1 Institut für Neurophysiologie, Universität Wien, Währingerstrasse17, A-1090 Vienna and , 2 Konrad Lorenz Institut für Evolutions- and Kognitionsforschung, A-3422 Altenberg, Austria
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Abstract |
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Introduction |
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To address the question of supramodal object representation, an ideal method should be one that, on one hand, measures synchronization of distributed neuronal ensembles and, on the other, can also be applied to humans. Electroencephalographic (EEG) spectral analysis analyzes the dynamics of large neuronal populations and their interactions directly in humans. By analyzing the EEG in the frequency domain, oscillatory activity and synchronization can be measured. The EEG, i.e. the macroscopic potentials measured at scalp electrodes, is constituted by the fluctuating pattern of synchronization and desynchronization of different groups of neurons within the tissue underneath the electrode (Elul, 1972). Since a scalp electrode picks up the summed activity of some square centimeters of cortical tissue (Cooper et al., 1965
), EEG measures mostly concerted activity of large-scale cell ensembles. Indeed, with EEG recordings above human visual cortex, there is evidence that EEG amplitude reflects the state of synchronization within functional visual cell ensembles (Lutzenberger et al., 1995
; Tallon et al., 1995
; von Stein and Petsche, 1995
; von Stein et al., 1995a
; Müller et al., 1996
). This relationship between scalp EEG and intracortical synchronization may explain why EEG analysis has been successfully applied to study cognitive processes in humans such as language processing, spatial reasoning and motor activity (e.g. Giannitrapani, 1975; Pfurtscheller and Aranibar., 1977; Duffy et al., 1981; Rappelsberger et al., 1991; Klimesch et al., 1993; von Stein et al., 1993; Joliot et al., 1994; Gevins et al., 1997). The correlation between EEG channels, however, is usually not analyzed. This is surprising in view of the success of cross-correlation analysis in intracortical recordings (for review, see e.g. Aertsen and Arndt, 1993). In this paper, we have applied spectral coherence analysis to human EEG. Coherence gives information about the amount of correlated activity between electrodes and thus about inter-areal synchronization. In several studies we measured coherence between signals at different scalp locations and found that EEG coherence is a good and more sensitive measure for higher cognitive processes than EEG amplitude (Rappelsberger and Petsche, 1988
; Petsche et al., 1992
, 1996
; von Stein et al., 1993
; von Stein, 1994a; Johnson et al., 1996
; Sarnthein et al., 1998
). In particular, coherence was found to be sensitive to different language-related tasks (Weiss and Rappelsberger, 1996
). With respect to the new concepts on distributed representation and to multimodal representations in particular, coherence seems to be specifically important as it does not detect local activity but the correlated signal components between distant cortical sites. In this paper, we used coherence and amplitude analysis to study in detail whether multimodal object representation is reflected in human EEG.
EEG was recorded in 19 human subjects during presentation of different semantic stimuli: high imagery objects of daily life hammer, table, dog, etc. were presented as pictorial representations, as written words and as spoken words (Fig. 1a). The aim was to analyze whether a common activation can be determined in all three modalities, a correlate of supramodal object-representation. As control conditions, non-sense pictures, visually presented non-sense words and spoken non-sense words were presented. Correlates of semantic representations could thus be determined by looking for EEG patterns common to the three semantic (sense) conditions, and by looking for differences between the sense and the non-sense condition in each modality.
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Materials and Methods |
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In our study we examined 23 healthy, right-handed subjects (25.9 ± 2.0 years old, 12 males). EEG was recorded while subjects were seated comfortably in an acoustically shielded room perceiving stimuli. Stimuli were presented on a black and white computer screen (70 Hz frame rate) at a distance of 50 cm from the subjects. Contrast and luminance were adjusted individually to minimize subject's eye strain and to reduce muscle artefacts. Stimuli were presented in consecutive blocks of nine different conditions. Conditions 1, 3 and 7 were chosen to induce semantical processing, the other conditions served as controls. (The experimental design is shown in Fig. 1ac.)
The words were such that the objects they denote were easily represented in a simple pictorial way. They were concrete German nouns two syllables in length that denote objects encountered very frequently in daily life. The non-sense words were pronounceable and had the same length (two syllables) as the sense words (e.g. Naugan, Redor). To imitate German nouns they were written with a capitalized initial letter. The (sense) pictures showed the same objects denoted by the words. Only 50% of the words were exactly the same in all three modalities; the other 50% were of similar content but not the same.
The 40 items within each block were randomized. Interstimulus intervals were 23 s in all conditions. Between each block there was a period of 1 min when the subjects were asked to relax with their eyes opened (resting condition, EO eyes open). During this resting condition, subjects were instructed to fixate a point ~2 m in front of them in order to minimize disruptive effects of eye movements on the EEG. These averaged resting conditions served as baseline for our analysis. The sequence of blocks was chosen such that a maximum time interval was achieved between similar blocks in order to avoid short-term memorization of the items (Fig. 1c).
Behavioral Tests
Subjects were instructed to carefully perceive the meaning of the words and to let associations and imagery arise. Neither memorization nor immediate behavioral response to stimuli was requested. The reason for omitting behavioral responses was that we wanted to determine correlates of semantic association which may occur anywhere in the time window after stimulus onset; frequency analysis is an adequate method to determine such responses since this method is not restricted to stimulus-locked responses but detects changes occurring anywhere in the time window of analysis. To do so, however, we had to keep this window free of other processes. In particular, behavioral responses have been shown to affect the spectral composition of the EEG several seconds prior to movement onset (Pfurtscheller and Aranibar, 1977; Stancak and Pfurtscheller, 1996). This excludes a design where subjects expect to perform a behavioral response.
Instead of demanding an immediate behavioral response, subjects' active participation and depth of perception was monitored after the experiment. Five minutes following the last condition we carried out an episodic memory test, the rememberknow (R/K) paradigm (Tulving, 1993). A series of the previously presented words was presented auditorily to each subject. For each item we investigated whether the subjects' memory was a remember or know memory, i.e. whether the item was remembered as a full past experience (episodic memory) or only as the fact of its occurrence (semantic memory). For this, the subject was asked to travel back in time and remember the situation when he/she saw or heard the word and to tell the experimenter what associations he was having, visual, olfactory, auditory or any other (Tulving, 1972
), and further, whether he/she remembered the modality of its presentation, auditorily, visually or pictorially (source memory). The number of episodically remembered events, semantically remembered events and not remembered events were noted (Table 1
); further, occurrences of episodic memories were subdivided according to the nature of associations, imagery or other associations. The R/K paradigm was carried out for both the single and the double presentations. Since subjects had been explicitly told prior to the experiment that they were not going to be tested on memory afterwards, the learning situation has to be considered incidental. The reliability of the obtained imagery scores was evaluated using EEG spectral parameters.
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EEGs were recorded from 19 electrodes positioned on the scalp according to the 10/20 system (Homan et al., 1987) (Fig. 2
). The averaged signals from both ear lobes served as electrical reference. The digitization rate was 128 Hz, the data was filtered between 0.5 and 35 Hz. Eyeblinks and muscle artefacts were eliminated by visual inspection. Only subjects with muscle artefacts in less than three channels were included in the study, resulting in n = 19 subjects. The 2 s epochs of EEG during perception of items were Fourier-analyzed with a frequency resolution of 0.5 Hz. Averaged power spectra Cxx were computed for each electrode for each condition (average of all 40 items). Similarly, averaged cross-power spectra Cxy were calculated between pairs of electrodes. For data reduction only electrode-pairs that yielded sufficiently high coherence values were considered; these turned out to be the pairs of neighboring electrodes and the symmetrical interhemispheric electrode pairs (Fig. 3a,b
), which usually have coherence values >0.2. Data reduction of the spectra was performed by averaging adjacent spectral lines to obtain average values for five frequency bands: theta (47.5 Hz), alpha (812.5 Hz), beta1 (1318 Hz), beta2 (18.524 Hz), gamma (24.532 Hz). The final step was the computation of amplitude (square root of power) and squared coherence per frequency band. Squared coherence K2xy (f) describes the linear relationship of two signals x and y. It is equal to the normalized average cross-power spectrum according to
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In this way we obtained amplitude values for 19 scalp locations and coherence values for 38 pairs of electrodes for all nine stimulus conditions. The calculation of coherence is based on averaging the real and imaginary parts of the cross-spectra independently in order to detect repeating phase relationships between the signals. Thus coherence is a statistical measure of the dominant synchronization between two signals for any stable phase lag between them. For a detailed description of the method, see Rappelsberger et al. (1986).
The question was whether amplitude or coherence parameters during the task differed significantly from those during the resting condition for any cortical site or pair of electrodes. As an example for a significant change, Figure 4 shows the coherence values for one electrode pair for all 19 subjects. Each subject's coherence value is based on an average of 40 perception-epochs, thus yielding coherence estimates of 80 degrees of freedom. In 17 of 19 subjects, the averaged coherence during perception was higher than during the resting condition. The significance of parameter-changes for the group of 19 subjects was evaluated using paired Wilcoxon tests. The obtained rank sums were converted to error probabilities, yielding an error probability of P < 0.01 in the example shown in Figure 4
. Those electrode pairs where a change in coherence reached a significance level of P < 0.1 or better are marked with a square in the topographic probability maps. The size of the square reflects significance levels 0.01, 0.02, 0.05 and 0.1; filled squares represent an increase of coherence with respect to the resting condition, empty squares a decrease (e.g. Fig. 5a
). Separate maps are computed for each frequency band. Analogously, changes in amplitude are depicted by a square on the electrode site. The problem of multiple comparisons cannot easily be overcome by an alpha correction due to to the large number of electrode pairs in the coherence maps. The statistics should therefore be considered descriptive rather than confirmatory. A change in coherence was only considered relevant if (i) the error probability in the Wilcoxon test reached P < 0.05 or better, and (ii) the change appeared consistently in several subsets of data which were recorded during similar stimulus conditions. Increases of amplitude or coherence were interpreted as task-related enhancement of correlated cortical activity.
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Results |
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Figure 5a demonstrates the changes in coherence as induced by auditory (left) and visual (right) processing of objects. A major difference between the modalities is found in the coherence pattern in the low-frequency range (812 Hz, alpha). In auditory processing there was an increased coherence between temporal, presumably auditory cortical electrodes and several other electrodes over occipito-temporo-parietal cortex, bilaterally (Fig. 5a
, left). During visual processing, coherence changed more posteriorly, mostly between occipital and parietal electrodes, and between occipital and temporal electrodes (Fig. 5a
, right). Although the observed change in coherence was mostly a decrease, this does not mean that the underlying cortical areas were inactivated. Since EEG signals are measured at distant sites, a decrease in coherence between them may well stem from a functional compartimentalization of the active cortical area. Our investigations of primary processes, however, are described elsewhere (von Stein, 1994
; von Stein et al., 1994a
) and will not be discussed further in this paper. The aim of this work is to describe an EEG correlate of concept integration and supramodal object representation.
Changes of Coherence During Perception of Objects
What are the areas involved in all three ways to perceive the objects? There was one pattern that was present in all cases of object perception, whether shown as pictures, written as words or named auditorily: an increase of coherence between temporal and parietal recording sites, in the beta1 (1318 Hz) frequency range. Figure 6a shows the changes of coherence in this frequency range for all three modes of perception. Solid lines connect pairs of electrode sites where coherence increased significantly. Among the multiple connections, only coherence between temporal (T6/T5) and parietal (P4/P3) electrodes was significantly enhanced in all three conditions (Fig. 6a
, thick line). The increase was significant with P < 0.01 for the auditory presentation and P < 0.05 for the visual verbal and pictorial presentation, in a group of 19 subjects (see also Fig. 4
). The change in coherence was bilateral, except during pictorial presentation. The temporo-parietal increase of coherence was the only pattern of activity that was common to the three semantic conditions. In all three cases, temporo-parietal coherence appeared in one and the same frequency range, the beta1 range (1318 Hz). Patterns of induced coherence in the alpha frequency range did not show any overlap across modalities but were specific for the modality (see Fig. 5a
), patterns in the gamma range did not show any relation at all with either semantics or modality (see below, Fig. 11a
). During other conditions, such as presentation of abstract words, a continuous text or during letter strings (see below), coherence at temporo-parietal electrodes did not change. Therefore, we conclude that the enhancement of coherence between temporal and parietal electrodes seems specific for modality-independent processing of objects and might reflect a higher-order semantic aspect of processing.
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EEG Changes During Non-sense Items
Comparing this finding with the coherence-changes in non-sense words gives further interesting results (Fig. 6b). First, the pattern of coherence for each non-sense condition (written, auditory, pictorial) is rather similar to its corresponding sense condition in Figure 6a
. (To judge how dissimilar coherence patterns can be, see, for example, the changes of coherence in the gamma range, Fig. 11
). In addition, the common pattern of coherence across conditions also remains the same: during both visually and auditorily presented non-sense words, the same temporo-parietal electrodes as in Figure 6a
showed increased coherence, and in the same frequency range (beta1). In this case, however, the coherence-increase was unilateral, more on the right hemisphere, except for the pictorial presentation where it was bilateral. What is the correlate of this right temporo-parietal cross-modal pattern? Comparing the coherence-changes for the pronounceable visual and auditory non-sense words with their non-pronounceable control condition (letter strings) in Figure 8a
suggests a possible solution. During letter strings there was no increase in temporo-parietal coherence. Therefore the increase in right temporo-parietal coherence during non-sense words may be constrained by the pronounceability of a string of letters. Such a supramodal process, which relies on the fact that words are transferable from one modality to the other, might be termed supramodal Gestalt representation.
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Summing up, processing of meaningful words is reflected in bilateral increase in temporo-parietal coherence; when the semantic content is missing, the activation tends to be restrained to the right side.
Interhemispheric Coupling
This result suggests a lateralization in the cross-modal processing of meaningful words semantic representation on the left, word-Gestalt representation on the right. It is interesting to know how both parts relate to each other. Important evidence may already be gained from frequency analysis: besides knowing that both are activated during processing, we also know that they are activated in the same frequency range. Therefore we might expect a cooperative behavior between the two. Figure 9 supports this consideration. The interhemispheric coherence between left and right temporal electrodes is increased in nearly all these conditions, in the same frequency range (1318 Hz). Remember that changes of coherence may occur in five different frequency ranges. It is therefore significant that the same frequency band is affected in all these cases. This is evidence that a coupling or information transfer between both cortical processes can indeed be observed.
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Pictorial presentation of objects (Fig. 6a) induced increasing coherence at the same electrodes and in the same frequency range, but only at right temporo-parietal electrodes. Since pictures contain semantic information, this result does not support our current interpretation. During perception of pictures, however, coherence is increased between the two electrodes at temporal cortex: T3 and T5 (on the left hemisphere), and T4 and T6 on the right (Fig. 6a
). Also, coherence between the two hemispheres is increased at multiple temporal sites (Fig. 9
). Thus, instead of a bilateral temporo-parietal activation as in the other semantic conditions, we found a bilateral involvement of temporal electrodes during processing of pictures. A strong involvement of temporal cortex in the processing of pictures is in line with the role of temporal cortex in visual processing.
Sense Minus Non-sense
Finally, we examined correlates of semantic processing by computing the difference between meaningful words and non-sense words. We have computed this difference separately for auditory words and for written words (Fig. 10a,b). A significant difference between words with meaning with respect to words without meaning was revealed at left temporal electrodes in the low-frequency range (Fig. 10a
): coherence in the 812 Hz (alpha) range increased between anterior and posterior left temporal electrodes during auditory processing of meaningful words (P < 0.05). The same comparison was made independently for the visual condition (Fig. 10b
). Coherence changed at the same site between anterior and posterior temporal electrodes in the alpha range. Thus, for both modalities the comparison with the non-semantic control condition points to the same area i.e. left temporal cortex as being responsible for semantic processing. Therefore, as in the previous finding, also this approach indicates that left temporal cortex was engaged in processing of meaning; the frequency range, however, was alpha (812 Hz) in this case.
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As an example for spectral changes in higher-frequency ranges, Figure 11 shows the changes of coherence and amplitude in the gamma frequency range (2432 Hz). Coherence changes at several sites in most of the conditions. The pattern of changes, however, cannot be attributed to one of the two aspects of processing addressed with this study, semantics or modality. Reasons that no sensory-specific gamma changes were observed might be, on the one hand, that the gamma range investigated here was chosen as rather low to avoid contamination with muscle artefacts. On the other hand, sensory-specific changes in gamma might occur on a shorter timescale; the window of our analysis, however, was chosen to be rather long (2 s) to detect late, longer-lasting semantical components of processing.
Behavioral Data
The behavioral data collected to judge memory and active imagery during the experiment show that subjects were actively participating and semantically perceiving the stimuli. Results of the R/K episodic memory paradigm are shown in Table 1. On average (Table 1b
) 50% of the items were retrieved episodically (remembered, R), 15% were retrieved semantically (known, K) and 35% were not retrieved at all (none), for single presentations (Table 1b
). These numbers are in good agreement with other studies (i.e. Düzel et al., 1997). Further, between the different modalities, visually presented words led to the best episodic memory performance (59%), followed by auditory words (50%) and, finally, by pictures (41%); for semantic memory a slight tendency for the opposite was found (visual, 11%; auditory, 14%; pictorial, 20%). Thus on average, and most strongly for the episodically remembered events, an interaction was observed between sequence of presentation and modality of presentation: visual words, which are the first items presented during the experiment, scored highest, while pictures scored lowest. While this could be accounted for by a verbal overshadowing effect (Schooler and Engstler-Schooler, 1990
), another interpretation is that pictorial material, although usually better retrieved than verbal material, leaves less space for own active associations and is thus weaker encoded episodically. This is in line with findings of better memory performance for words embedded in a highly imaginable content than for words alone (Vogel et al., 1987
). With double presentation the average number of both remembered and known items increased and the number of forgotten items dropped (from 35 to 12%), as expected. Interestingly, the increase was far higher for knowing (from 15 to 30%) than for remembering (from 50 to 55%), which is in agreement with the general notion of episodic memory (i.e. Tulving, 1972, 1993). In conclusion, the results of the R/K paradigm reproduce previous findings about memory performance and demonstrate that subjects were actively perceiving the items presented during the experiment, showing both episodic and semantic encoding. Further, the total number of recollected words (both episodically and semantically) was 65%. This is evidence that the presented words had been deeply rather than shallowly encoded (Craik and Lokhard, 1972
), i.e. subjects had paid attention to the words' meaning rather than to their sound or graphemic properties.
The reliability of the R/K test and the corresponding imagery scores (e.g. Table 1a) was confirmed by the fact that separate analysis of the EEG of subjects with mental imagery and without mental imagery revealed significant differences. In particular, EEG amplitude above the occipital cortex was significantly higher in subjects who had visualized the items compared to subjects who had not; additionally, this difference turned out to be reproducible across modalities (von Stein et al., 1995b
).
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Discussion |
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These findings suggest that supramodal processing might be correlated with an enhanced coherence of 1318 Hz activity between temporal and parietal electrodes. In a recent investigation in our laboratory, a similar study was performed with the aim of differentiating between coherence maps related to concrete and to abstract concepts (Weiss and Rappelsberger, 1996; S. Weiss et al., in preparation). Words evoking different types of concepts were presented to 15 subjects either auditorily or visually. Apart from interesting differences between concrete and abstract concepts, coherence increased between T5 and P3, and T6 and P4 in the 1318 Hz range, during both auditory and visual presentation of words designating concrete objects. Thus, for the same stimulus conditions, coherence increased between exactly the same electrodes and in the same frequency range as in the present study. Thus the study reproduced with high similarity the results of the present study. With this support we may conclude that modality-independent processing of objects is indeed accompanied by an increased coherence between temporal and parietal electrodes.
Relationship Between Scalp EEG and Cortical Processing
How is this EEG result related to cortical processing? The EEG measured at a site on the scalp consists of the summed electrical field potentials from cortical neurons in a certain volume of tissue under the electrode. Cortical field potentials are generated by postsynaptic dendritic currents. They contribute to the scalp potential only if the dendrites are arranged perpendicularly to the scalp; effective contributions come mostly from the synchronized components of cortical activity (Cooper et al., 1965; Elul, 1972
, Nunez, 1995
). A dipolar field is produced by synchronous input, which can be singled out by averaging methods (evoked potential). It is a far-reaching field that influences the potential landscape over large parts of the scalp. In addition, there is a continuous change in cooperativity within the cortical network even without external stimulation (intrinsic potentials) (Lopes da Silva, 1991
; Bullock, 1995
). This changing pattern of synchronization and desynchronization within different neuronal groups of the network generates the macroscopic potentials of the ongoing EEG (Elul, 1972
). Since intrinsic potentials stem from the network interactions they generate low-amplitude fields which are effective only in close proximity, thus they influence the potentials mostly at individual scalp electrodes. Changes in the degree of synchronization of this intrinsic activity are reflected in changes in EEG amplitude at the scalp. Our study of EEG spectral parameters aimed at detecting stimulus-induced changes of this intrinsic activity.
While EEG amplitude predominantly reflects the amount of underlying synchronized activity, the degree of interaction between two signals can be measured by coherence. Coherence is a statistical measure: the value of coherence depends on the amount of repeated correlations between events in the frequency domain. The phase relationship between the two signals is irrelevant; however, it must be stable. Since the signal at each electrode site reflects the network activity under the electrode, coherence should measure interactions between two neuronal populations; while cortico-cortical correlations occupy only a minute part of the power of the EEG signal at each site, the statistical nature of coherence helps to unravel them from the noise if they repeat consistently.
In the light of these considerations our finding of increased coherence between temporal and parietal electrodes provides evidence that supramodal semantic processing induces an increased interaction between neuronal populations of temporal and of parietal cortex. How precise is this localization? The spatial resolution of EEG has been estimated to be ~5 cm (Nunez, 1995; Srinivasan et al., 1998
). Thus the signal at one electrode stems from an area of ~10 cm2 of cortical tissue. Since this is about the size of a cortical area this means that the signals measured at the two electrodes in question stem from gross anatomical areas like temporal cortex (T5) and parietal cortex (P3). It is important to note that besides indicating the degree of cortico-cortical interactions, an increase in coherence might stem from two other situations. The second possibility is that the two sites receive common input from a third area (e.g. the thalamus), a situation which cannot be differentiated from true cortico-cortical interactions by scalp recordings alone. We are, however, only interested whether a common dynamics occurs at two sites at all. A third, more trivial possibility is that interelectrode coherence is just due to activity volume-conducted to the two electrodes. Given the spatial resolution of 5 cm, volume conduction along the 7 cm interelectrode distance should be a minor contribution. Nevertheless, to distinguish between volume-conducted coherence and physiological coherence, it is helpful to consider additionally the behavior of EEG amplitude in the respective electrodes: a volume-conducted signal will lead to an increased amplitude at both electrode sites. Since in our case no common increase in amplitude was seen and in some cases amplitudes even decreased, volume conduction is less likely and our results can be interpreted as true physiological coupling between the two cortical sites. It must, further, be mentioned that while under certain conditions EEG results allow us to draw conclusions about increases or decreases of correlated activity in the cortical network, we cannot exclude the possibility that single neurons also react specifically in the investigated process. In conclusion, the result of this study was that supramodal processing was accompanied by an enhanced coherence between signals at T5/T6 and P3/P4, which is in agreement with an increase of coherent activity between neuronal populations of temporal cortex and parietal cortex in a frequency range of 1318 Hz.
Synchronized Activity as Basis for Binding of Objects
A growing body of evidence shows that correlated activity between similar neurons plays a role in cortical processing. The first findings (Eckhorn et al., 1988; Gray et al., 1989
) in cat area 17 have shown that, for example, neurons of similar preferred orientations tend to correlate their firing when activated with a coherently moving stimulus. Since then the functional significance of synchronization has been shown in a number of studies, making it a possible mechanism for binding of features in the visual domain (e.g. Eckhorn, 1994; König and Engel, 1995). Synchronization has been found locally, but also between neurons from different visual areas (Eckhorn et al., 1988
; Frien et al., 1994
; Bressler, 1996
) and across the two visual hemispheres (Engel et al., 1991
), and recently the relevance of coherent activity has also been found in other cortical areas such as the motor cortex (Murthy and Fetz, 1992
) and the auditory cortex (deCharms and Merzenich, 1996
). Few studies, however, have shown correlations between neurons from different modality-specific areas. If coherent activity between the different feature representations of an object is a possible mechanism to guarantee their binding into a unified percept (for review, see König and Engel, 1995), then coherent activity should also be observed between neuronal populations from areas serving different submodalities or modalities. Such coherent activity between different modality-specific areas during object perception, however, has not yet been shown. The few reports on such inter-areal correlations have been found during sensory-motor integration (Roelfsema et al., 1997
). Although our results clearly take place on a crude spatial scale compared to intracortical recordings, they nevertheless are first evidence that object perception indeed seems to induce coherent activity between functionally different cortical areas. Coherent activity between temporal and parietal cortex during supramodal concept processing may demonstrate the existence of widely distributed neuronal ensembles representing multimodal entities.
The Role of Temporal and Parietal Cortex in Semantic Processing
The localizatory aspect of our main result, that EEG coherence increases over parieto-temporal cortex, agrees well with the assignment of symbolic representation to this area described in the early literature. The area of posterior parietal cortex, especially left angular gyrus, is known as a supramodal, integrating area (e.g. Luria, 1973). Lesions in that region lead to failure in cross-modal matching. Patients who are able to identify an object among others in one modality are not able to recognize it in another modality (Butters and Brody, 1968). Thus, it seems that this area is necessary to integrate different modalities to form a concept. Patients with parietal agnosias suffer from the same deficit in forming higher integrative concepts (Kolb and Whishaw, 1985
, p. 384). More recent clinical studies have confirmed the importance of left parietal cortex in semantic processing. Semantic processing on the single word level breaks down with parietal lesions (Hart and Gordon, 1990
). Pretrial DC potentials at parietal sites are predictors for semantic facilitation (Stamm et al., 1987
). Temporal cortex, on the other hand, is part of the ventral stream of visual processing and a higher-order visual association cortex. It plays a crucial role in processing of complex visual form and particularly in object recognition (e.g. Desimone et al., 1984). The posterior part of temporal cortex is thought to be more involved in visual perception, whereas anterior temporal cortex is more related to complex visual memories (Iwai and Mishkin, 1968
). Temporal cortex has also been shown to be involved in visual imagery (e.g. Roland and Gulyas, 1994). An increase of correlated activity between both structures temporal cortex and parietal cortex during perception of objects as suggested by our results might therefore indicate an interaction between visual representations (temporal cortex) and higher-order representations (parietal cortex) for multimodal semantic integration in humans. Regarding connections between temporal and parietal cortex, recent findings in macaque monkeys have shown that area TEO, in rough correspondence with the area covered by our electrode T5, has extensive connections to parietal cortex (Webster et al., 1994
). Anatomical studies (Mesulam, 1990
) have demonstrated that parietal cortex is a highly connected, supramodal center in the cortex, connected with different sensory cortices, association cortices, subcortical structures, etc. The high degree of connectivity provided by this cortical area supports the proposed function of multimodal semantic integration. The interactions between temporal and parietal cortex are also interesting with respect to the integration of information carried by the dorsal and the ventral stream. It has often been proposed that the two have to be integrated at various levels (DeYoe and van Essen, 1988
). We here provide evidence that interactions between temporal and parietal cortex might indeed exist during higher-level semantic processing of objects.
Interestingly, we did not find evidence for local activations showing overlap across conditions, i.e. we did not find overlap in amplitude-changes. This result would explain seemingly contradictory results regarding visuo-auditory integration that were found by Posner (see Posner et al., 1988; Peterson et al., 1988). In these positron emission tomographic studies, aimed at similar supramodal processes as our study, no overlapping pattern of activity for auditory and visual processing of words was found. This difference is interesting, shedding some light on the different aspects of cortical processing gathered with different methods. We, too, did not find overlapping changes in amplitude; overlapping activity appeared only in the interactions between two sites, i.e. in coherence. An increase in coherence without an increase in amplitude might be interpreted as increased phase-locking between the neuronal activity of two cortical sites without an increase in the number of active neurons. Thus, what was commonly induced during auditory processing and visual processing of words seems not to be a global activation of one specific area but rather a phase-locking between neurons of different cortical areas, i.e. members of a distributed ensemble.
Besides an overlapping activation for presentations denoting objects, we found a second overlapping activation for all presentations dealing with entities that are transferable between modalities: pronounceable non-sense words, visual and auditory, induced a similar increase of coherence between temporal and parietal electrodes but unilateral at the right hemisphere. This suggests that only left temporo-parietal synchronization is specific for processing of semantics. This finding is in agreement with a considerable amount of literature describing a left hemispheric dominance for processing of language and semantics (for a review on hemispheric specialization, see e.g. Kimura, 1973). Right temporo-parietal coherence in our data appears less specifically whenever supramodal integration takes place. The only constraint seems that the items exist in more modalities and can be transferred between them; non-pronounceable non-sense words (letter strings) did not produce this activity. This activation might be tentatively interpreted as a phonologicalgraphological integrative process which could be called multimodal Gestalt representation. If we interpret right temporo-parietal activation in this sense, this may also explain why pictures of objects, with their pronounced Gestalt aspect, predominantly activated right temporo-parietal cortex. Also, from clinical experience, agnosias for pictures are more related to lesions in right parietal cortex. The site of our activation right temporo-parietal fits well with the known role of right parietal cortex in processing spatiotemporal patterns. Perceptual integration of geometric patterns is disrupted in patients with right parietal lesions (Warrington and Taylor, 1973). Prosodic information as well as musical thinking and processing of other non-verbal entities have also been related to the right hemisphere (Kimura, 1973
). A right hemisphere lateralization has been found in discrimination of non-verbal environmental sounds as opposed to semantically related sound sources (Schnider et al., 1994
). Sensory feature integration as proposed by our results is therefore a probable task for right parietal cortex. The discussion of hemispheric lateralization of coherence patterns, however, has to be viewed with due caution since we did not test our results in this respect. Our main statement thus is that multimodal processing is accompanied by an enhanced coherence between signals from temporal cortex and parietal cortex.
Multimodal Binding or Attentional Mechanisms?
Because of its high connectivity, posterior parietal cortex is considered as part of the attentional system (Posner et al., 1988; Mesulam, 1990
). An interesting alternative idea to the binding proposal is to interpret the activity of this area during processing of objects as a process of selective attention. It might be the searchlight of attention (Treisman, 1988
) binding together the different aspects of an object. Posterior parietal cortex might be where the searchlight is located, and its light might be a synchronization of 1318 Hz. Indeed, parietal lobe, especially right parietal lobe, has been found to play a fundamental role in attention as evident in the neglect syndrome (e.g. Heilman and Watson, 1977). Further, attention-related 1020 Hz oscillations have been found in cats (Chatila et al., 1992
). Therefore, the observed increase of coherent activity in the 1318 Hz range between parietal and temporal electrodes might be interpreted as a parietally driven attentional process subserving binding into semantical entities. This interpretation would not contradict the previous solution of binding by synchrony (von der Malsburg and Schneider, 1986
; Singer 1993
), where oscillatory activity is thought to guarantee the coupling of widespread cortical ensembles and thus bind together the distributed representations belonging to an entity. An interesting patient described by Friedman-Hill et al. (1995) has problems attributing color and shape to one object. While correctly describing all single features present on a screen, he miscombines them among the objects. This means he has problems integrating or binding different features into one entity. The site of his lesion is parietal cortex, as in our experiment. Thus the result of our experiment seems to combine both types of concepts. It indicates that parietal cortex plays a role in binding features into entities, and, additionally, that synchronous activity in the 1318 Hz range plays a role in this process. Further, there is evidence that what is referred to as attention might be activation of backward connections from higher cortical areas possibly due to internally emerging synchronization (Cauller, 1996
). Our results favor this idea. Top-down (semantically) driven synchronization might bind the different modality inputs into one semantical entity. The interplay between dynamics in different frequencies such as alpha, beta1 and probably in a shorter time window gamma might play a crucial role in this process.
Synchronization in the Beta1 Range (1318 Hz)
A last question remains: why was the synchronization frequency the beta 1 (1318 Hz) and not the gamma (20100 Hz) range as in visual cortex?
In the binding by synchrony discussion the focus is mainly on gamma oscillations. The reason this specific frequency stands out may lie in the spatial averaging obtained by the type of electrode used to record it. Electrodes perceiving signals from larger populations might perceive different ensembles with different frequencies. According to our EEG results, several further, lower-frequency ranges play roles in cortical processing (Petsche et al., 1992, Rappelsberger and Petsche, 1988
; von Stein et al., 1994b
). Reports exist on specific activity in the delta range (0.54 Hz: Kaplan, 1995
), theta range (48 Hz: Machleidt et al., 1989
; Sarnthein et al., 1998
), and several frequencies in the beta range (Rappelsberger et al., 1991
; Petsche et al., 1993; von Stein et al., 1993
; von Steine and Petsche, 1995b). Also alpha (812 Hz) activity has been shown to be specific for cognitive processes, which contradicts the assignment of alpha as a mere resting rhythm (von Stein et al., 1994a
; von Stein and Petsche, 1995b; Petsche et al., 1997
). Among the different frequency ranges, the beta1 range (1318 Hz) in particular has repeatedly been found during higher-level cortical processing. For example, Rappelsberger and Petsche (1988) found an increase of beta1-coherence during a spatial imagination task; Weiss and Rappelsberger (1996) found beta1 frequencies to be the most responsible for basic semantic processing. Flexer et al. (1995) used artificial networks to classify EEG maps and found the best results for classifying different spatial tasks in the beta1 range. Stimulus-induced oscillations in the 1318 Hz range have also been reported in intracortical recordings in cat visual cortex (Bringuier 1992).
The reason for different frequency ranges in cortico-cortical synchronization might be different dynamics of interaction. Several parameters influence the dynamics of activation-spreading in a cortical network, among them the matrix of connectivity, the length of connections, intracellular parameters such as refractory period, EPSP kinetics, asynchronous gain, etc., and more. Since the matrix of connectivity depends on learning, similar stored representations might have similar connectivity matrices and therefore similar synchronization patterns such as the frequency of synchronization. The length of connections, i.e. the size of included networks, may be similar for similar kinds of representations; representations involving networks with the same spatial extension, e.g. networks of the same sensory region, may lead to similar frequencies, etc. The most likely explanation why our observed frequency lies in a lower range than visual cortex synchronization is the latter one, namely the size of the representing ensembles. It has been shown theoretically as well as empirically (Eckhorn, 1994) that larger ensembles produce lower synchronization frequencies than smaller ensembles. Semantic ensembles, which are multimodal, should be more widely extended than the ensembles between columns in visual cortex. Therefore we propose that the low frequency range of our synchronization is a manifestation of cooperative activity in a large neuronal ensemble representing heteromodal entities.
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Notes |
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Address correspondence to Dr Astrid von Stein, Institut für Neuroinformatik, Universität Zürich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland. E-mail: astrid.vonstein{at}ini.phys.ethz.ch.
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