki, 1964. EthicalPrinciples for Medical Research Involving Human Subjects. Helsinki,Finland (http://www.wma.net/e/policy/b3.htm).

Yuval-Greenberg, S., Deouell, L.Y., 2007. What you see is not (always) whatyou hear: induced gamma band responses reflect cross-modal interac-tions in familiar object recognition. J. Neurosci. 27, 1090–1096.

Zappasodi, F., Pasqualetti, P., Ercolani, M., Pizzella, V., Rossini, P.M.,Tecchio, F., 2006. Hand cortical representation at rest and duringactivation: gender and age effects in the two hemispheres. Clin.Neurophysiol. 117, 1518–1528.

264 F. Tecchio et al. / NeuroImage 40 (2008) 256–264