From Tones to Speech: Magnetoencephalographic Studies

Transcription

1 Chapter 28 From Tones to Speech: Magnetoencephalographic Studies Bernd Lütkenhöner and David Poeppel Abbreviations AEP auditory evoked magnetic field AM amplitude modulation CV consonant-vowel DC direct current EEG electroencephalography ERP event related potential FM frequency modulation fmri functional magnetic resonance HP Huggins pitch MEG magnetoencephalography MMF mismatch field MMN mismatch negativity POR pitch onset response RIS regular interval sound RMS root-mean-square SF sustained field STG supratemporal gyrus TN tone embedded in noise is excellently suited for studying brain dynamics. For a magnetic field arising from a single circumscribed brain area, MEG offers high source localization accuracy as well. But this is not the situation in typical experiments. In general, the observed activity is more complex and comprises contributions from several simultaneously active sources. A unique interpretation of the recorded data does not exist under such circumstances, and any conclusion depends on modeling assumptions about the number and configuration of the underlying neuronal sources. A proper understanding of experimental MEG results therefore requires at least an elementary knowledge of the theoretical foundations. Thus, this introduction will briefly explain the basics of MEG. Comprehensive reviews can be found elsewhere (Williamson and Kaufman 1987; Hämäläinen et al. 1993; Baillet et al. 2001; Hämäläinen and Hari 2002; Lütkenhöner and Mosher 2007). 1.1 From Neural Currents to the Magnetic Fields: The Forward Problem 1 Basics of Magnetoencephalography Electrical activity in the brain generates a weak magnetic field in the vicinity of the head. Recording this signal with sensitive detectors is called magnetoencephalography (MEG). The technique may be considered the magnetic counterpart of electroencephalography (EEG), where the signal is recorded from electrodes attached to the scalp. An outstanding feature of MEG (as well as EEG) is that its temporal resolution is virtually unlimited. Thus, MEG D. Poeppel ( ) Department of Psychology, New York University, New York, NY 10003, USA david.poeppel@nyu.edu The main sources of the magnetic field recorded by MEG are postsynaptic currents in cortical pyramidal cells. These primary currents cause volume currents in the surrounding conductive medium so that a closed circuit is formed. Microscopic details of the currents are not reflected in the MEG signal. Thus, a relatively coarse, macroscopic model can be used to describe the generation of this signal. It is sufficient for that purpose to imagine a limited brain area as a battery, where the current flowing inside the battery represents the sum of all primary currents in the respective area. At a certain distance from the battery, the strength of the magnetic field is proportional to the product of current and battery length, which is called the dipole moment (typically expressed in nanoamperemeters, nam). The model is usually simplified even further by assuming a battery of infinitesimal length. In this way, the battery model turns into the model J.A. Winer, C.E. Schreiner (eds.), The Auditory Cortex, DOI / _28, Springer Science+Business Media, LLC

2 598 B. Lütkenhöner and D. Poeppel of a current dipole. Given sufficient knowledge of the primary currents in the brain and the conductivity profile of the head, the MEG signal can be accurately predicted. This issue is often called the forward problem. While the EEG signal crucially depends on the volume currents in brain, skull and scalp, the MEG signal depends, under typical experimental conditions, mainly on the primary currents. Thus, as an approximation, MEG can be used to visualize cortical events directly through the skull (Hämäläinen and Hari 2002). A peculiarity of MEG is that it is mainly sensitive to currents (dipoles) that are oriented tangentially to the inner surface of the skull. By contrast, sources oriented perpendicularly to that surface (radial sources) are generally considered as silent sources. MEG is also relatively insensitive to sources located deep in the brain, so that the observed field is mainly of cortical origin. Figure 28.1 illustrates the recording of a magnetic field (thin arrows) caused by a tangential dipole in the cortex (thick arrow). Radially oriented magnetometers distributed about the head would record the spatial pattern displayed as a contour map in Fig. 28.2, which provides a view of the measurement surface from above. 1.2 Interpretation of Magnetic Fields To interpret measured data they have to be explained in terms of the underlying sources in the brain. This type of problem is called the inverse problem. If the magnetic field were of a dipolar nature (as in Fig. 28.2), the parameters of the underlying dipole could be easily estimated using an iterative optimization procedure. But typical experimental data are more complicated and the number of contributing sources is usually not obvious. A fundamental dilemma is that the inverse problem does not have a unique solution. Thus, any MEG measurement allows, in principle, an infinite number of interpretations. Although certain types of solutions can generally be excluded based on prior knowledge, assumptions and plausibility considerations, some uncertainty always remains. Because of these difficulties, many analysis techniques have been developed over the years. Two main classes can be distinguished: parametric and imaging techniques. In the first case, it is typically postulated that the observed magnetic field resulted from a limited number of current dipoles (far fewer than the number of measuring channels). In the second case, a huge number of dipoles with known locations and directions (generally far more than the number of measuring channels) is assumed, and the task is to estimate the activation strengths of these dipoles (the image) from the measured data. Parametric methods have the advantage of a considerable data reduction, but they have to be tailored to the type of experiment, and the choice of an inappropriate model risks Fig Measurement of the magnetic field caused by a single current dipole in a simplistic model of the head. Scalp and skull are represented by spherical shells (black and dark gray, respectively). Moreover, a primitive cortex with a single sulcus at the top is plotted (light gray). Apical dendrites of cortical pyramidal cells are oriented roughly perpendicular to the cortical surface, and this is also the preferred orientation of the current dipoles considered in MEG. One such dipole is displayed (thick arrow). Because of its placement in the depth of a sulcus, it represents a tangential source, which is a favorable condition for MEG. While such a dipole causes a non-zero magnetic field almost everywhere, the strongest field is in a plane perpendicular to the dipole. This plane is represented by the gray area with the coordinate grid; arrows indicate the strength and the direction of the magnetic field at the respective location. A single magnetometer coil detects only one component of the three-dimensional magnetic field vector, and typically the radial component (roughly perpendicular to the scalp) is chosen. For a given measurement surface (white curve), the strength of that component exhibits two maxima (indicated by dotted lines emanating from the center of the sphere). A radially oriented magnetometer coil (with two leads) is shown at either location. Directly above the dipole, the magnetic field is purely tangential, i.e. its radial component is zero. A magnetic field here may be recorded with a planar gradiometer consisting of two oppositely wound coils (dark gray)

3 28 Magnetoencephalography 599 Fig Radial component of the magnetic field caused by a single current dipole. The spherical measurement surface (Fig. 28.1) is viewed from above. Thin circles, angular distances of 10, 20,...,90 from the pole of the sphere; dotted circle, the outermost contour of the scalp. The contour map shows how the radial component of the magnetic field depends on the measurement location. On the meridian that corresponds to the direction of the dipole (arrow), the radial component is zero (thick solid line). Magnetic flux directed out of the head (defined as the positive polarity) is shown as the solid curves in the upper half of the plot.a completely symmetric pattern is found in the bottom half of the plot, but here the magnetic flux is directed into the head (dashed curved lines). The locations corresponding to maximum and minimum are indicated by x-marks generally unique and numerically stable. More problematic may be the interpretation of the result. The choice of the dipole model is often justified with the argument that the goodness of fit (percent of the variance explained by the model) is greater than, e.g., 90%. But such an argument is acceptable only in the case of relatively noisy data. If the data exhibit an excellent signal-to-noise ratio, such a goodnessof-fit would basically confirm that more than one source contributed to the observed field. Even goodness-of-fit values above 99% do not exclude the possibility that two or more cortical sources with a distance of several centimeters were active (Lütkenhöner 1998). In the case of multiple sources, the location of the estimated current dipole often corresponds to the center of gravity of the sources. But this is not certain, especially if the primary currents flow in opposite directions so that the associated magnetic fields partially cancel each other (Lütkenhöner and Mosher 2007). In spite of these problems, the single-current-dipole approach is both useful and powerful. The better a single dipole can explain the data, the more difficult it is to find a convincing alternative model: because of a lack of information in the data, analyses with more complex models tend to be critically dependent on constraints and assumptions. Thus, the above-mentioned problems do not disqualify the dipole model itself, they merely suggest that a careful interpretation of the model parameters are necessary (Lütkenhöner et al. 2006). In any case, the critique of dipole models must acknowledge that the alternative models face with similarly daunting challenges in generating convincing neurophysiological interpretations. serious misinterpretations. Images are easiest to calculate, but they may be hardest to interpret, and in the worst case they can yield a misleading impression of the activity in the brain (Lütkenhöner and Mosher 2007). It is clear from the above that the interpretation of MEG data crucially depends on models and that each methodological approach has specific advantages and drawbacks. Approaches that will be of particular relevance later in this chapter are now considered in more detail. 1.3 Estimation of a Single Current Dipole Magnetic fields exhibiting an approximately dipolar spatial pattern (Fig. 28.2) are quite common. This is the reason why a data interpretation in terms of a single current dipole still belongs to the most popular approaches. Fitting a dipole to experimental data is analytically straightforward, and unless the data are too noisy or fundamentally in conflict with the assumption of a single dipolar source the solution is 1.4 Multi-dipole Approaches A natural extension of the single-dipole model is a multidipole model. As long as the dipoles are well separated (e.g., one dipole in the auditory cortex of each hemisphere), this approach has basically the same constraints as the singledipole approach. Additional problems emerge, however, if some of the dipoles are located relatively near one another. Even with prior knowledge of their exact locations it may be difficult to obtain independent estimates of the dipole moments (which may be assumed to reflect the net activities in the respective cortical regions). Moreover, it is not a trivial task to determine the number of dipoles actually needed. Because of such difficulties, it is often better to use a simpler model that imperfectly describes the major features of the data than to add many dipoles until the correspondence between model prediction and data is almost perfect. A frequent shortcoming of the latter approach is that many solutions of similar sophistication exist (Lütkenhöner and Mosher 2007).

4 600 B. Lütkenhöner and D. Poeppel 1.5 Synthetic Sensors: Beamformers Synthetic (or virtual) sensors can be realized by linearly combining the signals provided by the actual sensors (Vrba and Robinson 2002). They usually have an improved spatial specificity and are often used to interrogate the activity going on in a specific brain region. An early example is the software lens (Freeman 1980). A general methodological framework is provided by the theory of beamforming (Van Veen and Buckley 1988). An ideal beamformer would correspond to a spatial filter which allows activity from a location of interest pass while blocking other activity. Although such an ideal technique does not exist, many methods can be considered variations of beamforming, even single-dipole modeling (Lütkenhöner 2003). In the latter case, the estimated dipole moment is essentially a linear combination of the signals measured by the individual sensors, with coefficients depending (among other factors) on the location and the orientation of the dipole. Thus, the estimation of a dipole moment may be considered a measurement with a synthetic sensor focusing on the dipole. For two dipoles, the beamformer for the first dipole blocks the signal from the second dipole, and vice versa (Lütkenhöner and Mosher 2007). More general implementations of beamforming require not only knowledge about the noise, but also strong assumptions about source models and statistics (Hillebrand and Barnes 2005). When applied to auditory data elicited by relatively simple acoustic signals, beamforming techniques can yield source reconstructions that are comparable to dipole models while also incorporating information about time and frequency attributes of the neuronal activity (Sekihara et al. 2001). As our anatomic models of human auditory cortex become more refined, perhaps the distinct modeling approaches will become less similar. For now, there is no principled reason in studies of human auditory cortex to prefer one method over the other without strong prior hypotheses about the source configuration that are more amenable to one or the other data-analytic approach. 1.6 Spectro-Temporal Approaches: Peaks, Peak Activation Sequences, Oscillations, Phase Much effort is required to perform and justify source analysis, but it is reasonable to assert that the MEG data relating most closely to neurophysiological considerations derive from analyses of the timing and morphology of the neuromagnetic activity elicited by acoustic stimulation. Briefly, four types of analyses are commonly encountered. First, as is also typical for EEG, individual response peaks (described below) are examined for variations in peak amplitudes and latencies as a function of the experimental manipulation (Roberts et al. 2000; Salajegheh et al. 2004). Second, the cortical activation sequence is characterized, i.e., at what time-point are peaks visible and where are they generated (Salmelin et al. 1994). Third, the role that oscillatory activity plays is studied, particularly the contribution of the canonical bands (theta, alpha, beta, gamma) to the cortical construction of perceptual representations in speech (Palva et al. 2002) and nonspeech (Luo et al. 2005) regimes. Finally, the role of phase is investigated, motivated by the fact that phase information is central to encoding auditory signals in non-speech (Patel and Balaban 2004), speech (Luo and Poeppel 2007) or binaural phase (Ross et al. 2007) conditions. 1.7 Relation to Other Techniques: Unique Contributions of Magnetoencephalography Functional magnetic resonance imaging (fmri) has emerged as a dominant technique in cognitive neuroscience, and its remarkable spatial resolving power is impressive. Scanners with a field strength of three Tesla or more are now widely available, making it possible to generate functional images with an in-plane resolution of 1 mm or better, thereby enriching the understanding of the functional anatomy of the human auditory cortex. The highly model-dependent spatial resolution of MEG sources is about 5 10 mm. Because the imaging approaches are hemodynamically based (so far), their temporal resolution does not match the rate at which many auditory phenomena occur (milliseconds), and the electromagnetic recording techniques EEG and MEG therefore remain essential to study the dynamics of the neuronal encoding and representation of acoustic signals. In particular, hypotheses that connect insights from human recordings to animal physiology are most powerful at the level of electrophysiological phenomena, and it follows that questions of coding are best addressed by considering electrophysiological data with the appropriate temporal resolution (Luo et al. 2006). In this context, MEG provides a unique contribution to auditory neuroscience, complementing what EEG offers. First, a practical feature of MEG is that preparation time is brief. A subject can be in the scanner within min because the time consuming task of applying and checking electrodes is obviated. Second, the anatomic (sulcal) location of large parts of auditory cortex in the human brain (Morosan et al. 2001) make MEG ideally suited for electrophysiological studies. Many neuromagnetic fields originate from the dorsal aspect of the superior temporal gyrus and the planum (Fig ), and their net activation is

5 28 Magnetoencephalography 601 optimally sited for capture by MEG. Third, MEG is especially well suited to investigate lateralized phenomena. For biophysical reasons noted above evoked responses measured with EEG/ERP are best visible and quantified most precisely at midline electrodes, making hemispherically asymmetric effects more difficult to characterize. Effects related to speech, language, and pitch processing, which are often lateralized, are effectively captured by MEG, enabling us to build more nuanced models of the neurocomputational principles underlying auditory lateralization. 2 Auditory Evoked Magnetic Fields: Basic Phenomena Elementary auditory stimuli such as clicks or tone bursts generally elicit an auditory evoked magnetic field (AEF) with a stereotyped, highly reproducible time course. Some aspects of this temporal pattern are conserved in many experimental conditions, and the goal of many MEG studies is to examine how a specific feature (e.g., peak amplitude or latency) depends on certain stimulus properties (e.g., frequency or intensity). The question as to where in the cortex the respective phenomenon originates is generally of secondary importance in such studies. Thus, difficulties inherent to the solution of the inverse problem can be largely ignored. If the main focus is on the time course of the AEF, it may suffice to consider the channel with the strongest signal or to calculate the root-mean-square (RMS) value for an appropriate subset of channels. Alternatively, an elementary source analysis may be performed by representing the auditory cortex of each hemisphere by a single current dipole with invariant location and direction, but time-dependent dipole moment. The estimated dipole moment may then be interpreted as the signal of a synthetic channel (beamformer) focusing on auditory cortex. Regarding the signal-to-noise ratio, the dipole moment is usually superior to single measurement channels. 2.1 On-Response, Sustained Field, and Off-Response The transition between sound and silence evokes an onresponse at sound onset, a somewhat smaller off-response at sound offset, and a sustained field (SF) which lasts from onset to offset (Fig. 28.3a). These three phenomena are ubiquitous in MEG studies of audition, although they are typically not as distinct as in the present example. The underlying study (Lammertmann and Lütkenhöner 2000) is special in that very long stimuli were presented at a rather Fig (a) Time course of the response elicited by a10-s long tone burst. The curve refers to the moment of a dipole representing the entire auditory cortex of one hemisphere. Transitions between silence and sound and vice versa elicit a pronounced on- and a somewhat smaller off-response. Stimulus persistence is reflected in a sustained field (SF). (b) On-response (black curve) and off-response (gray curve) on an enlarged scale. The on-response has deflections with latencies 50, 100, and 200 ms, termed P50m, N100m, and P200m. The P50m is absent in the off-response. Derived from prior work (Lammertmann and Lütkenhöner 2001, Fig.9) low rate. Moreover, experiment and analysis were designed to allow analysis of near-dc components of the response, which normally have a poor signal-to-noise ratio; by this means it was possible to analyze the temporal dynamics of the SF. In the present example, the SF decreases with a time constant of 3.6 s, falls to a much lower level immediately after stimulus offset, and then decays to the baseline (mean potential before the presentation of the next stimulus) with a time constant of 2.7 s. Qualitatively consistent results were obtained using a special direct-current (DC) MEG technique (Mackert et al. 1999). A similar waveshape, consisting of on- and off-response and a sustained component, was found with functional magnetic resonance imaging (fmri) in both Heschl s gyrus and superior temporal gyrus (Harms and Melcher 2002).

6 602 B. Lütkenhöner and D. Poeppel 2.2 Waves P50m, N100m, and P200m The on-response typically shows three prominent peaks with latencies around 50, 100, and 200 ms (Fig. 28.3b, black curve). It has been suggested to denote AEF peaks by addition of the suffix m to the names of the electrical counterparts (Hari et al. 1980). The three peaks are therefore denoted as P50m, N100m, and P200m, with the initial letter indicating the polarity of the peak and the number denoting the approximate latency. Although intracortical recordings suggest a more complex view (Steinschneider et al. 1994), it is assumed that a positive polarity essentially reflects a depolarization in cortical layers III or IV and a negative polarity a depolarization near the cortical surface, perhaps in layer II (Eggermont 2007). The latter condition evidently corresponds to intracellular currents that flow from superficial to deeper cortical layers (by definition, current flows from plus to minus). A peak of positive polarity thus corresponds to currents in the opposite direction. By combining MEG and magnetic resonance imaging, this has been confirmed for the peaks N100m and P200m (Lütkenhöner and Steinsträter 1998). The N100m is a rather robust phenomenon that generally dominates the on-response. The P200m, by contrast, is much more variable and may be of such low amplitude that a clear peak is absent in some subjects (Hari et al. 1982; Jacobson et al. 1992b; Lütkenhöner et al. 2006). P200m is significantly enlarged in musicians when compared to individuals without musical training (Kuriki et al. 2006). The earlier finding of a reduced P200m/N100m amplitude ratio in tinnitus patients (Hoke et al. 1989) presumably results from a problematic selection of the normal-hearing reference group since subsequent studies could not replicate that finding (Jacobson et al. 1991; Jacobson and McCaslin 2003). A high interindividual variability also impedes the investigation of the P50m, which is not consistently observed in all subjects (Pantev et al. 1996; Onitsuka et al. 2003; Lütkenhöner et al. 2006). The off-response (Fig. 28.3a) is typically displayed with time zero referring to the stimulus offset (Fig. 28.3b, gray curve). There is a clear N100m, but no P50m, in accord with earlier studies (Hari et al. 1987; Pantev et al. 1996); the off-counterpart of the P200m is inconspicuous (small peak with respect to the dashed line). In another study (Pantev et al. 1996), an off-p200m was seen in 4 of 10 subjects. Microelectrode recordings from rat auditory cortex suggest that the off-response may be formed by a rebound after inhibitory input (Takahashi et al. 2004). Off responses seem to be more prominent in infants (Wakai et al. 2007). Both the on and the off-response are highly dependent on stimulation parameters. The phenomenon studied most systematically is the N100m on-response. The amplitude of this wave is roughly proportional to the square root of sensation level measured in db (Bak et al. 1985), except near the threshold of hearing, where the amplitude is proportional to sensation level (Lütkenhöner and Klein 2007). Another crucial parameter is the interstimulus interval (Hari et al. 1982). If the interstimulus interval is reduced from 16 to 1 s, the N100m amplitude declines by about a factor of 3 4 (Campbell and Neuvonen 2007). Sequences of six short tone bursts presented at 500 ms intervals, with a 3.4 s silent interval between two sequences, elicited a strong amplitude reduction between the first and the second N100m, but no major difference between the second and the subsequent N100m responses (Lammertmann et al. 2001). Comparable results were found in auditory evoked potentials recorded with scalp electrodes (Fruhstorfer et al. 1970) or intracranially in patients undergoing presurgical evaluation (Rosburg et al. 2004). Although interstimulus intervals as brief as 500 ms (or less) do not usually prevent the development of the N100m, even a 1-s interval does not always ensure that the peak is found (Lütkenhöner et al. 2001). Other factors shaping the N100m response are stimulus duration (Joutsiniemi et al. 1989) and rise time (Biermann and Heil 2000). Moreover, the response significantly depends on the stimulus type, e.g., noise or tone (Lütkenhöner et al. 2006). Aspects such as temporal integration (Forss et al. 1993; Loveless et al. 1996), spectral composition (Jacobson et al. 1992a; Stufflebeam et al. 1998; Roberts et al. 2000; Seither-Preisler et al. 2003), and pitch (Crottaz-Herbette and Ragot 2000; Seither-Preisler et al. 2006b) also influence measurement. 2.3 Transition Responses An AEF is elicited not only by a transition between silence and sound (on response) and vice versa (off response), but also by a transition from one sound to another, as studies of responses to vowel onsets after voiceless fricative consonants (Kaukoranta et al. 1987) and to noise/square wave transitions (Mäkelä et al. 1988) show. A transition between sounds usually involves a change in stimulus energy. Thus, the elicited response is not necessarily specific to the nature of the transition. A pitch-specific response without contamination by an energy-related component was measured by analyzing a transition from a noise to a regular interval sound (RIS) with the same intensity and bandwidth, but eliciting a sensation of pitch (Krumbholz et al. 2003). The transition from noise to RIS elicited a prominent pitch onset response (POR). The latency and size of the POR were directly related to the pitch value and its salience. Figure 28.4 shows exemplary data from a subsequent study (Seither-Preisler et al. 2006a). The

7 28 Magnetoencephalography 603 first two seconds, representing the response to noise, qualitatively agree with the response to a tone burst (Fig. 28.3a). Then, however, a noise-ris transition elicits a POR that is at least as strong as the N100m. There is an approximately linear increase in POR amplitude with the logarithm of noise duration (Seither-Preisler et al. 2004), and RIS induces a larger SF than noise (Fig. 28.4). A similar finding was made by comparing the responses to regular and irregular click trains (Gutschalk et al. 2002, 2004). Other data, testing binaural pitch, converge with the POR data reported above (Chait et al. 2006). Comparison of the cortical and behavioral responses to Huggins Pitch (HP), a stimulus requiring binaural processing to elicit a pitch percept, with responses to tones embedded in noise (TN) perceptually quite similar but physically different signals confirm this idea. As in the above studies, the stimuli were crafted to separate the electrophysiological responses to onset of the pitch percept from the stimulus onset. These data show that, although physically distinct, both HP and TN are mapped onto similar substrates on lateral Heschl s gyrus by 150 ms post-onset. Cumulatively, the data across laboratories provide critical evidence that the pitch-onset response reflects central pitch mechanisms, in agreement with models postulating a single, central pitch extractor sensitive to abstract properties of pitch. A final example of the relevance of transitions lies in how elementary auditory experiences (objects) arise (Chait et al. 2007a,b). The acoustic biotope varies as a consequence of the (dis)appearance of acoustic sources, often manifested as transitions in the pattern of ongoing activity. How does the system detect and process such transitions? MEG data suggest that the dynamics and response morphology of the temporal-edge detection processes depend on the nature of the change. Measurements of auditory cortical responses to transitions between a sequence of random frequency tone pips (disorder) and a constant tone (order) show that these transitions embody key features of auditory edges. Early responses (from 50 ms post-transition) reveal that order-disorder transitions, and vice versa, are mediated by slightly different neural mechanisms. This suggests that cortex optimally adjusts to stimulus statistics even when this is not required for overt behavior. The response profile (Fig. 28.7) bears a striking similarity to that measured from another order-disorder transition, between interaurally correlated and uncorrelated noise, radically different stimuli (Chait et al. 2005). This parallelism suggests a general mechanism that operates early in the processing stream on the abstract statistics of the auditory input, and is putatively related to the processes of constructing a new representation of the auditory scene. 2.4 Mismatch Negativity One experimental approach that has been used extensively employs mismatch designs. In mismatch negativity (MMN) studies (in the case of MEG, mismatch field or MMF), a sequence of stimuli is presented such that one stimulus is often repeated and acts as a standard while a second stimulus is interspersed occasionally and is a deviant. The evoked response difference (subtraction) between deviant and standard stimulus is the mismatch response, and is an easily implemented and reliable indicator of change detection in an acoustic sequence. For example, small deviations in frequency, amplitude, timbre, etc. can be tested with the MMN/F design (Näätänen and Alho 1995). Higher-order sequences are also often investigated (e.g., a sequence of speech sounds, or words), highlighting the utility of the mismatch response to assess change detection more generally. The MMN/MMF likely has several cortical generators, including at least one in the superior temporal cortex and one in frontal cortex (Alho 1995). Change detection using MMN is distinct to the transient responses discussed above. Fig Time course of the response elicited by a 2-s segment of random noise followed, without a gap, by a 1-s segment of regular interval sound (RIS) of identical bandwidth and intensity. The noise onset elicits a prototypical on-response (as in Fig. 28.3), with P50m, N100m, and P200m waves. A strong response is elicited also by the transition from noise to RIS sound; it is called pitch-onset response (POR). RIS sound evokes a stronger sustained field (SF) than noise. Derived from prior work (Seither-Preisler et al. 2006a, Fig. 5b)

8 604 B. Lütkenhöner and D. Poeppel 2.5 Faster Transient Responses The existence of a response peak around 50 ms (P50m) implies that there is earlier activity (rising slope of the P50m). However, experiments such as those considered above (Figs. 28.3, 28.4) are unsuited for a more detailed consideration of early activity. To achieve a sufficient enhancement of the signal-to-noise ratio at the beginning of the response, the number of averaged epochs must be increased by at least an order of magnitude, which is practicable only with shorter stimuli presented at a relatively high rate. In the example presented here (Fig. 28.5), a response was elicited by clicks presented at mean intervals of 350 ms. As distinct from the previous figures, not a single waveform (estimated dipole moment) is shown, but the time courses in single magnetometer channels. About 20 ms after click presentation (corresponding to the travel time from the periphery to the cortex), the activity sharply increases to a first maximum around 30 ms, P30m. After a brief reduction in the overall activation level, a second maximum occurs at 60 ms. The two peaks and the intervening valley are considered counterparts of the Pa, Nb, and Pb (also called P1) waves of the middle-latency auditory evoked potential (Picton et al. 1974; Eggermont and Ponton 2002). But it is appropriate to be cautious since both the magnetic and the electrical responses represent a conglomerate of contributions from various cortical sources, and the mixing ratios may not be entirely the same. This would explain why simultaneous recordings of the two types of responses may show significant differences (Yvert et al. 2001). The N100m and P200m waves are absent, owing to the high stimulus repetition rate (Fig. 28.5). 2.6 Steady-State Responses If the stimulus repetition rate is further increased, the situation arises that even faster response components such as the P30m do not fade away before the presentation of the next stimulus. The consequences are illustrated with an example (Fig. 28.6). The curves show responses to three different series of clicks. The interval between two clicks is initially 100 ms, corresponding to an instantaneous rate of 10 Hz (times of click presentation indicated by dotted vertical lines). Then the interval is continuously reduced until the periodic rate indicated on the left is reached. During the time range marked in gray, the click presentation is strictly periodic and so the response becomes periodic as well. This type of response is called steady-state response. Steady-state responses to clicks presented at 20 and 40 Hz are strong, whereas only weak steady-state responses are found at 30 Hz (Fig. 28.6). The effect is qualitatively explained by Fig Time course of the response elicited by a click (interstimulus interval ms). The response waveforms at 37 locations over one hemisphere were superimposed; the maximum response of each polarity was highlighted (two white curves on black background). The peaks near 30 and 60 ms (dotted lines) and the valley between presumably correspond to the waves Pa, Nb, and Pb of the middle-latency auditory evoked potential. The negative peak is not conspicuous in this example, but the interindividual variability is high, and much more pronounced Nb correlates were found in other experiments (Yoshiura et al. 1996). Later activity has a relatively small amplitude in the present example, and the asymmetry between the two polarities suggests a non-dipolar spatial pattern. Derived from prior work (Lütkenhöner et al. 2003b, Fig. 1a) considering how the major peaks of the transient responses to the individual clicks would sum. The explanation is consistent with a convolution model for steady-state activity (Gutschalk et al. 1999), and with earlier work in which 20- and 40-Hz responses were successfully reconstructed from the 10-Hz response (Hari et al. 1989). The latter study concluded that an amplitude enhancement at a specific frequency (40 Hz in particular) can be explained without hypothesizing resonance properties of the cortical network. Moreover, it was shown that a latency derived from the relationship between phase of the response and stimulation rate (group delay) is of questionable physiological relevance. Apart from click trains, many other periodic stimuli have been used to elicit steady-state responses (Picton et al. 2003). Steady-state responses have been used profitably to test, for example, how simultaneous amplitude modulation (AM) and frequency

9 28 Magnetoencephalography 605 Fig Transition from a sequence of transient responses to a periodic (steady-state) response, and vice versa. In the initial 200 ms, the three response curves are indistinguishable. The first click (clickpresentation times marked by vertical dotted lines) elicits a P30m and a second positive peak at 80 ms (inverted triangle and a vertical bar, respectively). The second click at 100 ms elicits a P30m around 130 ms (inverted triangle). This second P30m apparently superimposes on the falling slope of the 80-ms peak elicited by the first click. The intervals between subsequent clicks are next reduced step-by-step until the periodic repetition rate indicated on the left of each curve is reached, which modulation (FM) are encoded (Luo et al. 2006) and how long acoustic sequences (typical of speech or music) are reflected in the steady-state responses (Patel and Balaban 2004). 3 Domains of Magnetoencephalographic Research in Auditory Cognition Large portions of the human auditory system are located in sulcal cortex, on dorsal aspects of the superior temporal gyrus (Fig ). This includes core and belt areas associated with the anatomic structures of Heschl s gyrus is maintained for about 400 ms (time range marked in gray). The second half of the click series is a mirror image of the first half. The periodic response caused by periodic stimulation is the steady-state response. In this example, the amplitude of the steady-state response is high at click repetition rates of 20 and 40 Hz, and lower at 30 Hz. In the first two cases, P30m peaks likely coincide with 80-ms responses to previous clicks, whereas in the latter case they are assumed to occur in the middle of two 80-ms responses. Based on prior work (Lütkenhöner et al. 2004, Fig.2) (transverse temporal gyrus), the planum temporale, and the planum polare. This anatomic fact coupled with the millisecond temporal resolution of MEG renders the technique optimally suited for recording neurophysiological activity noninvasively with remarkably high fidelity, and for investigating how acoustic signals are transformed to yield the auditory representations that form the basis for speech perception, music cognition, and other aspects of auditory cognition. While many taxonomic schemes are possible, we adopt a simple classification to organize the numerous studies using MEG, identifying several (somewhat overlapping) domains

10 606 B. Lütkenhöner and D. Poeppel of research: (a) work on elementary perceptual attributes derived from acoustic signals pitch, loudness, and timbre; (b) work on elementary processing strategies used to generate perceptual representations, including streaming, integration, binding, and change detection; (c) research on speech processing, ranging from isolated vowels to connected speech; (d) research on music; and (e) studies on multisensory and sensory-motor interaction and integration. We briefly highlight selected data on how MEG studies contribute to auditory neuroscience, and specifically to models of auditory cortex function. 3.1 The Construction of Elementary Auditory Attributes Auditory perceptual representations, regardless of their cognitive identity (e.g., speech versus non-speech), necessarily reflect basic attributes (such as loudness, pitch, timbre) that derive from the physics of the signal. MEG studies have been successful at identifying some of the relations between early neuromagnetic activity and basic perceptual representations. (i) As noted above, the N100m can be exploited to investigate aspects of loudness perception. Threshold and non-threshold loudness phenomena can be quantified and segregated at the (temporal and spatial) level of N100m generation (Reite et al. 1982; Bak et al. 1985; Stufflebeam et al. 1998; Lütkenhöner and Klein 2007). This is of special interest since psychophysical research shows that a stable percept of loudness is generated at 200 ms post-stimulus onset (Moore 2003). Models of loudness perception therefore must consider the differing results of a 200-ms time constant, on the one hand, and early sensitivity to intensity evident in the N100m, on the other. Both types of data are highly robust and replicable and require explanation. (ii) The computational basis of pitch is a vast field. Several recent MEG studies (cf. Section 2.3) make a critical contribution in that regard. Considerable data show that a response generated on the lateral aspect of Heschl s gyrus can be viewed as a pitch-onset-response (POR), regardless of whether the pitch is evoked monotonically or dichotically (Krumbholz et al. 2003; Seither-Preisler et al. 2004, 2006a,b; Chait et al. 2006). Such data thus implicate a local region in lateral Heschl s gyrus in the calculation of pitch at a relatively abstract level, given that the experiments used rather different stimulation that included click trains, iterated rippled noise, dichotic Huggins pitch, and other materials. In this domain, too, the MEG data on the POR (peaking at ms after pitch onset) support certain models of pitch and challenge others. (iii) How cortical neurons represent timbral information, or more generally aspects of the spectral envelope, has become a topic of research from single-unit studies to fmri and MEG. Viewing the N100m alone, provides evidence that this response covaries in latency with envelope modulations (Roberts et al. 2000; Ritter et al. 2007). For example, the latency of the N100m elicited by low-frequency signals ( Hz) is systematically affected the by spectral envelope (e.g., the difference between sine-, square-, and saw-tooth waves). Specifically, both F0 and the spectral envelope concurrently affect latency. Because the N100m cannot sample more than 40 ms of signal (cf. Section 3.2), very brief segments of signal suffice to construct usable representations of the sound spectral envelope. Naturally, this is also relevant for how speech sounds are encoded (cf. Section 3.3). 3.2 Elementary Operations in Auditory Cortex The cortical construction of perceptual representations relies on processing algorithms that are, by and large, shared across domains. These processes include temporal integration, auditory stream segregation, and change detection. The N100m response again provides a sensitive measure to evaluate such basic operations. (i) The afferent auditory pathway is subject to temporal summation and integration by the neural substrate subserving the analysis. Neural elements reflect both a temporal integration constant and the temporal resolution afforded by a given response. An N100m temporal integration window of ms must be assumed, i.e., the N100m is affected by acoustic information up to 40 ms, but signals outside this temporal window do not affect its properties (Gage and Roberts 2000; Gage et al. 2006), except at rather low stimulus levels (Lütkenhöner and Klein 2007). Within this brief temporal integration window, acoustic events (e.g., gaps) can be resolved to 2 ms. N100m timing and amplitude show a resolution that is well matched to psychophysical gap detection thresholds while integrating over durations commensurate with the temporal order threshold. Other evoked fields (Fig. 28.3) can be examined in similar studies, forming the basis for a larger-scale model of temporal integration in human auditory cortex. (ii) Work on stream segregation found auditory cortex displays many aspects of streaming (Micheyl et al. 2007).

11 28 Magnetoencephalography 607 Interpretable auditory objects or streams must be assembled from complex input arising from many sources, and here, too, early evoked fields are useful as dependent measures. There is a strong correlation between early cortical activity up to and including the N100m and the representation of separate streams (Gutschalk et al. 2005). (iii) The detection of change has principally been investigated using the mismatch response (MMN or MMF) a response pattern that also has significant supratemporal sources. Local changes in stimulus statistics are also reflected in neuromagnetic responses (cf. Section 6.1). The response to change can be used to test where, when, and how changes in stimulus statistics are detected (Fig. 28.7) (Chait et al. 2007a). Stimulus statistics are reflected in change responses by ms post change onset. 3.3 Speech Processing: Overview Investigating the cortical basis of speech processing has been central to MEG research. Because auditory evoked responses exhibit such stereotypical morphology and timing (Fig. 28.3), how these responses are modulated by speech input has been a foundational question. Clicks or brief tone burst elicit the cascade of responses visualized as the P50m, N100m, and P200m. How the responses elicited by single vowels or consonant-vowel (CV) syllables or even single words appear in comparison has been studied extensively, even in nonspeech control experiments (Mäkelä et al. 1988). In this context, it is critical to note that speech perception is not monolithic. The theoretical and neurobiological machinery invoked is quite distinct when studying isolated vowels, isolated CV syllables, isolated Fig Grand average of the across channels and subjects responses for a stimulus of brief tone pips at randomly changing frequencies between 222 and 2000 Hz (random condition) alternating with a tone (constant condition). Grey line, the no-change control condition. Contour maps show the magnetic field distribution at critical time points. Both panels illustrate a robust N100m response at the beginning of the stimulus. (a) The response profile from random-to-constant shows a single large response after the transition, with a contour resembling the N100m. (b) Constant-to-random response with two peaks after the change onset. Because the response profile, timing and distribution differ between these two conditions that are matched along several stimulus dimensions, auditory cortex may maintain an on-line model of the local statistics of the stimulus. The direction of change is critical since in one case a representation is constructed from randomly distributed pips (RC); in the other condition (CR) the representation is destroyed. Adapted from the original (Adapted from Chait et al. 2007b)

12 608 B. Lütkenhöner and D. Poeppel words, or connected speech (Hickok and Poeppel 2007; Poeppel et al. 2007). Consequently, generalizations about the neural basis of speech perception should consider that psycholinguistically distinct levels of analysis are associated with varied neurobiological implementation. A further terminological clarification is necessary, since we focus on speech perception but not language comprehension more broadly construed. Language comprehension can be driven by auditory (speech), visual (text, sign), or somatosensory (Braille) information and operates on supramodal linguistic representations. Speech perception is the set of processes transforming acoustic input into a format suitable for language comprehension and further computation (morphology, syntax, etc.). We focus on speech perception, among the many MEG studies on language. Ignoring this fundamental distinction can lead to profound confusion about which computational subroutines are actually at stake. The study of speech has occurred largely independently from work on basic attributes and operations (cf. Sections 1 and 2). Although basic perceptual attributes such as pitch and primitive operations such as integration apply to all sounds, few MEG studies link basic auditory cognition and speech properties. Many speech studies have inquired whether special signal properties are reflected in neuromagnetic responses. We consider research on vowels and syllables, words, and connected speech. 3.4 Vowels, Consonants, and Syllables A few distinct approaches are taken, focusing on spatial mapping, on the speech/nonspeech distinction, and on linguistic abstraction (from sound to phonology). Some studies adopt a strongly localizationist perspective, and therefore show extensive source modeling data on the responses elicited by vowels or consonant-vowel (CV) syllables (cf. Section 4 for analysis of dipole localization and spatial mapping). This research often assumes that there are likely to be spatially organized maps in superior temporal cortex that reflect where speech sounds are represented (phonemotopy) and has been used for the analysis of vowels (Diesch et al. 1996; Obleser et al. 2003b) and consonants (Obleser et al. 2003a, 2006). The N100m is subjected to dipole localization as a function of stimulus type (cf. Section 1). For the role of the sustained field, fewer data are available with implications for auditory cortex models. The vowels /a/ and /i/, which are well separated in formant (F1-F2) space, lie far apart on the anterior dorsal aspect of the supratemporal gyrus (STG), the planum polare. In contrast, the spatial position of vowels more closely aligned in formant space is correspondingly closer in auditory cortex. Such work builds on two crucial features: the putative existence of maps in cortex that encode the relevant acoustic features spatially (here corresponding to frequency) and, second, the ability of MEG source modeling to resolve the relevant spatial differences. This relates the systematic representation of speech sounds to cortical maps (speech sound identity is determined by its position on a cortical sheet), and to the results from phonology, raising the possibility that what is mapped may be more abstract than frequency and amplitude. Consonants with conflicting place-of-articulation features are also mapped more distantly in cortical space, which suggests a potential mapping from place of articulation to brain space. That measurements based largely on the N100m stimulate such hypotheses is noteworthy. It remains controversial whether such maps of speech sounds can be reliably identified in human auditory cortex. The N100m response to speech sounds also varies in systematic ways in time (latency) (Diesch et al. 1996; Roberts et al. 2000; Obleser et al. 2003b). Complementary investigations incorporate the temporal dynamics of the acoustic signals and the ensuing neuromagnetic responses. Perhaps temporal coding principles play a critical role in speech sound representation. F0 as well as spectral peaks are reliably reflected in the N100m latency, an approach that connects more clearly with the auditory elements discussed above. It remains unclear whether models relying more on phonemotopy or on, phonemochrony, best capture the neuronal representation of vowels and consonants. Connecting more explicitly with neural coding models that derive from animal research will be a vital step. A different approach tests whether speech versus matched non-speech signals elicit responses differing in amplitude, latency, and spectral properties. Measuring the N100m to isolated vowels (/a/, /u/) and CV syllables (/pa/, /ka/) and comparing the responses to materials closely matched spectrotemporally showed that the left N100m is significantly larger and differentiates between the stimulus types, whereas the speech-nonspeech distinction is not robustly visible in the right hemisphere N100m (Fig. 28.8) (Parviainen et al. 2005). The gamma band response differed between speech and nonspeech by 60 ms poststimulus onset, with the right hemisphere showing particular sensitivity to nonspeech and the left to speech (Palva et al. 2002). Such data suggest that this distinction emerges ms after the onset of the signal. If the N100m reflects at most 40 ms of signal (cf. Section 6.2), such data require a model that explains how 40 ms worth of acoustic signal can be identified as speech versus nonspeech, given the receptive field properties of neurons in the auditory hierarchy. The data imply that small durations of signal can support subtle distinctions between signal types in the N100m, generated in superior temporal cortex. The responses to syllables are typically more complex even superficially, encompassing components elicited by the

13 28 Magnetoencephalography 609 properties of syllable onsets are also reflected in the N100m, whose amplitude, timing, and lateralization is sensitive to the distinction between stops and continuants (Gage et al. 1998). Such sensitivity transcends acoustic-phonetic factors. Data from (phonological) nasalization restrictions in English show that MEG responses between ms poststimulus onset reflect knowledge of the abstract phonological generalizations that a speaker brings to the perceptual task (Flagg et al. 2006). Finally, the response to syllables is conditioned by top-down expectations such that the response to a syllable sharply differs after the N100m, from 200 ms forward, when presented in isolation versus contexts that facilitate lexical access and other higher-order linguistic subroutines (Bonte et al. 2006). Mismatch designs (cf. Section 6.4) are tools to study abstract phonological representations in speech by using experimental designs showing auditory cortex sensitivity to a change in loudness or pitch, for example. Psycholinguistic studies use subtle, often crosslinguistic, mismatch designs to test how the native language phonology constrains the early analysis of speech sounds. This approach has established that both language-specific and abstract (phonological) representations can be probed by using mismatch designs, which in turn implies that, by 150 ms, these effects are established (Näätänen et al. 1997; Phillips et al. 2000; Kazanina et al. 2006). 3.5 Words Fig The top panels show areal means from several channels for the N100m recorded from both hemispheres. In the left hemisphere, the speech condition has robustly larger responses, discriminating between speech, matched complex sounds, and tones. The lower panels show the dipole model fit in the three-dimensional brain (Sylvian fissure marked) and dipole strength over time (bottom traces). Again, the speech condition showed the largest response in the left, whereas the other two conditions were not differentiated at the N100m. Adapted from the original source (Adapted from Parviainen et al. 2005) syllable features such as bursts, closure releases, and voicing onsets. The neuromagnetic fields depend on a variety of acoustic-phonetic, phonological, and semantic-contextual factors. Early work capitalizing on MEG s temporal resolution established that intrasyllabic distinctions could be identified. The burst of energy associated with closure release and the onset of voicing, when sufficiently separated in time, as in a voiceless stop such as /t-a/, can be resolved, yielding responses resembling an N100m and N100m, (see Mäkelä et al for non-speech controls). The detailed acoustic In contrast to the basic approach exemplified by research on isolated speech sounds, an intermediate level is represented by spoken word recognition. The typical concerns are where, when, and how the recognition process occurs. This implies that acoustic information must interact with long term memory (words), and must therefore be transformed into a usable representation. Experimental design plays a more crucial role in this research. A study using a canonical mismatch design concluded that, by 150 ms after stimulus onset, lexical access has been initiated and phonological and semantic information are evident (Pulvermüller et al. 2006). Because the MMNm originates on dorsal STG and peaks at 150 ms, it is not surprising to see effects at that latency. Another study implemented the mismatch idea by presenting quadruplets of words which either generated semantic or phonological expectations and assessing the evoked fields. Phonological information is reflected reliably by the N100m whereas semantic information appeared from 200 ms on. These data are, thus, more compatible with a view that acoustic-phonetic-phonological analysis executed in superior temporal cortex precedes semantic processing, although both types of information are readily available very early in processing (Uusvuori et al. 2008).

14 610 B. Lütkenhöner and D. Poeppel 3.6 Connected Speech Some experiments use connected speech to study how ecologically natural speech is segmented. Listeners are presented sentences at different compression ratios, thereby parameterizing and assessing their intelligibility. A principal component analysis of the MEG showed correlations between auditory cortical phase-locking to the speech envelope and speech intelligibility. Successful phase-locking of the response to the envelope seems a key feature to insure intelligibility. At compression values compromising sentence intelligibility, phase locking was also poor (Ahissar et al. 2001). Analysis of single trials of spoken sentences show that the phase pattern of the cortical theta band (4 8 Hz) response tracks and discriminates between spoken sentences. This discrimination ability is correlated with intelligibility (Fig. 28.9). The data suggest that a 200 ms temporal window (period of theta oscillation) segments the incoming speech signal, resetting and adjusting to track speech signal dynamics. The mechanism for this cortical speech analysis may be based on the stimulus-induced modulation of inherent cortical rhythms (Luo and Poeppel 2007), a view supported by concurrent EEG-fMRI recordings (Giraud et al. 2007). Together, both studies strongly implicate the syllable as a computational primitive for the representation of spoken language, showing at the very least that connected speech is segmented into syllable-sized temporal elements. 4 Magnetoelectroencephalographic Studies on the Structure of Human Auditory Cortex In early MEG studies, high source localization accuracy was considered the principal advantage compared to EEG. A more reserved view now seems appropriate. Source Fig (a) Spectrograms of three sentence stimuli and single-trial MEG traces. The analysis evaluated phase coherence across single trials of neuromagnetic responses to the same stimulus (within-group coherence) and compared the response to mixed trials (across-group). (b) Phase dissimilarity and power dissimilarity plots for one channel (left) and 20 auditory channels (right). Strong phase dissimilarity was seen in the theta band, 4 8 Hz, suggesting that phase coherence in that band discriminates the sentence types in single trials. (c) Contour plots of the phase dissimilarity plots showing the distribution over auditory cortex (right lateralized). Other responses showed no organized spatial pattern. The recordings suggest that the phase of the theta band response encodes the acoustic envelope of the sentences in single trials of MEG data. Reproduced from the original source (Reproduced with permission from Luo and Poeppel 2007)

15 28 Magnetoencephalography 611 localization from MEG is always uncertain, unless the signal arises from one focal source, or a few such well separated sources (Halgren 2004). This prerequisite is not fulfilled in typical MEG studies of audition so that inferences regarding the structure of auditory cortex need to be examined critically. 4.1 Localization of Primary Auditory Cortex MEG is relatively insensitive to subcortical activity. Thus, supposed that the activation of belt regions of the auditory cortex by nonspecific afferents (Lakatos et al. 2005) can be neglected, the very beginning of the AEF elicited by a short stimulus may be assumed to result essentially from a single focal source: primary auditory cortex. A source analysis of the earliest phase of the response should consequently allow localization of that region. A study of the P30m elicited by clicks, focusing on the initial rise about 20 ms postclick, suggests that this is indeed possible (Lütkenhöner et al. 2003b). Coregistration of the estimated dipoles with magnetic resonance images suggested that primary auditory cortex is near the retroinsular origin of Heschl s gyrus, in good agreement with intracranial recordings (Liégeois-Chauvel et al. 1994). 4.2 Tonotopic Maps A study of steady-state AEF elicited by amplitude-modulated tones showed that a 4 5 octave frequency change shifted the location of the estimated dipole by about 1 cm, which was interpreted as indicative of a tonotopic map in auditory cortex (Romani et al. 1982). This report triggered numerous investigations with other experimental setups (Lütkenhöner et al. 2003a). Not all authors found evidence of a tonotopic map in their data, but if they did, the typical conclusion was straightforward, with higher frequencies activating more medial regions of auditory cortex than lower frequencies. For the N100m such effects may be highly reproducible in individual subjects (Lütkenhöner and Steinsträter 1998), but the interindividual variability is bewildering (Lütkenhöner et al. 2003a). In most cases, the dipole location either exhibited no significant frequency dependence, or the dipoles for the investigated frequencies were not orderly aligned, or the data disagreed with the single-dipole hypothesis. These results do not support the utility of MEG as a tool for the study of tonotopic maps in auditory cortex. The main obstacle for a successful examination of tonotopy appears to be that the AEF typically arises from multiple sources (Hari 1990; Schreiner 1998; Eggermont and Ponton 2002). With some caveats, the location of the estimated dipole may be considered the center of gravity of the activated cortical areas, where the strength of cortical activation assumes the role of the mass. It is unlikely that stimulus parameters such as frequency will affect the strength and timing of the activities in different cortical areas in precisely the same way. Thus, the center of gravity estimated from multiple sources can, unfortunately, give the illusory impression of a single source with frequency-dependent location, but this would be a pseudotonotopy, without a valid structural correlate in auditory cortex (Lütkenhöner and Mosher 2007). 4.3 Dipoles Representing Multiple Cortical Sources Even though MEG seems to be unable to reveal structural details of single cortical areas, a cautious interpretation of estimated dipole locations may lead to valuable conclusions. A dipole source analysis may suggest, for example, that one type of activity predominantly originates from more anterior cortical regions than another. But it is crucial to appreciate that each dipole likely represents multiple, spatially distinct sources whose precise locations are unknown. In the case of two sources, for example, the estimated dipole would be expected to be on a line joining them, with the exact location depending on the dipole moments associated with each source. The estimated dipole location is commonly interpreted as the center of gravity of the contributing sources, as noted above But this view requires caution. If the net currents in the contributing sources are in opposite direction (so that the respective magnetic-field contributions partially cancel), the estimated dipole may be outside the activated cortical region (Lütkenhöner and Mosher 2007). Despite this potential pitfall, the idea of a center of gravity is a useful concept if applied with care. The N100m components of on- and off-response appear to have a similar origin (Hari et al. 1987; Pantev et al. 1996; Noda et al. 1998; Lammertmann and Lütkenhöner 2001). In right-handed subjects, the right-hemispheric N100m dipole was found 6 mm anterior to the left-hemispheric counterpart (Nakasato et al. 1995). While the sources are predominantly in planum temporale (Lütkenhöner and Steinsträter 1998), there seems also to be a component originating in the lateral part of Heschl s gyrus (Godey et al. 2001). Another study (Sams et al. 1993) suggested an anterior and a posterior subcomponent. The posterior subcomponent adapts as sound novelty decreases, and this subcomponent is likely involved in the gating of novel sounds to awareness (Jääskeläinen et al. 2004).

16 612 B. Lütkenhöner and D. Poeppel While the P50m dipole was found at a similar location as the N100m dipole (Hari et al. 1987; Mäkelä and Hari 1987; Kanno et al. 2000), the P200m dipole was consistently found at a more anterior location (Hari et al. 1987; Pantev et al. 1996; Lütkenhöner and Steinsträter 1998; Lammertmann and Lütkenhöner 2001). The dipole estimated for the SF either had a similar locus as the N100m dipole (Lammertmann and Lütkenhöner 2001) or was slightly more anterior (Hari et al. 1987; Pantev et al. 1996). Other authors (Gutschalk et al. 2004) distinguished an anterior and a posterior component of the SF. While the anterior component was related to temporal pitch processing, the posterior component was sensitive to stimulus intensity. The localization results obtained for the P30m are not completely consistent. A dipole in the vicinity of the N100m dipole has been seen (Yoshiura et al. 1996), or the source was localized in the dorso-postero-medial part of Heschl s gyrus (Godey et al. 2001), in approximate agreement with other results (Yvert et al. 2001). The POR dipole is described as 12 mm anterior to the N100m dipole, on average (Krumbholz et al. 2003). The POR may represent a source, or sources, on medial Heschl s gyrus (Fig ), adjacent to a larger region in the anterolateral half of Heschl s gyrus where functional imaging studies find a pattern of activation that is highly correlated with the degree of regularity in RISs (Griffiths et al. 1998; Patterson et al. 2002). 5 Conclusions and Perspectives Other than invasive (clinically motivated) recording in the human auditory cortices, MEG remains the key method to assess auditory function electrophysiologically. If we accept the view that many of the elementary mechanisms identified in animal preparations will be foundational for human auditory function as well (bandwidth, modulation, spectrotemporal receptive field organization), it is desirable and necessary to understand how human auditory cortex responds to basic attributes of auditory signals. A major advantage of MEG is that it enables concurrent psychophysical and physiological studies. The experimental dimension both physiologically and psychophysically should be emphasized more strongly. While a core goal will be to understand those functions that appear to be unique properties of human audition, such as speech perception, building more explicit models from animal work can guide future research. In the animal domain there are serious efforts to correlate hemodynamics with electrophysiology. MEG research can contribute to this effort by developing experimental paradigms of interest to animal physiologists. On balance, we advocate using MEG largely as an electrophysiological tool (with superior sensitivity in single subject studies at the level of single trials) rather than an imaging method with more modest prospects for precise localization. The power of MEG in localizing functional sources within a reasonable anatomic context such as the gyral and sulcal location can provide a framework for the appropriate electrophysiological analysis of the encoding that particular responses perform. MEG can serve as a functional bridge between human auditory processing and physiology as performed in awake behaving animals. References Fig Dipole locations of the POR (black) and the N100m (gray) for one listener, estimated from four measurement sessions and projected into a three-dimensional reconstruction of the listener s left temporal lobe. The dipoles are shifted upward by 3 cm from their actual position to prevent them from being partially obscured beneath the cortical surface. While the dominant generators of the N100m appear to be in planum temporale, the POR may arise from a source, or sources, on medial Heschl s gyrus. Scenery from the original source (Krumbholz et al. 2003, Fig. 6) modified and viewed from a different perspective Ahissar E, Nagarajan S, Ahissar M, Protopapas A, Mahncke H, and Merzenich MM (2001) Speech comprehension is correlated with temporal response patterns recorded from auditory cortex. Proceedings of the National Academy of Sciences of the United States of America 98: Alho K (1995) Cerebral generators of mismatch negativity (MMN) and its magnetic counterpart (MMNm) elicited by sound changes. Ear and Hearing 16: Baillet S, Mosher JC, and Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Processing Magazine 18: Bak CK, Lebech J, and Saermark K (1985) Dependence of the auditory evoked magnetic field (100 msec signal) of the human brain on the intensity of the stimulus. Electroencephalography and Clinical Neurophysiology 61: Biermann S and Heil P (2000) Parallels between timing of onset responses of single neurons in cat and of evoked magnetic fields in human auditory cortex. Journal of Neurophysiology 84: