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Language-related brain activity revealed by independent component analysis
Clinical Neurophysiology 115 (2004) 385–395 www.elsevier.com/locate/clinph
Language-related brain activity revealed by independent component analysis Carlo Salustria,*, Eugene Kronbergb a
Institute of Cognitive Science and Technology (CNR) – Unita` MEG, Ospedale San Giovanni Calibita Fatebenefratelli – Isola Tiberina, 00186 Rome, Italy b Center for Neuromagnetism, New York University School of Medicine, New York, NY, USA Accepted 17 September 2003
Abstract Objective: When an individual engages in a cognitive task, a multitude of diverse processes are activated in his/her brain and it is reasonable to assume that multiple brain sources are simultaneously active at any one time. Magnetoencephalographic (MEG) data recorded in such circumstances provide a picture of spatial distribution and time course of the sum of the magnetic fields generated by all these sources. Thus, the experimenter faces the challenge of separating the multiple contributions to the total recorded signal before attempting a localization of their sources and studying their functional roles. Methods: We describe in this paper how independent component analysis of MEG data collected in a word/pseudo-word reading experiment elegantly solves this problem. Results: Using a few statistical assumptions, independent component analysis resolved simultaneously active brain sources in the rightfrontal, left-parietal and left-frontal areas, all showing well defined dipolar field distributions. Discussion: We describe the characteristics of these contributions and discuss the language-related functional roles that appear to be associated to some of the independent sources. We report in particular on one source, localized near Broca’s area, which showed to be affected by reading words but not pseudo-words. q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Magnetoencephalography; Independent component analysis; Language
1. Introduction Understanding the mechanisms underlying the production and the use of language represents a target of utmost importance both per se and for the wide number of pathologies in which language is involved. In this context localization of language-related areas and identification of their functional roles are obviously major targets of ongoing research. Despite the fact that this issue has been and is widely studied by means of different techniques as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging, positron emission tomography (PET) and more (for a review, see Pulvermuller, 1999, 2001 and references therein), no unified picture has been achieved. This is not surprising since it is reasonable to think that a multitude of diverse cognitive processes are likely to be activated in the brain when * Corresponding author. Tel.: þ 39-6-6837-382. E-mail address: [email protected] (C. Salustri).
a human engages in any activity requiring language, meaning that at least several, if not many brain sources must be assumed to be simultaneously active at any one time. Consequently, techniques like EEG and MEG face the challenge of separating the multiple contributions to the total recorded signal, as a first basic step towards the more ambitious target of localizing their sources and studying their functional roles. This task is further complicated by the presence in the recorded signal of artifacts of different origins, as cardiac activity, head or body movements and muscle contractions: in experiments involving reading, for example, brain MEG signals are often dominated by artifacts due to eye blinking which, lasting sometimes hundreds of milliseconds, can easily bury most of the cognitive response. A number of robust mathematical techniques, as for example orthogonal signal projection (Samonas et al., 1997), adaptive noise cancelling (Widrow et al., 1975), singular value decomposition (Kanjilal et al., 1997), are commonly used for artifact rejection and for extracting essential features from the data but they do not
1388-2457/$30.00 q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2003.09.015
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provide detailed spatio-temporal information specific to the multiple brain sources that simultaneously contribute to the total signal. In this paper we describe the application of a relatively novel technique, independent component analysis (ICA), to brain data collected in a word/pseudo-word reading experiment by means of a whole-head MEG system. Our aim is to show how this technique is capable of resolving and localizing multiple simultaneously active brain sources, providing the full time course of their individual activations. ICA finds linear projections of the data that maximize their statistical independence (Hyvaerinen and Oja, 2000; Brown et al., 2001; Tzyy-Ping et al., 2001) and our results suggest the possibility of interpreting these projections as a direct description of the underlying neuronal sources as well as of the overlying artifacts. The clearcut separation of multiple sources provides insight into the functional roles that farapart brain areas appear to have in processing language.
2. Methods The problem of separating simultaneously active sources belongs to the family of blind source separation, the term ‘blind’ indicating that signals which cannot be measured directly and about which we have little or no information are recovered from measurements of mixtures of them. It is also known as the ‘cocktail party’ problem (Brown et al., 2001) since its nature is well described by the analogy of a number of people talking in a room while a number of microphones record the room sound. The sound traces recorded by each microphone represent a weighted mixture of all the people’s voices, the weights depending on the microphone location in the room, and the problem is to extract each single person’s voice from the total sound the microphones have recorded. Much in the same way, what is available in an MEG experiment is the head surface distribution of the total magnetic field as recorded by N sensors distributed over the subjects head. If we assume that these N sensor recordings x1(t)……xN(t) are each a weighted mixtures of N statistically independent sources s1(t)……sN(t), our recordings can be written as x1 ðtÞ ¼ a11 s1 ðtÞ þ a12 s2 ðtÞ þ · · ·· · · þ a1N sN ðtÞ x2 ðtÞ ¼ a21 s1 ðtÞ þ a22 s2 ðtÞ þ · · ·· · · þ a2N sN ðtÞ · · xN ðtÞ ¼ aN1 s1 ðtÞ þ aN2 s2 ðtÞ þ · · ·· · · þ aNN sN ðtÞ or in matrix notation xðtÞ ¼ AsðtÞ The matrix A, which is unknown to us, is called the ‘mixing’ matrix since it mixes up the independent sources s which, in turn, are called ‘latent’ variables, since they cannot be
measured directly. ICA ignores any time structure of the data and estimates the matrix A that best delivers statistical independence of the sources s. From the mixing matrix A we directly extract information on the source spatial locations and, by computing its inverse B ¼ A 21, we obtain the time course of the sources’ activations: sðtÞ ¼ BxðtÞ The application of ICA to MEG (as well as EEG) data is based on 3 fundamental assumptions: (1) the existence of signal sources statistically independent from each other; (2) that the mixing of their contributions is instantaneous and linear; and (3) that both the source signals and the mixing process are spatially stationary. Independence, which implies uncorrelatedness, may seem difficult to assume with simultaneously active brain sources: nevertheless it must be emphasized that the concept of independence does not refer to the physiology of the neural sources but to the statistics of their amplitude distributions. Moreover, some ICA algorithms search for linear transformation of the data that maximize their nonGaussianity and both evoked fields and artifacts have a nonGaussian distribution (Vigario and Oja, 2000; Hyvaerinen et al., 2001). The mixing process can also be reasonably assumed instantaneous, since MEG activity is well below 1 kHz and the quasi-static approximation of the Maxwell equations holds (Hamalainen et al., 1993): ICA then considers each time point of the MEG traces separately. Brain sources are normally described with current dipoles and within this model, the concept of spatial stationarity of the source signals and of their mixing process corresponds to the existence of sources with fixed locations and orientations but time varying amplitudes (Scherg and von Cramon, 1995; Mosher et al., 1992). A detailed discussion of ICA assumptions and requirements can be found in almost any publication regarding the mathematics of ICA. 2.1. Experiment Verbal stimuli consisted of a visually delivered random series of words and pseudo-words. The word set was made of 168 function words (like ‘from’, ‘after’, ‘beyond’, etc.) and 168 content words (like ‘chair’, ‘mountain’, ‘car’, etc.), jointly matched for frequency (log frequency from 0.47 to 3.1) and length (3 –10 characters) (Kucera and Francis, 1967). The pseudo-word set was made of 168 function and 168 content non-words constructed by changing one to two letters in real words, maintaining them orthographically legal, pronounceable, and 3– 10 characters in length. The mean diagram frequency of both words and pseudo-words was calculated by summing the frequencies of each consecutive letter pair in the string and dividing by the number of pairs. Pseudo-words and content words resulted
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Fig. 1. Timing of the stimulus presentation.
quite similar on this measure, indicating that the pseudowords were word-like. Both words and pseudo-words were produced as letter strings by a standard personal computer and delivered one at the time to the subject lying in a magnetically shielded room, via a fiber optic system (Silent Vision, AVOTEC) engineered to avoid electromagnetic noise. Each presentation epoch was organized in the following way (see Fig. 1): a 200 ms long attention mark appeared on the screen 1 sec prior the appearance of the verbal stimulus: then, the verbal stimulus appeared on the screen for 150 ms and was followed 1200 ms later by a 150 ms long question mark; 2 s of blank screen separated one epoch from the next one. The subject’s task was simply to judge whether the presented stimulus was a word or a pseudo-word, wait for the appearance of the question mark and then press one of two buttons accordingly. It is important to notice that the subject’s task was not overtly to discriminate between function and content words. The delayed question mark was introduced in order to well separate cognitive processes and motor activation. Stimuli were presented in blocks, each block containing 56 stimuli (28 words and 28 pseudo-words): after each block subjects were allowed to rest their eyes for a few minutes without moving their heads with respect to the recording apparatus. The number of stimuli delivered changed for each subject according to the level of fatigue he/she would report: the minimum was 6 blocks (336 epochs), the maximum was 13 blocks (728 epochs). Each recording session lasted then between 45 and 90 min, including resting times, depending on subject endurance. Ten subjects participated in the experiment after signing an informed consent. Brain magnetic fields were recorded with a 148-channel MEG system (Magnes 2500WH, 4D Neuroimaging) at 678 Hz sampling rate.
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other. Standard FFT techniques did not seem to us the best choice for spectral analysis since they are known to suffer from ‘leakage’, i.e. the transfer of information originating from one frequency into another frequency (this phenomenon is also commonly called ‘bias’). In general leakage is a concern for processes whose spectra cover a large dynamic range and most importantly when data have a limited temporal span: in fact, it can be shown from statistical theory that the standard periodogram is approximately an unbiased estimator of the true spectral density function only when sample sizes are very large. For finite sample sizes, this approximation can be quite poor due to the above mentioned leakage. Moreover, the periodogram variance does not decrease with increasing sample size. For these reasons, we decided to use a more modern technique called multi-taper (Thomson, 1982; Percival and Walden, 1993; Mitra and Pesaran, 1999), which resolves the problems of bias and variance by averaging over a set of orthogonal basis functions, the so-called Slepian sequences (Slepian and Pollak, 1961). These sequences wk(t) of orthogonal functions, defined in the time interval t ¼ 1, 2, …, T, are parameterized by a bandwidth parameter W, such that there are K ¼ 2WT 2 1 basis functions whose spectra are confined to a frequency band [ f 2 W, f þ W ] around the frequency of interest f. For a givenPdata sequence x(t), the tapered Fourier transform gk( f) ¼ t x(t) wk(t) exp(2 2pft) is computed and a P direct estimate of the spectrum is given by SMT( f) < (1/K) k j gk( f) j 2. The average across tapers reduces the variance by a factor of 1/K and the effects of leakage are drastically reduced. The multi-taper technique has been recently applied to the analysis of neurobiological time series (Mitra and Pesaran, 1999, Llinas et al., 1999). Each single 3.5 s long epoch of the recorded MEG (Fig. 1) was passed through a multi-taper algorithm with a bandwidth parameter W ¼ 0.57 Hz, leading to K ¼ 3. The extracted single-epoch frequency spectra were then averaged separately for each subject. Fig. 2 shows the resulting
2.2. Data analysis and results First of all we performed a spectral analysis of the data. MEG recordings have in general a rather large dynamic range and in principle one can expect the simultaneous sources to have rather diverse frequency contents; on the other hand, one cannot rule out the possibility that they may also show frequency peaks very close to or overlapping each
Fig. 2. Averaged power spectral density for 3 representative subjects.
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power spectral density (PSD) for 3 subjects both in the case of words and pseudo-words. Besides an expected strong presence of low frequencies, the spectra showed two rather sharp peaks slightly before and after 10 Hz: the first peak varied across subjects from as low as 6 Hz to about 9 Hz, whereas the second peak varied only between 11 and 12.5 Hz. All subjects’ spectra also showed a wide PSD increase centered between 20 and 25 Hz but its relative amplitude and frequency spread varied across subjects. Both words and pseudo-words produced the same frequency peaks although the PDS appeared slightly (never exceeded 10%) but consistently lower for pseudo-words than for words. It is worth noticing that this double peak pattern was invisible in the majority of the subjects when data were analyzed with standard FFT instead of a multitaper algorithm. The recorded brain magnetic fields were dominated by high-amplitude waves that lasted some hundreds of milliseconds after the stimuli and showed highly bihemispheric patterns. The averaged brain response of subject 1 of Fig. 2, filtered between 1 and 55 Hz, is shown in Fig. 3 (lower portion) together with the channel distribution of the averaged magnetic fields at 4 representative time points (upper portion). The channel distribution pattern was shared by all subjects and would well represent two dipolar sources located on both eyes’ sides and oriented in the same direction, suggesting a strong artifact caused by eye blinking. Applying a high-pass filter did not rid the data of these bi-hemispheric contributions and no reliable dipolar patterns could be identified in the time span of hundreds of
milliseconds after the stimulus with the exception of visual evoked fields in the occipital areas. Data suggested a scenario in which the cognitive response to the word/pseudo-word presentation, if it existed, was buried under high-amplitude bi-hemispherical waves generated by a variety of non-language related sources. We separated the recorded MEG data in two groups according to whether the corresponding stimulus was a word or a pseudo-word, filtered them between 1 and 55 Hz and separately passed them through an ICA algorithm (available for download at Dr Scott Makeig’s web site http://www.sccn.ucsd.edu/~scott/ica.html). The ICA algorithm assumed the existence of as many components as the number of sensors, i.e. extracted 148 independent components and ordered them according to the variance their projections accounted for, the first component accounting for the highest variance. The spatio-temporal characteristics of the highest-ranking components extracted by ICA led us to interpret them as related to eye blinks and heartbeat. The left column of Fig. 4 shows the head distribution in one subject of a component that we interpreted as associated to eye blinks: this component was extracted by ICA in all subjects, it showed no difference between word and pseudo-word stimulation and its distribution strikingly resembles the one obtained by averaging the data shown in Fig. 3. This type of artifact can be due either to eye muscle contraction or to a change of the retinal dipoles’ directions consequent to the ocular movements, or both: in the assumption that the activation of an independent component represents the time course of the contribution of
Fig. 3. Lower part: averaged magnetic fields recorded from subject 1 of Fig. 2. Data were filtered between 1 and 55 Hz. Upper part: channel distribution at 4 representative time points.
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Fig. 4. Head distribution (left part) and activation time course (right part) of the highest-rank component from subject 1 of Fig. 2.
the underlying neuronal source to the total variance, we defined time epochs corresponding exactly to the epochs in the original data and averaged the activations across epochs: the right side of Fig. 4 shows the averaged activation time course of the component whose head distribution is shown on the left side. ICA extracted at least one heart-related component in all subjects and more than one in some subjects: this is due to the fact that ICA assumes sources fixed not only in space but also in orientation: consequently dipolar activations like the heart QRS and T cycle are ‘seen’ by ICA as two or more statistically independent activations. Fig. 5 shows 3 randomly chosen epochs of activation of the second and seventh components extracted from one subject. None of the remaining high-ranking components showed a repetitive pattern of activation visible by simple eye inspection of the single epochs: they rather appeared as series of spindles. Nevertheless some of them showed outstanding dipolar distributions located near areas of the brain that are generally associated with language processing and an interesting temporal evolution strongly affected by the stimulus presentation. We used both simple visual inspection of the head distributions and examination of their frequency contents to relate components to each other in the two word classes. Although many of the components extracted by ICA from our data showed an activation time course clearly related to the stimulus presentation, we devote the rest of this paper to describing those independent components that accounted for a high percentage of the variance and whose projections showed a well defined dipolar structure. In general we will proceed describing components in descending order of projected variance. Three areas in particular showed strong and well localized dipolar distribution of high-rank independent components: right-frontal, left-parietal and left-frontal; one component with multi-dipolar distribution was also extracted from all subjects. 2.2.1. Right-frontal activation A component was extracted from 100% of the subjects showing a marked dipolar distribution in the right frontal area (Fig. 6a for one representative subject). Using again multi-taper techniques we calculated the frequency spectrum within each epoch of the activation time course of this
component and averaged them across epochs for each subject. Fig. 6b shows the grand-average of these spectra across subjects. Since the spectrum is marked by a sharp 12 Hz peak we investigated the relationship of the component’s time-course with the stimuli presentation within the 9– 15 Hz narrow band. When averaging epochs one must remember that a link to the stimulus can happen in different ways: as an amplitude modulation or as a phase reset or both. A standard average of the activation timecourse does not bring complete information on amplitude variations since phase incoherence can cause the traces to cancel each other and appear as an amplitude drop. For the same reason, time-limited phase coherence linked to the stimulus could appear as an amplitude increase simply because of the drop in the rest of the trace. This is particularly evident when data are filtered in a narrow band. For this reason we computed the Hilbert transform of each single epoch and averaged the absolute value of the analytic signal across epochs: the latter results in an all-positive envelope of the trace without any loss of phase information. Consequently, Hilbert transforming represents the ideal method to measure amplitude variations not due to phase coherence. Fig. 6c shows the grand-averaged absolute value of the analytic signal, normalized for variations of power across subjects.
Fig. 5. Three randomly chosen epochs of the activation time course of the second and seventh components extracted from one subject.
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Fig. 6. (a) Head distribution of the independent component extracted in the right frontal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
2.2.2. Left-parietal activation A total of 80% of the subjects showed also a dipolar component in the left central-posterior areas (Fig. 7a for the head distribution of this component in one representative subject). Proceeding as for the previous component, we computed and grand-averaged the power spectrum, shown in Fig. 7b, which features characteristics remarkably similar to the right-frontal component, marked by a prominent frequency peak around 12 – 13 Hz. The component’s time course was filtered then between 9 and 15 Hz before computing the Hilbert transform. Fig. 7c shows the grandaverage of the absolute value of its analytic signal.
2.2.3. Left-frontal activation Most interesting, though puzzling, is the rather frontal component that ICA extracted in the left hemisphere since this is the one that delivered a substantial difference between words and pseudo-words: in fact ICA extracted this component in all subjects from the runs where the verbal stimuli were made of words, whereas it did not uncover any dipolar left-frontal structure in the runs where the stimuli were made of pseudo-words. Fig. 8a shows an example of the head distribution of this component. Contrary to the previous two, this component showed frequency patterns rather diverse among subjects, including twin peaks
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Fig. 7. (a) Head distribution of the component extracted in the left parietal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
between 8 and 17 Hz, resulting in a power spectral density grand-average lacking outstanding features (Fig. 8b). Nevertheless, in the 7– 15 Hz frequency range the Hilbert grand-average (words only) still shows a modest but evident modulation after the stimulus arrival (Fig. 8c). 2.2.4. Multi-dipolar activation All subjects featured a multi-dipolar component that appeared activated at the time of the presentation of both stimuli (attention mark and verbal stimulus, no difference between words and pseudo-words): this component showed an activation rather distributed over the head with higher intensity in the occipital area (Fig. 9a). Although the timing
of this component was extremely consistent across subjects, the frequency spectra showed much variability, causing again a lack of outstanding peaks in the grand average: besides the ubiquitous strong low frequency, subjects showed frequency peaks, and twin peaks, from 7.5 to 13 Hz.
3. Discussion Drastically reducing leakage effects in extracting the frequency spectra by using multi-taper analysis allowed us to resolved two peaks at frequencies slightly lower and higher than 10 Hz, respectively, observed in all subjects.
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Fig. 8. (a) Head distribution of the component extracted in the left frontal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9– 15 Hz narrow band.
A division in ‘lower and upper alpha bands’ was established back in the seventies by Matousek and Peterse´n (1973) and by Hermann et al. (1978). Lopes da Silva (1993) reported it in a study of 243 subjects and our frequency peaks agree very well with the results of that study. We observed this two-frequency configuration also in a separate measurement of spontaneous activity (unpublished) performed in our laboratory. Although the existence of multiple and overlapping ‘10 Hz’ activities is by now well established (Andrew and Pfurtscheller, 1997; Lutzenberger, 1997; Makeig et al., 2002, see Hari et al., 1997 for an MEG study), it remains strangely uncommon to see double-picked alpha frequency spectra reported in the literature.
However, we believe that the major outcome of this work is the evidence that ICA is effective in resolving multiple brain sources that simultaneously contribute to the total recorded signal: this result cannot be achieved by standard averaging techniques. Moreover, the assumption of statistical independence reduces (in theory eliminates) the need of an accurate choice of the ranges of data filtering: a wide spectrum of frequencies can be safely included in the analysis allowing each source to be characterized by its full frequency content. The spatio-temporal features of the independent components extracted and presented in this paper strongly suggest that they may directly represent the characteristics
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Fig. 9. (a) Head distribution of the multi-dipolar component extended all over the head shown for two representative subjects. (b) Grand-averaged absolute value of the analytic signal in the 7–15 Hz narrow band. (c) Component’s frequency spectrum (power spectrum density grand-averaged across subjects).
of underlying neuronal sources, providing meaningful evidence of cognitive processes. Fig. 6c shows two successive decreases of the independent component’s amplitude in the right-frontal area: a descend starting after the appearance of the attention mark and a sharp drop after the presentation of the verbal stimulus; a recovery is visible after 600 –800 ms. The rather moderate but monotone descend between the attention mark and the stimulus arrival and the fact that the modulation in the entire time course appears the same for both words and pseudo-words seem to suggest that this frontal source is sensitive to the expectation of the event of importance, before undergoing a strong suppression after the event arrival.
Most interesting is of course the result of the leftfrontal independent component that was affected by words but not by pseudo-words and is localized in the vicinity of areas 44 and 45 (Broca’s region). Imaging studies have already shown the activation of inferior frontal areas (together with superior temporal) when words are perceived (Fiez et al., 1996; Mazoyer et al., 1993; Zatorre et al., 1992). Over the inferior frontal areas of the left hemisphere, also previous electric and magnetic studies (Law et al., 1993; Pulvermuller, 1996) have shown that words evoke stronger responses than matching pseudowords. This difference is reported to be significant only in the 20 – 35 Hz frequency range: although this is in general agreement with the evoked responses recorded
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by us (see the frequency range 20 –30 Hz in Fig. 2), the left-frontal independent component extracted by our ICA shows its well-defined dipolar structure in the 7 –15 Hz range: this suggests that this component is linked to the mu-rhythm suppression caused by the ongoing word processing. From a Hebbian viewpoint (Hebb, 1949), one could reasonably say that the word presentation ignites the activation of a relatively vast assembly of cells and the size and connectivity of this assembly causes the underlying mu-rhythm to be suppressed for the time of this activation. In this view, the presentation of pseudowords does not trigger an equally widespread mu-rhythm suppression simply because it does not ignite the activation of equally well-defined cell assemblies. If this interpretation is correct, the sensitivity of this component to the nature of the incoming stimulus and its localization near areas 44 and 45 seem to contradict the old localizationist view that describes those areas as hosting only the motor representation of verbal processes (Geschwind, 1970): in fact in our experiment there was no induced motor activity during the mental processing. In the same way, also the view that assigns only the acustic representation of a verbal process to Wernicke’s area (Geschwind, 1970) would be contradicted: in fact stimuli in our experiment were presented only visually and Fig. 7c shows that they still trigger a sharp drop in the amplitude of the left-parietal independent component which is localized in the vicinity of Wernicke’s area. The fact that this component is not affected by the appearance of the attention mark suggests that the amplitude suppression is attributable to the actual cognitive process alone. Considering the characteristics and consistency of its frequency spectrum and that there seems to be no substantial difference between words and pseudo-words, the sinking of this component seems to represent well a suppression of the mu-rhythm caused by the processing of the verbal input: this also extends significantly the notion of a mu-rhythm modulated by somatosensory activity to including a dependence on cognitive processes. It is worth noticing at this point that, although we have described here only high-rank components with a welldefined dipolar distribution, lower-rank components also showed strong dependence on the stimulus arrival and frequency characteristics that we believe contribute to the above described double-peaked spectra. We are in the process of analyzing these components. To conclude, we believe that our results suggest a scenario in which low-frequency oscillations (7 – 14 Hz) are constantly active in distributed regions of the brain and are locally suppressed by external inputs within stimulusspecific areas: we speculate that during this low-frequency suppression, higher-frequency activations may occur, possibly on a more local scale. As a consequence of the smaller neuronal population involved, these higher-frequencies do not produce sufficient power to be effectively extracted by
current analysis methods. We are presently testing new data pre-processing which may help extract higher frequency sources.
Acknowledgements The authors sincerely thank Dr Robert M. Chapman for providing the sets of verbal stimuli, Dr Kevin Sauve’ for his invaluable contribution in the data collection and Dr Scott Mekaig and his co-workers for making their ICA algorithm available to the public.
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Language-related brain activity revealed by independent component analysis Carlo Salustria,*, Eugene Kronbergb a
Institute of Cognitive Science and Technology (CNR) – Unita` MEG, Ospedale San Giovanni Calibita Fatebenefratelli – Isola Tiberina, 00186 Rome, Italy b Center for Neuromagnetism, New York University School of Medicine, New York, NY, USA Accepted 17 September 2003
Abstract Objective: When an individual engages in a cognitive task, a multitude of diverse processes are activated in his/her brain and it is reasonable to assume that multiple brain sources are simultaneously active at any one time. Magnetoencephalographic (MEG) data recorded in such circumstances provide a picture of spatial distribution and time course of the sum of the magnetic fields generated by all these sources. Thus, the experimenter faces the challenge of separating the multiple contributions to the total recorded signal before attempting a localization of their sources and studying their functional roles. Methods: We describe in this paper how independent component analysis of MEG data collected in a word/pseudo-word reading experiment elegantly solves this problem. Results: Using a few statistical assumptions, independent component analysis resolved simultaneously active brain sources in the rightfrontal, left-parietal and left-frontal areas, all showing well defined dipolar field distributions. Discussion: We describe the characteristics of these contributions and discuss the language-related functional roles that appear to be associated to some of the independent sources. We report in particular on one source, localized near Broca’s area, which showed to be affected by reading words but not pseudo-words. q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Magnetoencephalography; Independent component analysis; Language
1. Introduction Understanding the mechanisms underlying the production and the use of language represents a target of utmost importance both per se and for the wide number of pathologies in which language is involved. In this context localization of language-related areas and identification of their functional roles are obviously major targets of ongoing research. Despite the fact that this issue has been and is widely studied by means of different techniques as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging, positron emission tomography (PET) and more (for a review, see Pulvermuller, 1999, 2001 and references therein), no unified picture has been achieved. This is not surprising since it is reasonable to think that a multitude of diverse cognitive processes are likely to be activated in the brain when * Corresponding author. Tel.: þ 39-6-6837-382. E-mail address: [email protected] (C. Salustri).
a human engages in any activity requiring language, meaning that at least several, if not many brain sources must be assumed to be simultaneously active at any one time. Consequently, techniques like EEG and MEG face the challenge of separating the multiple contributions to the total recorded signal, as a first basic step towards the more ambitious target of localizing their sources and studying their functional roles. This task is further complicated by the presence in the recorded signal of artifacts of different origins, as cardiac activity, head or body movements and muscle contractions: in experiments involving reading, for example, brain MEG signals are often dominated by artifacts due to eye blinking which, lasting sometimes hundreds of milliseconds, can easily bury most of the cognitive response. A number of robust mathematical techniques, as for example orthogonal signal projection (Samonas et al., 1997), adaptive noise cancelling (Widrow et al., 1975), singular value decomposition (Kanjilal et al., 1997), are commonly used for artifact rejection and for extracting essential features from the data but they do not
1388-2457/$30.00 q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2003.09.015
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provide detailed spatio-temporal information specific to the multiple brain sources that simultaneously contribute to the total signal. In this paper we describe the application of a relatively novel technique, independent component analysis (ICA), to brain data collected in a word/pseudo-word reading experiment by means of a whole-head MEG system. Our aim is to show how this technique is capable of resolving and localizing multiple simultaneously active brain sources, providing the full time course of their individual activations. ICA finds linear projections of the data that maximize their statistical independence (Hyvaerinen and Oja, 2000; Brown et al., 2001; Tzyy-Ping et al., 2001) and our results suggest the possibility of interpreting these projections as a direct description of the underlying neuronal sources as well as of the overlying artifacts. The clearcut separation of multiple sources provides insight into the functional roles that farapart brain areas appear to have in processing language.
2. Methods The problem of separating simultaneously active sources belongs to the family of blind source separation, the term ‘blind’ indicating that signals which cannot be measured directly and about which we have little or no information are recovered from measurements of mixtures of them. It is also known as the ‘cocktail party’ problem (Brown et al., 2001) since its nature is well described by the analogy of a number of people talking in a room while a number of microphones record the room sound. The sound traces recorded by each microphone represent a weighted mixture of all the people’s voices, the weights depending on the microphone location in the room, and the problem is to extract each single person’s voice from the total sound the microphones have recorded. Much in the same way, what is available in an MEG experiment is the head surface distribution of the total magnetic field as recorded by N sensors distributed over the subjects head. If we assume that these N sensor recordings x1(t)……xN(t) are each a weighted mixtures of N statistically independent sources s1(t)……sN(t), our recordings can be written as x1 ðtÞ ¼ a11 s1 ðtÞ þ a12 s2 ðtÞ þ · · ·· · · þ a1N sN ðtÞ x2 ðtÞ ¼ a21 s1 ðtÞ þ a22 s2 ðtÞ þ · · ·· · · þ a2N sN ðtÞ · · xN ðtÞ ¼ aN1 s1 ðtÞ þ aN2 s2 ðtÞ þ · · ·· · · þ aNN sN ðtÞ or in matrix notation xðtÞ ¼ AsðtÞ The matrix A, which is unknown to us, is called the ‘mixing’ matrix since it mixes up the independent sources s which, in turn, are called ‘latent’ variables, since they cannot be
measured directly. ICA ignores any time structure of the data and estimates the matrix A that best delivers statistical independence of the sources s. From the mixing matrix A we directly extract information on the source spatial locations and, by computing its inverse B ¼ A 21, we obtain the time course of the sources’ activations: sðtÞ ¼ BxðtÞ The application of ICA to MEG (as well as EEG) data is based on 3 fundamental assumptions: (1) the existence of signal sources statistically independent from each other; (2) that the mixing of their contributions is instantaneous and linear; and (3) that both the source signals and the mixing process are spatially stationary. Independence, which implies uncorrelatedness, may seem difficult to assume with simultaneously active brain sources: nevertheless it must be emphasized that the concept of independence does not refer to the physiology of the neural sources but to the statistics of their amplitude distributions. Moreover, some ICA algorithms search for linear transformation of the data that maximize their nonGaussianity and both evoked fields and artifacts have a nonGaussian distribution (Vigario and Oja, 2000; Hyvaerinen et al., 2001). The mixing process can also be reasonably assumed instantaneous, since MEG activity is well below 1 kHz and the quasi-static approximation of the Maxwell equations holds (Hamalainen et al., 1993): ICA then considers each time point of the MEG traces separately. Brain sources are normally described with current dipoles and within this model, the concept of spatial stationarity of the source signals and of their mixing process corresponds to the existence of sources with fixed locations and orientations but time varying amplitudes (Scherg and von Cramon, 1995; Mosher et al., 1992). A detailed discussion of ICA assumptions and requirements can be found in almost any publication regarding the mathematics of ICA. 2.1. Experiment Verbal stimuli consisted of a visually delivered random series of words and pseudo-words. The word set was made of 168 function words (like ‘from’, ‘after’, ‘beyond’, etc.) and 168 content words (like ‘chair’, ‘mountain’, ‘car’, etc.), jointly matched for frequency (log frequency from 0.47 to 3.1) and length (3 –10 characters) (Kucera and Francis, 1967). The pseudo-word set was made of 168 function and 168 content non-words constructed by changing one to two letters in real words, maintaining them orthographically legal, pronounceable, and 3– 10 characters in length. The mean diagram frequency of both words and pseudo-words was calculated by summing the frequencies of each consecutive letter pair in the string and dividing by the number of pairs. Pseudo-words and content words resulted
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Fig. 1. Timing of the stimulus presentation.
quite similar on this measure, indicating that the pseudowords were word-like. Both words and pseudo-words were produced as letter strings by a standard personal computer and delivered one at the time to the subject lying in a magnetically shielded room, via a fiber optic system (Silent Vision, AVOTEC) engineered to avoid electromagnetic noise. Each presentation epoch was organized in the following way (see Fig. 1): a 200 ms long attention mark appeared on the screen 1 sec prior the appearance of the verbal stimulus: then, the verbal stimulus appeared on the screen for 150 ms and was followed 1200 ms later by a 150 ms long question mark; 2 s of blank screen separated one epoch from the next one. The subject’s task was simply to judge whether the presented stimulus was a word or a pseudo-word, wait for the appearance of the question mark and then press one of two buttons accordingly. It is important to notice that the subject’s task was not overtly to discriminate between function and content words. The delayed question mark was introduced in order to well separate cognitive processes and motor activation. Stimuli were presented in blocks, each block containing 56 stimuli (28 words and 28 pseudo-words): after each block subjects were allowed to rest their eyes for a few minutes without moving their heads with respect to the recording apparatus. The number of stimuli delivered changed for each subject according to the level of fatigue he/she would report: the minimum was 6 blocks (336 epochs), the maximum was 13 blocks (728 epochs). Each recording session lasted then between 45 and 90 min, including resting times, depending on subject endurance. Ten subjects participated in the experiment after signing an informed consent. Brain magnetic fields were recorded with a 148-channel MEG system (Magnes 2500WH, 4D Neuroimaging) at 678 Hz sampling rate.
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other. Standard FFT techniques did not seem to us the best choice for spectral analysis since they are known to suffer from ‘leakage’, i.e. the transfer of information originating from one frequency into another frequency (this phenomenon is also commonly called ‘bias’). In general leakage is a concern for processes whose spectra cover a large dynamic range and most importantly when data have a limited temporal span: in fact, it can be shown from statistical theory that the standard periodogram is approximately an unbiased estimator of the true spectral density function only when sample sizes are very large. For finite sample sizes, this approximation can be quite poor due to the above mentioned leakage. Moreover, the periodogram variance does not decrease with increasing sample size. For these reasons, we decided to use a more modern technique called multi-taper (Thomson, 1982; Percival and Walden, 1993; Mitra and Pesaran, 1999), which resolves the problems of bias and variance by averaging over a set of orthogonal basis functions, the so-called Slepian sequences (Slepian and Pollak, 1961). These sequences wk(t) of orthogonal functions, defined in the time interval t ¼ 1, 2, …, T, are parameterized by a bandwidth parameter W, such that there are K ¼ 2WT 2 1 basis functions whose spectra are confined to a frequency band [ f 2 W, f þ W ] around the frequency of interest f. For a givenPdata sequence x(t), the tapered Fourier transform gk( f) ¼ t x(t) wk(t) exp(2 2pft) is computed and a P direct estimate of the spectrum is given by SMT( f) < (1/K) k j gk( f) j 2. The average across tapers reduces the variance by a factor of 1/K and the effects of leakage are drastically reduced. The multi-taper technique has been recently applied to the analysis of neurobiological time series (Mitra and Pesaran, 1999, Llinas et al., 1999). Each single 3.5 s long epoch of the recorded MEG (Fig. 1) was passed through a multi-taper algorithm with a bandwidth parameter W ¼ 0.57 Hz, leading to K ¼ 3. The extracted single-epoch frequency spectra were then averaged separately for each subject. Fig. 2 shows the resulting
2.2. Data analysis and results First of all we performed a spectral analysis of the data. MEG recordings have in general a rather large dynamic range and in principle one can expect the simultaneous sources to have rather diverse frequency contents; on the other hand, one cannot rule out the possibility that they may also show frequency peaks very close to or overlapping each
Fig. 2. Averaged power spectral density for 3 representative subjects.
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power spectral density (PSD) for 3 subjects both in the case of words and pseudo-words. Besides an expected strong presence of low frequencies, the spectra showed two rather sharp peaks slightly before and after 10 Hz: the first peak varied across subjects from as low as 6 Hz to about 9 Hz, whereas the second peak varied only between 11 and 12.5 Hz. All subjects’ spectra also showed a wide PSD increase centered between 20 and 25 Hz but its relative amplitude and frequency spread varied across subjects. Both words and pseudo-words produced the same frequency peaks although the PDS appeared slightly (never exceeded 10%) but consistently lower for pseudo-words than for words. It is worth noticing that this double peak pattern was invisible in the majority of the subjects when data were analyzed with standard FFT instead of a multitaper algorithm. The recorded brain magnetic fields were dominated by high-amplitude waves that lasted some hundreds of milliseconds after the stimuli and showed highly bihemispheric patterns. The averaged brain response of subject 1 of Fig. 2, filtered between 1 and 55 Hz, is shown in Fig. 3 (lower portion) together with the channel distribution of the averaged magnetic fields at 4 representative time points (upper portion). The channel distribution pattern was shared by all subjects and would well represent two dipolar sources located on both eyes’ sides and oriented in the same direction, suggesting a strong artifact caused by eye blinking. Applying a high-pass filter did not rid the data of these bi-hemispheric contributions and no reliable dipolar patterns could be identified in the time span of hundreds of
milliseconds after the stimulus with the exception of visual evoked fields in the occipital areas. Data suggested a scenario in which the cognitive response to the word/pseudo-word presentation, if it existed, was buried under high-amplitude bi-hemispherical waves generated by a variety of non-language related sources. We separated the recorded MEG data in two groups according to whether the corresponding stimulus was a word or a pseudo-word, filtered them between 1 and 55 Hz and separately passed them through an ICA algorithm (available for download at Dr Scott Makeig’s web site http://www.sccn.ucsd.edu/~scott/ica.html). The ICA algorithm assumed the existence of as many components as the number of sensors, i.e. extracted 148 independent components and ordered them according to the variance their projections accounted for, the first component accounting for the highest variance. The spatio-temporal characteristics of the highest-ranking components extracted by ICA led us to interpret them as related to eye blinks and heartbeat. The left column of Fig. 4 shows the head distribution in one subject of a component that we interpreted as associated to eye blinks: this component was extracted by ICA in all subjects, it showed no difference between word and pseudo-word stimulation and its distribution strikingly resembles the one obtained by averaging the data shown in Fig. 3. This type of artifact can be due either to eye muscle contraction or to a change of the retinal dipoles’ directions consequent to the ocular movements, or both: in the assumption that the activation of an independent component represents the time course of the contribution of
Fig. 3. Lower part: averaged magnetic fields recorded from subject 1 of Fig. 2. Data were filtered between 1 and 55 Hz. Upper part: channel distribution at 4 representative time points.
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Fig. 4. Head distribution (left part) and activation time course (right part) of the highest-rank component from subject 1 of Fig. 2.
the underlying neuronal source to the total variance, we defined time epochs corresponding exactly to the epochs in the original data and averaged the activations across epochs: the right side of Fig. 4 shows the averaged activation time course of the component whose head distribution is shown on the left side. ICA extracted at least one heart-related component in all subjects and more than one in some subjects: this is due to the fact that ICA assumes sources fixed not only in space but also in orientation: consequently dipolar activations like the heart QRS and T cycle are ‘seen’ by ICA as two or more statistically independent activations. Fig. 5 shows 3 randomly chosen epochs of activation of the second and seventh components extracted from one subject. None of the remaining high-ranking components showed a repetitive pattern of activation visible by simple eye inspection of the single epochs: they rather appeared as series of spindles. Nevertheless some of them showed outstanding dipolar distributions located near areas of the brain that are generally associated with language processing and an interesting temporal evolution strongly affected by the stimulus presentation. We used both simple visual inspection of the head distributions and examination of their frequency contents to relate components to each other in the two word classes. Although many of the components extracted by ICA from our data showed an activation time course clearly related to the stimulus presentation, we devote the rest of this paper to describing those independent components that accounted for a high percentage of the variance and whose projections showed a well defined dipolar structure. In general we will proceed describing components in descending order of projected variance. Three areas in particular showed strong and well localized dipolar distribution of high-rank independent components: right-frontal, left-parietal and left-frontal; one component with multi-dipolar distribution was also extracted from all subjects. 2.2.1. Right-frontal activation A component was extracted from 100% of the subjects showing a marked dipolar distribution in the right frontal area (Fig. 6a for one representative subject). Using again multi-taper techniques we calculated the frequency spectrum within each epoch of the activation time course of this
component and averaged them across epochs for each subject. Fig. 6b shows the grand-average of these spectra across subjects. Since the spectrum is marked by a sharp 12 Hz peak we investigated the relationship of the component’s time-course with the stimuli presentation within the 9– 15 Hz narrow band. When averaging epochs one must remember that a link to the stimulus can happen in different ways: as an amplitude modulation or as a phase reset or both. A standard average of the activation timecourse does not bring complete information on amplitude variations since phase incoherence can cause the traces to cancel each other and appear as an amplitude drop. For the same reason, time-limited phase coherence linked to the stimulus could appear as an amplitude increase simply because of the drop in the rest of the trace. This is particularly evident when data are filtered in a narrow band. For this reason we computed the Hilbert transform of each single epoch and averaged the absolute value of the analytic signal across epochs: the latter results in an all-positive envelope of the trace without any loss of phase information. Consequently, Hilbert transforming represents the ideal method to measure amplitude variations not due to phase coherence. Fig. 6c shows the grand-averaged absolute value of the analytic signal, normalized for variations of power across subjects.
Fig. 5. Three randomly chosen epochs of the activation time course of the second and seventh components extracted from one subject.
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Fig. 6. (a) Head distribution of the independent component extracted in the right frontal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
2.2.2. Left-parietal activation A total of 80% of the subjects showed also a dipolar component in the left central-posterior areas (Fig. 7a for the head distribution of this component in one representative subject). Proceeding as for the previous component, we computed and grand-averaged the power spectrum, shown in Fig. 7b, which features characteristics remarkably similar to the right-frontal component, marked by a prominent frequency peak around 12 – 13 Hz. The component’s time course was filtered then between 9 and 15 Hz before computing the Hilbert transform. Fig. 7c shows the grandaverage of the absolute value of its analytic signal.
2.2.3. Left-frontal activation Most interesting, though puzzling, is the rather frontal component that ICA extracted in the left hemisphere since this is the one that delivered a substantial difference between words and pseudo-words: in fact ICA extracted this component in all subjects from the runs where the verbal stimuli were made of words, whereas it did not uncover any dipolar left-frontal structure in the runs where the stimuli were made of pseudo-words. Fig. 8a shows an example of the head distribution of this component. Contrary to the previous two, this component showed frequency patterns rather diverse among subjects, including twin peaks
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Fig. 7. (a) Head distribution of the component extracted in the left parietal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
between 8 and 17 Hz, resulting in a power spectral density grand-average lacking outstanding features (Fig. 8b). Nevertheless, in the 7– 15 Hz frequency range the Hilbert grand-average (words only) still shows a modest but evident modulation after the stimulus arrival (Fig. 8c). 2.2.4. Multi-dipolar activation All subjects featured a multi-dipolar component that appeared activated at the time of the presentation of both stimuli (attention mark and verbal stimulus, no difference between words and pseudo-words): this component showed an activation rather distributed over the head with higher intensity in the occipital area (Fig. 9a). Although the timing
of this component was extremely consistent across subjects, the frequency spectra showed much variability, causing again a lack of outstanding peaks in the grand average: besides the ubiquitous strong low frequency, subjects showed frequency peaks, and twin peaks, from 7.5 to 13 Hz.
3. Discussion Drastically reducing leakage effects in extracting the frequency spectra by using multi-taper analysis allowed us to resolved two peaks at frequencies slightly lower and higher than 10 Hz, respectively, observed in all subjects.
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Fig. 8. (a) Head distribution of the component extracted in the left frontal area shown for one representative subject. (b) Component’s frequency spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9– 15 Hz narrow band.
A division in ‘lower and upper alpha bands’ was established back in the seventies by Matousek and Peterse´n (1973) and by Hermann et al. (1978). Lopes da Silva (1993) reported it in a study of 243 subjects and our frequency peaks agree very well with the results of that study. We observed this two-frequency configuration also in a separate measurement of spontaneous activity (unpublished) performed in our laboratory. Although the existence of multiple and overlapping ‘10 Hz’ activities is by now well established (Andrew and Pfurtscheller, 1997; Lutzenberger, 1997; Makeig et al., 2002, see Hari et al., 1997 for an MEG study), it remains strangely uncommon to see double-picked alpha frequency spectra reported in the literature.
However, we believe that the major outcome of this work is the evidence that ICA is effective in resolving multiple brain sources that simultaneously contribute to the total recorded signal: this result cannot be achieved by standard averaging techniques. Moreover, the assumption of statistical independence reduces (in theory eliminates) the need of an accurate choice of the ranges of data filtering: a wide spectrum of frequencies can be safely included in the analysis allowing each source to be characterized by its full frequency content. The spatio-temporal features of the independent components extracted and presented in this paper strongly suggest that they may directly represent the characteristics
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Fig. 9. (a) Head distribution of the multi-dipolar component extended all over the head shown for two representative subjects. (b) Grand-averaged absolute value of the analytic signal in the 7–15 Hz narrow band. (c) Component’s frequency spectrum (power spectrum density grand-averaged across subjects).
of underlying neuronal sources, providing meaningful evidence of cognitive processes. Fig. 6c shows two successive decreases of the independent component’s amplitude in the right-frontal area: a descend starting after the appearance of the attention mark and a sharp drop after the presentation of the verbal stimulus; a recovery is visible after 600 –800 ms. The rather moderate but monotone descend between the attention mark and the stimulus arrival and the fact that the modulation in the entire time course appears the same for both words and pseudo-words seem to suggest that this frontal source is sensitive to the expectation of the event of importance, before undergoing a strong suppression after the event arrival.
Most interesting is of course the result of the leftfrontal independent component that was affected by words but not by pseudo-words and is localized in the vicinity of areas 44 and 45 (Broca’s region). Imaging studies have already shown the activation of inferior frontal areas (together with superior temporal) when words are perceived (Fiez et al., 1996; Mazoyer et al., 1993; Zatorre et al., 1992). Over the inferior frontal areas of the left hemisphere, also previous electric and magnetic studies (Law et al., 1993; Pulvermuller, 1996) have shown that words evoke stronger responses than matching pseudowords. This difference is reported to be significant only in the 20 – 35 Hz frequency range: although this is in general agreement with the evoked responses recorded
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by us (see the frequency range 20 –30 Hz in Fig. 2), the left-frontal independent component extracted by our ICA shows its well-defined dipolar structure in the 7 –15 Hz range: this suggests that this component is linked to the mu-rhythm suppression caused by the ongoing word processing. From a Hebbian viewpoint (Hebb, 1949), one could reasonably say that the word presentation ignites the activation of a relatively vast assembly of cells and the size and connectivity of this assembly causes the underlying mu-rhythm to be suppressed for the time of this activation. In this view, the presentation of pseudowords does not trigger an equally widespread mu-rhythm suppression simply because it does not ignite the activation of equally well-defined cell assemblies. If this interpretation is correct, the sensitivity of this component to the nature of the incoming stimulus and its localization near areas 44 and 45 seem to contradict the old localizationist view that describes those areas as hosting only the motor representation of verbal processes (Geschwind, 1970): in fact in our experiment there was no induced motor activity during the mental processing. In the same way, also the view that assigns only the acustic representation of a verbal process to Wernicke’s area (Geschwind, 1970) would be contradicted: in fact stimuli in our experiment were presented only visually and Fig. 7c shows that they still trigger a sharp drop in the amplitude of the left-parietal independent component which is localized in the vicinity of Wernicke’s area. The fact that this component is not affected by the appearance of the attention mark suggests that the amplitude suppression is attributable to the actual cognitive process alone. Considering the characteristics and consistency of its frequency spectrum and that there seems to be no substantial difference between words and pseudo-words, the sinking of this component seems to represent well a suppression of the mu-rhythm caused by the processing of the verbal input: this also extends significantly the notion of a mu-rhythm modulated by somatosensory activity to including a dependence on cognitive processes. It is worth noticing at this point that, although we have described here only high-rank components with a welldefined dipolar distribution, lower-rank components also showed strong dependence on the stimulus arrival and frequency characteristics that we believe contribute to the above described double-peaked spectra. We are in the process of analyzing these components. To conclude, we believe that our results suggest a scenario in which low-frequency oscillations (7 – 14 Hz) are constantly active in distributed regions of the brain and are locally suppressed by external inputs within stimulusspecific areas: we speculate that during this low-frequency suppression, higher-frequency activations may occur, possibly on a more local scale. As a consequence of the smaller neuronal population involved, these higher-frequencies do not produce sufficient power to be effectively extracted by
current analysis methods. We are presently testing new data pre-processing which may help extract higher frequency sources.
Acknowledgements The authors sincerely thank Dr Robert M. Chapman for providing the sets of verbal stimuli, Dr Kevin Sauve’ for his invaluable contribution in the data collection and Dr Scott Mekaig and his co-workers for making their ICA algorithm available to the public.
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