Spatiotemporal reconstruction of the auditory steady-state response to frequency modulation using magnetoencephalography

NeuroImage 49 (2010) 745–758

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Spatiotemporal reconstruction of the auditory steady-state response to frequency modulation using magnetoencephalography Rebecca E. Millman a,⁎, Garreth Prendergast a, Pádraig T. Kitterick b, Will P. Woods a, Gary G.R. Green a a b

York Neuroimaging Centre, The Biocentre, York Science Park, Heslington, YO10 5DG, UK Department of Psychology, University of York, York, YO10 5DD, UK

a r t i c l e

i n f o

Article history: Received 15 May 2009 Revised 9 July 2009 Accepted 13 August 2009 Available online 21 August 2009 Keywords: Frequency modulation Auditory steady-state response Phase-locking Magnetoencephalography Beamforming Virtual electrodes

a b s t r a c t The aim of this study was to investigate the mechanisms involved in the perception of perceptually salient frequency modulation (FM) using auditory steady-state responses (ASSRs) measured with magnetoencephalography (MEG). Previous MEG studies using frequency-modulated amplitude modulation as stimuli (Luo et al., 2006, 2007) suggested that a phase modulation encoding mechanism exists for low (b 5 Hz) FM modulation frequencies but additional amplitude modulation encoding is required for faster FM modulation frequencies. In this study single-cycle sinusoidal FM stimuli were used to generate the ASSR. The stimulus was either an unmodulated 1-kHz sinusoid or a 1-kHz sinusoid that was frequency-modulated with a repetition rate of 4, 8, or 12 Hz. The fast Fourier transform (FFT) of each MEG channel was calculated to obtain the phase and magnitude of the ASSR in sensor-space and multivariate Hotelling's T2 statistics were used to determine the statistical significance of ASSRs. MEG beamformer analyses were used to localise the ASSR sources. Virtual electrode analyses were used to reconstruct the time series at each source. FFTs of the virtual electrode time series were calculated to obtain the amplitude and phase characteristics of each source identified in the beamforming analyses. Multivariate Hotelling's T2 statistics were used to determine the statistical significance of these reconstructed ASSRs. The results suggest that the ability of auditory cortex to phase-lock to FM is dependent on the FM pulse rate and that the ASSR to FM is lateralised to the right hemisphere. © 2009 Elsevier Inc. All rights reserved.

Introduction Biologically relevant sounds contain patterns of changes in loudness (amplitude modulation) and pitch (frequency modulation). Amplitude modulation (AM) is crucial for the recognition of speech, at least in quiet (e.g. Drullman et al., 1994; Shannon et al., 1995). However, more recently it has been shown that frequency modulation (FM) is important for speech perception in noisy backgrounds for both normal-hearing listeners (e.g. Zeng et al., 2005) and hearingimpaired listeners (e.g. Lorenzi et al., 2006). Despite extensive studies of FM processing in animals (e.g. Eggermont, 1994; Liang et al., 2002; Wang et al., 2003) and in humans using 1) psychoacoustics (e.g. Zwicker, 1952; Kay, 1982; Moore and Sek, 1994, 1995, Moore and Sek, 1996), 2) electroencephalography (EEG) (e.g. Picton et al., 1987, 2003; Dimitrijevic et al., 2001), and 3) magnetoencephalography (MEG) (e.g. Mäkelä et al., 1987; Luo et al., 2006, 2007), the mechanisms underlying the perception of FM remain unclear. It is debatable whether AM and FM are processed using the same mechanism (e.g. Zwicker, 1956,

⁎ Corresponding author. Fax: +11 1904 435356. E-mail address: [email protected] (R.E. Millman). 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.08.029

1962; Saberi and Hafter, 1995; Wang et al., 2003) or whether there is a modulation frequency dependent change in the underlying mechanisms at some critical modulation frequency (e.g. Kay, 1982; Moore and Sek, 1994, 1995, 1996; Luo et al., 2006, 2007; Wang et al., 2003). There is a lack of agreement in the literature regarding the modulation frequency at which a change in the mechanism may occur. The transition may occur for FM modulation frequencies between 5 and 10 Hz (e.g. Kay, 1982; Moore and Sek, 1994, 1995, 1996; Luo et al., 2006, 2007) or for modulation frequencies greater than 16 Hz (e.g. Wang et al., 2003). FM may be transduced into AM as early as the auditory filters in the auditory periphery (e.g. Zwicker, 1956, 1962; Saberi and Hafter, 1995), which provides the basis of a single underlying mechanism for the processing of AM and FM. Some psychoacoustical studies suggest that the mechanism depends on the modulation depth/index and that highly detectable AM and FM are not encoded in the same way (e.g. Edwards and Viemeister, 1994). Other psychoacoustical studies (e.g. Moore and Sek, 1994, 1995, Moore and Sek, 1996) suggest that the mechanisms for the perception of FM are dependent on the carrier frequency and the modulation frequency. The model put forward by Moore and Sek (1996) to predict results from psychoacoustical experiments suggests that a temporal mechanism (phase-locking) dominates for carrier frequencies up to 4–5 kHz and at low

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modulation frequencies, i.e. below 5–10 Hz, and a place mechanism (changes in the excitation pattern on the basilar membrane) dominates for carrier frequencies greater than 4–5 kHz and also for lower carrier frequencies when the modulation frequency is greater than 5–10 Hz. Some neurophysiological evidence (e.g. Liang et al., 2002; Wang et al., 2003) also supports the idea that the encoding of FM is modulation frequency dependent. Wang and colleagues argue that low AM and FM modulation frequencies are encoded by synchronization (phase-locking) to the modulation frequency but higher modulation frequencies (N16 Hz) are encoded in auditory cortex in a non-synchronised manner using a rate-based code (e.g. Liang et al., 2002; Wang et al., 2003). Magnetoencephalographic and electroencephalographic auditory steady-state responses (ASSRs) can be used to measure responses in human cortical areas to AM and FM. The ASSR has been shown to phase-lock to the modulation frequency of amplitude-modulated sounds for a wide range of modulation frequencies (∼4–100 Hz) (e.g. Picton et al., 1987, 2003; Ross et al., 2000; Simon and Wang, 2005). The response to AM is represented by a spectral peak at the Fourier component corresponding to the stimulus modulation frequency in the fast Fourier transform (FFT) of the ASSR (e.g. Picton et al., 2003; Ross et al., 2000; Simon and Wang, 2005). This response is a complex magnetic field that contains both amplitude and phase information (e.g. Picton et al., 1987; Ross et al., 2000; Simon and Wang, 2005). The phase and magnitude of the FFT can be used to characterize the response to modulated sounds. Ross et al. (2005) argue that the ASSR for modulated sounds is lateralized to the right hemisphere, whereas others (e.g. Herdman et al., 2003; Simon and Wang, 2005; Popescu et al., 2008) argue for bilateral representation of the ASSR. Picton et al. (1987, 2003) measured ASSRs for sinusoidally modulated AM and FM stimuli using EEG over a range of modulation frequencies (2–54.7 Hz). Their analyses of the reliability of ASSRs to FM, based on the Hotelling's T2 statistic (Hotelling, 1931), showed that there were only significant (p b 0.05) phase-locked responses to one low FM modulation frequency (4.9 Hz) and the higher modulation frequencies tested (19.5–54.7 Hz). Therefore they could not find reliable ASSRs for FM modulation frequencies between 8.8 and 15.6 Hz. Luo et al. (2006, 2007) measured ASSRs to complex FM–AM stimuli using MEG. The AM modulation frequency was 37 Hz and the FM modulation frequency was varied from 0.3 to 8 Hz (Luo et al., 2006) and from 2.1 to 30 Hz (Luo et al., 2007). Luo et al. (2006, 2007) used broadband FM stimuli with a relatively large frequency deviation of up to 660 Hz applied to a sinusoidal carrier frequency of about 500 Hz. Luo et al. (2006, 2007) reported bilateral spectral peaks in the FFT of the ASSR in MEG sensor-space at both the AM and FM modulation frequencies, which they termed “sideband representation.” Luo et al. (2006, 2007) suggested that two encoding-type parameters are involved in the processing of the FM component of FM–AM stimuli; phase encoding of low modulation frequencies (b5 Hz) is suggested but an additional form of AM encoding is required for modulation frequencies ≥5 Hz. The aim of the present work was to characterise ASSRs for FM in both MEG sensor-space and source-space. To our knowledge, this is the first study that has used MEG beamformers and virtual electrode analyses to investigate FM processing, although previous studies have used beamforming and source reconstruction techniques to characterise the ASSR for AM (e.g. Herdman et al., 2003; Brookes et al., 2007; Popescu et al., 2008). Beamforming can be used to localise the sources of the ASSR with a relatively high spatial resolution (b1 cm). As beamforming analyses create independent spatial filters throughout the brain, the combination of beamforming and virtual electrode analyses can be used to reconstruct independent time series of neural activity throughout the brain. Virtual electrodes are one way to take advantage of the fine temporal resolution (b1 ms) offered by MEG in

source-space. Virtual electrode analyses were used to reconstruct the time series at each neural source identified by the beamforming analyses and determine how FM is represented in primary and nonprimary auditory cortical areas. Specifically we expected to see Fourier components corresponding to the FM modulation frequency (Luo et al., 2006, 2007) in the FFTs of the ASSRs for all FM modulation frequencies tested in MEG sensor-space and source-space. Materials and methods Participants Thirteen participants (age range 19–34 years) with no history of neurological disorders took part in this study. Two participants were excluded from the analyses. One of these participants moved excessively (N0.8 cm) during the scan (as determined by head coil measurements) and the other participant was excluded on the basis of physiological artifacts. Consequently 11 participants were included in the final analyses. All participants gave their written consent prior to the experiment and the local ethics committee approved the study. Stimuli Data from pilot experiments (unpublished) showed an improved signal-to-noise ratio (SNR) for the sensor-space ASSR for single-cycle sinusoidal FM relative to that for sinusoidally modulated FM. Therefore pulses of sinusoidal FM were used as auditory stimuli. Stimuli were created using custom programs in MATLAB (The MathWorks, Natick, MA) with a sampling frequency of 44.1 kHz. Stimuli were delivered binaurally via Etymotic insert earphones (Etymotic Research ER3-A) at a sound level of about 83 dB SPL. Frequency-modulated stimuli were created with single-cycle sinusoidal pulses. The equation describing the FM stimuli was as follows: xðt Þ = cosð2πfc t + βmðt Þ = fm Þ

ð1Þ

where x is the signal, t is time, fc is the carrier frequency, β is the ‘modulation index,’ fm is the pulse rate, and m(t) is the modulation waveform. The modulation waveform, m(t), is given by: mðt Þ = 0:5ð1 − cosðπt = dÞÞ during a pulse or mðt Þ = 0 otherwise

ð2Þ

where d is the duration of each pulse. The modulation index of FM is defined by the differential of the phase angle. Therefore the frequency sweep (Δf) of the single-cycle sinusoidal FM pulse is given by: Δf =

β sinðπd = DÞ 4D

ð3Þ

where D is the period of the pulse rate. Fig. 1 shows the temporal waveform, the frequency deviation and the FFT of the 4-Hz FM stimulus. A 1-kHz sinusoid was used as the carrier frequency. The 1-kHz carrier was either an unmodulated control or frequency-modulated with a pulse rate of 4, 8, or 12 Hz. For the 4-Hz FM condition, the half-duration of the pulse was 24 ms. Single-cycle pulses for the 8- and 12-Hz pulse rates were created with single-cycle pulses of duty cycles equivalent to the duty cycle used in the 4-Hz FM condition. Therefore for 8-Hz FM the half-duration of the single-cycle pulse was 12 ms and for 12-Hz FM the half-duration of the single pulse was 8 ms. The modulation index for each FM stimulus corresponded to a frequency deviation of 158 Hz. All stimuli were gated on and off with 20-ms linear ramps. ASSRs were measured in a passive listening task. A block design presentation was used and each stimulus was presented in a block

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Fig. 1. Example of a single-cycle FM pulse stimulus. The carrier frequency is a 1-kHz sinusoid. The carrier is modulated with an FM pulse with a pulse rate of 4 Hz and with a frequency sweep of 158 Hz. The single-cycle pulse had a half-duration of 24 ms. For the purposes of this figure, the stimulus duration was 1 s (for the MEG experiment the stimulus duration was 240 s). (A) The stimulus waveform. N.B. The carrier frequency is artificially low. (B) The instantaneous frequency of the stimulus. (C) The FFT of the stimulus.

with a 240-s duration. The duration of each stimulus block resulted in many stimulus repetitions, e.g. for the 4-Hz FM condition the number of stimulus repetitions was 960. Blocks of stimuli were presented to participants in a pseudo-random order. MEG recording Data were collected using a Magnes 3600 whole-head 248channel magnetometer (4-D Neuroimaging Inc., San Diego). The data were recorded with a 678.17-Hz sample rate and band-pass filtered between 1 and 200 Hz. Prior to recording individual facial and scalp landmarks (left and right preauricular points, Cz, nasion, and inion) were spatially coregistered using a Polhemus Fastrak System. The landmark locations in relation to the sensor positions are derived on the basis of a precise localization signal provided by five spatially distributed head coils with a fixed spatial relation to the landmarks. These head coils provided a measurement of a participant's head movement at the beginning and end of each stimulus block. To carry out artifact rejection, the data from each 240-s experimental block were divided into 1-s epochs. Following visual inspection of the raw data, epochs contaminated with either physiological or non-physiological artifacts were manually removed. Co-registration For the source-space analyses, the landmark locations were matched with the individual participants' anatomical magnetic resonance (MR) scans using a technique adapted from Kozinska et al. (2001). T-1 weighted MR images were acquired with a GE 3.0-T Signa Excite HDx system (General Electric, Milwaukee, USA) using an eight-channel head coil and a 3-D fast spoiled gradientrecalled sequence. TR/TE/flip angle = 8.03 ms/3.07 ms/20°, spatial resolution of 1.13 mm × 1.13 mm × 1.0 mm, in-plane resolution of 256 × 256 × 176 contiguous slices. For the group analyses in MEG source-space, the individuals' data were spatially normalized to the Montreal Neurological Institute (MNI) standard brain, based on the average of 152 individual T-1 weighted structural MR images (Evans et al., 1993).

Sensor-space analyses The 1-s epochs of artifact-free data were averaged for each condition. The FFT of each of the 248 MEG channels was calculated to obtain the phase and magnitude of the ASSR, i.e. the complex magnetic field (e.g. Simon and Wang, 2005), for each condition. The magnitude and phase of the Fourier components of interest (4, 8, and 12 Hz) were plotted on a 2-D contour plot of the individual participants' heads in sensor-space using custom software in MATLAB (e.g. see Fig. 3). Source-space analyses MEG beamformer analyses were used to localise neural sources of the ASSR to FM. The sources of the ASSR were localized with a vectorised linearly-constrained minimum-variance beamformer (VLCMV) (Van Veen et al., 1997; Huang et al., 2004) using a 2-mm grid. An MEG beamformer calculates the contribution of a given voxel in the brain to the signal measured at the MEG sensors. Independent beamformers are constructed for each voxel. Each beamformer is an optimal spatial filter dedicated to a given grid point. The MEG sensors are linearly weighted to focus the sensor array on a given target source location. The output of the spatial filter generates the total power at the target location over a given temporal window and within a given frequency band. Non-parametric unpaired t statistics (e.g. Nichols and Holmes, 2002) were used to determine sources with significant (p ≤ 0.05) changes in power between the unmodulated sinusoid control and the FM conditions for each voxel. Both the “active” (FM stimuli) and “control” (unmodulated sinusoid) windows were 1 s in duration. Stimuli were presented diotically in the present study and it is therefore probable that correlated sources would appear in both hemispheres in response to the auditory stimuli (e.g. Herdman et al., 2003; Brookes et al., 2007; Popescu et al., 2008). A VLCMV beamformer will only recover little or none of the original source power for two highly correlated and spatially separated sources (e.g. Van Veen et al., 1997). There is disagreement in the literature regarding the degree of correlation between sources before correlation become an issue; examples of reported correlation coefficients

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where correlation is problematic for beamforming analyses range from 0.63 (e.g. Popescu et al., 2008) to ≥0.90 (e.g. Huang et al., 2004; Brookes et al., 2007). In the present study beamforming analyses were carried out 1) using all 248 channels (“whole-head” beamforming) to generate the covariance matrix, or 2) only using 133 channels in each hemisphere (“half-head” beamforming), a technique previously published by Herdman et al. (2003) to overcome the correlated sources issue. Statistical contrasts were carried out to determine sources where there were statistically significant differences in power between the unmodulated sinusoid (control) and the FM conditions. The data for each experimental condition were filtered using a 7-Hz wide bandpass filter centred on the Fourier component of interest (i.e. 4, 8, and 12 Hz). Subsequent statistical contrasts were carried out using the outputs of these 7-Hz wide band-pass filters. For example, statistical contrasts between the unmodulated sinusoid and the 4-Hz FM conditions were made after the data were filtered using a 7-Hz wide filter centred on the 4-Hz Fourier component. Sources were considered significant where the contrast between the FM condition and the unmodulated sinusoid control condition yielded p values where p ≤ 0.05. Source localisations are reported in terms of the University of Oxford's FMRIB Software Library (FSL) (http://www.fmrib.ox.ac. uk/fsl/) slice numbers and Talairach co-ordinates (http://www. talairach.org/). The slice numbers were converted into Talairach space using the MNI-to-Talairach (MTT) transform (Lancaster et al., 2007). The anatomical location of each source in Talairach space was determined through the Münster T2T-Converter (http:// wwwneuro03.uni-muenster.de/ger/t2tconv/). Virtual electrode analyses Virtual electrodes were used to reconstruct the source activity at voxels of interest identified by the beamforming analyses. Virtual electrodes are constructed for a given voxel by applying the weights from the beamformer spatial filters to the sensor data, i.e. similar to the synthetic aperture magnetometry (SAM) “virtual channel” (Robinson and Rose, 1992). One of the differences between the virtual electrodes and the SAM “virtual channel” is that the virtual electrodes used in the present study transform the data into 3-D space using the x, y and z co-ordinates, thereby retaining the reconstruction of source activity in all three orthogonal directions. The SAM “virtual channel” (Robinson and Rose, 1992) only considers activity in the direction of the primary variance component, i.e. principal component analysis (PCA). Another difference in the virtual electrodes used in the present study was that the source activity was reconstructed as time series. Reconstruction of the virtual electrode source activity vector (vkd) is described by: T

vkd = wkd × B

ð4Þ

where wkdT is a vector of beamformer weights and B is the matrix of magnetic field values. The co-ordinates where significant increases in power were identified by beamforming analyses in the group image were converted back into the slice numbers for each individual. Therefore virtual electrodes were calculated from the equivalent location for each participant. The FFT of each virtual electrode was calculated to obtain the phase and magnitude of the reconstructed ASSR. Multivariate Hotelling's T2 tests Multivariate analyses were applied to frequency-domain representations of the MEG data at the sensor and source level. Phase and magnitude data from the unmodulated sinusoid control and each of the FM conditions were subjected to dependent-sample T2 tests

(Hotelling, 1931; Johnson and Wichern, 1992). In sensor-space, the analysis was performed on the FFT of the averaged data from the artifact-free 1-s epochs. The phase and magnitude were extracted from the Fourier components of interest (4, 8, and 12 Hz) for each sensor within three frequency bins corresponding to the Fourier components. The resulting Hotelling's T2 maps across the sensor array were subjected to a threshold of p ≤ 0.001 (uncorrected). In sourcespace, the multivariate tests were applied to the average FFT for each virtual electrode component reconstructed within a 1–25 Hz bandpass filter. A cluster-based permutation test was used to account for the comparison of correlated data across adjacent frequency bins (Maris and Oostenveld, 2007). The threshold for the cluster analysis was chosen as the Hotelling's T2 value corresponding to an alpha level of 0.95 at the appropriate degrees of freedom. The permutation test identified those supra-threshold clusters of Hotelling's T2 values whose absolute size was larger than 95% of the observed absolute cluster sizes. Thus, the resulting clusters were deemed to be significant at the p b 0.05 level (two-tailed t-test). Results Sensor-space results Fig. 2 shows the FFT of all 248 MEG sensors for the unmodulated sinusoid condition (top left panel), the 4-Hz FM condition (top right panel), the 8-Hz FM condition (lower left panel), and the 12-Hz FM condition (lower right panel) in a representative participant. The ASSR corresponds to the Fourier component at the frequency of interest (4, 8, or 12 Hz). An arrow is used to indicate the Fourier component corresponding to each FM pulse rate. Visual inspection of Fig. 2 clearly shows that the ASSR to 4-Hz FM is the most robust and this result was consistent for all individual participants. For the representative participant shown in Fig. 2 there is also a smaller ASSR to 8-Hz FM. There is no identifiable phase-locked response to 12-Hz FM in the FFT of any of the participants. Fig. 3 shows a representation of the complex magnetic field (e.g. Simon and Wang, 2005) for the Fourier component of interest in an individual participant. In the upper panel the magnitude of the Fourier component of interest is plotted on a 2-D contour plot of the participant's head. The phase of the Fourier component of interest is plotted on a 2-D contour plot of the participant's head in the lower panel. The left-hand figure in each panel represents 4-Hz FM, the middle figure represents 8-Hz FM, and the right-hand figure represents 12-Hz FM. Note that each subplot in the upper panel of Fig. 3 is scaled individually because of the variance in the magnitude of the responses across conditions. The contour plots shown in Fig. 3 of the magnitude and phase of the Fourier component of interest are consistent with an auditory cortical origin for the ASSRs but a more precise localisation cannot be inferred from sensor-space analyses. The results shown in Fig. 3 suggest that the magnitude of the 8-Hz ASSR is weaker than that of the 4-Hz ASSR. The dipolar pattern of the phase of the 8-Hz ASSR is also less well defined relative to the 4-Hz ASSR. An interesting aspect of the phase results is that the configuration of the phase of the FFT of the ASSR to 8-Hz FM is “flipped” relative to the response to 4-Hz FM, i.e. positive and negative phase values lie in roughly opposite locations on the head for the 4-Hz ASSR relative to the 8-Hz ASSR. This result is consistent for all participants in at least one hemisphere. There is no clear pattern of dipolar activity in the magnitude of ASSR for 12-Hz FM. For the representative participant shown in Fig. 3, the phase configuration for the 12-Hz ASSR is similar to that measured in the 8-Hz ASSR and this was the case for the majority of participants. For one participant the phase configuration of the 12-Hz ASSR is more similar to that measured in the 4-Hz FM condition. In the remaining participants, there is no discernable phase configuration for the 12-Hz ASSR.

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Fig. 2. The FFT of the magnetic field in every channel in a representative subject. FFTs are shown for each condition; unmodulated carrier (top left), 4-Hz FM (top right), 8-Hz FM (bottom left), and 12-Hz FM (bottom right). Arrows indicate the Fourier coefficient corresponding to each FM pulse rate.

The representation of the phase and magnitude of the ASSR to 4Hz FM suggests bilateral dipolar activity in the representative participant shown in Fig. 3. However, the contour plots of the magnitude of the Fourier component of interest suggest that the

representation of the ASSR may be more robust in the right hemisphere. Multivariate Hotelling's T2 statistics (see Methods section) were used to construct Hotelling's T2 maps to determine whether the ASSR is represented bilaterally in sensor-space. Fig. 4

Fig. 3. The magnitude (top panel) and phase (lower panel) of the FFT at the Fourier coefficient corresponding to the pulse rate in a representative subject. N.B. The figures in the top panel are scaled individually.

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R.E. Millman et al. / NeuroImage 49 (2010) 745–758 Table 1 Overview of significant sources (p ≤ 0.05) in 4-Hz FM condition using “whole-head” and “half-head” beamformers. Type of beamformer

Whole-head

Half-head

Coordinates Slice (mm)

Brain region

Talairach

x

y

z

x

y

z

9 10 11 13 9 10 14

51 59 59 59 50 59 73

35 33 42 41 35 35 33

66 65 63 59 66 61 44

− 24 − 13 − 10 − 10 − 24 −9 18

1 2 16 13 3 2 0

Mid rSTG Ant. rSTG Inf. rPoCG rTTG Mid rSTG Ant. rSTG rIFG

r, right; STG, superior temporal gyrus; Ant., anterior; Inf., inferior; PoCG, postcentral gyrus; TTG, transverse temporal gyrus; IFG, inferior frontal gyrus.

Fig. 4. Hotelling's T2 maps. Maps of significant Hotelling's T2 values (p ≤ 0.001, uncorrected) are shown for the 4-Hz FM (left) and 8-Hz FM (right) ASSRs.

shows a map of significant (p ≤ 0.001, uncorrected) Hotelling's T2 values for the 4- and 8-Hz FM conditions. For the 12-Hz FM condition, the Hotelling's T2 values did not exceed the a priori threshold of p ≤ 0.001 (uncorrected). The Hotelling's T2 maps in Fig. 4 suggest that the ASSRs for 4- and 8-Hz FM are lateralised to the right hemisphere.

anterior STG [Brodmann's area (BA) 21] and right inferior postcentral gyrus (rPoCG) (see Table 1). 8-Hz FM. There were no significant (p ≤ 0.05) changes in power between the 8-Hz FM condition and the unmodulated sinusoid control condition for the 5–11 Hz frequency band tested. 12-Hz FM. There were no significant (p ≤ 0.05) changes in power between the 12-Hz FM condition and the unmodulated sinusoid control condition for the 9–15 Hz frequency band tested.

Source-space results “Half-head” VLCMV beamformer “Whole-head” VLCMV beamformer 4-Hz FM. The upper panel of Fig. 5 shows the sources for 4-Hz FM identified by the “whole-head” group beamforming analyses. The data were band-pass filtered between 1 and 7 Hz, i.e. within the frequency region where the ASSR to 4-Hz FM would occur. In the group results three sources in associative auditory cortex were found with significant (p ≤ 0.05) increases in power relative to the unmodulated sinusoid control condition. The sources of this increase in power localised to the right mid superior temporal gyrus (rSTG), right

4-Hz FM. The lower panel of Fig. 5 shows the sources identified by the group “half-head” beamforming analyses for the 4-Hz FM condition. The data were band-pass filtered between 1 and 7 Hz. No significant changes in power were found in the left hemisphere. In the group results four sources of significant (p ≤ 0.05) increases in power were found in the primary auditory cortex and associative areas in the right hemisphere. These sources, in and around auditory cortical areas, localised to the right transverse temporal gyrus (rTTG) (BA 42), the right anterior STG (BA 22), and the right mid STG (BA 22) (see Table

Fig. 5. Group beamforming results for 4-Hz FM. Locations of the ASSR to 4-Hz FM (p ≤ 0.05) are shown. Beamforming results are shown for “whole-head” beamforming (top panel) and “half-head” beamforming (lower panel). In the lower panel beamforming images for the left and right hemispheres were calculated separately and then combined for visual display.

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Fig. 6. Reconstruction of ASSR waveforms using all three VE components from the three sources identified by the “whole-head” beamformer in a representative participant. The VE time series waveforms from the locations identified by the “whole-head” beamformer are shown for the unmodulated sinusoid control condition (top panel) and the 4-Hz FM condition (lower panel).

1). Another significant increase in power was found in the right inferior frontal gyrus (rIFG) (BA 47) (see Table 1). 8-Hz FM. There were no significant (p ≤ 0.05) changes in power in response to 8-Hz FM (band-pass filtered between 5 and 11 Hz) in the group results in either the left or the right hemispheres. 12-Hz FM. There were no significant (p ≤ 0.05) changes in power in response to 12-Hz FM (band-pass filtered between 9 and 15 Hz) in the group results in either the left or the right hemispheres. Virtual electrode results “Whole-head” VLCMV beamformer The “whole-head” beamformer analyses found three significant sources in the 4-Hz FM condition (see Table 1). These significant changes in power localised to associative auditory cortex (mid rSTG, anterior rSTG, and inferior rPoCG) in the right hemisphere in the 4-Hz FM condition. Fig. 6 shows the waveforms of the VE time series and Fig. 7 shows the FFTs of the VE time series reconstructed by the virtual electrodes seeded in the individual participants in the locations equivalent to the sources found in the “whole-head” group analyses. Fig. 7 shows the FFTs of the VE time series overlaid for all the individual participants. VE time series were reconstructed within a band-pass filter of 1–25 Hz. The top panels of Figs. 6 and 7 show the VE time series or FFT in the unmodulated sinusoid control condition and the lower panels show the VE time series or FFT in the 4-Hz FM condition. The reconstructed activity in the x (blue), y (green), and z (red) directions, i.e. all three components in 3-D space, are shown. For all three sources found in the 4-Hz FM condition, the FFTs of the VE time series (see Fig. 7) in 10 out of the 11 participants displayed a peak at the Fourier component corresponding to 4 Hz, in at least one of the x, y, or z directions. As demonstrated in Fig. 7, the

4-Hz Fourier component with the largest magnitude was generally reconstructed in the y-axis component (green line). However, the data in Fig. 7 also suggest that information about the magnitude of the 4-Hz signal is also present in the x (blue line) and z (red line) directions. Multivariate Hotelling's T2 tests (see Methods section), taking into account both the phase and magnitude of the Fourier components in the FFTs of the VE time series, were used to determine significant differences between the FM conditions and the unmodulated control condition (see Table 2). Table 2 shows that there was a significant difference (p b 0.05) between the Hotelling's T2 value for the 4-Hz Fourier component in the 4-Hz FM conditions and the unmodulated sinusoid control condition for two of the three locations identified in the “whole-head” beamformer analyses. When both the phase and the magnitude of the Fourier components of interest are taken into account, the 4-Hz Fourier component was represented in the direction of the x component of the VE for mid rSTG. In anterior rSTG, the Hotelling's T2 test did not identify a significant 4-Hz component in any of the VE components but a significant Fourier component corresponding to the 2nd harmonic (8 Hz) of the stimulus was found in the x direction of the VE. For the VE placed in inferior rPoCG, the 4-Hz Fourier component was represented in all three directions of the VE components. “Half-head” VLCMV beamformer The “half-head” beamformer analyses found four significant sources in the 4-Hz FM condition (see Table 1). These sources localised to the rTTG, anterior rSTG, mid rSTG, and rIFG. Fig. 8 shows the FFTs of the time series of the virtual electrodes seeded in the individual participants in the location equivalent to the peaks found in the group analyses with the “half-head” beamformer. The top panel of Fig. 8 shows the FFTs of the VE time series in the sinusoid condition and the lower panel shows the FFTs of the VE time series in the 4-Hz

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Fig. 7. Reconstruction of the FFTs of the VE time series using all three VE components from the three sources identified by the “whole-head” beamformer. FFTs of the time series for all individual participants are overlaid. The FFTs of the virtual electrode time series from the locations identified by the “whole-head” beamformer are shown for the unmodulated sinusoid control condition (top panel) and the 4-Hz FM condition (lower panel). The arrow indicates the Fourier coefficient corresponding to the FM pulse rate (4 Hz).

out of 11 participants, in at least one of the x, y, or z directions. The largest 4-Hz Fourier component was generally found in the FFTs of the VE time series in rTTG. Inspection of the FFTs of the VE time series showed that the 4-Hz Fourier components with the largest magnitudes were generally found in the y direction (green line).

FM condition. VE time series were reconstructed within a band-pass filter of 1–25 Hz. The activity in the x (blue), y (green), and z (red) directions, i.e. all three components in 3-D space, are shown. For the VE time series of the 4-Hz FM condition, a peak in the FFT at the Fourier component corresponding to 4 Hz was evident in 10

Table 2 Summary of the results of the multivariate Hotelling's T2 tests of significance (p ≤ 0.05) for the Fourier coefficients corresponding to the 4-Hz FM pulse rate and its second harmonic. Type of beamformer

Location of VE

Direction of VE component

Significant Hotelling's T2? 4 Hz

8 Hz

Whole-head

Mid rSTG

x y z x y z x y z x y z x y z x y z x y z

Yes – – – – – Yes Yes Yes Yes – – Yes – – – – Yes – – Yes

– – – Yes – – – – – – – – – – – Yes – – – – –

Ant. rSTG

Inf. rPoCG

Half-head

rTTG

Mid rSTG

Ant. rSTG

rIFG

VE, virtual electrode; r, right; STG, superior temporal gyrus; Ant., anterior; Inf., inferior; PoCG, postcentral gyrus; TTG, transverse temporal gyrus; IFG, inferior frontal gyrus.

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Fig. 8. Reconstruction of the FFTs of the VE time series using all three VE components from the four sources identified by the “half-head” beamformer. FFTs of the time series for all individual participants are overlaid. The FFTs of the virtual electrode time series from the locations identified by the “half-head” beamformer are shown for the unmodulated sinusoid control condition (top panel) and the 4-Hz FM condition (lower panel). The arrow indicates the Fourier coefficient corresponding to the FM pulse rate (4 Hz).

However, as demonstrated in Fig. 8, the 4-Hz Fourier component was also present in the x and z directions. This effect was particularly noticeable in rIFG where the 4-Hz Fourier component with the greatest magnitude was reconstructed in the z direction in 7 out of 11 participants. Table 2 shows the results of the multivariate Hotelling's T2 tests of statistical significance applied to the FFTs of the VEs placed in the locations identified by the “half-head” beamformer analyses. A significant difference (p b 0.05) between the 4-Hz FM condition and the unmodulated sinusoid control condition was found in all locations. However, the VE direction with the significant 4-Hz Fourier component varied with location. The 4-Hz Fourier component was represented in the x direction of the VE in rTTG and mid rSTG and in the z direction of the VE in anterior rSTG and rIFG. In anterior rSTG, the 4-Hz Fourier component was represented in the z direction of the VE whilst the 2nd harmonic (8 Hz) of the FM pulse rate was represented in the x direction of the VE. Discussion The aim of this study was to use MEG to investigate the mechanism underlying the processing of FM over a range of FM pulse rates. The results presented here suggest that the processing of FM is dependent on the pulse rate of the FM stimulus. Both the results from sensorspace and source-space analyses are consistent with the idea that auditory cortical areas in the right hemisphere can phase-lock to a low FM pulse rate (4-Hz FM). The results from the multivariate Hotelling's T2 statistical tests in MEG sensor-space also suggested that the right hemisphere can phase-lock to 8-Hz FM. The VLCMV beamformer failed to localise an ASSR for 8-Hz FM. The results from the present

study provide evidence that ASSRs for low FM pulse rates (up to ∼8 Hz) are represented by phase-locked activity and that higher modulation frequencies must be encoded in a different manner. Evidence of FM processing strategy from sensor-space analyses For the FM pulse rates used in the present study, we found evidence of cortical phase-locking to the 4-Hz FM and, to a lesser extent, the 8-Hz FM stimuli in the results from the sensor-space analyses. These findings are consistent with the EEG study by Picton et al. (1987). Picton et al. (1987) measured ASSRs for a range of sinusoidal FM modulation frequencies between 2.0 and 54.7 Hz. They found reliable ASSRs to FM with a modulation frequency of 4.9 Hz but the reliability of the response decreased over a range of modulation frequencies between 8.8 and 15.6 Hz. Whereas Picton et al. (1987) identified a reliable ASSR for 6.8-Hz FM, the reliability of the response had fallen below statistical significance as the FM modulation frequency was increased to 8.8 Hz. The results from the present study suggest that there was statistically significant phaselocking to the 8-Hz FM stimulus. Picton et al. (1987) reported that ASSRs became reliable again (based on the results from Hotelling's T2 tests) for FM modulation frequencies greater than 19.5 Hz. Therefore we could expect to find significant ASSRs to the sinusoidal pulsed FM stimuli used in the present study for FM pulse rates greater than 19 Hz. Both the results from the present study and the results from Picton et al. (1987) failed to find reliable ASSRs to FM modulation frequencies within the range of ∼12 Hz. Unlike Luo et al. (2006, 2007), we did not find “sideband representation” at the Fourier component corresponding to the 12-Hz FM stimulus used in the present study. There are several reasons why the results from the

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present study may be inconsistent with previous publications for FM modulation frequencies greater than 8 Hz (Luo et al., 2007). First, there are important differences between the type of FM stimuli that we used here and in the studies by Luo et al. (2006, 2007). We used a simple single-cycle sinusoidal FM pulses and Picton et al. (1987, 2003) used sinusoidal FM. Luo et al. (2006, 2007) used a complex AM–FM stimulus with a large FM frequency deviation of 660 Hz applied to a relatively low-frequency carrier and it is not apparent that this is the best stimulus to use to characterise the ASSR to FM itself (see Choice of FM stimulus). Secondly, we took a different approach in determining statistically significant Fourier components corresponding to the FM pulse rates of interest based on multivariate Hotelling's T2 statistics in MEG sensorspace. Luo et al. (2006, 2007) used confusion matrix analyses between AM–FM conditions based only on the magnitude of the Fourier component of interest to determine whether the ASSR was present. Picton et al. (1987) used Hotelling's T2 statistic but they did not compare the Fourier components of the ASSR to modulated stimuli with an unmodulated control stimulus. In the present study multivariate Hotelling's T2 tests were used to compare the ASSR in the modulated and unmodulated sinusoid (control) conditions at a given Fourier component corresponding to the FM pulse rate. The multivariate Hotelling's T2 test takes into account both the phase and amplitude of the response, which are both important characteristics of the ASSR. As the ASSR is a complex magnetic field, it is important to consider not only the magnitude but also the phase of the ASSR. The 2-D contour plots (see Fig. 3) of both the magnitude and phase of the ASSR to FM and the maps of Hotelling's T2 values in sensor-space (see Fig. 4) reveal that phase-locking decreases with increasing FM pulse rate. Luo et al. (2006, 2007) did not show complex magnetic field for Fourier components at frequencies corresponding to the FM modulation frequencies. Therefore it is not possible to make a direct comparison of the complex magnetic fields generated by FM stimuli and obtained by Luo et al. (2006, 2007) with the results from the present study. The contour plots of the phase of the Fourier components of interest show that the phase of the ASSR to 8- and 12-Hz FM is “flipped” relative to the response to 4-Hz FM, i.e. positive and negative phase values lie in roughly opposite locations on the head for the 4-Hz ASSR relative to the 8- and 12-Hz ASSR. It is possible that this “phase flip” represents some kind of coding transition as the FM pulse rate is increased from 4 to 8 Hz. Alternatively this “phase flip” may reflect differences in the timing of responses to different FM pulse rates or differences in the sources generating the ASSR for different FM modulation frequencies. It is not possible to determine which explanation is more likely from the available data.

showed there was no phase-locked response to 12-Hz FM. Moreover, there were no other phase-locked components in the FFTs in sensorspace that may reflect the 12-Hz FM stimuli, e.g. we did not find any Fourier components corresponding to harmonics of the FM pulse rate of the original stimulus. The lack of an ASSR to the 12-Hz FM stimulus is consistent with the EEG study by Picton et al. (1987) using sinusoidal FM. If FM-to-AM transduction occurred for the 12-Hz FM stimulus, then the resulting AM cannot be measured by either MEG or EEG. The present MEG study could not determine the neural mechanism/s involved in the perception of 12-Hz FM. Consistent with the EEG study using sinusoidal FM stimuli by Picton et al. (1987), we could not find a phase-locked ASSR for the 12-Hz FM stimulus used in the present study. It is possible that higher FM modulation frequencies are encoded by a non-synchronised rate-based mechanism (e.g. Wang et al., 2003). Liang et al. (2002) and Wang et al. (2003) suggest that cortical neurons can phase-lock to modulated sounds with modulation frequencies up to about 16 Hz with a maximum at 8 Hz. Therefore the proposed modulation frequency (N16 Hz) at which the transition from a synchronised to nonsynchronised based mechanism (e.g. Wang et al., 2003) takes place is not consistent with the lack of phase-locked responses to 12-Hz FM found in the present study. Alternatively we cannot rule out the possibility that we may not have chosen optimal FM stimulus parameters to promote phaselocking for the 12-Hz FM stimulus used in this study, although the 12Hz FM stimulus was highly detectable. The FM frequency deviation used here was much less than used by Luo et al. (2006, 2007), as our aim was to keep the majority of spectral components within the auditory filter centred on the 1-kHz carrier frequency (see Choice of FM stimulus). Previously it was thought that auditory cortex could only phase-lock to low modulation frequencies, i.e. b16 Hz (e.g. Wang et al., 2003). Liang et al. (2002) reported that the optimal FM frequency deviation varied from neuron to neuron in their study and they chose to tailor their FM stimuli to individual neurons. Therefore auditory cortical neurons may be able to phase-lock to higher FM modulation frequencies as long as stimuli contain appropriate FM frequency deviations. Auditory cortical neurons are also sensitive to stimulus parameters such as pulse width and spectral bandwidth (Bendor and Wang, 2008) and the duration of the positive phase of each modulation cycle and the duty cycle, at least for AM stimuli (Krebs et al., 2008). In the present study, the stimuli were designed so that the each FM stimulus varied in pulse half-duration with pulse rate so that each pulse rate had an equivalent half-duration. It remains to be seen whether adjusting the modulation parameters of sinusoidal pulsed FM stimuli, i.e. varying FM frequency deviation, the duty cycle or duration, will result in phase-locked responses to higher FM modulation frequencies that can be measured with MEG and EEG.

Possible FM encoding strategies Cortical representation of harmonic structure If highly detectable FM and AM are both processed by a single mechanism, FM-to-AM transduction may be the underlying mechanism (e.g. Zwicker, 1956, 1962; Saberi and Hafter, 1995). FM-to-AM transduction may have produced the phase-locked ASSRs for 4- and 8Hz FM. Therefore in the cases of the ASSR for 4- and 8-Hz FM, it is possible that the FM stimuli were transduced into AM with corresponding modulation frequencies. However, if FM-to-AM transduction is the general mechanism for the perception of FM then we may expect to see activity phase-locked to the harmonics (i.e. integer multiples of the original FM modulation frequency) of the FM stimulus rather than the FM modulation frequency itself. Consistent with the results of Picton et al. (1987), there was some evidence for phase-locked activity in the 2nd harmonic of 4-Hz FM, for example, see Fig. 2 and Table 2, but the ASSR at the 2nd harmonic was generally much smaller than the ASSR at the FM modulation frequency itself. The multivariate Hotelling's T2 tests conducted in MEG sensor-space

Previous EEG (e.g. Picton et al., 1987) and MEG studies (e.g. Ross et al., 2000) have reported harmonic structure in ASSRs to sinusoidally modulated stimuli. The modulation spectrums of sinusoidally modulated stimuli contain components corresponding only to the modulation frequency, i.e. the cortical representation of sinusoidal modulation contains harmonic structure that is not present in the stimulus waveform. The modulation waveforms used in the present study for single-cycle FM pulse stimuli result in modulation spectra that contain harmonics at integer multiples of the FM pulse rate. The use of single-cycle FM pulses raises the possibility that the harmonic structure of the stimulus could be reflected in the representation of the ASSR at the cortical level. For the FM stimuli used in the present study, one could predict that a linear system would generate ASSRs for single-cycle FM pulses containing frequency components corresponding to the FM pulse

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rates (4, 8, or 12 Hz) and their harmonics. The FFTs in sensor-space (Fig. 2) and FFTs of VE time series (Figs. 7 and 8) for the 4-Hz FM condition suggest that the FM modulation frequency (4 Hz) and its 2nd harmonic (8 Hz) are reflected in the cortical response to the 4-Hz single-cycle FM pulse. However, the results of the sensor-space analyses did not suggest that the harmonic structures of the 8- and 12-Hz FM stimuli are reflected at the cortical level (see Fig. 2). Therefore this study has demonstrated that auditory cortex can phase-lock to 8-Hz FM, whether the 8-Hz in the FM stimulus is the primary FM pulse rate (as in the 8-Hz FM condition) or it is the 2nd harmonic of the primary FM pulse rate (as in the 4-Hz FM condition). We did not find any evidence of cortical phase-locking to the 12-Hz FM stimulus, either in the 12-Hz FM condition where 12-Hz is the primary FM pulse rate or in the 4-Hz FM condition where 12-Hz is the 3rd harmonic. The lack of phase-locking to either primary FM pulse rates or their harmonics with frequencies greater than 8 Hz is consistent with the idea that auditory cortex cannot phase-lock to FM pulse rates within the range of 8.8–15.6 Hz (Picton et al., 1987). In summary, the harmonic structure of the 4-Hz ASSR suggests that the cortical mechanisms underlying the processing of only low pulse rate FM stimuli are concerned with the detailed structure of the temporal modulation. Right hemisphere lateralisation of the ASSR for FM Standard (i.e. “whole-head”) beamforming techniques may fail to localise a bilateral representation of the ASSR for AM in both left and right hemispheres (e.g. Brookes et al., 2007; Popescu et al., 2008). The failure of standard MEG beamformers to localise bilateral sources for the ASSR to AM has been attributed to the problems that beamformers encounter with correlated sources (e.g. Van Veen et al., 1997; Huang et al., 2004). More sophisticated beamforming techniques (e.g. Brookes et al., 2007; Popescu et al., 2008) have been developed to localise a bilateral ASSR for AM. “Half-head” beamforming has been used to successfully localise bilateral sources for the ASSR (e.g. Herdman et al., 2003; Popescu et al., 2008). However, Popescu et al. (2008) reported that “half-head” beamforming resulted in ASSR sources that were erroneously lateralised, whereas Herdman et al. (2003) localised medial ASSR sources using “half-head” beamforming. The reason for this discrepancy is unclear. Both the “whole-head” and “half-head” VLCMV beamformers localised the 4-Hz ASSR to auditory cortical areas in the right hemisphere only. The results from the sensor-space analyses based on multivariate Hotelling's T2 statistics presented in Fig. 4 also suggest that ASSR for FM is lateralised to the right hemisphere. Sensor-space analyses are not affected by the presence of correlated sources. Therefore the results from the sensor-space analyses and the VLCMV beamformer analyses in the present study are consistent with the idea that the ASSR for FM is lateralised to the right hemisphere. The right hemisphere lateralisation of the ASSR for FM found in the present study is also in accordance with previous studies (e.g. Ross et al., 2005) using the source-space projection technique to localise ASSRs for AM stimuli. Source-space projection is not affected by correlated sources. Alternatively the lateralised representation of the ASSR for FM compared with the bilateral representation of the ASSR for AM (e.g. Herdman et al., 2003; Brookes et al., 2007; Popescu et al., 2008) may reflect hemispheric differences in the processing of AM and FM stimuli. Moreover, ASSRs for FM may be lateralised to the right hemisphere because this hemisphere is dominant for pitch processing (e.g. Zatorre and Belin, 2001; Boemio et al., 2005). An alternative explanation of the right hemisphere lateralisation of FM processing is that auditory cortical areas in the right hemisphere may be specialised to process the low frequencies in speech stimuli (e.g. Luo and Poeppel, 2007). Auditory areas in the right hemisphere show spontaneous changes in power in the theta (3–6 Hz) frequency range, even in the absence of specific auditory stimulation (Giraud et al., 2007).

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Choice of FM stimulus The concept of the critical bandwidth (e.g. Zwicker, 1961; Scharf, 1970) and its relationship to the auditory filters in the auditory periphery (e.g. Moore and Glasberg, 1983) becomes important for stimulus design when using highly detectable modulated stimuli. The characteristics of the auditory filter for a given sinusoidal or other narrowband carrier frequency (to which the modulation is applied) and the modulated stimulus parameters should be taken into account if the aim of the experiment is to measure the perception of modulated sounds. The sound level of the stimulus is important as greater sound levels results in larger auditory filter bandwidths (e.g. Baker and Rosen, 2006; Unoki et al., 2006). The bandwidth of a given auditory filter will determine the appropriate modulation parameters for measuring responses to either AM or FM stimuli. As a rule of thumb, for sinusoidal carriers, the frequency separation between the carrier frequency and the modulation frequency should not exceed more than half the auditory filter bandwidth for a given carrier frequency. However, sideband detection may become an issue for modulation frequencies less than half the auditory filter bandwidth (e.g. Moore and Glasberg, 2001). Once sidebands associated with a modulated stimulus lie outside of the critical band for a given carrier frequency, the sidebands become resolved by adjacent auditory filters. At this point the stimulus is no longer processed as “pure” modulation with all spectral components processed within one auditory filter. The carrier in this study was a 1-kHz sinusoid presented at about 83 dB SPL. The auditory filter bandwidth for a 1-kHz sinusoid presented at sound levels of 70 dB SPL and above is at least 180 Hz (Baker and Rosen, 2006; Unoki et al., 2006). The FM pulse rates in the present study were limited to 4, 8, and 12 Hz as these FM modulation frequencies lie within the auditory filter for the 1-kHz carrier frequency. For highly detectable FM stimuli it is usually necessary to use a large FM modulation index or frequency deviation. The spectra of suprathreshold FM stimuli are therefore often complex and contain many sidebands at harmonics of the modulation frequency. Ideally, all sidebands associated with a suprathreshold FM stimulus should not spread significantly outside the critical bandwidth for the FM carrier frequency (e.g. Moore et al., 1991). However, this requirement is difficult to achieve with highly detectable FM stimuli and nonetheless may not be sufficient to prevent FM-to-AM transduction. Larger FM modulation indices or frequency deviations promote the transduction from FM to AM (e.g. Regan and Tansley, 1979; Plack and Carlyon, 1994). In the present study, the carrier frequency was 1-kHz and the FM frequency deviation was restricted to 158 Hz for all the FM stimuli. Comparison of “whole-head” and “half-head” VLCMV beamforming results The VLCMV beamformers only identified sources for the 4-Hz ASSR and therefore the discussion of the comparison between “wholehead” and “half-head” beamforming will focus on 4-Hz FM ASSR. It has recently been shown that using fewer channels for VLCMV beamforming analyses (e.g. “half-head” beamforming) improves source localisation for the ASSR to AM relative to “whole-head” VLCMV beamformers using simulated data and MEG data from an individual (e.g. Popescu et al., 2008). The results of the present study extend the finding that “half-head” beamforming is better than “whole-head” beamforming for identifying ASSRs to the group level. An interesting difference between previous simulations (e.g. Popescu et al., 2008) and the present study is that here the group results from the “whole-head” beamformer show focal sources for the 4-Hz ASSR that are localised to lateral right auditory cortex. Previous simulations (e.g. Popescu et al., 2008) and results from individuals (e.g. Dalal et al., 2006) report that “whole-head” beamformers erroneously produce a large diffuse source that is more medial and superior to the expected

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location for simple auditory stimuli. This difference between the results from an individual in previous studies (e.g. Dalal et al., 2006; Popescu et al., 2008) and the group results from the present study emphasises the importance of assessing group data when making comparisons with simulations. In this study, both the “whole-head” and “half-head” beamformers identified ASSR sources to 4-Hz FM in mid and anterior rSTG in the right hemisphere. The “half-head” beamfomer found a source in the rTTG, whereas a source was found in inferior rPoCG using the “whole-head” beamformer (see Table 1), which is probably due to less accurate source localisation with the “whole-head” beamformer. The “half-head” beamformer also identified an additional source in rIFG (BA 47). Brodmann's area 47 has been implicated in the processing of the temporal fine structure of language and music (e.g. Levitin and Menon, 2003) and therefore it could be expected that this brain region may be involved in the processing of FM stimuli. The location of the sources in primary auditory cortex (rTTG) and surrounding non-primary areas (mid rSTG and anterior rSTG) is consistent with some of the peaks found in fMRI studies that also contrasted single FM tones and an unmodulated sinusoid baseline (e.g. Hall et al., 2002; Hart et al., 2003). Single EEG depth electrode studies using AM stimuli (e.g. Liégeois-Chauvel et al., 2004; Gourevitch et al., 2008) have also reported multiple sources for modulated stimuli that are at least partly consistent with the sources for the ASSR to FM found by the MEG beamformers in the present study. One previous MEG study (Gutschalk et al., 1999) using ASSRs to clicks found two sources in both medial and lateral primary auditory cortex using equivalent current dipole analyses. Failure of the VLCMV beamfomer to localise ASSRs for all FM conditions The VLCMV beamformer only localised sources associated with the representation of the 4-Hz FM ASSR and not the 8- and 12-Hz FM ASSRs. The sensor-space and source-space analyses both showed that the 12-Hz FM stimuli did not generate any significant phase-locked activity. Therefore it is not surprising that the VLCMV beamformer failed to localise a 12-Hz ASSR. However, the multivariate Hotelling's T2 tests in MEG sensor-space suggested that the 8-Hz FM stimulus did generate a significant phase-locked 8-Hz ASSR, although the FFTs of the FM conditions in sensor-space (Fig. 2) suggest that the SNR for the 8-Hz FM condition was relatively lower than that for the 4-Hz FM condition. The VLCMV beamformer localises sources based on changes in power in phase-locked and (mainly) non-phase-locked activity. The failure of the VLCMV beamformers to localise any significant changes in power for the 8-Hz FM condition could be attributed to the relatively small SNR of phase-locked activity in the 8-Hz FM condition. Different types of beamforming analyses designed to localise sources based on phase-locked activity (e.g. Cheyne et al., 2006; Bardouille and Ross, 2008) may be more successful in localising phase-locked responses at less favourable SNRs. Choice of statistical thresholding procedure The choice of image thresholding procedure will influence the interpretation of the beamforming results in terms of any lateralisation of the ASSR and the number of significant sources. The majority of beamforming studies (e.g. Herdman et al., 2003; Brookes et al., 2007; Popescu et al., 2008) on the ASSR to AM usually report one source in each auditory cortex. In part this discrepancy may be due to a more arbitrary thresholding procedure used in previous studies, for example, Popescu et al. (2008) show maps of normalised power thresholded at 60% of the maximum in their beamformer images. Herdman et al (2003) reported that they used “non-parametric” pseudo t statistics that did not incorporate variance from individuals into the group level analyses. In the present study both the “whole-

head” and “half-head” beamforming analyses identified multiple sources for the 4-Hz ASSR to FM. Another difference between the present study and those previously published (e.g. Herdman et al., 2003; Brookes et al., 2007; Popescu et al., 2008) is that the previous studies contrasted a modulated stimulus with a “passive” pretrigger baseline, instead of the unmodulated sinusoid used as the control in the present study. In the present study the uncertainty over reliability and choice of statistical threshold was overcome by using nonparametric unpaired t statistics (e.g. Nichols and Holmes, 2002). Beamforming images were thresholded at a significance level of p ≤ 0.05 for the contrast between the FM conditions and an unmodulated sinusoid control condition. Virtual electrode reconstructions of the ASSR The discussion of virtual electrode analyses will focus on the 4-Hz ASSR because the VLCMV beamfomers could not localise activity in auditory cortex and associative areas for the 8- and 12-Hz FM stimuli. Virtual electrode analyses were used to determine whether a 4-Hz Fourier component was present at each source identified by the beamformer analyses in and around auditory cortex. A significant 4Hz Fourier component was found in all but one of the locations identified by either the “whole-head” or the “half-head” beamforming analyses. Popescu et al. (2008) used simulated data to show that using fewer channels to generate the beamformer covariance matrix, i.e. “half-head” beamforming, also affects the accuracy of the reconstruction of “activation curves” and therefore may affect the accuracy of reconstructed virtual electrode time series. Both the “whole-head” and the “half-head” (see Fig. 5) beamformer analyses identified a source for the 4-Hz ASSR in mid and anterior rSTG (see Table 1). An interesting difference between Figs. 7 and 8 is that the magnitude of the reconstructed 4-Hz Fourier component in the FFTs of the VE time series varies at comparable locations identified by either the “half-head” or the “whole-head” beamforming analyses. In the location in anterior rSTG, the magnitude of the 4-Hz component in the x, y, and z directions is greater in the VE reconstructed from the “halfhead” beamformer relative to the 4-Hz Fourier component reconstructed from the “whole-head” beamformer. If one considers that rTTG (“half-head” beamformer) and rPoCG (“whole-head” beamformer) are comparable locations, then the magnitudes of the 4-Hz Fourier components in the x, y, and z directions are also greater for the reconstructions from the “half-head” beamformer. Therefore the VE reconstructions in anterior rSTG and rTTG/rPoCG are consistent with the results from simulation experiments (Popescu et al., 2008), i.e. the “half-head” beamformer offers improved reconstruction of the VE time series relative to the “whole-head” beamformer. Although the magnitude of the 4-Hz Fourier component corresponding to the 4-Hz ASSR is usually greatest in the y direction (green line), in some cases there were also peaks in the FFTs of the VE time series at the 4-Hz Fourier component in the x (blue line) and z (red line) directions. For example, in Fig. 7 the magnitudes of the 4-Hz Fourier component are larger in anterior rSTG in the x (blue line) and z (red line) directions. When a multivariate Hotelling's T2 statistic is applied, which takes into account the magnitude and the phase of the Fourier component of interest, to the FFTs of the VE time series (see Table 2), the results also show that the signal is represented in VE components in different directions. An interesting result was found for the location in inferior rPoCG identified by the “whole-head” beamformer where the Fourier component of interest is represented in all VE directions. The FFTs of the VE time series in anterior rSTG (Fig. 7, lower middle panel) suggest that there is a peak in the magnitude of the FFT at the Fourier component corresponding to 4-Hz FM and a smaller peak corresponding to the second harmonic (8 Hz). However, the results of the multivariate Hotelling's T2 test show that there is no statistically significant component at 4 Hz but there is a significant

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effect for the 8-Hz component of the ASSR for 4-Hz FM. The outcomes of the Hotelling's T2 tests emphasise the contribution of the phase of the ASSR to the determination of significant results from the Hotelling's T2 test. For the VE located in the anterior rSTG identified by the “half-head” beamforming analyses, the Fourier component corresponding to the 4-Hz FM stimulus is represented in the z direction whilst the 2nd harmonic (8 Hz) of the 4-Hz stimulus is represented in the x direction of the VE. Therefore some of the results shown in Table 2 demonstrate that it is important to consider all the individual components in virtual electrode analyses. If, for example, virtual electrode or SAM “virtual channel” analyses consider only the principal component from PCA analyses, then in some cases these analyses will miss information about the signal represented in the other VE components. Summary This study used MEG and the ASSR to investigate the mechanisms underlying the processing FM stimuli. The main findings of the present study can be summarised as follows: (i) Taken together, the results from the present study and previous studies (e.g. Picton et al., 1987; Ross et al., 2000; Simon and Wang, 2005) suggest that both AM and FM are represented by phase-locked activity corresponding to the modulation frequency, at least for low modulation frequencies. (ii) MEG sensor-space analyses suggested that auditory cortex can phase-lock to 4- and 8-Hz FM pulse rates. (iii) MEG source-space analyses using both “whole-head” and “halfhead” VLCMV beamformers only localised the ASSR for 4-Hz FM, which may be a result of the more favourable SNR for the 4-Hz ASSR. (iv) Both “whole-head” and “half-head” VLCMV beamformers found multiple significant (p ≤ 0.05) sources for the ASSR to 4-Hz FM in and around auditory cortical areas in the right hemisphere. This right hemisphere lateralisation of the representation of the ASSR in MEG source-space is consistent with the sensor-space analyses based on maps of Hotelling's T2 values in the present study. (v) VE analyses used reconstructions based on the x, y, and z directions. Statistical tests of significance based on a multivariate Hotelling's T2 values showed that the reconstructed ASSR is generally, but not always, greatest in a single direction of the VE components. Information about the signal is sometimes reconstructed in two or three directions and therefore all three VE component directions should be considered in VE analyses. Acknowledgments We thank two anonymous reviewers for helpful comments on a previous version of the manuscript. References Baker, R.J., Rosen, S., 2006. Auditory filter nonlinearity across frequency using simultaneous notched-noise masking. J. Acoust. Soc. Am. 119, 454–462. Bardouille, T., Ross, B., 2008. MEG imaging of somatosensory neural networks using inter-trial coherence in vibrotactile steady-state responses. NeuroImage 42, 323–331. Bendor, D., Wang, X., 2008. J. Neural response properties of primary, rostral, and rostrotemporal core fields in the auditory cortex of marmoset monkeys. Neurophysiol. 100, 888–906. Boemio, A., Fromm, S., Braun, A., Poeppel, D., 2005. Hierarchical and asymmetric temporal sensitivity in human auditory cortices. Nat. Neurosci. 8, 389–395. Brookes, M.J., Stevenson, C.M., Barnes, G.R., Hillebrand, A., Simpson, M.I.G., Francis, S.T., Morris, P.G., 2007. Beamformer reconstruction of correlated sources using a modified source model. NeuroImage 34, 1454–1465.

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