Dynamics of gamma-band activity in human magnetoencephalogram during auditory pattern working memory

NeuroImage 20 (2003) 816 – 827

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Dynamics of gamma-band activity in human magnetoencephalogram during auditory pattern working memory Jochen Kaiser,a,* Barbara Ripper,a Niels Birbaumer,a,b and Werner Lutzenbergera a

MEG Center, Institute of Medical Psychology and Behavioral Neurobiology, University of Tu¨bingen, 72076 Tu¨bingen, Germany b Center for Cognitive Neuroscience, University of Trento, 38100 Trento, Italy Received 19 March 2003; revised 3 June 2003; accepted 3 June 2003

Abstract Both electrophysiological research in animals and human brain imaging studies have suggested that, similar to the visual system, separate cortical ventral “what” and dorsal “where” processing streams may also exist in the auditory domain. Recently we have shown enhanced ␥-band activity (GBA) over posterior parietal cortex belonging to the putative auditory dorsal pathway during a sound location working memory task. Using a similar methodological approach, the present study assessed whether GBA would be increased over auditory ventral stream areas during an auditory pattern memory task. Whole-head magnetoencephalogram was recorded from N ⫽ 12 subjects while they performed a working memory task requiring same– different judgments about pairs of syllables S1 and S2 presented with 0.8-s delays. S1 and S2 could differ either in voice onset time or in formant structure. This was compared with a control task involving the detection of possible spatial displacements in the background sound presented instead of S2. Under the memory condition, induced GBA was enhanced over left inferior frontal/anterior temporal regions during the delay phase and in response to S2 and over prefrontal cortex at the end of the delay period. ␥-Band coherence between left frontotemporal and prefrontal sensors was increased throughout the delay period of the memory task. In summary, the memorization of syllables was associated with synchronously oscillating networks both in frontotemporal cortex, supporting a role of these areas as parts of the putative auditory ventral stream, and in prefrontal, possible executive regions. Moreover, corticocortical connectivity was increased between these structures. © 2003 Elsevier Inc. All rights reserved. Keywords: Magnetoencephalography (MEG); ␥-band activity (GBA); Working memory; Delayed matching-to-sample; Auditory ventral stream; Auditory pattern processing; Syllables; Statistical probability mapping; ␥-band coherence

Introduction Both neurophysiological research in animals and human brain imaging studies have suggested separate auditory dorsal and ventral pathways for the processing of spatial and object-related information, respectively (Rauschecker, 1998). Sound location-specific neurons were situated more frequently in caudolateral belt areas surrounding the macaque primary auditory cortex, whereas cells responding preferentially to monkey vocalizations were found both in the anterolateral auditory belt (Rauschecker and Tian, 2000; * Corresponding author. Institute of Medical Psychology and Behavioral Neurobiology, University of Tu¨bingen, Gartenstrasse 29, 72074 Tu¨bingen, Germany. Fax: ⫹49-7071-29-5956. E-mail address: [email protected] (J. Kaiser). 1053-8119/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/S1053-8119(03)00350-1

Tian et al., 2001) and in ventrolateral prefrontal cortex (Romanski and Goldman-Rakic, 2002). Anatomical projections have been identified which link posterior belt areas with posterior parietal and dorsolateral prefrontal regions (Hackett et al., 1999; Kaas and Hackett, 1999; Kaas et al., 1999) and connect anterior auditory belt regions with anterior temporal and ventral prefrontal cortex (Romanski et al., 1999a, 1999b). Providing support for the extended topography of regions involved in auditory processing, Poremba et al., (2003) have recently shown that auditory stimulation activated the entire superior temporal sulcus and large parts of both inferior parietal and prefrontal cortex. Based on observed overlaps with previously identified visual areas, these findings were interpreted as support for separate auditory processing pathways. In humans, neuropsychological studies have suggested

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separate cortical substrates for sound recognition and localization (Griffiths et al., 1996; Clarke et al., 2000, 2002). Several functional imaging studies have demonstrated the involvement of areas posterior to auditory cortex and in posterior parietal areas in both sound localization (Bushara et al., 1999; Weeks et al., 1999; Maeder et al., 2001) and sound motion processing (Griffiths et al., 1998, 2000; Baumgart et al., 1999; Griffiths and Green, 1999; Lewis et al., 2000; Bremmer et al., 2001; Warren et al., 2002). Conversely, anterior temporal (Belin et al., 2000, 2002; Scott et al., 2000) and inferior frontal regions (Pugh et al., 1996; Binder et al., 1997; Maeder et al., 2001; Opitz et al., 2002) have been shown to be activated during auditory pattern processing. Whereas most of the studies cited above assessed bottom-up driven processing of auditory spatial or pattern information, top-down, task-induced activations of parietal and prefrontal regions were demonstrated during an audiospatial working memory paradigm (Martinkauppi et al., 2000). Alain et al., (2001) reported enhanced regional cerebral activity measured with functional magnetic resonance imaging (fMRI) in inferior frontal gyrus during a pitch delayed matching-to-sample task, whereas processing of sound location activated posterior temporal, parietal, and superior frontal activations. While hemodynamic approaches successfully identified the topographies of dorsal and ventral areas involved in the processing of different types of auditory materials, these methods have been unable to elucidate the time course of activations along the processing streams. Oscillatory activity has also been used to investigate auditory spatial and pattern processing in humans. Electroencephalogram (EEG) research has demonstrated enhancements of induced ␥-band activity (GBA, ⬃30 –100 Hz) during the perception of coherent, gestalt-like objects (Lutzenberger et al., 1995; Mu¨ ller et al., 1996; Tallon-Baudry et al., 1996; Keil et al., 1999). Recent work has implicated GBA also in top-down driven tasks involving spatial selective attention (Gruber et al., 1999; Mu¨ ller et al., 2000) and learning (Miltner et al., 1999; Gruber et al., 2002). GBA has therefore been interpreted as a correlate of the synchronization of cortical networks underlying different types of cognitive processes (Tallon-Baudry and Bertrand, 1999; Keil et al., 2001). While GBA effects in EEG were rather extended both topographically and spectrally, in MEG we have found local GBA enhancements in narrow frequency bands above 50 Hz. In keeping with the dual streams hypothesis, GBA in MEG was enhanced over posterior temporoparietal areas during auditory space processing (Kaiser et al., 2000a, 2002a; Kaiser and Lutzenberger, 2001) and over both anterior temporal and inferior frontal regions during perception of auditory pattern changes (Kaiser et al., 2002b). This method thus yielded topographies that corresponded well with functional imaging studies, and it allowed us to identify exact time courses and coherence patterns of synchronized neuronal activity.

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GBA in EEG also reflected the internal activation of object representations in the absence of external stimulation, e.g., during short-term memory studies in the visual domain (Tallon-Baudry et al., 1998, 1999). In MEG, we have recently shown GBA during an auditory spatial working memory task requiring same– different judgments about the lateralization angle of two noise stimuli S1 and S2 presented with a 0.8-s delay (Lutzenberger et al., 2002). ␥-Band spectral amplitude was increased in the middle of the delay period over posterior parietal cortex at ⬃59 Hz and over right frontal cortex at the end of the delay at ⬃67 Hz. Furthermore, parietofrontal ␥-band coherence was increased across the delay phase. These findings supported the role of putative auditory dorsal stream areas in the memorization of sound locations and demonstrated the relevance of functional connectivity between “frontal executive” and “posterior slave systems” (Baddeley, 1992) for working memory. The aim of the present study was to assess GBA during auditory pattern memory in humans. Here we followed closely both paradigm and analysis methodology used in our previous, auditory spatial memory study (Lutzenberger et al., 2002). Same– different judgments were now to be made between pairs of syllables potentially differing in either voice onset time or formant structure. This was compared with a nonmemory control condition involving the detection of possible spatial displacements of a background sound. Based on our previous findings, we expected GBA enhancements during the delay period over anterior temporal/inferior frontal, putative auditory ventral stream areas, as well as over prefrontal cortex. We also hypothesized that coherence would be increased between these regions during the delay period which would reflect the increased coupling between possible frontal executive and sensory storage areas.

Materials and methods Subjects Fourteen adults gave their informed and written consent to participate in the study. Two subjects had to be excluded from the analyses because of hearing problems and excessive artifacts, respectively. This left N ⫽ 12 participants (4 females, 8 males, mean age 27 years, age range 22–36 years). The study was approved by the ethics committee of the University of Tu¨ bingen Medical Faculty. Procedure and stimulus material Subjects were seated upright in a magnetically shielded room (Vakuum-Schmelze, Hanau, Germany). They were instructed to sit still and keep their eyes open, looking at a fixation cross in the center of their visual field about 2 m in front of them. Auditory stimuli were presented binaurally

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Fig. 1. Trial structure of memory and control conditions. Under both conditions, low-pass filtered midline noise and the presentation of the first syllable (S1) were followed by a delay phase of 800-ms midline noise. Then under the memory condition (a), a second syllable (S2) appeared for 200 ms. Subjects had to compare S1 and S2 and detect identical stimulus pairs. In the figure, the syllables /da/ and /ta/ were chosen as examples, but there were also other syllable combinations as described under “Procedure and stimulus materials”. Under the volume condition (b), after the delay phase another 200 ms of noise was presented either from midline or with a spatial deviation from midline by 15° either to the left or to the right (as indicated by the arrows in the “S2” box). The two horizontal bars above the time axis show the two latency windows for spectral analysis (a1, 0.6 –1.2 s; and a2, 1.0 –1.6 s).

via air-conducting tubes with ear inserts. The experiment was divided into two separate recording blocks: an auditory pattern memory condition and a control task. The order in which the conditions were presented was counterbalanced between subjects. Prior to the recordings, participants were informed about which task they were to perform. The trial structure of both tasks is depicted in Fig. 1. The onset of the trial was signaled by low-pass filtered midline noise (at 6 kHz: ⫺24 dB/octave) presented for 300 ms with an intensity of 52 dB (a) (rise and fall times: 10 ms). Then a language stimulus S1 was presented for 200 ms with an intensity of 70 dB (a). Language stimuli were the four syllables /ta/, /te/, /da/, and /de/. They differed in their voice onset times (60 ms for /ta/ and /te/, 20 ms for /da/ and /de/) and their formant structure; see Ackermann et al., (1999) for details of the stimulus synthesis. During the following delay phase the midline noise was presented again for 800 ms. Under the memory condition (Fig. 1a), this was followed by a second task-relevant language stimulus S2 which also lasted for 200 ms. The subjects were instructed to compare S1 and S2. If both sounds were identical, they had to trigger a light barrier by raising both index fingers, whereas they were not to respond if S1 and S2 differed. The four syllables were randomly presented as S1 at equal probabilities. S2 was identical to S1 in 20% of the trials, while the probability for a nonidentical S2 was 80%. S2 could differ from S1 in either in voice onset time or in formant structure, but never in both parameters. For example /da/ (S1) could be followed by either /da/ (identical S2), /ta/, or /de/ (nonindentical S2), but never by /te/. The

duration of the intertrial interval was randomized between 1700 and 2700 ms. Under the control condition (Fig. 1b), subjects had to detect a possible spatial displacement in the background sound presented at the end of the delay phase. Up to S2, stimulation was identical to the memory condition. Then, instead of S2, either the midline noise of the delay phase was presented for 200 ms or it was replaced by the same sound convoluted with generic head-related transfer functions (Gardner and Martin, 1995; see http://sound.media. mit.edu/KEMAR.html) to yield the impression of sounds lateralized at 15°. This lateralization angle had been determined on the basis of prior behavioral testing to obtain comparable difficulty levels for both conditions. Subjects were instructed to lift their index fingers when the perceived presentation angle of the background sound remained the same (go trials, 20%), whereas no response was required when there was a spatial displacement (nogo trials, 80%). Under both conditions subjects were instructed to pay more attention to accuracy than to speed. Answers had to be given before the onset of the subsequent trial. Each condition comprised 150 trials, i.e., 30 go and 120 no-go trials. Prior to each experimental condition subjects performed 60 practice trials. For the first 24 of these trials, subjects were informed about the sequence of identical and nonidentical S1–S2 combinations (and their equivalents in the control condition). For the remaining practice trials, subjects had to respond to the stimuli and were given verbal feedback about their performance by the experimenter. Data recordings MEG was recorded using a whole-head system (CTF, Inc., Vancouver, Canada) comprising 151 first-order magnetic gradiometers with an average distance between sensors of about 2.5 cm. One sensor had to be excluded because of a technical problem, leaving 150 sensors for analysis. The amplitude resolution of the CTF system amounts to 0.3 fT, enabling the detection of low-amplitude signal changes. The signals were sampled at a rate of 312.5 Hz with an anti-aliasing filter at 100 Hz. Recording epochs lasted from 200 ms pre-trial onset to 1800 ms post-trial onset. The subject’s head position was determined with localization coils fixed at the nasion and the preauricular points at the beginning and the end of each recording to ensure that head movements did not exceed 0.5 cm. Only trials without a motor response (no-go trials) were entered in the statistical analysis. To reduce eye movement and blink artifacts we rejected trials containing signals exceeding 1.5 pT in frontotemporal sensors. This left an average of 110 (SD ⫽ 13) trials in the memory condition and 111 (SD ⫽ 10) trials in the control condition.

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Data analysis Data analysis was based on the following procedure. First, spectral analysis served to identify the frequency range with the most robust difference between memory and control conditions. To enable the detection of temporally dynamic processes, this was done for two overlapping time windows. Significance of the observed spectral power values for each frequency bin and MEG sensor was tested using a statistical probability mapping procedure described in detail in the next paragraph. Second, time courses across the entire recording epoch and topography of these effects were assessed after filtering in the frequency range with the most pronounced difference between conditions. The latency ranges with the most significant spectral amplitude differences between conditions were then identified using statistical probability mapping. Third, task-related coherence changes were investigated. Here the analyses were restricted both to the relevant frequency range and to the pairwise combinations between those sensors with spectral amplitude effects and the remaining sensors. Again, statistical probability mapping was applied to identify significant coherence changes. Statistical probability mapping The basis of the statistical approach was the requirement that two consecutive frequency bins be simultaneously significant. Under the null hypothesis, the probability for this event equals the product of the single probabilities. Using a search range of 40 –90 Hz corresponds to 41 frequency bins (frequency resolution: 1.22 Hz, see section on spectral analysis below). With a single probability of P ⫽ 0.0028 a combined probability is obtained of Pc ⫽ 0.0028 ⫻ 0.0028 ⬍ 0.05/ (151 ⫻ 41) that approaches the very conservative Bonferroni correction for multiple comparisons. Our experience with previous data sets (Kaiser et al., 2000a, 2002a, 2002b) has shown that for spectral power analysis, a significance criterion between 0.005 and 0.002 for two consecutive frequency bins usually yielded results that also withstood randomization test procedures, while for the analysis of time courses, a stricter criterion of P ⬍ 0.001 for two consecutive latency windows had to be used because of the higher correlation between neighboring data points. However, instead of applying a predefined significance level, here we used a more exact method to determine significance criteria on the basis of permutation tests (Noreen, 1989; Blair and Karniski, 1993). This statistical probability mapping included corrections both for multiple comparisons and for possible correlations between data either from neighboring frequency bins (for spectral and coherence analysis) or from time points (for time course analysis). The starting point was the comparison of group average spectral amplitude values for experimental versus control condition at each sensor and each frequency bin between 40 and 90 Hz. This yielded the observed distribution of the t values for all frequency bins (time points) ⫻

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sensors. To ensure that tests for two consecutive frequency bins (time points) were significant, a new distribution of the minimal t values tm was computed for all pairs of neighboring frequency bins (time points) i, and sensors j: t mij ⫽ min共t i, j,t i⫹1, j兲

(1)

Now the t value tm and its corresponding P value P0.05 were determined for which 5% of the observed tmi,j were larger. In the case of highly correlated data, P0.05 would be close to or smaller than 0.05, whereas for highly independent data, P0.05 would be greater than 0.05. The next step was to assess the random distribution of maximal t values in the present data set by exchanging the values of memory and control conditions (or the signs of the differences between both conditions) for all sensors j and frequency bins (time points) i at a time for a given subject. This was done for all possible 2n combinations of N subjects. Each of these combinations now yielded a new maximum t value. The distribution of these maximal t values tmax for each of the nrand ⫽ 2n permutations was computed as t max ⫽ maxij共t mij兲. The corrected t value tcorr was now defined as the value where P0.05 ⫻ nrand of the obtained tmax were greater. This corrected t value tcorr was then applied as significance criterion to the observed data. Spectral analysis Spectral analysis was conducted on single trial basis in the range of 40 –90 Hz for two overlapping 600-ms time windows (Fig. 1). The first window, a1, was chosen from 600 to 1200 ms post-trial onset, thus excluding the first 100 ms immediately after the offset of S1 to avoid contamination with the “off-response” to S1. The second window, a2, was chosen from 1000 to 1600 ms, thus including the later part of the delay phase and the presentation of S2. Selecting a time window of 600 ms resulted in records of 187 points which were zero-padded to obtain 256 points. To reduce the frequency leakage for the different frequency bins, the records were multiplied by Welch windows (Press et al., 1992, p. 547). Then a fast Fourier transform was carried out with a frequency resolution of 1.22 Hz. Square roots of the power values were computed to obtain more normally distributed spectral amplitude values. These values were averaged across epochs to obtain measures of the total spectral activity for both conditions. This comprised both phaselocked and nonphase-locked induced oscillatory responses. The latter has been related to higher level cognitive processes (Pulvermu¨ ller et al., 1999; Tallon-Baudry and Bertrand, 1999). Spectral activity was compared between memory and control conditions applying the statistical probability mapping method described in the previous paragraph.

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Exploration of source structure Our previous studies have always yielded only single areas of spectral amplitude enhancement at the surface (Kaiser et al., 2000a; Lutzenberger et al., 2002). Single activation areas suggested that the generator was not a dipole which in MEG would produce two areas of surface activation corresponding to the dipole’s in- and outflowing flux wells. Dual activation areas at the surface would lead to the conclusion that the source dipole was located in an area in between the two surface areas. In contrast, as we have shown and discussed in detail previously (Kaiser et al., 2000a), a quadrupolar or octopolar arrangement of multiple dipoles elicits a strong field over the area circumscribed by the dipoles but much weaker outer fields (the latter not being detectable with our statistical probability mapping method). This would imply that the sources should be located close to the area below the sensor with the highest GBA. Narrowly localized single areas of ␥-band activity enhancements at the surface could only be generated by single dipoles in extremely lateral cortical regions because their second maximum would not be covered by the sensor helmet. However, to exclude the possibility that our significance criterion was too strict to find a second maximum generated by a single dipole source, the observed spectral amplitude enhancements were explored by repeating the statistical probability mapping with an uncorrected criterion of P ⬍ 0.2 for two adjacent frequency bins. Time course and topographical localization To explore the time course and the topographical localization of the observed spectral amplitude changes between memory and control condition, the signals across the entire recording interval of 200 ms before to 1800 ms after trial onset (625 points) were padded to obtain 1024 points, multiplied with cosine windows at their beginnings and ends, and filtered in the frequency range in which the statistical probability mapping had yielded significant effects. Noncausal, gaussian curve-shaped Gabor filters (width: ⫾2.5 Hz, length in the time domain: 100 ms) in the frequency domain were applied to the signals on a single-epoch basis for both conditions. The filtered data were amplitude-demodulated by means of a Hilbert transformation (Clochon et al., 1996) and then averaged across epochs for each condition. Differences in amplitude between memory and control condition in the filtered frequency band were assessed with the statistical mapping procedure described above. To depict the topographical localization of the observed differential spectral amplitude enhancements we assigned the sensor positions with significant spectral amplitude effects of each subject to common spatial coordinates (“common coil system”). Sensor positions with respect to the underlying cortical areas were determined using a volumetric magnetic resonance image of one representative subject. The error which is introduced by not using individual sensor locations was estimated in previous studies by using a single dipole localization of the first auditory evoked component

(N1m) (Kaiser et al., 2000a, 2000b). The comparison of individual sensor locations and the “common coil system” revealed differences ranging below the spatial resolution determined by the sensor spacing of 2.5 cm. This justified the application of a common coil system for the purpose of the present study where no exact source localization was attempted. Exploratory analysis of lower frequency ranges There have been suggestions that power changes in lower frequency ranges like ␣ and ␪ may correlate with the maintenance of information in working memory (Jensen et al., 2002; Jensen and Tesche, 2002). Therefore we explored the activity in lower frequency ranges by analyzing taskrelated differences in the filtered activity for ␪ and ␣ between 4 and 10 (⫾2.5) Hz in 1-Hz steps and for the ␤-band at 22 (⫾5) Hz with a significance criterion of P ⫽ 0.001. Coherence analysis ␥-Band coherence was investigated for sensors showing significant GBA differences between the memory condition and the control condition. We computed coherence for all pairwise combinations including only those sensors with a significant difference in ␥-band spectral amplitudes between conditions. This means that for each sensor 149 coherence values were entered into the analysis. Moreover, coherence analysis was restricted to the frequency range where significant spectral amplitude effects were found. Coherence between two waveforms x and y is defined as ␥xy2 (f) ⫽ (Gxy (f ))2/(Gxx (f ) Gyy (f )), where Gxy (f ) is the mean crosspower spectral density and Gxx (f ) and Gyy (f) are the respective mean autopower spectral densities (Glaser and Ruchkin, 1976). Differences in coherence between memory and control condition were statistically evaluated in the whole sample with the statistical mapping procedure described above.

Results Behavioral data Subjects showed an overall performance of 82.5 (SD ⫽ 12.4) % hits and 95.4 (SD ⫽ 6.6) % correct rejections for the memory condition and 92.5 (SD ⫽ 11.8) % hits and 97.2 (SD ⫽ 2.4) % correct rejections for the control condition. The sum of misses was higher in the memory than the control condition (memory: 5.2 (SD ⫽ 1.1); control: 2.2 (SD ⫽ 1.0), t(11) ⫽ 2.4, P ⫽ 0.035), whereas there was no task-related difference in the numbers of false alarms (memory: 5.5 (SD ⫽ 2.3); control: 3.4 (SD ⫽ 0.8)). Reaction times for hits did not differ between conditions (memory: 643 (SD ⫽ 36) ms, control: 602 (SD ⫽ 28) ms).

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Fig. 2. Frequency spectrum in a sensor showing significant differences between conditions for the entire sample of N ⫽ 12 subjects (after subtraction of system noise). (Top) Spectral amplitude absolute values in fT between 55 and 80 Hz for both memory and control conditions (bold and thin lines, respectively). (Bottom) The results (P values) of t tests comparing spectral amplitudes between conditions. Vertical connecting dotted lines indicate the borders of the frequency range where the difference between both conditions reached significance.

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changes, time courses of P values for both conditions, and projections of the findings onto realistic brain models. Fig. 5 depicts time-frequency P value plots for relevant sensors. The following spectral amplitude differences met the criterion of tcorr ⫽ 4.43 for signals filtered at 67 ⫾ 2.5 Hz. Under the memory condition, there were spectral amplitude enhancements relative to the control condition in a left inferior frontal/anterior temporal sensor peaking at ⬃700 ms after trial onset, i.e., in the earlier part of the delay phase. The average difference between both conditions in the corresponding time interval of 680 –720 ms amounted to 1.37 (SD ⫽ 0.60) fT, t(11) ⫽ 4.62, P ⬍ 0.001. This was followed by a significant spectral amplitude increase in a prefrontal sensor peaking toward the end of the delay phase at ⬃1200 ms after trial onset. The average difference between memory and control tasks in the latency interval of 1180 to 1220 ms amounted to 1.03 (SD ⫽ 0.22) fT, t(11) ⫽ 4.69, P ⬍ 0.001. Following the presentation of S2, a GBA enhancement was observed at another left inferior frontal/anterior temporal sensor which reached its maximum at ⬃1550 ms after trial onset. The average difference between both con-

Spectral amplitudes Fig. 2 depicts the frequency spectrum between 55 and 80 Hz at one sensor for which significant effects were identified. The following effects met the corrected t value of tcorr ⫽ 3.92. Higher spectral amplitude in the memory compared with the control condition was found exclusively in a rather narrow frequency range centered at ⬃67 Hz. Spectral amplitude at this frequency was increased in sensors over left inferior frontal/anterior temporal and in a sensor over prefrontal cortex. In contrast, no spectral amplitude increase was found for the control relative to the memory condition in any frequency bin. Time course and topographical distribution To explore time course and topography of the spectral amplitude differences, the data records were Gabor filtered in the frequency range where the spectral analysis had yielded significant differences between both conditions (center frequency: 67 Hz, width: ⫾2.5 Hz). Fig. 3 shows the topography of the significant GBA enhancements both for the P corrected statistical probability mapping and for a more liberal, uncorrected criterion of P ⬍ 0.2 for two consecutive time windows. Fig. 4 gives a summary of findings of the present and a previous study on auditory spatial working memory (Lutzenberger et al., 2002), including the topography of relative spectral amplitude and coherence

Fig. 3. Statistical probability mapping of spectral amplitude differences between memory and control conditions. Topographies are shown for the entire group for signals filtered at 67 ⫾ 2.5 Hz at 700 ms after trial onset (top) and at 1200 and 1550 ms (middle and bottom, respectively) projected onto two-dimensional MEG sensor maps including anatomical landmarks (seen from above, nose up). (Left) Isocontour plots of significant spectral amplitude increases (white areas) under the memory compared to the control condition meeting the corrected t value derived from the permutation test-based method correcting for multiple comparisons described under Materials and methods. Relative amplitude enhancements under the memory condition were found over left frontotemporal areas and over prefrontal cortex. (Right) The results after application of a liberal, uncorrected P value of 0.2 for two consecutive time windows. Here areas of spectral amplitude increases were more extended over inferior frontotemporal and prefrontal areas, showing that spectral amplitude enhancements also existed outside the latency windows with the maximal activation for each site. However, the different time courses at each sensor indicated that these surface activations were unlikely to be generated by a single dipole located in between prefrontal and left frontotemporal areas.

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ditions in the corresponding time interval of 1530 –1570 ms amounted to 0.94 (SD ⫽ 0.18) fT, t(11) ⫽ 5.12, P ⬍ 0.001. The question of the possible source structure underlying the observed effects was explored by repeating the statistical probability mapping with a more liberal, uncorrected criterion of P ⬍ 0.2 for two adjacent time windows (Fig. 3, right column). Visual inspection of the results of this analysis demonstrated that spectral amplitude increases were mainly limited to those prefrontal and frontotemporal regions where the stricter analysis had already yielded effects. Now the areas were extended in all three latency windows, reflecting the fact that spectral amplitude enhancements were not strictly limited to one of those locations during a particular time window. However, different time courses at the various sites exclude the possibility that both prefrontal and inferior frontal/anterior temporal surface activations may have reflected the activity of a common dipolar source. Instead we interpret these findings as support for the notion that the observed activations were generated by more complex sources in the vicinity of the areas with GBA maxima.

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onset (spectral amplitude difference: 3.3 (SD ⫽ 0.8) fT, t(11) ⫽ 4.20, P ⫽ 0.001). Over bilateral temporal areas, ␪ activity was enhanced in response to S2 at 1400 –1500 ms after trial onset (spectral amplitude difference: 39.0 (SD ⫽ 6.2) fT, t(11) ⫽ 6.24, P ⬍ 0.0001), probably reflecting the auditory evoked fields to this stimulus which was absent under the control condition. There were no task-related differences in the ␣- or ␤-bands. Coherence Coherence analysis was conducted for the sensors which showed spectral amplitude enhancements in the frequency range of 65– 69 Hz. Only one coherence increase met the criterion of tcorr ⫽ 2.87. This coherence enhancement was observed in the memory condition between the left inferior frontal/anterior temporal and a right prefrontal sensor adjacent to the prefrontal sensor showing the GBA enhancement at ⬃1220 ms after stimulus onset (Fig. 7). This coherence increase amounted to 0.07 (SD ⫽ 0.02), t(11) ⫽ 3.33, P ⫽ 0.007.

Exploratory analysis of lower frequency ranges The analysis of spectral power differences between memory and control conditions yielded effects only in the low ␪ range (4 ⫾ 2.5 Hz). Here spectral amplitude was enhanced in the memory compared with the control condition. Fig. 5 (bottom) depicts a time-frequency plot for a midline frontal sensor showing enhancements in low ␪ activity that were most pronounced during the delay phase. The P value time courses and topographies of ␪ activity are presented in Fig. 6. Over bilateral prefrontal cortex ␪ activity peaked during the early part of the delay phase at 700 – 800 ms after trial onset. Here the spectral amplitude difference amounted to 4.6 (SD ⫽ 0.9) fT, t(11) ⫽ 5.19, P ⬍ 0.0001. This was followed by a second frontal peak at the end of the delay phase between 1150 and 1200 ms after trial

Discussion The present study investigated oscillatory activity in human MEG during an auditory working memory task requiring the comparison of two syllables S1 and S2 potentially differing in either formant structure or voice onset time. This was compared with a control task involving the immediate detection of a possible change in the perceived spatial location of a background noise. A statistical probability mapping procedure based on permutation tests served to identify the following activations. In line with our predictions, GBA was increased in the memory condition over left inferior frontal/anterior temporal cortex during the middle of the delay phase, over prefrontal cortex toward the end of

Fig. 4. Summary of findings (top) and comparison with the results of our previous auditory spatial working memory study (bottom) partly reprinted, with permission, from Lutzenberger et al., 2002; copyright 2002 by the Society for Neuroscience. (a) Locations of the sensors showing relative spectral amplitude and coherence increases at 67 ⫾ 2.5 Hz under the memory condition. GBA increases were localized in two sensors over left inferior frontal/anterior temporal cortex (solid red and turquoise circles). Here a coherence increase (symbolized by the broken red line) was observed with a prefrontal sensor (broken red circle). GBA enhancements were also observed over midline prefrontal cortex (blue circle). (b) Time courses of spectral amplitude differences for the sensors that showed significant effects. The curves depict the results of t test comparisons between memory and control conditions, i.e., P values for time points between 50 ms pre- to 1800 ms post-trial onset. The curves are displayed in the same colors as the corresponding sensors in (a). Note the good agreement between both studies. Activations during the delay phase were localized over the putative auditory stream areas relevant for each type of processing. This was followed at the end of the delay period by prefrontal activations and different areas within the same auditory stream during S2. (c) Projection of areas underlying sensors with significant GBA increases onto a realistic brain model derived from a representative subject’s magnetic resonance image. Areas are displayed in the same colors as the corresponding sensors in the top left. Note that the assignment of sensor positions to cortical areas was based on group data. These images thus serve to illustrate the approximate location of the areas with increased GBA without providing an exact localization. Slight differences between sensor locations in (a) and (c) may be attributable to activity in sulci which are not visible in the two-dimensional images in (a). Fig. 5. Time-frequency plots showing the distribution of P values for t tests of spectral amplitude differences between memory and volume conditions in the entire group. Here four relevant sensors are presented that showed the statistically most pronounced effects in the ␥- and ␪-bands. From top to bottom, the plots represent sensors (1), (2), and (3) in Fig. 4 and a midline prefrontal sensor at the center of ␪ activity shown in the top right map in Fig. 6. P values are shown for the entire trial (smoothed with 8-ms windows) and between 2 and 90 Hz. Warm colors indicate higher spectral amplitude in the memory compared with the control condition, whereas cold colors represent higher spectral amplitude in the control compared to the memory condition. Time-frequency regions of spectral amplitude differences meeting our significance criterion are highlighted with white frames.

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Fig. 6. Time course and topographical distributions of ␪ activity. (Top left) Time courses for a prefrontal sensor (solid line) and a temporal sensor (broken line). The two-dimensional topographical maps (seen from above, nose up) depict isocontour plots for ␪ spectral amplitudes at three different latency windows corresponding to the peaks of activity shown on the top left. Prefrontal ␪ activity peaked both at the beginning and again toward the end of the delay period. In response to S2 the auditory evoked fields gave rise to bilateral frontotemporal ␪ increases.

the delay phase, and again over the left frontotemporal region after the presentation of S2. Moreover, there was a memory-related coherence increase in this frequency range between left frontotemporal and prefrontal areas during the delay phase. These findings suggest that auditory pattern information may first be encoded and maintained in areas along the putative auditory ventral stream during the middle of the delay phase. The timing of prefrontal GBA at the end of the delay phase suggested a role of executive networks in comparison and decision making processes. Encoding of S2 seemed again to rely on frontotemporal circuits. However, none of the time courses of these activations was compatible with stimulus maintenance throughout the entire delay period. A potential correlate of this function may have been increased coupling between higher-order sensory and prefrontal regions as reflected by the ␥-band coherence enhancement. The present paradigm was highly comparable to our previous, auditory spatial working memory study (Lutzenberger et al., 2002), except for the inversion of the types of auditory information to be processed under the memory and control conditions. In the previous investigation, subjects had to memorize auditory spatial information in the memory task and detect auditory pattern changes under the control condition, whereas the opposite was true in the present study. The main previous results included increased GBA over left posterior parietal cortex during the delay phase of the sound source lateralization matching task which was followed by frontal and midline parietal activations at the end of the delay period and during S2 (Lutzenberger et al.,

2002). Taking into account the fact that the present task required auditory pattern instead of spatial memory, the current results almost perfectly paralleled our previous findings (Fig. 4). We replicated both the temporal sequence of GBA increases over putative sensory storage areas during the delay phase, over prefrontal cortex at the end of this period, and again over higher-order sensory areas in response to S2 (for the two latter activations, GBA was even found in exactly the same frequency band between studies) and the coherence increase between sensory processing regions and prefrontal areas. However, in contrast to our previous investigation, in the present study we did not find a GBA increase for the control task over the corresponding (here: dorsal) auditory stream areas that met our strict significance criterion. After application of a less strict criterion, however, areas of relative spectral amplitude decrease in the memory condition (i.e., increase in the control condition) became apparent over left parietal, putative dorsal stream areas (Fig. 3, right column, top and middle). In both the present and our previous study (Lutzenberger et al., 2002), an experimental condition involving working memory for a certain type of auditory information was compared with a nonmemory control condition requiring the processing of another type of auditory information. Theoretically, differences between both conditions could be attributable to purely memory-related activations. However, the clear differences in topography between the previous auditory spatial and the present auditory pattern memory studies suggest that these activations can be attributed to the

Fig. 7. Coherence for the left inferior frontal–right prefrontal sensor pair showing significant differences between conditions. (Top) Coherence absolute values between 55 and 80 Hz for both memory and control conditions (bold and thin lines, respectively). (Bottom) The results (P values) of t tests comparing coherence values between conditions. Vertical connecting dotted lines indicate the borders of the frequency range where the difference between both conditions reached significance.

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processing of the specific type of memorized auditory information. In the present study the memory task was somewhat more difficult than the control condition, raising the possibility that the observed differences in spectral amplitudes could be attributed to unspecific arousal. We explored this issue by excluding the two subjects with the largest difference in performance between memory and control task. As a result, the performance difference between conditions was no longer present, whereas the GBA effects remained significant. This observation, in combination with the high spatial and temporal specificity of the present findings, precludes global arousal as an explanation for the present results. The present findings also shared other features of magnetoencephalographic GBA that we have consistently found in our previous investigations (Kaiser et al., 2000a, 2002a, 2002b; Kaiser and Lutzenberger, 2001; Lutzenberger et al., 2002). GBA increases were restricted to narrow frequency ranges, the most robust effects were in frequencies above 50 Hz, and the effects were topographically limited to single sensors. These characteristics are in contrast to GBA research in EEG (e.g., Tallon-Baudry and Bertrand, 1999; Keil et al., 2001) where effects were usually observed over topographically more extended areas and in wider spectral bands often involving lower frequency ranges. The difference in bandwidths between methods may partly be accounted for by the fact that we compared activations directly between conditions instead of comparing task-related activity with a preceding baseline, thus excluding ranges of common activation across conditions. Moreover, MEG appears to be more sensitive than EEG to the activity of smaller, more local networks that may be characterized by higher synchronization frequencies. This would be compatible with electrocorticographic recordings where local networks have been reported to oscillate in frequencies up to 100 Hz in both motor and auditory paradigms (Crone et al., 1998, 2001). The exploratory analysis of lower frequency bands yielded significant enhancements of prefrontal ␪ activity during the delay phase under the memory condition. In contrast, no significant differences between both tasks were detected in the ␣- or ␤-bands. Increases in frontal ␪ activity have been related to both increasing memory load and practice in verbal and spatial memory tasks in EEG (Gevins et al., 1997). In MEG frontal induced ␪ activity has been found to be increased with working memory load during the retention and memory scanning phases of a Sternberg task (Jensen and Tesche, 2002). In the present paradigm, ␪ spectral amplitude enhancements seemed to precede ␥-band activity over prefrontal areas in time, supporting a possible interrelationship between both signals (Jensen and Lisman, 1996). There was also a ␪ activity enhancement following S2, the timing and topography of which suggested that it merely reflected the auditory evoked field to S2. Note that the evoked fields to S2 did not seem to influence the ␥-band

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effects, the topography of which was highly unlikely to reflect stimulus processing over primary sensory areas. The GBA topography was consistent with human brain imaging studies showing auditory pattern processing to elicit activations in anterior temporal cortex, e.g., during passive listening to vocal sounds (Belin et al., 2002), and in inferior frontal areas, e.g., during phoneme or pitch discrimination (Pugh et al., 1996) or the perception of intelligible speech (Scott et al., 2000). More relevant to our investigation was Alain et al.’s (2001) delayed matching-to-sample study that yielded enhanced hemodynamic activation in fMRI in inferior prefrontal gyrus during pitch compared with sound location working memory. Here effects were more dominant in the right hemisphere, whereas in our study inferior frontal GBA increases were lateralized to the left. This hemispheric difference may be attributable to the different types of stimuli: pitch has been found to be processed predominantly on the right (Zatorre et al., 1992), whereas both environmental sounds (Maeder et al., 2001) and verbal materials were preferentially processed in lefthemisphere regions (Binder et al., 1997; Wagner et al., 1998). In the present study subjects had to maintain information on both voice onset time and formant structure in working memory as they could not anticipate on which of these two dimensions S2 might differ from S1. While we cannot distinguish between activations that may have been specific to one of these two stimulus dimensions, both seemed to be processed predominantly in the left hemisphere. The high topographical correspondence between our GBA findings and brain imaging data support the recently suggested link between synaptic processes underlying oscillatory cortical signals and blood oxygen level-dependent responses (Logothetis et al., 2001). In contrast to brain imaging techniques based on hemodynamic signals, the present method yielded additional information about temporal dynamics and connectivity patterns of the involved cortical regions. According to the present data, stimulus maintenance appears to be mainly performed by frontotemporal, putative higher-order auditory networks, whereas maintenance-related prefrontal activation (Owen et al., 1999; Petrides, 2000) may have been too small to be detectable in MEG. An important feature of working memory maintenance seemed to be the replicated coherence enhancement, possibly reflecting increased cross-talk between sensory and prefrontal regions. These memory-related connectivity changes warrant further investigation. The inferior frontal/anterior temporal topography of GBA enhancements during the present auditory pattern working memory task is consistent with the role of this area as part of the putative auditory ventral “what” processing stream (Rauschecker, 1998; Rauschecker and Tian, 2000) suggested by neurophysiological (Tian et al., 2001; Romanski and Goldman-Rakic, 2002) and imaging studies in monkeys (Poremba et al., 2003). The present study is one in a sequence of investigations conducted in our laboratory that have assessed magnetoencephalographic GBA during both

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passive and active processing of auditory spatial and pattern information. Auditory spatial processing was associated with posterior temporoparietal GBA increases in passive mismatch paradigms with verbal (Kaiser et al., 2000a) or nonverbal stimuli (Kaiser et al., 2002a), in an auditorymotor integration task (Kaiser and Lutzenberger, 2001), and during working memory for sound locations (Lutzenberger et al., 2002). Conversely, pattern mismatch perception gave rise to left anterior temporal/inferior frontal GBA enhancements for language sounds, animal vocalizations, and complex artificial sounds (Kaiser et al., 2002b). Interestingly, the present findings even involved one of the same sensors showing spectral amplitude effects during passive listening to auditory pattern change. In our opinion, this demonstrates both the high replicability of GBA findings across studies and the relevance of this region for the putative auditory “what” processing pathway. In summary, the present auditory pattern working memory task led to top-down driven GBA increases over inferior frontal/anterior temporal putative auditory dorsal “what” stream areas. This task also involved prefrontal networks and was associated with increased coherence between frontotemporal and prefrontal regions. Increased GBA during the delay period of a short-term memory task may be interpreted as a correlate of the activity of cortical networks involved in the mental representation of sensory information in the absence of external stimulation (Tallon-Baudry et al., 1998, 1999). In addition to replicating previous results (Kaiser et al., 2002b; Lutzenberger et al., 2002), GBA thus served to confirm and extend functional imaging results on auditory pattern working memory by providing additional information on the temporal dynamics of cortical activation and on corticocortical coupling.

Acknowledgment This research was supported by Deutsche Forschungsgemeinschaft (SFB 550/C1).

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