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Neuroscience Letters 548 (2013) 120– 125

Contents lists available at SciVerse ScienceDirect

Neuroscience Letters

j o ur nal homep age: www.elsev ier .com/ locate /neule t

ronto-temporal interactions in the theta-band during auditoryeviant processing

eong Woo Choia, Jong Ku Leea, Deokwon Kob, Gwan-Taek Leeb,i-Young Jungb,∗∗, Kyung Hwan Kima,∗

Department of Biomedical Engineering, College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do, 220-710,epublic of KoreaDepartment of Neurology, Korea University Medical Center, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul, 136-705, Republic oforea

i g h l i g h t s

Strong fronto-temporal TBPS was observed during auditory deviant processing.Reciprocal fronto-temporal TBGC was observed during auditory deviant processing.Fronto-temporal interactions are crucial to the MMN generation.

a r t i c l e i n f o

rticle history:eceived 29 January 2013eceived in revised form 24 May 2013ccepted 27 May 2013

a b s t r a c t

Mismatch negativity (MMN) is a negative component of event-related potential (ERP) that reflectsauditory deviant detection. Previous studies repeatedly suggested that MMN is generated by thefronto–temporal network. However, it is still unclear how the frontal and temporal areas interact. Toverify this, we observed the spatiotemporal pattern of inter-regional functional connectivity using phase

eywords:ismatch negativity (MMN)

ronto-temporal interactionshase synchrony (PS)

synchrony and Granger causality, by analyzing event-related electroencephalograms (EEGs) elicited bystandard and deviant tones in an oddball paradigm. Strong theta-band phase synchrony and bidirec-tional Granger causality were observed between the frontal and temporal areas during the processing ofauditory deviants, especially at the temporal interval of MMN. Our results support the hypothesis that

ions a

ranger causality (GC)vent-related potential (ERP)

fronto–temporal interact

. Introduction

Mismatch negativity (MMN) is a negative component of audi-ory event-related potential (ERP), elicited when infrequent (oreviant) sounds occur in a sequence of repetitive (or standard)ounds [19]. This component can be obtained by subtracting the

RP of the standard stimuli from the ERP of the deviant stimuli,nd it usually shows the largest peak in frontal and temporal areasf the human brain at 100–250 ms post-stimuli [25]. MMN can be

∗ Corresponding author at: 208 Baekwoon Hall, Department of Biomedicalngineering, College of Health Sciences, Yonsei University, 234 Maeji-ri, Heungup-yun, Wonju, Gangwon-do 220-710, Republic of Korea. Tel.: +82 33 760 2364;

ax: +82 33 763 1953.∗∗ Co-corresponding author at: Department of Neurology, Korea University

edical Center, Korea University College of Medicine, Inchon-Ro 73, Anam-Dong,ungbuk-Gu, Seoul 136–705, Republic of Korea. Tel.: +82 2 920 6649;ax: +82 2 925 2472.

E-mail addresses: jungky@korea.ac.kr (K.-Y. Jung), khkim0604@yonsei.ac.krK.H. Kim).

304-3940/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.neulet.2013.05.079

re crucial to the generation of MMN during auditory deviant processing.© 2013 Elsevier Ireland Ltd. All rights reserved.

observed even when not attending to a stimulus, which suggeststhat MMN is associated with pre-attentive auditory processing [20].For this reason, MMN is known to reflect ‘primitive intelligence’originating in the auditory cortex [20], and it has helped to eluci-date important issues in the fields of cognitive neuroscience andclinical neuroscience [18,29].

Many MMN studies have focused on its neuronal generationmechanisms [6,12,31]. The most common interpretation postu-lating MMN generation is that MMN is elicited by a comparisonbetween current auditory input and a previous auditory memorytrace and that it reflects an on-line updating of the model for pre-dicting auditory inputs [21,31]. These studies concluded that MMNis an index of the prediction error that results from sound thatdeviates from auditory regularities. Additionally, numerous pre-vious studies have reported that two different MMN generatorsin the frontal and temporal areas are involved in auditory deviant

detection [8,17]. In particular, the temporal generator is thoughtto reflect a sensory memory mechanism, whereas the frontal gen-erator is thought to be associated with cognitive roles such asautomatic attention-switching and comparison between previous

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nd input sounds. Consequently, the neuronal network comprisinghese two MMN generators has been thought to play a functionalole in generating the MMN component. However, how the twoMN generators interact with each other remains unknown.In addition to conventional ERP analysis, oscillatory neural activ-

ties in theta-band (4–8 Hz) have been suggested to be crucial ineviant processing using electroencephalogram (EEG) and mag-etoencephalogram (MEG) [5,10]. Considering aforementionedognitive roles of the MMN generators, these could be in line withany previous studies showing the emergence of theta rhythm

n the human brain in a variety of cognitive tasks such as sus-ained attention, successful memory encoding, and maintenancef information in short-term memory (STM) [13]. Interestingly,he theta oscillations observed during auditory deviant processingere found at frontal and temporal areas, similarly to the locations

f MMN generators [5,10]. We have also recently confirmed thathe auditory deviant processing is accompanied by phase reset-ing and power modulation in frontal and temporal theta rhythm14]. Also, it has been suggested that the function of large andistributed networks is related to slow oscillations such as thetahythm [28].

Therefore, we hypothesized that it could be observed the inter-ctions between two MMN generators by exploring the functionalonnections in large-scale between the frontal and temporal areasn theta-band. To verify this, we first analyzed the functionalonnectivity between the frontal and temporal areas in theta-and during auditory deviant processing by inter-regional phaseynchrony [15,30]. We additionally analyzed the causal relationsetween the frontal and temporal areas because the functionaloles of two MMN generators have been known to be differ-nt according to the procedure of auditory deviant processing.imilarly, several previous studies postulated that the two MMNenerators are temporally separated, with the frontal MMN gen-rator being activated later than the temporal one [17,23,24]. Thisrocess can be observed by calculating the Granger causality (GC)

ndex between the frontal and temporal areas [26].The main purpose of this study was to explore how the frontal

nd temporal MMN generators interact with each other duringuditory deviant processing. We did this by observing the spa-iotemporal pattern of phase synchrony and the GC index betweenask-relevant scalp areas (especially the frontal and temporal areas)uring a passive auditory oddball task.

. Methods

.1. Subjects and experimental protocols

Thirteen healthy, right-handed university students (6 males,ge: 23.31 ± 1.60 years) participated in this study after having givenheir written informed consent. The experimental protocol waspproved by the Institutional Review Board of the Korea Universityedical Center.Pure tones (90 dB SPL) were generated by the Cool Edit program

V.2.1 Software, Adobe Systems). Commercial software (Presenta-ion; Neurobehavioral systems, Albany, CA) was used to design theuditory oddball paradigm and present the pure tones as stimuli tooth ears simultaneously through a pair of earphones. All subjectsere sitting in a comfortable chair and instructed to read a book

n order to ignore the stimuli during task performance. The odd-all paradigm was based on randomly ordered sequences of twoones: a standard tone (1000 Hz, p = 0.8; n = 960) and a deviant tone

1030 Hz, p = 0.2; n = 240). Tone duration was 100 ms with a 10 msising/falling time and an inter-stimulus interval (ISI) of 1000 ms.ach subject performed three blocks of the oddball task containing00 tones each.

tters 548 (2013) 120– 125 121

2.2. Electroencephalogram (EEG) recording

During task performance, EEGs were recorded using a 32 chan-nel EEG amplifier (Grass Neurodata Acquisition System Model 12,Grass Technologies, Quincy, MA, USA) with 27 electrodes that wereplaced on the scalp according to the international 10–20 system,with extended coverage of lower temporal area (F9/10, T9/10, andP9/10). The reference electrode was set to Pz, and impedance ofeach electrode was kept below 5 k�. A band pass filter (0.1–100 Hz)and a 60 Hz notch filter were applied. Two electrooculogram (EOG)channels (placed on the left and right outer canthi) were added toconfirm eyeball movement for removing EOG artifacts. All signalswere recorded with a sampling rate of 400 Hz.

2.3. Preprocessing and event-related potential (ERP) analysis

EEG data were re-referenced to average reference. ERP wave-forms within a −400∼600 ms interval after stimulus onset wereobtained. Stereotyped artifacts such as ocular and muscular arti-facts were corrected using independent component analysis (ICA),as described in [3]. Baseline correction was performed by sub-tracting the mean level during the 100 ms interval before stimulusonset. We removed those single-trial ERP waveforms from furtheranalysis if severe contamination from non-stereotyped artifacts,such as drift or high-frequency muscular artifacts, were confirmedby visual inspection. Additionally, EEG segments were excludedfrom further analysis if the absolute value of EOG exceeded±100 �V.

2.4. Theta-band phase synchrony (TBPS) analysis

To analyze inter-regional functional connectivity, phase syn-chrony indices among electrode pairs were calculated. We excluded4 electrodes (F9, F10, P9 and P10), which were closely adjacentto F7, F8, P7 and P8, of the reported 27 scalp electrodes since thePLVs among adjacent electrodes would be meaningless due to thevolume conduction. Single-trial EEG signals were first transformedinto narrowband signals in the theta-band by band pass filtering(4–8 Hz) using a linear phase finite impulse response (FIR) filter.The instantaneous phase for each time point was calculated fromthe narrowband signal and its Hilbert transform. To quantify thedegree of phase synchrony, the phase-locking value (PLV) betweentwo electrodes was calculated for each time point by averaging thephase difference over all trials [15].

We then determined whether the PLVs between two specificelectrodes indicated significant phase synchrony. For this purpose,we devised a double-threshold strategy based on two criteria [2].The first step involved checking whether the PLV change underinvestigation was meaningful with respect to the PLVs of surrogatedata, which were obtained from 200 random shuffling of trials foreach electrode pair [15]. Using the distribution of PLVs calculatedfrom the surrogate data, we were able to determine the level of sig-nificance, as explained in [15]. We set the significance level at 1%,i.e., we decided that phase synchrony was significantly increasedif it was higher than the first percentile of the PLV values fromsurrogate data.

We then checked whether the PLV increased significantly by thepresented stimuli compared with the pre-stimulus baseline PLV.After obtaining the baseline distribution of PLVs during the pre-stimulus period (300 ms, 75 samples) by averaging across trials,we again set the significance level at 1%. When both criteria weremet, it was decided that the PLV indicated significant PS.

2.5. Spectral Granger causality (GC) analysis

To analyze inter-regional causal relations, a GC index was calcu-lated in the frequency domain. According to Granger’s suggestion,

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22 J.W. Choi et al. / Neuroscie

time series X2 causes another time series X1 if the predictionrror of X1 estimated by joint autoregressive (AR) modeling of X1nd X2 is significantly reduced compared to that estimated by ARodeling using only X1 [9]. Additionally, the GC index in the fre-

uency domain can be derived by Fourier transform of the AR modelstimated in the time domain [7].

The spectral GC indices were calculated using the latest ver-ion of the Granger causality connectivity analysis (GCCA) toolboxeleased by Seth, and the procedure for calculating the GC indices isllustrated in greater detail in [27]. Eight electrodes (Fp1, Fpz, Fp2,7, F3, Fz, F4, and F8) within frontal areas and 4 electrodes (T9, T7,8, and T10) within temporal areas were selected to observe theausal relations between the frontal and temporal areas. The dataength (300 ms after stimuli onset) of each time series for AR model-ng was determined by considering the maximum period (∼250 ms)f theta rhythm (4–8 Hz). The optimal order of the AR model washosen by Akaike information criteria (AIC) [1].

According to the GCCA toolbox, the spectral GC indices betweenwo time series were represented as a function of frequencyith 1 Hz steps, and the theta-band GC (TBGC) indices were

hen obtained by averaging the GC indices within the theta-band4–8 Hz). The TBGC indices among all pairs of electrodes wereescribed as a changed ratio of TBGC indices compared with thoseuring the pre-stimulus duration (300 ms). Consequently, the TBGC

ndices were statistically compared between stimuli (i.e., standards. deviant) for each causal direction by paired sample t-test.

. Results

.1. MMN component

Fig. 1(a) shows the grand-averaged ERP and MMN waveformst 2 main electrodes on the frontal and central areas. The MMNaveforms were obtained from subtraction of the ERP to standard

timuli from the ERP to deviant stimuli. An apparent MMN com-onent was observed at about 150–250 ms after stimulus onset,s denoted by the black arrow in Fig. 1(a). Additionally, voltage-istributed topographies in Fig. 1(b) showed maximum negativeotentials over the fronto-central areas. A subset of results on theRP analysis has been reported in our recent study [14].

.2. Theta-band phase synchrony (TBPS)

Fig. 2(a) shows the spatiotemporal patterns of TBPS. The graynd red solid lines denote the significant TBPS among the wholerain areas, and only between the frontal and temporal areas,espectively. The number of significant TBPS was described belowhe each graph. Twelve electrodes (Fp1, Fpz, Fp2, F7, F3, Fz, F4,8, T9, T7, T8, and T10) within the frontal and temporal areasere used to observe the fronto-temporal TBPS. At an early period

∼100 ms), a stronger fronto-temporal TBPS was observed fortandard stimuli. However, this pattern began to be reversed at theMN period (150–250 ms), and a much greater fronto-temporal

BPS was observed for deviant stimuli after the MMN duration∼250 ms).

Fig. 2(b) shows the ratio of significant fronto-temporal con-ections to significant total connections for both stimuli. Thelack solid line denotes the chance level of the ratio between theaximal possible numbers of fronto-temporal connections and

verall connections. Remarkably higher fronto-temporal connec-ions compared with the chance level were observed only for

eviant stimuli and were especially highest at the MMN period.he largest difference in the ratio of significant fronto-temporalonnections between two stimuli was also observed at the MMNeriod.

Cz). The MMN waveforms were obtained from subtraction of the ERP to standardstimuli from the ERP to deviant stimuli, and denoted by the black arrow. (b) Voltage-distributed topography of the MMN component (150–250 ms after stimuli onset).

For a direct comparison of fronto-temporal PLVs betweenstandard and deviant stimuli, the averaged PLVs among frontaland temporal electrode pairs during MMN epoch were normalizedwith respect to the PLVs of baseline period. The normalized fronto-temporal PLVs for deviant was significantly larger than thosefor standard (standard: 0.00 ± 0.02, deviant: 0.03 ± 0.04, t = −2.68,p = 0.03 by paired-sample t-test).

3.3. Theta-band Granger causality (TBGC)

Fig. 2(c) shows the causal relations in the theta-band amongfrontal and temporal areas for both stimuli. The upper left brokenyellow square denotes a causal direction from frontal areas to tem-poral areas, and the lower right square denotes a causal directionfrom temporal areas to frontal areas. As shown in Fig. 2(c), the TBGCindices were much greater for deviant stimuli among most elec-trode pairs. In particular, it was prominently increased for deviantstimuli in both causal directions, i.e., from frontal to temporal andtemporal to frontal areas.

Fig. 2(d) shows the statistical comparison of the TBGC indicesbetween causal directions, and between stimuli. For this com-parison, the TBGC indices between all electrode pairs wereaveraged within each scalp area. As noted above, the TBGCindices were much greater for deviant stimuli in both direc-tions, but the difference was significant in only the direction

from temporal to frontal area (temporal-to-frontal: 0.19 ± 0.06for standard, 1.47 ± 0.52 for deviant, t = −2.23, p = 0.05; frontal-to-temporal: 0.58 ± 0.24 for standard, 2.35 ± 0.90 for deviant,t = −1.69, p = 0.12).

J.W. Choi et al. / Neuroscience Letters 548 (2013) 120– 125 123

Fig. 2. Fronto-temporal interactions in theta-band. (a) The spatiotemporal patterns of theta-band phase synchrony (TBPS). The number of significant connections was denotedbelow the each graph. The gray and red solid lines denote the significant TBPS among the whole brain areas, and only between the frontal and temporal areas, respectively.The time after stimuli onset written in ms indicates the midpoint of the 50 ms interval from which the phase synchrony pattern was obtained. (b) The ratio of significantfronto-temporal connections to significant total connections for both stimuli. The black solid line denotes the chance level that represents the ratio of maximally possiblefronto-temporal connections to maximally possible total connections. (c) The causal relations in the theta-band among frontal and temporal areas for both stimuli. The upperleft broken yellow square: a causal direction from frontal areas to temporal areas. The lower right broken yellow square: a causal direction from temporal areas to frontalareas. (d) The statistical comparison of theta-band Granger causality (TBGC) indices between stimuli for each causal direction. The error bar: standard error. *p < 0.05 bypaired sample t-test. F: frontal areas. T: temporal areas.

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. Discussion

We explored the differences in spatiotemporal patterns of phaseynchrony in the theta-band between standard and deviant stimuliuring an auditory oddball task. In particular, we focused onhe TBPS between the frontal and temporal areas to verify theypothesis that the fronto-temporal network contributes to MMNeneration. Causal relations between the frontal and temporalreas were also analyzed by TBGC to observe how they interactith each other. We compared our results with previous studiesesigned to prove the MMN generation hypothesis.

The largest difference in spatiotemporal patterns of TBPSetween two stimuli could be observed from the fronto-temporalBPS. The fronto-temporal TBPS was sharply decreased for standardtimuli but gradually increased for deviant stimuli at the MMNeriod. This increase of fronto-temporal TBPS for deviant mighteem to be derived from the global increase of total TBPS. Thislobal-effect is thought to reflect the large-scale integration amongultiple brain processes, which is known to evolve with a tempo-

al dynamics in a low frequency range [28]. Thus, the contributionf fronto-temporal TBPS to auditory deviant processing could note revealed sufficiently by total TBPS. That is why we calcu-

ated the ratio of significant fronto-temporal connections to totalonnections. As shown in Fig. 2(b), the largest difference in theatio of significant fronto-temporal connections between deviantnd standard was observed also at ∼200 ms, the middle of MMN

period. Furthermore, the ratio of fronto-temporal connections wasremarkably higher than the chance level. This may suggest thatthe fronto-temporal connections were most prominent duringthe MMN epoch, and thus were closely related to MMN genera-tion. In general, inter-regional PS during a specific cognitive taskreflects the integration of task-relevant scalp areas and the dynamicinteractions among them [15,30]. This may indicate that the fronto-temporal TBPS is elicited by integrations and interactions betweenthe frontal and temporal areas during auditory deviant processing.Similarly, in a recent MEG study, Hsiao et al. [11] reported thatfronto-temporal neuronal networks in the several frequency bandswere involved in detecting auditory mismatch by observing thephase synchrony among cortical regions. Additionally, Lin et al. [16]reported that the phase synchrony of auditory deviant-evoked MEGsignals was significantly enhanced in the fronto-temporal regionsafter a successful surgery for temporal lobe epilepsy patients. Ourresults could additionally provide electrophysiological evidencethat fronto-temporal interactions are involved in MMN generation.

More interestingly, in these recent MEG studies, significantfronto-temporal networks were also elicited with slow rhythmsincluding the theta rhythm [11,16]. They suggested consistentlythat the theta rhythm in a large-scale could be an oscillatory char-

acteristic underlying the auditory deviant processing. This couldbe comparable to the theta oscillations increased during the atten-tional control processes and the maintenance of STM information,which are closely related to the cognitive roles of MMN generators

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13]. Also, it has been suggested that the slow oscillations underlpha-band and fast oscillations such as gamma rhythm woulde associated with the function of global and local networks,espectively [28]. In line with these, a recent MEG study reportedhat auditory changes induced local changes in clustering of fronto-emporal connections in gamma-band at the MMN period [22].verall, these results may indicate that the MMN would be gen-rated by dynamical oscillatory activities of the fronto-temporaletworks in different scales, especially, with theta rhythm.

Fronto-temporal interactions during auditory deviantrocessing could also be observed clearly from causal rela-ions among the TBGC indices between the frontal and temporalreas. The TBGC indices for deviant stimuli, compared withtandard stimuli, were considerably greater in reciprocal direc-ions between the two areas. This means that the frontal andemporal areas dynamically interacted and strongly influ-nced each other during auditory deviant processing [26].t is meaningful that the fronto-temporal interactions werebserved consistently in our results even though GC and PLVre different indices representing the relation of functional con-ectivity between two time series. Our findings also appear toe consistent with a recent study that used another methodor connectivity analysis, namely dynamic causal modelingDCM), and suggested that the mechanisms of MMN genera-ion involve plasticity in reciprocal causalities among multipleources within the hierarchical levels of the primary auditoryortex (A1), the superior temporal gyrus (STG) and the infe-ior frontal gyrus (IFG), (see [6] for a detailed explanation ofCM).

Several previous studies reported that the two MMN genera-ors in the frontal and temporal areas are temporally separated17,23,24]. For example, a combined functional magnetic resonancemage (fMRI)-EEG study showed that the early neural activity at0–120 ms duration could be localized with dipoles in the STG,hereas the later one at 140–170 ms duration could be localizedith dipoles in the IFG [23]. Additionally, Maess et al. [17] pro-osed in their MEG study that the early component of magneticMN is associated with the sensory, non-comparator mechanism,hereas the later one is associated with the cognitive, comparatorechanism. These findings were consistent with the hypothe-

is that the frontal MMN generators are activated later than theuditory cortex generators [24]. We could not observe the tem-orally distinct causal relations between the frontal and temporalreas due to the limitation of the window size for GC analysisecause we considered the maximum period of theta rhythm toe 250 ms [27]. However, in our results, the significantly greaterBGC indices for deviant stimuli compared with standard stimuliere observed only in the direction from temporal to frontal

reas during the 250 ms after stimulus onset. This finding, in theiew of information flow, means that the information during audi-ory deviant processing is propagated more strongly from theemporal to frontal areas [26]. Thus, it can support the previoustudies, as mentioned above, suggested that the activation of tem-oral MMN generators is temporally prior to that of frontal MMNenerators. We expect that the fronto-temporal causality can bebserved temporally in detail when using the narrow windowize for GC analysis in faster rhythms such as beta- or gamma-and.

The existence and exact location of the frontal MMN generatorsre not confirmed [4]. Further study is necessary to identify frontalnd temporal MMN generators at cortical level. Localization of sev-ral cortical oscillations should be accompanied in order to explore

nteractions between MMN generators.

In conclusion, we verified that fronto-temporal interactionsre strongly involved in auditory deviant processing. Our cur-ent study appears to strongly support the hypothesis that the

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tters 548 (2013) 120– 125

fronto-temporal network is involved in the MMN generation. Inparticular, multiple methods for analyzing inter-regional connec-tivity produced comparable results, in spite of the limitation due tothe volume conduction effects in scalp EEG.

Acknowledgment

This research was supported by the Original TechnologyResearch Program for Brain Science through the National ResearchFoundation of Korea (NRF) funded by the Ministry of Education,Science and Technology (No. 20100018840), and by the NationalResearch Foundation of Korea (NRF) grant funded by the Koreagovernment (MEST) (No. 20110029740).

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