Spatiotemporal reconstruction of auditory steady-state responses to acoustic amplitude modulations: Potential sources beyond the auditory pathway

NeuroImage 148 (2017) 240–253

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NeuroImage journal homepage: www.elsevier.com/locate/neuroimage

Spatiotemporal reconstruction of auditory steady-state responses to acoustic amplitude modulations: Potential sources beyond the auditory pathway

MARK

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Ehsan Darestani Farahani , Tine Goossens, Jan Wouters, Astrid van Wieringen Research Group Experimental ORL, Department of Neurosciences, KU Leuven – University of Leuven, Herestraat 49 bus 721, 3000 Leuvetabn, Belgium

A R T I C L E I N F O

A BS T RAC T

Keywords: EEG Auditory steady-state response Independent component analysis Auditory pathway Source localization

Investigating the neural generators of auditory steady-state responses (ASSRs), i.e., auditory evoked brain responses, with a wide range of screening and diagnostic applications, has been the focus of various studies for many years. Most of these studies employed a priori assumptions regarding the number and location of neural generators. The aim of this study is to reconstruct ASSR sources with minimal assumptions in order to gain indepth insight into the number and location of brain regions that are activated in response to low- as well as high-frequency acoustically amplitude modulated signals. In order to reconstruct ASSR sources, we applied independent component analysis with subsequent equivalent dipole modeling to single-subject EEG data (young adults, 20–30 years of age). These data were based on white noise stimuli, amplitude modulated at 4, 20, 40, or 80 Hz. The independent components that exhibited a significant ASSR were clustered among all participants by means of a probabilistic clustering method based on a Gaussian mixture model. Results suggest that a widely distributed network of sources, located in cortical as well as subcortical regions, is active in response to 4, 20, 40, and 80 Hz amplitude modulated noises. Some of these sources are located beyond the central auditory pathway. Comparison of brain sources in response to different modulation frequencies suggested that the identified brain sources in the brainstem, the left and the right auditory cortex show a higher responsiveness to 40 Hz than to the other modulation frequencies.

Introduction Auditory steady-state responses (ASSRs) are periodic brain responses elicited by periodically varying input sounds. Pure tones or noise bands modulated at different frequencies, are typical acoustic stimuli for ASSR measurements. ASSRs are synchronized to the rate of the acoustic input (e.g., the modulation rate) and can be detected by means of frequency-based analyses (Picton et al., 2003). They are wellknown and efficient for studying auditory temporal processing, hearing screening (Aoyagi et al., 1993; Lins and Picton, 1995; Luts et al., 2006), assessing supra threshold hearing, and monitoring the state of arousal during anesthesia (Picton et al., 2003). Moreover, there is a growing interest to evaluate ASSRs in the psychopathological domain: ASSRs are considered bio-markers for different mental disorders, such as schizophrenia (O’Donnell et al., 2013) and Alzheimer's disease (van Deursen et al., 2011). In order to gain a better understanding of the brain processes underlying such mental disorders and, more generally of the mechanisms underlying auditory temporal processing, it is of

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Corresponding author. E-mail address: [email protected] (E.D. Farahani).

http://dx.doi.org/10.1016/j.neuroimage.2017.01.032 Received 16 December 2016; Accepted 13 January 2017 Available online 18 January 2017 1053-8119/ © 2017 Elsevier Inc. All rights reserved.

great (clinical) interest to investigate neural generators of ASSRs. Existing models of the brain regions involved in the generation of ASSRs can be divided into cortical models and distributed models. In cortical models, only two active regions in the left and the right primary auditory cortex (one source in each hemisphere) are identified in response to amplitude modulated sounds, whereas in distributed models, several cortical and subcortical regions are identified. The cortical model of ASSR generators is supported by source localization studies using magnetoencephalography (MEG) (Gutschalk et al., 1999; Herdman et al., 2003; Kuriki et al., 2013; Lazzouni et al., 2010; Pantev et al., 1996; Teale et al., 2008). Herdman et al. (2003) applied synthetic aperture magnetometry, a specific approach to analyze MEG data, to localize the cortical activity in response to 40 Hz amplitude modulated (AM) sounds. They detected one active area in each hemisphere, located in the superior temporal plane. In another MEG study, Popescu et al. (2008) evaluated the outcomes of two different beamformer methods to localize the sources of ASSRs to 40 Hz acoustic modulations. Both methods identified one source in the

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be required for these investigations. The main objective of the present study is to reconstruct ASSR sources and to investigate the number of brain sources responding to acoustic amplitude modulations. To accomplish this, it is indispensable to evaluate all cerebral regions and to avoid applying constraints on the number of sources and their locations. Moreover, the high temporal resolution of the recording method is essential to evaluate the synchronized (steady-state) activity of the neural generators in response to AM stimuli. The spatiotemporal reconstruction in this study can provide a basis for further connectivity analysis. The second objective is to investigate how different modulation frequencies affect the model of ASSR generation with regard to the spatial and temporal properties. This study is novel and unique, as it does not only focus on localizing sources without prior constraints but also investigates ASSRs in response to low- as well as high-frequency acoustic amplitude modulations. In the present study, the location and time series of activity of ASSR sources are reconstructed based on EEG data in response to white noise amplitude modulated at 4, 20, 40, and 80 Hz. We apply independent component analysis (ICA) as a source separation method with no prior assumptions regarding the number and/or location of sources. Subsequently, to determine the location of each source, an ECD is fitted using a 4-shell spherical head model. The independent components with significant ASSRs were recognized and clustered across subjects by means of a probabilistic clustering method based on a Gaussian mixture model. In addition, we compared the strength and phase of ASSR sources among the four modulation frequencies.

Heschl's gyrus (part of the primary auditory cortex) of each hemisphere. This outcome is in line with the observation by Kuriki et al. (2013). These researchers employed a single equivalent current dipole (ECD) per hemisphere to localize the sources of ASSRs to 40 Hz AM tones and observed one dipole that was located in the central part of Heschl's gyrus. The distributed model for ASSR generation is supported by studies using electroencephalography (EEG). Most EEG studies have used dipole source analysis to localize ASSR sources (Herdman et al. 2002; Poulsen et al., 2007; Spencer, 2012). Herdman et al. (2002) used a dipole source model containing a midline brainstem generator with vertical and lateral components and two symmetrical sources in the left and right superior temporal planes, each consisting of tangential and radial components. The location and orientation of these dipoles were determined based on the ASSRs recorded by 47 scalp electrodes. Other EEG studies used distributed source analysis to localize ASSR generators (Mulert et al., 2011; Reyes et al., 2005). Using this method Reyes et al. (2005) identified 7 ( ± 1) sources with considerable phase dispersion in response to a 40 Hz AM tone. Outcomes of functional imaging studies corroborate the distributed model of ASSR generation. Steinmann and Gutschalk (2011) compared the neural activity in response to a 40 Hz AM tone versus an unmodulated tone using fMRI, to estimate the activity related to a 40 Hz steady-state response. They investigated the regions of interest along the auditory pathway and identified three distinct active regions, namely Heschl's gyrus, the medial geniculate body (MGB) and the inferior colliculus (IC). Reyes et al. (2004) applied position emission tomography (PET) to locate the active brain regions associated with the processing of a 40 Hz AM tone. These researchers reported that, in addition to the left and right auditory cortices, the left MGB and right middle frontal gyrus were active when stimulating the right ear. Although the middle frontal gyrus does not belong to the central auditory pathway, evidence has been provided for anatomical connections between the prefrontal cortex and primary auditory cortex (Kaas and Hackett, 2000; Pandya, 1995). The study of Reyes et al. (2004) suggested that the neural network underlying the generation of ASSRs may go beyond the brain regions belonging to the central auditory pathway. However, it has to be considered that the ASSRs are the result of an electrophysiological phased-locked activity and the sources of electrophysiologic responses may not induce a change in metabolic level (Reyes et al., 2005). Importantly, the number and location of ASSR generators may differ in response to AM sounds with different modulation frequencies. It is suggested that both cortical and subcortical sources are activated by AM stimuli, whereas the cortical sources are more sensitive to lowfrequency modulations (less than 40 Hz) and the subcortical sources are optimally activated by high-frequency modulations (more than 80 Hz) (Giraud et al., 2000; Herdman et al. 2002; Kuwada et al., 2002; Mauer and Döring, 1999). Low-frequency modulations, ≤16 Hz in particular, are known to be important for speech perception (Drullman et al., 1994; Poeppel, 2003). In an fMRI study, Giraud et al. (2000) investigated the active brain regions in response to noise that was amplitude modulated at frequencies ranging from 4 to 256 Hz. They suggested that the lower brainstem and the inferior colliculus are preferentially responsive to higher frequencies (32–256 Hz), whereas the MGB and the primary auditory cortex show preference for lower frequencies (4–32 Hz). Although the cortical regions seem to be more sensitive to low-frequency modulations, in some studies, they were found dominant in response to higher modulation frequencies as well (Hari et al., 1989; Schoonhoven et al., 2003). The long-term goal of our research is to gain in-depth insight into the functional connectivity of different neural sources playing a role in the response to auditory modulated stimuli. The latter are typical basic stimuli to model the temporal speech envelope in human communication which play a key role in speech intelligibility (Shannon et al., 1995). Good temporal resolution and moderate spatial resolution will

Materials and methods Participants The ASSR data were adopted from the study of Goossens et al. (2016). Nineteen right-handed young adults (20–30 years of age, 9 men) with clinically normal audiometric thresholds in both ears (≤ 20 dB HL, 125 Hz − 4 kHz), participated in this study. All subjects were Dutch native speakers and showed no indication of mild cognitive impairment as assessed by the Montreal Cognitive Assessment Task (score ≥26/30) (Nasreddine et al., 2005). None of the participants had a medical history of brain injury, neurological disorders or tinnitus.

Stimuli and procedures White noise with a bandwidth of 1 octave, centered at 1 kHz, was 100% amplitude modulated at 3.91, 19.53, 40.04, and 80.08 Hz and presented to the right ear via ER-3A insert phones. The intended modulation frequencies, i.e., 4, 20, 40, and 80 Hz, were adjusted to above-mentioned frequencies to ensure that each epoch of 1.024 s contained an integer number of cycles (John and Picton, 2000). Participants were lying on a bed and watched a silent movie with subtitles. This set-up induced non-attentive listening to the modulated sounds and prevented the listeners from falling asleep. Stimulus intensity was set to 70 dB SPL. The order of modulation frequencies was randomized among subjects. For each of the four modulation frequencies, the stimuli were continuous and 5-minute EEG signals were recorded (for more details see Goossens et al., 2016).

EEG recordings ASSRs were recorded using the ActiveTwo system of BioSemi at a sampling rate of 8192 Hz with a gain of 32.25 nV/bit. The EEG signal was picked up by 64 active Ag/AgCl electrodes mounted in head caps according to the 10–10 electrode system (American Clinical Neurophysiology Society, 2006). 241

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Fig. 1. Block diagram of the applied approach to reconstruct and investigate the ASSR sources.

times the number of channels squared. This meets the recommended time points for a satisfactory ICA decomposition (Delorme and Makeig, 2004). Data points which did not fit the model (i.e., standard deviation ≥5) were excluded. This rejection procedure was repeated five times, namely after iterations 3, 6, 9, 12, and 15. The number of outcome ICs was equal to the number of input channels. Since some of the channels had been rejected (during preprocessing) the number of input channels varied between 57 and 63 (62.6 ± 0.96).

EEG analysis: spatiotemporal reconstruction The EEG data analyses were performed using custom scripts in Matlab R2013a. Fig. 1 illustrates the applied approach for reconstructing and investigating brain source activity. In the following paragraphs the different processing steps are explained in more detail. Preprocessing Each of the 64 EEG channels was filtered by a zero phase high-pass filter with a cut-off frequency of 2 Hz (12 dB/octave) to exclude lowfrequency distortions caused by skin potentials and the DC component of the amplifier. The filtered EEG channels were referenced to the vertex electrode (i.e., Cz) and divided into epochs of 1.024 s. Subsequently, three procedures were applied to omit noisy data.

Dipole fitting: IC localization The brain source locations and projection weights (scalp topography of each IC) are assumed to be stationary (spatially fixed) for the duration of the training data (Onton et al., 2006). Therefore, the projection weights of every IC can be used to estimate the equivalent dipole location of that IC. In this study, source localization was conducted by fitting an equivalent current dipole model to the projection weights of the ICs using DIPFIT 2.3 which is a plug-in for EEGLAB (Delorme and Makeig, 2004). In this procedure a 4-shell spherical head model, consisting of 4 spherical surfaces (i.e., scalp, skull, cerebrospinal fluid (CSF), and brain), was selected as the volume conductor model of the head (Kavanagh et al., 1978). The conductivity values of brain and CSF were set to 0.33 (Geddes and Baker, 1967), and 1.79 S/m (Baumann et al., 1997), respectively. The conductivity of the scalp and the brain are suggested to be similar, whereas the conductivity of the skull is much lower (Geddes and Baker, 1967; Gonçalves et al., 2003; Oostendorp et al., 2000). An accurate estimation of the relative conductivity between the skull and the brain is indispensable for an accurate source localization, because the brain to skull conductivity ratio (BSCR) can change the estimated source locations significantly (Acar et al., 2016; Acar and Makeig, 2013). By virtue of the average of BSCRs reported in the recent studies using invasive and non-invasive methods (Baysal and Haueisen, 2004; Ferree et al., 2000; Gonçalves et al., 2003; Oostendorp et al., 2000; Zhang et al., 2006), we estimated the BSCR to be 24.5. The channel location information was aligned to the standard MNI (Montreal Neurological Institute) brain template (Mazziotta et al., 2001).

1. Channel rejection: the mean of the peak to peak (PtoP) amplitude of all epochs was calculated for each of the 64 electrode channels separately (as an index of PtoP for each channel). A channel was rejected if its PtoP amplitude was three times larger than the median PtoP amplitude across all channels. 2. Recording rejection: an EEG recording was rejected if more than 6 channels had to be rejected. 3. Epoch rejection: epochs were rejected if their absolute amplitude exceeded 100 µV. However, if by applying this criterion, less than 192 epochs could be retained, we gradually increased the critical amplitude up to and including 160 µV (step size of 20 µV). Finally, the first 192 artifact free epochs were preserved for subsequent analyses. Independent Component Analysis (ICA) After preprocessing, Independent Component Analysis (ICA) was applied to the cleaned EEG data of each participant in order to separate the source activities which are known to be linearly mixed due to volume conduction. ICA decomposes EEG data into maximally independent (i.e., spatially fixed and temporally distinct) source time series (Onton and Makeig, 2009). In the present study, the adaptive mixture ICA (AMICA) algorithm (Palmer et al., 2008, 2006) was applied because of its superior performance relative to many other ICA algorithms (Delorme et al., 2012). The performance of AMICA is based on the remaining mutual information between independent components (ICs) and the number of ICs with dipolar scalp projections (those are compatible with a single cortical source). AMICA was performed on the 192 artifact-free epochs in concatenated form. Each epoch of 1.024 s contained 8388 samples of data. Thus, the number of time points to estimate the mixing matrix of ICA ranges from 405 to 495

Recognizing ASSR sources Each IC, based on 192 epochs, shows the time course activity of an independent brain source. The epochs were transformed to the frequency domain by means of a discrete Fourier transformation. R = {r1, r2, ...,rn}, R ∈  is the set of complex responses for n epochs at the frequency bin corresponding to the modulation frequency. The signal-to-noise ratio (SNR) of ASSR was calculated in the modulation frequency bin (Eq. (1)). 242

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SNR =

PS ‖ mean (R )‖2 = ⎛ std (R) ⎞2 PN ⎜ n ⎟ ⎠ ⎝

the identified ASSR sources was performed based on a Gaussian mixture model (GMM) (for more information about GMM, see Appendix A). The number of GMM components was initialized to be equal to the average number of identified ASSR sources across subjects. The k-means algorithm with outlier detection (criteria: 3 standard deviation) implemented in EEGLAB (Delorme and Makeig, 2004) was used for the initialization procedure of the GMM. After fitting the GMM model, clusters were assigned to each data point by selecting the component that maximized the posterior probability. The posterior probability of each data point indicates the probability of belonging to a specific cluster. Hence, by means of this model-based clustering, a probabilistic interpretation was provided for each source and for the obtained clusters.

(1)

where PS , PN refer to the power of the response signal and the power of the EEG noise (i.e., neural background activity), respectively. The signal power was defined as the squared amplitude of the mean of responses across epochs (‖ mean (R )‖) and the noise power was defined on the basis of the standard error of the responses across epochs. The one-sample Hotelling T2 test (Hotelling, 1931) was applied to detect ICs that exhibited a significant ASSR (Hofmann and Wouters, 2012). An IC was recognized as an ASSR source when the Hotelling T2 test showed a significant difference (α=0.05) between the power of the response signal and the power of the EEG noise. Stated otherwise, the SNR of each IC (based on Eq. (1)) was the dependent variable of the Hotelling T2 test. Only the first 30 ICs which accounted for 87% (SD=4%) of the variance in the total channel power, were considered for the Hotelling T2 test. Many independent brain sources reflect volume conducted projection of near-synchronous local activity of a single compact cortical area (i.e., patch of cortex) (Delorme, 2012). Hence, these independent sources show a near-dipolar scalp projection. A near-dipolar scalp projection can be identified based on the residual variance of its dipole fitting procedure. A single equivalent dipole can be fitted to a neardipolar scalp projection with a low residual variance. Therefore, the residual variance between the scalp projection of an IC and its single equivalent dipole model can be used to distinguish between brain and non-brain components (i.e., the noisy ICs) (Delorme, 2012). In this study ICs of which the equivalent dipole model showed more than 15% residual variance were excluded from further analyses. Also, ICs were excluded when their equivalent dipoles were located outside the brain.

Spatial distribution of ASSR sources The spatial distribution of the ASSR sources was investigated by means of the fitted GMM model representing the probability density for the existence of a true equivalent dipole in a specific location. The center of each GMM's component shows the most probable location of the ASSR source while the covariance matrix shows the variability in dipole localization which can be due to imperfections regarding tissue conductivity estimates, head co-registration, the decomposition method, and/or between-subject variability in the source locations. Magnitude and phase distribution: polar diagram The magnitude and the phase angle of the ASSRs were estimated for each cluster separately. This estimation was based on the posterior probability of each source in the GMM model, yielding a mathematical expectation of the magnitude and phase. For each cluster, the expected value of the magnitude or phase ( Ai , i=1,…n, n is number of clusters) was calculated as: m

Source clustering A clustering algorithm was applied to the reconstructed sources in order to determine categories of ASSR sources among participants. Several studies demonstrated that the adjacent neurons in a patch of cortex show synchronous and simultaneous activity (Pascual-Marqui et al., 1994). Temporal coherence between adjacent neurons in response to auditory stimuli is reported in intracranial recording studies investigating tonotopical organization of the auditory cortex (Hullett et al., 2016; Nourski et al., 2014). On a larger scale, the centers of processing (i.e., neural ensembles) along the ascending auditory pathway generate responses with different phase delays (or apparent latencies) (Herdman et al. 2002; Picton et al., 2003). Thus, the phase angle of the ASSR sources provide a discriminative feature for clustering of the sources distributed in different regions. This physiological consideration was exploited in our clustering method by using the phase angle of the reconstructed sources at the modulation frequency, Arg (mean (R )), in addition to the location of reconstructed sources as the input feature vector of clustering. The phase angle is a circular quantity and cannot be used directly along with location data. The phase angle data (φ) were converted to their corresponding points on the unit circle, i.e., they were converted from polar coordinates to Cartesian coordinates, based on Euler's formula :

eiϕ = cos(ϕ) + i sin(ϕ)

E{Ai } = ⟨Ai ⟩ =

∑ j =1 Pji Aji m

∑ j =1 Pji

(3)

where m is the number of sources at cluster i, and P is the posterior probability assigned to each source. Explained variance of clusters The explained variance of an IC demonstrates the contribution of that IC to the scalp recorded data. In current study the explained variance of the ICs was estimated based on back projection of activity to scalp data. For each IC, the percentage of the variance accounted for (PVAF) in the scalp ASSR data (i.e., average across all epochs) was calculated using the following formula:

⎛ ∑ var (ASSR − backIC ) ⎞ ⎟⎟ PVAF (IC ) = 100 ⎜⎜1 − channel ∑channel var (ASSR ) ⎝ ⎠

(4)

Where ASSR is the average across all epochs, backIC is the back projection of the IC to the scalp data and var() is variance. Consequently, the relative contribution of source clusters to the scalp recorded ASSR was estimated using the average of the PVAF of the ICs belonging to that cluster.

(2)

Results

eiϕ along

with the location data The real and imaginary part of (phase: 2-dimension, location: 3-dimension) formed the required input vector for source clustering. The data of dipole location and phase angle were normalized and weighted by 3 and 1, respectively. We used a higher weight for dipole location data than for phase angle data in order to emphasize the spatial compactness of clusters.1 Clustering of

Reconstructed ASSR sources The number of identified ASSR sources for the different modulation frequencies were: 3.0 ± 1.8 (4 Hz), 4.3 ± 2.8 (20 Hz), 8.7 ± 3.3 (40 Hz), 4.1 ± 1.8 (80 Hz). To determine the source clusters, a GMM model was fitted to the identified ASSR sources for each of the four modulation frequencies. For the fitting procedure, the number of GMM components was initialized to be equal to the integer part of average number of identified sources at each modulation frequency (3, 4, 8, and 4

1 Empirically, we found that a scaling factor of 3 was enough to prevent clusters overlapping in 3-dimensional (3D) space. Higher scaling factors did not affect clustering outcomes.

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Fig. 2. The source clusters of ASSR at four modulation frequencies (4, 20, 40, and 80 Hz) are shown in axial, sagittal, and coronal projections. The background images are taken from standard MNI brain template (Mazziotta et al., 2001). The color of each ellipsoid depicts the expected value (based on Eq. (3)) of the SNR.

Anatomical labels associated with each cluster were obtained using a modified version of Talairach Daemon software (Lancaster et al., 2000). The labels were extracted at two levels, at the lobe level and at the gyrus level. The region inside each ellipsoid was investigated with a

sources for 4, 20, 40, and 80 Hz, respectively). Fig. 2 shows the identified source clusters in response to 4, 20, 40, and 80 Hz acoustic amplitude modulations. These clusters are depicted by means of ellipsoids. 244

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Table 1 Overview of the identified clusters of ASSR sources in response to 4, 20, 40, and 80 Hz. It lists the center location data based on MNI coordination system, the labels, the expected value of the SNR and the phase, and PVAF of each source cluster. Modulation frequency

n

Anatomical labels and their probability

Coordinates of the cluster center (xyz in mm)

Expected SNR (dB)

Expected phase (°)

PVAF (%) (SD)

4 Hz

C1

Left Frontal Lobe Postcentral Gyrus Medial limbic lobe Thalamus Right Frontal Lobe Precentral Gyrus Medial Occipital Lobe Cerebellum Medial frontal lobe Medial Frontal Gyrus Right Sub-lobar Cingulate Gyrus Left Temporal Lobe Precentral Gyrus Right Frontal Lobe Postcentral Gyrus Left Frontal Lobe Postcentral Gyrus Right Limbic Lobe Parahippocampal Gyrus Central Occipital Lobe Lingual Gyrus Left Temporal Lobe Parahippocampal Gyrus Medial Frontal Lobe Rectal Gyrus Medial Frontal Lobe Cingulate Gyrus Left Occipital Lobe Precuneus Right Frontal Lobe Precentral Gyrus Left Frontal Lobe Postcentral Gyrus Medial Limbic Lobe Anterior Cingulate Medial Occipital Lobe Lingual Gyrus

−35, −5, 40

8.2

−22

−6, −40, −8

7.2

143

34, −3, 32

6.4

103

10, −61, −10

9.4

106

−10, 31, −24

8.3

−31

23, −6, 19

8.1

19

−38, −23, 20

8.1

51

24, −21, 37

11.8

176

−33, −23, 35

11.0

162

26, −44, 5

10.8

48

0, −76, −8

9.7

54

−39, −37, −24

8.8

37

1, 35, −38

8.7

−82

−2, 32, 42

8.3

−79

−28, −67, 22

7.4

91

27, −24, 28

8.3

126

−30, −5, 34

7.3

164

13, 4, 1

7.2

−14

6, −73, −2

7.1

−50

2.1 (2.5) 6.9 (7.2) 4.7 (3.9) 4.9 (3.2) 8.4 (2.7) 6.0 (5.3) 3.6 (3.3) 5.3 (4.7) 3.4 (2.9) 4.9 (3.0) 4.0 (2.4) 4.5 (2.3) 11.0 (4.5) 2.6 (1.8) 2.2 (1.5) 4.0 (2.9) 4.7 (4.7) 7.4 (6.3) 3.2 (1.8)

C2 C3 20 Hz

C1 C2 C3 C4

40 Hz

C1 C2 C3 C4 C5 C6 C7 C8

80 Hz

C1 C2 C3 C4

0.63 0.17 0.20 0.08 0.64 0.18 0.30 0.26 0.57 0.13 0.38 0.08 0.35 0.09 0.40 0.18 0.45 0.29 0.29 0.13 0.50 0.24 0.46 0.20 0.72 0.38 0.65 0.27 0.40 0.17 0.20 0.16 0.50 0.10 0.28 0.09 0.53 0.16

resolution of 1 mm3. Larger ellipsoids yielded a larger number of identified labels. Therefore, a probability was assigned to each label, which expressed the repetition of a specific label normalized by the number of identified labels. For all of the modulation frequencies (4, 20, 40, and 80 Hz), the most likely labels for each source cluster are listed in Table 1. Source cluster characteristics for all of the modulation frequencies (4, 20, 40, and 80 Hz) are summarized in Table 1. It lists the center location, the labels, the explained variance (i.e., PVAF, based on Eq. (4)), the expected value of the SNR and the phase (based on Eq. (3)) of each source cluster. For each modulation frequency, we identified a source cluster at or in the vicinity of the left and the right auditory cortices. These clusters were located nearest to the average locations reported in the literature for cortical ASSR sources (x,y,z in MNI coordinates, left auditory cortex: −44, −21, 11, right auditory cortex: 42.5, −26, 17.4) (Reyes et al., 2005, 2004; Steinmann and Gutschalk, 2011; Teale et al., 2008). These nearest clusters were identified based on the Euclidean distance between their center and those reported in the literature. Throughout the remaining paper, these clusters are referred to as the left/right auditory cortex. For the 40 Hz ASSR, most of the identified source clusters are symmetrically spread out across the left and the right hemispheres (Fig. 2). A pair of symmetrical clusters is located in the postcentral gyrus (cluster 1 and 2), which is in the vicinity of the primary auditory cortex. With regard to temporal properties, these clusters show similar expected values of SNR and phase (Table 1). Clusters 1 and 2 show the highest expected SNR (Table 1) among all the identified clusters. The

second pair of symmetrical clusters is located more posterior and inferior, i.e., near the parahippocampal gyrus (clusters 3 and 5). Beside these clusters, two clusters located in the frontal lobe (clusters 6 and 7) exhibit similar phase (around −80°) and SNR (around 8.5 dB).

Effect of modulation frequency In addition to reconstructing the ASSR sources, we aimed at determining the extent to which the reconstructed ASSR sources differed for the different modulation frequencies. This was investigated from both a spatial and temporal perspective. As for the spatial perspective, regions of interest (ROIs) were defined around the brainstem, the left, and the right auditory cortices. These three regions were selected because they were common across the 4 modulation frequencies. In other words, for each modulation frequency, a source cluster was identified in each of these regions. Clusters which were not common across the 4 modulation frequencies (e.g. around the frontal lobe) could not be used for comparison. For each ROI, the location of the identified sources was compared across the 4 modulation frequencies. As for the temporal perspective, the SNR and phase of the identified source clusters were compared across the 4 modulation frequencies. Fig. 3 illustrates the location of the three source clusters, i.e., one per ROI, for the 4 modulation frequencies. To evaluate whether the location of the reconstructed sources differed significantly for the different modulation frequencies, a multivariate analyses of variance (mANOVA) was performed for each ROI separately. Since the location 245

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significant results were obtained for 5 pairs: brainstem – (20 Hz vs. 80 Hz) and (40 Hz vs. 80 Hz), left auditory cortex – (4 Hz vs. 80 Hz), right auditory cortex – (4 Hz vs. 20 Hz) and (40 Hz vs. 80 Hz). Importantly, in each ROI, the distance between the cluster centers of the different modulation frequencies was typically within 1 or 2 cm. Fig. 4 shows the polar plot of the expected value of the SNR (dB) and the phase angle (°) of the source clusters that were located in the ROIs. The phase angles of reconstructed sources were statistically compared across modulation frequencies using Watson-Williams test, a circular analogue of one-factor ANOVA (Berens, 2009). This analysis was performed separately for each ROI. The results of these analyses showed a significant effect of modulation frequency irrespective of the ROI (brainstem: F(3,85)=23.6, p < 0.001; left auditory cortex: F(3,67)=15.8, p < 0.001; right auditory cortex: F(3,88)=5.2, p < 0.01). Subsequently, for each ROI, we performed six Watson-Williams tests (i.e., one for each pair of modulation frequencies). For the brainstem clusters (Fig. 4. c), these post hoc comparisons indicated a significant effect (α=0.05) of modulation frequency for all pairwise comparisons, such that the expected phase angles decrease with increase of the modulation frequency. For the left auditory cortex clusters (Fig. 4. a), the post hoc comparisons indicated a significant effect (α=0.05) of modulation frequency for nearly all pairs of modulation frequencies (5 out of 6 pair). The phases increase with increase of the modulation frequency. Comparison between 40 Hz and 80 Hz phase angles was statistically non-significant. For the right auditory cortex clusters (Fig. 4. b), the sources of 40 Hz ASSRs showed higher phase angle than those of 20 Hz. No main effect of modulation frequency was found for other pairwise comparisons in the right auditory cortex. Fig. 5 represents the SNR for the identified sources located in the three ROIs. The SNR of the reconstructed sources were statistically compared across the different modulation frequencies by means of a Factorial Repeated Measures ANOVA with SNR as the dependent variable and ROI (3 categories: the brainstem, the left, and the right auditory cortex) and modulation frequency (4 categories: 4, 20, 40, and 80 Hz) as within-subject variables. Deviations from the normal distribution were detected by Shapiro-Wilk testing (α=0.05) in 5 out of the 12 test conditions (3 ROIs*4 modulation frequencies): brainstem – 4 Hz, left auditory cortex – 20 Hz, 80 Hz, right auditory cortex – 4 Hz, 20 Hz. To control for these violations of normality, we also applied Friedman's ANOVAs and Mann-Whitney post hoc analyses. As the parametric and non parametric analyses yielded similar results, only the results of the parametric analyses will be reported. The Factorial Repeated Measures ANOVA test showed a main effect of modulation frequency (F(3,242)=15.77, p < 0.001), whereas no main effect of ROI. No interaction between modulation frequency and ROI was found either. In a second analysis the SNR of reconstructed sources was compared statistically across modulation frequencies. This analysis was carried out separately for each ROI. One-way ANOVA test revealed a significantly higher SNR for the 40 Hz ASSR compared to all other modulation frequencies, irrespective of the ROI (brainstem: F(3,85)=5.35, p < 0.01; left auditory cortex: F(3,67)=6.17, p < 0.01; right auditory cortex: F(3,88)=10.8, p < 0.001). To go into more detail, we also carried out a series of t-tests and Mann-Whitney tests, thereby comparing the SNR of the different ASSRs pairwise, per ROI. Since the SNR of 40 Hz ASSR was significantly higher than those of the other modulation frequencies and for the sake of brevity, we only report the results of the tests including the 40 Hz ASSR . Since similar results were obtained from the parametric and nonparametric analyses only the parametric results are reported. The reconstructed sources of 40 Hz ASSRs showed higher SNR than those of 4 Hz in all three ROIs (the brainstem (t(42)=2.82, p < 0.01), the left auditory cortex (t(34)=2.33, p < 0.05), and the right auditory cortex (t(40)=4.77, p < 0.001)). Also, the response to 40 Hz stimuli showed a higher SNR than those to 80 Hz for all three ROIs (the brainstem t(33)=2.42, p < 0.05; the left auditory cortex t(35)=3.26, p < 0.01; the right

Fig.3. The identified clusters of ASSR sources located in the vicinity of the brainstem, the left and the right auditory cortex (ROIs) in response to 4, 20, 40, and 80 Hz acoustic modulations. The center and the covariance of each cluster were depicted in a coronal view (a) as well as an axial view (b).

of the reconstructed sources in each cluster belonged to a GMM component, these data inherently met the assumption of normality. The results of these analyses revealed a significant effect (α=0.05) of modulation frequency for all ROI. Subsequently, for each ROI, we carried out six mANOVAs (i.e., one for each pair of modulation frequencies) for post hoc comparisons. These post hoc comparisons indicated a significant effect (α=0.05) of modulation frequency for nearly all pairs of modulation frequencies (13 out of 18). Non246

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Fig. 4. The expected value of the SNR (dB) and the phase angle (°) of the source clusters for the 4, 20, 40, and 80 Hz ASSR, located inside or in the vicinity of the left auditory cortex (a), the right auditory cortex (b), and the brainstem (c). The error bar of phase angle shows the standard error of phase for each cluster.

i.e., 25 Hz and 55 Hz. By this, on average, 0.6 and 0.4 sources were identified per subject for 25 Hz and 55 Hz, respectively, showing that at least for nearly half of the subjects no ASSR source was recognized at all. Finally, we tried to fit a GMM model to the falsely identified sources. The number of GMM components was initialized to be equal to 2 or 3. However, the fitting algorithm did not converge, neither for 2 nor for 3 components. This shows that there was no focal point in the distribution of the falsely identified sources. In a second validation procedure, the proposed reconstruction method was examined on the silence data, i.e., EEG data with no auditory stimulation. These silence data had been recorded in two parts, each one 150 s, before and after the main ASSR recording of each subject. Similar to the first validation procedure, AMICA and dipole fitting were applied to the silence data. Subsequently, the SNRs of the ICs were calculated at 10 Hz and 30 Hz, i.e., two arbitrary frequencies. Hotelling T2 test on the SNRs showed an average of 0.5 and 0.6 sources per subject for 10 Hz and 30 Hz, respectively. Similarly to the first validation procedure the GMM fitting did not converge, neither for 2 nor for 3 components. In both validation procedures, the average number of identified sources per subject was less than 1, whereas for ASSRs in response to the 4, 20, 40, and 80 Hz acoustic modulations at least 3 sources were identified per subject (Fig. 2 and Table 1). These validation results suggest that the ICA approach is sensitive to ASSR sources and shows a low false positive error in recognizing the ASSR sources where there is no ASSR. Furthermore, in both validation procedures the GMM model did not fit to the identified sources. This indicates that there is no focal point in the distribution of the identified sources and that they do not follow a Gaussian distribution. In a third validation procedure the split-half reliability was evaluated. In this procedure the artifact-free epochs of each participant (192 epochs) were divided into two parts of 96 epochs each. Subsequently, the proposed source analysis approach (Fig. 1) was applied separately on each part of the data. To compare the location of the reconstructed sources in the first half, second half, and the entire data set, three mANOVAs were performed. These analyses were performed for the left and the right auditory cortex separately. No significant difference was found for any of these comparisons. These

Fig. 5. Average SNR (dB) of the identified clusters for 4, 20, 40, and 80 Hz modulation frequencies, located around the brainstem, the left and the right auditory cortex. Error bars represent ± 1SD. The 40 Hz ASSRs show significantly higher SNR than other frequencies in all ROIs (except for the comparison with 20 Hz sources located around the brainstem).

auditory cortex t(43)=3.01, p < 0.01). Finally, compared to 20 Hz ASSRs, 40 Hz ASSRs were larger for clusters located around the left auditory cortex (t(33)=2.09, p < 0.05) and the right auditory cortex (t(54)=3.85, p < 0.001). Validation of the reconstruction approach In order to examine the validity of the ICA approach (Fig. 1) to reconstruct ASSR sources, three validation procedures were performed. In the first validation procedure, the detection of ASSR sources was evaluated in response to two modulation frequencies that had not been presented to the participants. As such, AMICA and dipole fitting (as explained in 2.4.2 and 2.4.3) were applied to the 40 Hz modulation frequency data. Subsequently, to identify the ASSR sources the Hotelling T2 test investigated the SNR at two irrelevant frequencies, 247

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LORETA in the cerebellum and medial frontal lobe. Although the LORETA source near the auditory cortex is largely blurred, it is comparable with the source clusters indicated by ICA, i.e., in the auditory cortex (Fig. 2, 40 Hz: clusters C1 and C2) and clusters located inferior and posterior to the auditory cortex (Fig. 2, 40 Hz: clusters C3 and C5). LORETA has a bias towards returning large and distributed sources because of the smoothness constraint in its reconstruction procedure (Jatoi et al., 2014; Michel et al., 2004; Pascual-Marqui et al., 1994). As such, identifying multiple sources that are located near to each other is difficult when using LORETA. Accordingly, the comparison of LORETA and ICA results may demonstrate ability of the ICA approach to distinguish between multiple sources when they are located near to each other. The ICA approach provides a source reconstruction approach with minimal assumptions about the location of sources. Thus the outcomes of this method should be less dependent on the assumptions than other traditional methods such as dipole analysis and LORETA.

results suggest that the ICA approach is reliable again within-subject variability and shows consistent results when applying on different parts of a ASSR data set. Comparison with other source analysis methods In order to provide more insight into the ability of the ICA approach for reconstructing ASSR sources, we compared the outcomes of the current study with two other common methods: 1) dipole source analysis and 2) low resolution brain electromagnetic tomography (LORETA). Dipole source analysis Most EEG studies investigating ASSR sources have utilized dipole source modeling (Herdman et al. 2002; Poulsen et al., 2007; Spencer, 2012). They considered a 3-dipole source model, consisting of one fixed brainstem source and two symmetric cortical sources, and fitted the location and orientation of these dipoles using the grand average of the recorded EEG data. Dipole source analysis was performed using the 4shell spherical head model that we used in the ICA approach (2.4.3. Dipole fitting: IC Localization) with BESA research 5.3 software. The artifact-free epochs in response to the 40 Hz stimuli were averaged across all subjects. The grand mean averaged epoch was band-pass filtered between 38 and 42 Hz to emphasize the phase locked activity. This epoch was used to fit a dipole model with 3 regional sources in the brainstem and cortices (cf. Herdman et al. 2002; Poulsen et al., 2007). In a sequential fitting procedure firstly a regional source (i.e., three mutually orthogonal dipoles) was fitted and converged to a deep midline position (MNI coordinates: 2, −4, −11). Then, two symmetric regional sources were added to the initial single source. These two sources were fitted and converged to the medial superior temporal plane, near the auditory cortex (left hemisphere, MNI coordinates: −35, −20, −2; right hemisphere, MNI coordinates: 35, −20, −2)(Fig. 6).

Discussion Hypothetical models of ASSR generation In nearly all MEG studies, irrespective of the applied source localization method, only one ASSR source in the superior temporal plane of each hemisphere was identified (Kuriki et al., 2013; Lazzouni et al., 2010; Pantev et al., 2004; Teale et al., 2008). However, with MEG it is difficult to recognize radial sources. Moreover, it cannot easily pick up the activity of deep sources (Hämäläinen et al., 1993; Herdman et al., 2003, 2002). Therefore, it is not expected that MEG recordings can identify subcortical sources, whereas it is expected with EEG recordings. In EEG studies, dipole source analysis is commonly employed to localize ASSR sources (Herdman et al. 2002; Poulsen et al., 2007; Spencer, 2012; Spencer et al., 2009). In this approach, similar to dipole analysis in MEG studies, a symmetrical pair of dipoles is located in the superior temporal plane, oriented tangentially to the lateral scalp surface. Because EEG is sensitive to both tangential and radial source orientations, one additional dipole in each hemisphere is added to model the radial component of the ASSR (Scherg et al., 1988; Scherg and Cramon, 1986). Moreover, one or two deep midline dipoles accounted for residual or subcortical activity. Using the above-described dipole source model Herdman et al. (2002) suggested that the ASSR data can be interpreted reasonably well using a combination of cortical and subcortical dipoles. The data of these researchers demonstrated that both cortical and subcortical regions are responsive to AM stimuli. However, because of their prior assumptions regarding the number and location of dipoles it is possible that other sources in the cortical or subcortical areas are also responsive to AM stimuli. Functional imaging studies investigating 40 Hz ASSR generators have recognized multiple active regions. Activity in the secondary centers of the auditory pathway (the auditory cortex, MGB, and IC) has been reported by several researchers (Giraud et al., 2000; Harms and Melcher, 2002; Steinmann and Gutschalk, 2011). fMRI studies that employed “cardiac gating” for image enhancement, have also detected activity in the primary centers of the auditory pathway i.e., cochlear nucleus and the superior olivary complex (Griffiths et al., 2001; Guimaraes et al., 1998; Hawley et al., 2005; Sigalovsky and Melcher, 2006). Moreover, neuro-metabolic activity in the cerebellum in response to 40 Hz click trains has been reported using PET and fMRI scans (Pastor et al., 2008, 2002). In another PET study, Reyes et al. (2004) reported activity in the frontal lobe. The above-mentioned studies suggest that ASSR sources may be located beyond the central auditory pathway. This appears to be the case in the current study using brain potentials.

LORETA LORETA is a distributed source analysis method which estimates the current density of brain regions (for more details, see PascualMarqui et al., 1994). LORETA was performed based on the 4-shell spherical head model that we used in the ICA approach (2.4.3. Dipole fitting: IC Localization) with BESA research 5.3 software. Similar to the above-mentioned dipole analysis, the grand mean averaged epoch was band-pass filtered between 38 and 42 Hz. In contrast to the abovementioned dipole source analysis, the LORETA method does not require a prior dipole model and provides a distributed representation of the source activities at the outcome (Pascual-Marqui et al., 1994). The default setting of regularization parameter in BESA (i.e., truncated singular value decomposition with cutoff of 0.005%) was used for LORETA. Fig. 6 shows the identified sources using LORETA and dipole analysis. The first outcome of LORETA shows a blurred source with a maxima in the vicinity of the left auditory cortex (MNI coordinates: −38, −23, −24) (close to the left dipole determined by dipole source analysis). This source is largely distributed from the left to the right auditory cortex and shows relatively high activity near the dipoles determined by dipole source analysis. The maxima of the second and third sources were identified in the mid-frontal (MNI coordinates: −10, 47, 4) and mid-cerebellum (MNI coordinates: −3.5, −74, −19), respectively. These latter two sources are in line with the study of Reyes et al. (2005) who identified two sources in the middle frontal lobe and the cerebellum using LORETA. In sum, LORETA identified the main and large sources of the ASSR, similar to the dipole analysis, and two extra sources. These results show that there may be additional ASSR sources that are not detected using dipole analysis because of the limitations caused by its prior assumptions. The identified source clusters C4 and C7 (Fig. 2, 40 Hz) using the ICA approach were comparable with those sources identified by 248

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Fig. 6. The identified sources using LORETA and dipole analysis. Current density (LORETA) maps of 40 Hz ASSR over the grand mean averaged epoch are shown in axial, coronal, and sagittal projections. The location of LORETA sources (i.e., local maxima in current density map) is indicated by means of the red crossline superimposed on the appropriate MRI slices taken from standardized brain MRI provided by BESA software. The LORETA sources are: a) near left auditory cortex (MNI coordinates: −38, −23, −24) b) mid-frontal (MNI coordinates: −10, 47, 4) c) mid-cerebellum (MNI coordinates: −3.5, −74, −19). The sources that are identified by dipole analysis are illustrated by filled diamonds (MNI coordinates, deep midline: 2, −4, −11; medial superior temporal plane left hemisphere: −35, −20, −2; right hemisphere: 35, −20, −2).

tions between the primary auditory cortex and multiple regions in the frontal lobe (Kaas and Hackett, 2000; Pandya, 1995). Furthermore, supporting evidence for the activity in the parietal lobe comes from the EEG study of (Reyes et al., 2005). However, to verify that the identified sources located beyond the auditory pathway are effectively involved in ASSR generation, connectivity analyses are suggested for future studies. For each source cluster, we determined the variability of the source locations using the covariance matrix of source locations. These were illustrated by means of ellipsoids. The ellipsoids of the cortical source clusters of the 40 Hz ASSR were larger than the active regions typically encountered by fMRI studies (Giraud et al., 2000; Harms and Melcher, 2002; Steinmann and Gutschalk, 2011). The higher variability in our study may be due to the intrinsic lower spatial resolution of EEG compared to fMRI. Also, it is possible that utilizing a realistic head model and individual electrode position data, which were not available for this study, could decrease source location variability. For the 40 Hz ASSR, the identified source clusters around the auditory cortex show activity in the medial aspect of Heschl's gyrus which spreads superiorly. Based on the cytoarchitectonic description of the auditory cortex (Galaburda and Sanides, 1980), this area belongs to

Spatiotemporal reconstruction of 40 Hz ASSR To evaluate the existence of ASSR sources beyond the central auditory pathway we employed a reconstruction approach with no prior assumptions regarding the number and location of sources. In response to 40 Hz AM noise presented to the right ear, 8 source clusters were identified. These clusters were widely distributed across both cortical and subcortical regions. Our outcome is in agreement with the distributed model of ASSR generation. Moreover, it is in close correspondence with the EEG study by Reyes et al. (2005) in which three source localization approaches were applied and 7 ( ± 1) sources were identified. Regarding the location of ASSR sources, the clusters that were identified in the present study extended beyond the central auditory pathway, to the cerebellum, the frontal lobe and the parietal lobe. ASSR sources in the regions outside the auditory pathway have been reported before. Pastor et al. (2008, 2002) reported cerebellar activity in response to 40 Hz click trains. Moreover, Reyes et al. (2004) reported PET activity in the middle frontal gyrus. Using MEG, Molinaro et al. (2016) identified a source in the inferior frontal brain. Activity in the frontal lobe is in line with studies that identified anatomical connec249

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based on the changes of phase across modulation frequencies (Herdman et al. 2002; Picton, 1987). This analysis needs ASSR recordings at different modulation frequencies within each frequency range which was not done in this study. The reconstructed sources for 40 Hz ASSR exhibited higher SNRs than those of other frequencies (4, 20, and 80 Hz) (Fig. 5). This higher activity was observed in all ROIs except at the level of the brainstem where similar SNRs were observed for the 20 and 40 Hz ASSR. These results are comparable with those reported in the literature, irrespective of the level at which the ASSR response strength is evaluated, i.e., scalp or source level (Cohen et al., 1991; Dobie and Wilson, 1998; Herdman et al. 2002; Johnson et al., 1988). It is suggested that, in response to 40 Hz acoustic amplitude modulations, the brainstem and cortical sources are simultaneously active and that the overlapping fields of the multiple generators are recorded by scalp sensors. Because the latency between the activation at brainstem level and at cortical level approximately equals one cycle of the 40 Hz stimulus, the responses of the brainstem and cortical sources add together in a constructive way (Bohorquez and Ozdamar, 2008; Herdman et al. 2002; Mauer and Döring, 1999). However, it is possible that this explanation only applies to responses measured at the scalp. A plausible explanation for the larger 40 Hz response at source level is that the neural network underlying ASSRs shows an oscillatory behavior with an intrinsic resonance frequency around 40 Hz (Hari et al., 1989; Rance, 2008). In a MEG-based dipole source analysis, Schoonhoven et al. (2003) reported similar dipole locations in the auditory cortex for 40 Hz and 80 Hz ASSR. Their results indicate that the auditory cortex responds to both rapid and slow AM sounds. Similar observations were also reported by Hari et al. (1989) and Ross et al. (2000). This responsiveness to both rapid and slow AM stimuli is also observed in this study (Fig. 5). However, because MEG has difficulty detecting deep sources, these studies do not reject the contribution of subcortical sources for either low or higher frequencies. For each modulation frequency, the reconstructed sources around the brainstem exhibited a notable activity compared to the left and the right auditory cortex (Fig. 5). This is in line with the EEG study by Herdman et al. (2002), suggesting that the brainstem sources are active in response to low- as well as high-frequency acoustic modulations. In conclusion, we may say that, based on the responsiveness of the brainstem sources and sources located in auditory cortices, it is reasonable to assume that both cortical and subcortical sources are responsible for the ASSR generation in response to 4, 20, 40, and 80 Hz acoustic amplitude modulations. Our study also demonstrates that the identified brain sources show a higher sensitivity to 40 Hz than to the other modulation frequencies.

the medial koniocortex and the prokoniocortex. In a MEG study, Herdman et al. (2003) also detected 40 Hz ASSR activity in the medial koniocortex and prokoniocortex. These results are in line with a PET study by Pastor et al. (2002) who investigated active regions in response to 40 Hz click trains. Nevertheless, cortical sources of the 40 Hz ASSR are often shown to be located in the medial part of Heschl's gyrus (Giraud et al., 2000; Pantev et al., 1996; Steinmann and Gutschalk, 2011). It is possible that the extension of this active area into the prokoniocortex is the result of an imperfection in estimation of the skull conductivity (or BSCR). We used 24.5 for BSCR, based on the average of BSCRs reported in recent studies using invasive and noninvasive methods (Baysal and Haueisen, 2004; Ferree et al., 2000; Gonçalves et al., 2003; Oostendorp et al., 2000; Zhang et al., 2006). Another value for BSCR which is often reported is 80 (Rush and Driscoll, 1968). With this value, our cluster centers of 40 Hz ASSR, located in the auditory cortex, shifted toward the surface of the scalp, in particular laterally and superiorly (7, 1, 4 mm in x, y, and z direction). The quite large variability in BSCRs reported in the literature might be due to the measurement method and/or inter-subject variability (Acar et al., 2016). Acar et al. (2016) suggested an iterative method based on scalp projection of near-dipolar sources to estimate the skull conductivity of each participant separately. Effect of modulation frequency A novelty of this study is the investigation of the spatial and temporal properties of the reconstructed ASSR sources in response to 4 different modulation frequencies (4, 20, 40, and 80 Hz). Our study suggests that both cortical and subcortical sources are responsible for generating ASSRs in response to low- as well as high-frequency acoustic amplitude modulations and that these sources show higher sensitivity to 40 Hz than to other modulation frequencies (4, 20, 80 Hz). For each acoustic modulation, at least 3 distinct source clusters were identified which were located inside or in the vicinity of the brainstem, the left and the right auditory cortex (Fig. 3). This result suggests that both the brainstem and auditory cortex are active in response to different modulation frequencies and corroborates the theory suggesting that a distributed network of the brain regions is involved in the generation of ASSR. Moreover, for the 20 and 80 Hz ASSR a source cluster in the frontal lobe was identified, indicating that the distributed model of ASSR sources can be extended to regions beyond the central auditory pathway. To investigate whether modulation frequency affects the spatial properties of the reconstructed sources, the location of the source clusters was compared across 4, 20, 40, and 80 Hz acoustic amplitude modulations. A significant difference was observed in the majority of cluster pairs (i.e., in 13 out of 18 pairs). These results are seemingly in contrast with studies reporting no systematic difference between the location of the identified sources across different modulation frequencies (Ross et al., 2000; Schoonhoven et al., 2003). However, because of the considerable covariance of the source clusters, for the 4 Hz ASSR in specific, these statistical analyses should be interpreted with caution. As for the temporal properties of the reconstructed ASSR sources, their SNR and expected values of phase were assessed. The expected phase of the source clusters located around the brainstem decreases as the modulation frequency increases (Fig. 4. c). This systematic pattern at the brainstem is in line with that of previous studies showing that the latency of ASSRs (i.e., the time delay between stimulus onset and response) decreases for higher modulation frequencies (Herdman et al. 2002; Schoonhoven et al., 2003). A reverse systematic pattern was observed in the expected phases of the source clusters located in the left auditory cortex (Fig. 4. a). Since we do not know how many cycles have occurred between the expected phases of the consecutive modulation frequencies, interpretation of this systematic pattern is not directly possible. However, the latency of the ASSRs can be calculated indirectly

Validation of the ICA approach In current study, the localization was done at the level of the individual subject rather than at the level of the grand average. Based on these individual analyses, we managed to identify the small sources as well as the larger ones. This is in contrast with most other localization methods (either dipole fitting or distributed source analysis) which have bias towards returning large and distributed sources (Jatoi et al., 2014; Michel et al., 2004; Pascual-Marqui et al., 1994). This outcome, plus the very high time resolution of reconstructed sources, provide a strong framework for brain connectivity analysis. The following arguments suggest the ICA approach is able to accurately separate multiple sources of ASSRs from raw EEG data: 1. We identified several source clusters with distinct location and phase. These results indicate that the ICA approach is able to distinguish between phase-lagged sources. 2. We identified source clusters in similar positions around the left and right auditory cortex and the brainstem for the different 250

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located in the cortical and the subcortical regions, is activated in response to 4, 20, 40, and 80 Hz AM noises. Some of these sources are located beyond the central auditory pathway. Although other neuroanatomical studies also support the existence of sources beyond the auditory pathway, additional connectivity analyses are required to confirm that these sources are effectively involved in the generation of ASSRs. The comparison of brain sources in response to different modulation frequencies showed that the identified brain sources in the brainstem, the left and the right auditory cortex show a higher sensitivity to 40 Hz than to the other modulation frequencies. Results from this study indicated that the ICA approach is a useful technique for separation of multiple sources of ASSRs (small sources as well as the larger ones). The result of validation study (section 3.3) and comparison with other source analysis methods (section 3.4) showed that the ICA approach is sensitive to ASSR sources and the reconstructed sources are comparable with other source analysis methods.

modulation frequencies (Fig. 3). These results show that our results are cross-validated on different frequencies. 3. The SNR of source clusters located around the left and right auditory cortex and the brainstem were consistent with the ASSR literature (Fig. 5). 4. The results of the validation study suggest that the ICA approach is sensitive to ASSR sources and that it shows a low false positive error in recognizing the ASSR sources when there is no ASSR (section 3.3). 5. The reconstructed sources using the ICA approach were comparable with the result of other source analysis methods (section 3.4). Conclusion In this study a source decomposition technique was applied to reconstruct the time series of the ASSR generator's activity in response to AM noise. Subsequently, the location of each reconstructed source was estimated using ECD modeling. Sources that exhibited a significant ASSR were clustered by means of a probabilistic clustering method based on a GMM. This approach provides a spatiotemporal reconstruction of ASSR sources with no prior assumption regarding the number and location of sources. The results suggested that a widely distributed network of sources,

Acknowledgements This work was supported by the Research Council of KU Leuven through project OT/12/98.

Appendix A Gaussian mixture model (GMM) A mixture model is a probabilistic model for representing the mixture distribution of sub-populations within an overall population. The overall population is considered as a mixture of the components (sub-populations) and each component of this mixture is modeled through its probability distribution (Bouveyron and Brunet-Saumard, 2014). Given a random vector Y ∈ p with n observation {y1, y2 , ...,yn}, the mixture model P with k components is: k

P ( y) =

k

∑ πi fi ( y); ∑ πi=1 i =1

i =1

Where πi and fi represent the mixture proportion and the probability density function of the ith mixture component, respectively. The clusters are often modeled by the same parametric density function. When these density functions are assumed to be Gaussian, the resulting mixture model is called a Gaussian mixture model (GMM): k

P ( y) =

∑ πi fi ( y; μi , Σi ) i =1

where μi , Σ i are the mean and covariance matrix of the ith component, respectively. Different methods can be used to estimate the parameter of the GMM. The expectation-maximization (EM) algorithm, proposed by Dempster et al. (1977) is very efficient for this purpose (McLachlan and Peel, 2000). In modeling procedure with a fixed number of components (Gaussian distribution), the GMM parameters are initialized and the EM iteratively optimizes the parameter to fit better to the training data.

response (ASSR) explained using convolution. Clin. Neurophysiol. 119, 2598–2607. http://dx.doi.org/10.1016/j.clinph.2008.08.002. Bouveyron, C., Brunet-Saumard, C., 2014. Model-based clustering of high-dimensional data: a review. Comput. Stat. Data Anal. 71, 52–78. http://dx.doi.org/10.1016/ j.csda.2012.12.008. Cohen, L.T., Rickards, F.W., Clark, G.M., 1991. A comparison of steady-state evoked potentials to modulated tones in awake and sleeping humans. J. Acoust. Soc. Am. 90, 2467–2479. Delorme, A., Makeig, S., 2004. EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21. http://dx.doi.org/10.1016/j.jneumeth.2003.10.009. Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S., 2012. Independent EEG sources are dipolar. PLoS One 7, e30135. http://dx.doi.org/10.1371/ journal.pone.0030135. Dempster, A.P., Laird, N.M., Rubin, D.B., 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39, 1–38. Dobie, R.A., Wilson, M.J., 1998. Low-level steady-state auditory evoked potentials: effects of rate and sedation on detectability. J. Acoust. Soc. Am. 104, 3482–3488. Drullman, R., Festen, J.M., Plomp, R., 1994. Effect of reducing slow temporal modulations on speech reception. J. Acoust. Soc. Am. 95, 2670–2680. http://

References Acar, Z.A., Acar, C.E., Makeig, S., 2016. Simultaneous head tissue conductivity and EEG source location estimation. Neuroimage 124, 168–180. http://dx.doi.org/10.1016/ j.neuroimage.2015.08.032. Acar, Z.A., Makeig, S., 2013. Effects of forward model errors on EEG source localization. Brain Topogr. 26, 378–396. http://dx.doi.org/10.1007/s10548-012-0274-6. Aoyagi, M., Kiren, T., Kim, Y., Suzuki, Y., Fuse, T., Koike, Y., 1993. Frequency specificity of amplitude-modulation-following response detected by phase spectral analysis. Audiology 32, 293–301. http://dx.doi.org/10.3109/00206099309072945. Baumann, S.B., Wozny, D.R., Kelly, S.K., Meno, F.M., 1997. The electrical conductivity of human cerebrospinal fluid at body temperature. IEEE Trans. Biomed. Eng. 44, 220–225. http://dx.doi.org/10.1109/10.554770. Baysal, U., Haueisen, J., 2004. Use of a priori information in estimating tissue resistivities–application to human data in vivo. Physiol. Meas. 25, 737–748. http:// dx.doi.org/10.1088/0967-3334/25/3/013. Berens, P., 2009. CircStat: a MATLAB toolbox for circular statistics. J. Stat. Softw. 31, 1–21. http://dx.doi.org/10.18637/jss.v031.i10. Bohorquez, J., Ozdamar, O., 2008. Generation of the 40-Hz auditory steady-state

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NeuroImage 148 (2017) 240–253

E.D. Farahani et al.

Lazzouni, L., Ross, B., Voss, P., Lepore, F., 2010. Neuromagnetic auditory steady-state responses to amplitude modulated sounds following dichotic or monaural presentation. Clin. Neurophysiol. 121, 200–207. http://dx.doi.org/10.1016/ j.clinph.2009.11.004. Lins, O., Picton, T., 1995. Auditory steady-state responses to multiple simultaneous stimuli. Electroencephalogr. Clin. Neurophysiol. 96, 420–432. http://dx.doi.org/ 10.1016/0168-5597(95)00048-W. Luts, H., Desloovere, C., Wouters, J., 2006. Clinical application of dichotic multiplestimulus auditory steady-state responses in high-risk newborns and young children. Audiol. Neurotol. 11, 24–37. http://dx.doi.org/10.1159/000088852. Mauer, G., Döring, W.H., 1999. Generators of amplitude modula- tion following response (AMFR), In: Paper Presented at 16th Meeting of the Evoked Response Audiometry Study Group. Tromsø, Norway. Mazziotta, J., Toga, a, Evans, a, Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Boomsma, D., Cannon, T., Kawashima, R., Mazoyer, B., 2001. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1293–1322. http://dx.doi.org/10.1098/rstb.2001.0915. McLachlan, G., Peel, D., 2000. Finite Mixture Models. John Wiley & Sons. Michel, C.M., Murray, M.M., Lantz, G., Gonzalez, S., Spinelli, L., Grave De Peralta, R., 2004. EEG source imaging. Clin. Neurophysiol. 115, 2195–2222. http://dx.doi.org/ 10.1016/j.clinph.2004.06.001. Molinaro, N., Lizarazu, M., Lallier, M., Bourguignon, M., Carreiras, M., 2016. Out-ofsynchrony speech entrainment in developmental dyslexia. Hum. Brain Mapp. 2783, 2767–2783. http://dx.doi.org/10.1002/hbm.23206. Mulert, C., Kirsch, V., Pascual-Marqui, R., Mccarley, R.W., Spencer, K.M., 2011. Longrange synchrony of gamma oscillations and auditory hallucination symptoms in schizophrenia. Int. J. Psychophysiol. 79, 55–63. http://dx.doi.org/10.1016/ j.ijpsycho.2010.08.004. Nasreddine, Z., Charbonneau, S., Cummings, J.L., 2005. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, 695–699. http://dx.doi.org/10.1111/j.1532-5415.2005.53221.x. Nourski, K.V., Steinschneider, M., McMurray, B., Kovach, C.K., Oya, H., Kawasaki, H., Howard, M.A., 2014. Functional organization of human auditory cortex: investigation of response latencies through direct recordings. Neuroimage 101, 598–609. http://dx.doi.org/10.1016/j.neuroimage.2014.07.004. O’Donnell, B.F., Vohs, J.L., Krishnan, G.P., Rass, O., Hetrick, W.P., Morzorati, S.L., 2013. The auditory steady-state response (ASSR): a translational biomarker for schizophrenia. Suppl. Clin. Neurophysiol. 62, 101–112. Onton, J., Makeig, S., 2009. High-frequency broadband modulations of electroencephalographic spectra. Front. Hum. Neurosci. 3, 61. http://dx.doi.org/ 10.3389/neuro.09.061.2009. Onton, J., Westerfield, M., Townsend, J., Makeig, S., 2006. Imaging human EEG dynamics using independent component analysis. Neurosci. Biobehav. Rev. 30, 808–822. http://dx.doi.org/10.1016/j.neubiorev.2006.06.007. Oostendorp, T.F., Delbeke, J., Stegeman, D.F., 2000. The conductivity of the human skull: results of in vivo and in vitro measurements. IEEE Trans. Biomed. Eng. 47, 1487–1492. http://dx.doi.org/10.1109/TBME.2000.880100. Palmer, J.A., Kreutz-Delgado, K., Makeig, S., 2006. Super-Gaussian mixture source model for ICA. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.), 854–861. http://dx.doi.org/10.1007/11679363_106. Palmer, J.A., Makeig, S., Kreutz-Delgado, K., Rao, B.D., 2008. . Newton method for the ica mixture model. ICASSP, IEEE International Conference Acoust. Speech Signal Process. - Proceedings 1805–1808. 〈doi:10.1109/ICASSP.2008.4517982〉 Pandya, D., 1995. Anatomy of the auditory cortex. Rev. Neurol. 151, 486–494. Pantev, C., Okamoto, H., Ross, B., Stoll, W., Ciurlia-Guy, E., Kakigi, R., Kubo, T., 2004. Lateral inhibition and habituation of the human auditory cortex. Eur. J. Neurosci. 19, 2337–2344. http://dx.doi.org/10.1111/j.1460-9568.2004.03296.x. Pantev, C., Roberts, L.E., Elbert, T., Ross, B., Wienbruch, C., 1996. Tonotopic organization of the sources of human auditory steady-state responses. Hear. Res. 101, 62–74. Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18, 49–65. http://dx.doi.org/10.1016/0167-8760(84)90014-X. Pastor, M. a, Artieda, J., Arbizu, J., Marti-Climent, J.M., Peñuelas, I., Masdeu, J.C., 2002. Activation of human cerebral and cerebellar cortex by auditory stimulation at 40 Hz. J. Neurosci. 22, 10501–10506, (doi:22/23/10501 [pii). Pastor, M. a, Vidaurre, C., Fernández-Seara, M. a, Villanueva, a, Friston, K.J., 2008. Frequency-specific coupling in the cortico-cerebellar auditory system. J. Neurophysiol. 100, 1699–1705. http://dx.doi.org/10.1152/jn.01156.2007. Picton, T.W., 1987. Potentials evoked by the sinusoidal modulation of the amplitude or frequency of a tone. J. Acoust. Soc. Am. 82, 165–178. http://dx.doi.org/10.1121/ 1.395560. Picton, T.W., John, M.S., Dimitrijevic, A., Purcell, D., 2003. Human auditory steady-state responses. Int. J. Audiol. 42, 177–219. Poeppel, D., 2003. The analysis of speech in different temporal integration windows: cerebral lateralization as “asymmetric sampling in time”. Speech Commun. 41, 245–255. http://dx.doi.org/10.1016/S0167-6393(02)00107-3. Popescu, M., Popescu, E.A., Chan, T., Blunt, S.D., Lewine, J.D., 2008. Spatio-temporal reconstruction of bilateral auditory steady-state responses using MEG beamformers. IEEE Trans. Biomed. Eng. 55, 1092–1102. http://dx.doi.org/10.1109/ TBME.2007.906504. Poulsen, C., Picton, T.W., Paus, T., 2007. Age-related changes in transient and oscillatory brain responses to auditory stimulation in healthy adults 19-45 years old. Cereb.

dx.doi.org/10.1121/1.409836. Ferree, T.C., Eriksen, K.J., Tucker, D.M., 2000. Regional head tissue conductivity estimation for improved EEG analysis. IEEE Trans. Biomed. Eng. 47, 1584–1592. http://dx.doi.org/10.1109/10.887939. Galaburda, A., Sanides, F., 1980. Cytoarchitectonic organization of the human auditory cortex. J. Comp. Neurol. 190, 597–610. http://dx.doi.org/10.1002/cne.901900312. Geddes, L.A., Baker, L.E., 1967. The specific resistance of biological material—a compendium of data for the biomedical engineer and physiologist. Med. Biol. Eng. 5, 271–293. http://dx.doi.org/10.1007/BF02474537. Giraud, A., Lorenzi, C., Ashburner, J., Wable, J., Johnsrude, I., Frackowiak, R., Kleinschmidt, A., Wolfgang, J., Lorenzi, C., Ashburner, J., Johnsrude, I., Frackowiak, R., 2000. Representation of the temporal envelope of sounds in the human brain. J. Neurophysiol. 84, 1588–1598. http://dx.doi.org/10.1093/cercor/11.3.183. Gonçalves, S.I., De Munck, J.C., Verbunt, J.P.A., Bijma, F., Heethaar, R.M., Lopes da Silva, F., 2003. In vivo measurement of the brain and skull resistivities using an EITbased method and realistic models for the head. IEEE Trans. Biomed. Eng. 50, 754–767. http://dx.doi.org/10.1109/TBME.2003.812164. Goossens, T., Vercammen, C., Wouters, J., van Wieringen, A., 2016. Aging affects neural synchronization to speech-related acoustic modulations. Front. Aging Neurosci. 8, 133. http://dx.doi.org/10.3389/fnagi.2016.00133. Griffiths, T.D., Uppenkamp, S., Johnsrude, I., Josephs, O., Patterson, R.D., 2001. Encoding of the temporal regularity of sound in the human brainstem. Nat. Neurosci. 4, 633–637. http://dx.doi.org/10.1038/88459. Guimaraes, A.R., Melcher, J.R., Talavage, T.M., Baker, J.R., Ledden, P., Rosen, B.R., Kiang, N.Y.S., Fullerton, B.C., Weisskoff, R.M., 1998. Imaging subcortical auditory activity in humans. Hum. Brain Mapp. 6, 33–41. http://dx.doi.org/10.1002/(SICI) 1097-0193(1998)6:1 < 33::AID-HBM3 > 3.0.CO;2-M. Gutschalk, A., Mase, R., Roth, R., Ille, N., Rupp, A., H??hnel, S., Picton, T.W., Scherg, M., 1999. Deconvolution of 40 Hz steady-state fields reveals two overlapping source activities of the human auditory cortex. Clin. Neurophysiol. 110, 856–868. http:// dx.doi.org/10.1016/S1388-2457(99)00019-X. Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., Lounasmaa, O.V., 1993. Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65, 413–497. http:// dx.doi.org/10.1103/RevModPhys.65.413. Hari, R., Hämäläinen, M., Joutsiniemi, S.L., 1989. Neuromagnetic steady-state responses to auditory stimuli. J. Acoust. Soc. Am. 86, 1033–1039. http://dx.doi.org/10.1097/ 00001756-199509000-00001. Harms, M.P., Melcher, J.R., 2002. Sound repetition rate in the human auditory pathway: representations in the waveshape and amplitude of fMRI activation. J. Neurophysiol. 88, 1433–1450. http://dx.doi.org/10.1152/jn.00156.2002. Hawley, M.L., Melcher, J.R., Fullerton, B.C., 2005. Effects of sound bandwidth on fMRI activation in human auditory brainstem nuclei. Hear. Res. 204, 101–110. http:// dx.doi.org/10.1016/j.heares.2005.01.005. Herdman, A.T., Wollbrink, A., Chau, W., Ishii, R., Ross, B., Pantev, C., 2003. Determination of activation areas in the human auditory cortex by means of synthetic aperture magnetometry. Neuroimage 20, 995–1005. http://dx.doi.org/ 10.1016/S1053-8119(03)00403-8. Herdman, a T., Lins, O., Van Roon, P., Stapells, D.R., Scherg, M., Picton, T.W., 2002. Intracerebral sources of human auditory steady-state responses. Brain Topogr. 15, 69–86. Hofmann, M., Wouters, J., 2012. Improved electrically evoked auditory steady-state response thresholds in humans. JARO - J. Assoc. Res. Otolaryngol. 13, 573–589. http://dx.doi.org/10.1007/s10162-012-0321-8. Hotelling, H., 1931. The economics of exhaustible resources. J. Polit. Econ. 39, 137–175. http://dx.doi.org/10.1086/254195. Hullett, P.W., Hamilton, L.S., Mesgarani, N., Schreiner, C.E., Chang, E.F., 2016. Human superior temporal gyrus organization of spectrotemporal modulation tuning derived from speech stimuli. J. Neurosci. 36, 2014–2026. http://dx.doi.org/10.1523/ JNEUROSCI.1779-15.2016. Jatoi, M.A., Kamel, N., Malik, A.S., Faye, I., Begum, T., 2014. A survey of methods used for source localization using EEG signals. Biomed. Signal Process. Control 11, 42–52 . http://dx.doi.org/10.1016/j.bspc.2014.01.009. John, M.S., Picton, T.W., 2000. Human auditory steady-state responses to amplitudemodulated tones: phase and latency measurements. Hear. Res. 141, 57–79. http:// dx.doi.org/10.1016/S0378-5955(99)00209-9. Johnson, B.W., Weinberg, H., Ribary, U., Cheyne, D.O., Ancill, R., 1988. Topographic distribution of the 40 Hz auditory evoked-Related potential in normal and aged subjects. Brain Topogr. 1, 117–121. http://dx.doi.org/10.1007/BF01129176. Kaas, J.H., Hackett, T.A., 2000. Subdivisions of auditory cortex and processing streams in primates. Proc. Natl. Acad. Sci. USA 97, 11793–11799. http://dx.doi.org/ 10.1159/000013783. Kavanagh, R.N., Darcey, T.M., Lehmann, D., Fender, D.H., 1978. Evaluation of methods for three-dimensional localization of electrical sources in the human brain. IEEE Trans. Biomed. Eng. 25, 421–429. http://dx.doi.org/10.1109/TBME.1978.326339. Kuriki, S., Kobayashi, Y., Kobayashi, T., Tanaka, K., Uchikawa, Y., 2013. Steady-state MEG responses elicited by a sequence of amplitude-modulated short tones of different carrier frequencies. Hear. Res. 296, 25–35. http://dx.doi.org/10.1016/ j.heares.2012.11.002. Kuwada, S., Andersont, J.S., Batrat, R., Fitzpatrick, D.C., Teissier, N., ’angelo, W.R, D., 2002. Sources of the scalp-recorded amplitude- modulation following response. J Am. Acad. Audiol. 13, 188–204. Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T., 2000. Automated Talairach Atlas labels for functional brain mapping. Hum. Brain Mapp. 10, 120–131. http:// dx.doi.org/10.1002/1097-0193(200007)10:3 < 120::AID-HBM30 > 3.0.CO;2-8.

252

NeuroImage 148 (2017) 240–253

E.D. Farahani et al.

withprimarily temporal cues. Science 270, 303–304. Sigalovsky, I.S., Melcher, J.R., 2006. Effects of sound level on fMRI activation in human brainstem, thalamic and cortical centers. Hear. Res. 215, 67–76. http://dx.doi.org/ 10.1016/j.heares.2006.03.002. Society, A.C.N., 2006. Guideline 5: guidelines for standard electrode position nomenclature. Am. J. Electroneurodiagnostic Technol. 46, 222. Spencer, K.M., 2012. Baseline gamma power during auditory steady-state stimulation in schizophrenia. Front. Hum. Neurosci. 5, 190. http://dx.doi.org/10.3389/ fnhum.2011.00190. Spencer, K.M., Niznikiewicz, M. a, Nestor, P.G., Shenton, M.E., McCarley, R.W., 2009. Left auditory cortex gamma synchronization and auditory hallucination symptoms in schizophrenia. BMC Neurosci. 10, 85. http://dx.doi.org/10.1186/1471-2202-10-85. Steinmann, I., Gutschalk, A., 2011. Potential fMRI correlates of 40-Hz phase locking in primary auditory cortex, thalamus and midbrain. Neuroimage 54, 495–504. http:// dx.doi.org/10.1016/j.neuroimage.2010.07.064. Teale, P., Collins, D., Maharajh, K., Rojas, D.C., Kronberg, E., Reite, M., 2008. Cortical source estimates of gamma band amplitude and phase are different in schizophrenia. 42, 1481–1489. http://dx.doi.org/10.1016/j.neuroimage.2008.06.020. van Deursen, J.A., Vuurman, E.F.P.M., van Kranen-Mastenbroek, V.H.J.M., Verhey, F.R.J., Riedel, W.J., 2011. 40-Hz steady state response in Alzheimer's disease and mild cognitive impairment. Neurobiol. Aging 32, 24–30. http://dx.doi.org/10.1016/ j.neurobiolaging.2009.01.002. Zhang, Y., Van Drongelen, W., He, B., 2006. Estimation of in vivo brain-to-skull conductivity ratio in humans. Appl. Phys. Lett. 89, 34–36. http://dx.doi.org/ 10.1063/1.2398883.

Cortex 17, 1454–1467. http://dx.doi.org/10.1093/cercor/bhl056. Rance, G., 2008. The Auditory Steady-State Response: Generation, Recording, and Clinical Application. Plural Publishing. Reyes, S. a, Lockwood, A.H., Salvi, R.J., Coad, M., Lou, Wack, D.S., Burkard, R.F., 2005. Mapping the 40-Hz auditory steady-state response using current density reconstructions. Hear. Res. 204, 1–15. http://dx.doi.org/10.1016/ j.heares.2004.11.016. Reyes, S. a, Salvi, R.J., Burkard, R.F., Coad, M., Lou, Wack, D.S., Galantowicz, P.J., Lockwood, A.H., 2004. PET imaging of the 40 Hz auditory steady state response. Hear. Res. 194, 73–80. http://dx.doi.org/10.1016/j.heares.2004.04.001. Ross, B., Borgmann, C., Draganova, R., Roberts, L.E., Pantev, C., 2000. A high-precision magnetoencephalographic study of human auditory steady-state responses to amplitude-modulated tones. J. Acoust. Soc. Am. 108, 679–691. http://dx.doi.org/ 10.1121/1.429600. Rush, S., Driscoll, D.A., 1968. Current distribution in the brain from surface electrodes. Anesth. Analg. 47, 717–723. Scherg, M., Cramon, D. Von, 1986. Evoked dipole source potentials of the human auditory cortex. Electroencephalogr. Clin. Neurophysiol. Potentials Sect. 65, 344–360. http://dx.doi.org/10.1016/0168-5597(86)90014-6. Scherg, M., Vajsar, J., Picton, T.W., 1988. A source analysis of the late human auditory evoked potentials. J. Cogn. Neurosci. Neurosci., 1. Schoonhoven, R., Boden, C.J.R., Verbunt, J.P.A., De Munck, J.C., 2003. A whole head MEG study of the amplitude-modulation-following response: phase coherence, group delay and dipole source analysis. Clin. Neurophysiol. 114, 2096–2106. http:// dx.doi.org/10.1016/S1388-2457(03)00200-1. Shannon, R.V., Zen, F., Kamath, V., Wygonski, J., Ekeild, M., 1995. Speech recognition

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