Preattentive mechanisms of change detection in early auditory cortex: A 7 Tesla fMRI study

Neuroscience 253 (2013) 100–109

PREATTENTIVE MECHANISMS OF CHANGE DETECTION IN EARLY AUDITORY CORTEX: A 7 TESLA FMRI STUDY G. R. SZYCIK, a J. STADLER, b A. BRECHMANN b AND T. F. MU¨NTE c*

even during sleep or states of impaired consciousness attesting to its automaticity. Detection of an irregularity in sensory input may trigger orienting of attention toward this irregularity. The MMN has usually been recorded in oddball designs in which an infrequent, deviant tone is contrasted against a series of frequent, identical standard tones. Mechanistically, two processes devoted to auditory deviant detection can be differentiated (Schro¨ger and Wolff, 1996; Opitz et al., 2005). First, a sensory mechanism is thought to be driven either by different states of relative refractoriness of neurons processing the pitch of deviant and standard tones (Ja¨a¨skela¨inen et al., 2004) or, alternatively, by different states of adaptation. The refractoriness explanation builds on the fact that the auditory system is organized in a tonotopic fashion from the cochlea to the different cortical areas (Romani et al., 1982; Pantev et al., 1989). Thus, neural populations that are sensitive to the pitch of the standard stimulus in a typical MMN paradigm may – depending on the stimulation rate – have a different state of refractoriness compared to those populations responding to the pitch of the deviant. As the presentation rate of standards and deviants is different, the state of refractoriness of frequency-specific neurons differs. This mechanism is particularly relevant for sequences in which the deviant is defined by pitch differences but it cannot explain the MMN in complex tone sequences in which not a single standard is defined but rather a sequence of tones (e.g., ascending scales). As each tone of such a sequence is different in pitch, there is no refractoriness for the different neural populations responsible for the processing of the different pitches. Moreover, MMNs to duration deviants cannot be explained by a refractoriness account, as the standards and deviants share the same pitch (and are therefore processed by the same neural population) but differ only in their duration. With regard to differential adaptation, the neurons in the primary auditory cortex of animals have been shown to exhibit strong stimulusspecific adaptation, which can occur at very different time-scales ranging from hundreds of milliseconds to tens of seconds (Ulanovsky et al., 2004). Stimulusspecific adaptation has been proposed to be the singleneuron substrate of the MMN (Ulanovsky et al., 2003). This study has not been designed to distinguish refractoriness and adaptation accounts of a sensory mechanism. Importantly, however, a second cognitive mechanism can be distinguished from the sensory mechanism which is thought to reflect the comparison between a neuronal model of the standard auditory tone

a

Department of Psychiatry, Social Psychiatry and Psychotherapy, Medical School Hannover, Germany b

Leibniz Institute for Neurobiology, Magdeburg, Germany

c

Department of Neurology, University of Lu¨beck, Germany

Abstract—The auditory system continuously monitors the environment for irregularities in an automatic, preattentive fashion. This is presumably accomplished by two mechanisms: a sensory mechanism detects a deviant sound on the basis of differential refractoriness of neural populations sensitive to the standard and deviant sounds, whereas the cognitive mechanism reveals deviance by comparing incoming auditory information with a template derived from previous input. Using fast event-related high-resolution functional magnetic resonance imaging at 7 Tesla we show that both mechanisms can be mapped to different parts of the auditory cortex both at the group level and the singlesubject level. The sensory mechanism is supported by primary auditory areas in Heschl’s gyrus whereas the cognitive mechanism is implemented in more anterior secondary auditory areas. Both mechanisms are equally engaged by simple sine-wave tones and speech-related phonemes indicating that streams of speech and non-speech stimuli are processed in a similar fashion. Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Key words: auditory system, change detection, refractoriness, fMRI, 7 Tesla, speech.

INTRODUCTION A fundamental mechanism for survival in humans and other species is the constant monitoring of sensory input for changes that might be relevant. In the auditory modality, the mismatch negativity (MMN) of the eventrelated potential (ERP) represents a neural correlate of an early change detection process located in the auditory cortex (Na¨a¨ta¨nen et al., 1978) and is elicited by stimuli presented outside of the attentional focus and *Corresponding author. Address: Department of Neurology, University of Lu¨beck, Ratzeburger Allee 160, 23538 Lu¨beck, Germany. Tel: +49-45150052925; fax: +49-4515005457. E-mail address: [email protected] (T. F. Mu¨nte). Abbreviations: ANOVA, analysis of variance; EEG, electroencephalography; FDR, false discovery rate; fMRI, functional magnetic resonance imaging; GLM, general linear model; HG, Heschl’s gyrus; ISI, interstimulus intervals; MEG, magnetoencephalography; MMN, mismatch negativity.

0306-4522/13 $36.00 Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuroscience.2013.08.039 100

G. R. Szycik et al. / Neuroscience 253 (2013) 100–109

with the sensory memory representation of the deviant tone (Na¨a¨ta¨nen, 1990; Schro¨ger, 1997; Opitz et al., 2005). This cognitive mechanism is supported by a comparator system involving different memory representations (Maess et al., 2007). The term cognitive has been used by previous authors for this mechanism, because it involves the comparison of two different memory representations. In spite of the term ‘‘cognitive’’, this comparison process is assumed to proceed in an automatic and preattentive fashion. While not the topic of the present study, it is worth mentioning that the electrophysiological responses to deviants extend beyond the MMN. Deviant stimuli that capture and redirect the attention of the listener are usually associated with a P3a component, a waveform with a rather anterior scalp maximum and earlier peak than the standard P3 component often seen for attended target stimuli (Nager et al., 2003; Horva´th et al., 2008; Berti, 2013). Also, a longer lasting ‘‘reorienting negativity’’ (RON) has often been described (Roeber et al., 2003; Horva´th et al., 2008; Reiche et al., 2013). Previous research has shown that the MMN is also sensitive to speech-related sounds (Na¨a¨ta¨nen et al., 2007; Zaehle et al., 2009) and reflects the activation of memory-related networks for language sounds (Pulvermu¨ller and Shtyrov, 2006). Speech sounds also elicit stronger effects than non-speech stimuli in MMNtype designs (Pettigrew et al., 2004). With regard to functional imaging studies of speech sounds, earlier work had emphasized the role of the left superior temporal sulcus, whereas Ja¨ncke et al. (2002) clearly found activations in the planum temporale (PT) area of the auditory cortex. This initial finding has been replicated and extended by Elmer et al. (2012), who also demonstrated superior classification of speech sounds for musicians compared to controls which were associated with activation changes in the PT region. Previous reports have sought to define the neural sites supporting the sensory and cognitive change detection mechanisms using functional magnetic resonance imaging (fMRI) and source analysis of eventrelated potentials/fields. The sensory mechanism has been claimed to reside in the primary auditory cortex (Opitz et al., 2005), posterior to Heschl’s gyrus (HG) (Laufer et al., 2008) or to occupy the same location as the cognitive mechanism (Maess et al., 2007). The cognitive mechanism on the other hand has been located in the cuneus/posterior cingulate region (Laufer et al., 2009) or in the anterior part of HG (Opitz et al., 2005). Given this state of affairs, we used 7 Tesla highresolution fMRI to identify the spatial locations of both change detection mechanisms for simple tone stimuli and speech stimuli.

EXPERIMENTAL PROCEDURES All procedures were cleared by the ethical committee of the University of Magdeburg and conformed to the declaration of Helsinki.

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Participants Thirteen healthy right-handed native speakers of German (five women, mean age 27.3 years, range 21–39, SD 4.7) gave written informed consent to participate for a small monetary compensation. All acquired datasets were included in the analysis. All participants reported normal auditory and normal or corrected-to-normal visual acuity. Stimuli For the first experiment, 10 different sinusoidal tones (frequency in Hz: 600, 660, 726, 799, 878, 966, 1063, 1169, 1286, 1415), generated by adding 10% increments in frequency starting with the base tone of 600 Hz, were used. Tones had a duration of 250 ms, 5-ms rise- and fall times and were presented at a rate of 1 per second. We used a fast stimulation rate, because the contribution of refractoriness has been shown to decrease for longer interstimulus intervals (ISI, Cranford et al., 2003) and a constant ISI, because this increases expectancy for the next stimulus. For the second experiment, 10 phoneme stimuli (FA, GA, KA, LA, MA, NA, RA, VA, BA, PA) spoken by an experienced male German native speaker were used. The duration of the phoneme stimuli was on average 556 ms (SD 80 ms) and the presentation rate was also 1 per second. Audio information was presented via fMRI compatible electrodynamic headphones integrated into earmuffs to reduce residual background scanner noise (Baumgart et al., 1998). The sound level of the stimuli was individually adjusted to achieve good audibility during data acquisition. Temporally uncorrelated to the auditory stimuli of both experiments, visual stimuli (roman numerals I and II, white on black background, 4°  0.5° in height/width, duration 100 ms) were presented in random order at a rate of 1 per second (jitter 200 ms). The probability of I/II was 25/75%, respectively. Visual stimuli were projected via a mirror system by LCD projector onto a diffusing screen inside the magnet bore. Presentation software (Neurobehavioral Systems, Inc., Albany, Ca., USA) was used for stimulus delivery. During the tone experiment 600 tones were delivered, while during the phoneme experiment 1000 stimuli were presented. Different numbers of stimuli were presented for the tone and the phoneme experiment, as pilot studies had indicated that activation in the tone experiment was more robust and therefore required less stimuli. During both experiments subjects had to attend the visual stimulus series in order to press a button whenever the visual stimulus changed from I occurred. The tone sequence was irrelevant for the participants. Auditory stimuli of both experiments were grouped into two types of blocks of 10 sounds each (Fig. 1). In ‘‘oddball’’ blocks one deviant sound occurred at a pseudorandom position in the second half of the block (positions 6–10). The phonemes PA/BA served as deviant/standard stimuli in the phoneme experiment, whereas the 1286/1415 Hz tones were chosen as the deviant/standard stimuli in the tone experiment. In the ‘‘control’’ blocks all 10 different stimuli were presented

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Fig. 1. Stimulation design for the speech part of the study. The oddball condition block contained only two kinds of stimuli. The standard stimulus BA occurred nine times per block whereas the deviant PA occurred only one time at position 6–10. The control condition block comprised all 10 phonemes presented only once per block in random order. Care was taken that the PA stimulus occurred at the exact same position as in the oddball blocks. As illustrated, the deviance effect was derived by contrasting the deviant stimuli vs. standard stimuli for the oddball blocks. The sensory mechanism was isolated by comparing physically identical BA stimuli between oddball and control conditions. The cognitive mechanism was isolated by contrasting deviant PA stimuli from the oddball blocks and physically identical stimuli from the control condition blocks.

once per block in a pseudo-randomized order with the restriction, that the PA and 1286 Hz stimuli were presented at the same positions as in the ‘‘oddball’’ blocks, i.e. only at the positions 6–10. In the tone experiment 30 ‘‘oddball’’ blocks and 30 ‘‘control’’ blocks were presented, whereas in the phoneme experiment 50 blocks of each condition were delivered. The order of blocks was randomized. Image acquisition and analysis Magnetic-resonance images were acquired on a 7T MAGNETOM Siemens Scanner (Erlangen, Germany) located in Magdeburg, Germany and equipped with a CP head coil (Invivo, Pewaukee, Wisconsin, USA). A total of 600 T2⁄-weighted volumes for the non speech part of the experiment and of 1000 T2⁄-weighted volumes for the speech-related part of the experiment covering a sub-volume of the brain including early auditory areas on the dorsal surface of the temporal lobe and orientated parallel to the Sylvian fissure were acquired (TR 1000 ms, TE 24 ms, flip angle 60°, FOV 256  192 mm, matrix 128  96, 13 slices, slice thickness 2.0 mm, interslice gap 0.2 mm). Additionally, a 3D high-resolution T1-weighted volume (MPRAGE, TR 2200 ms, TI 1100 ms, flip angle 9°, matrix 192  256  256, 1 mm isovoxel) was obtained. The subject’s head was firmly padded with foam cushions during the entire measurement to avoid head movements. Analysis and visualization of the data were performed using Brain Voyager QX (Brain Innovation BV, Maastricht, The Netherlands) software. First, a correction for the temporal offset between the slices acquired in one scan was applied. For this purpose the data were sinc-interpolated. Subsequently, 3D motion correction was performed by realignment of the entire volume set to the first volume by means of trilinear interpolation. Thereafter, linear trends were removed and a high-pass filter was applied resulting in filtering out signals occurring less than three cycles during the whole time course. Structural and functional data were spatially transformed into the Talairach standard space using a 12-parameter affine transformation. Functional echo planar imaging (EPI) volumes were spatially smoothed

with a 4-mm full-width half-maximum isotropic Gaussian Kernel to accommodate residual anatomical differences across volunteers. After the preprocessing we analyzed the phoneme and tone experiments separately at the single-subject level and subsequently at the group level by means of random effects analysis. For the statistical model a fitted design matrix including all conditions of interest was specified using a hemodynamic response function. This function was created by convolving the rectangle function with the model of Boynton et al. (1996) using delta = 2.5, tau = 1.25 and n = 3. Thereafter a multisubject random effects general linear model (GLM) analysis was used for the identification of significant differences in hemodynamic responses. Additional single-subject GLMs for each subject were created. As regressors of no interest we used overall six translation and rotation vectors derived for each dataset during the 3D motion correction. Following Opitz et al. (2005), the statistical analysis included three contrasts of interest (Fig. 1). First, we calculated the contrast between the standard and deviant stimuli within the oddball condition blocks (henceforth deviance effect). This is the analysis which is commonly employed in change detection experiments. Second, we compared the standard stimulus from the oddball condition against the physically identical stimulus at the same position from the control condition blocks (henceforth: sensory effect). This effect captures the effect of refractoriness, which is thought to underlie the sensory effect, as the standard stimulus from the oddball condition should meet with neural ensembles in a state of refractoriness, whereas this is not the case for the physically identical stimulus from the control condition (cf. Fig. 1). Third, we contrasted deviant stimuli from the oddball condition blocks with the physically same stimuli from the control condition blocks (cognitive effect). In this case, the physically identical stimuli should be processed by nonrefractory neural populations. The only difference between the two conditions is that the stimulus from the oddball conditions is ‘‘deviant’’ with respect to the preceding string of standards. For the group analysis a threshold of p < 0.01 (tone experiment) or p < 0.001 (phoneme experiment) were

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chosen for identification of the activated voxels. The single-subject data were analyzed using a false discovery rate threshold of q(FDR) < 0.05 (Genovese et al., 2002). For those subjects and contrasts who showed widespread activation a more conservative threshold of q(FDR) < 0.01 or q(FDR) < 0.001 was used. The mean centers of mass of identified clusters are reported.

RESULTS Subjects had no problems with the visual task. During the tone-blocks 95.3% (SD 1.7) of the visual targets were correctly answered, whereas during the phoneme blocks 94.9% (SD 1.6) correct answers were obtained (t(12) = 0.03). The tone experiment led to activation of the auditory cortex for each of the three contrasts (Fig. 2A, Table 1). The deviance effect, corresponding to the detection of unexpected auditory events from an auditory stream, was characterized by large bilateral temporal activations. Similarly, the sensory effect,

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contrasting the standard stimulus from the oddball series with its physical equivalent from the control series, showed large activated clusters in bilateral auditory cortex. The ‘‘cognitive’’ effect involved a considerably smaller part of the auditory cortex. Next, we projected the individual centers of mass for each contrast of interest onto an axial slice (Z = 12) of the template brain (Fig. 2B), because this slice coincided best with the auditory cortex. Comparing the location of these points indicates that neighboring but distinct brain sites are involved in the processing of different aspects of deviant tones and that the sensory mechanism is located more caudal than the cognitive mechanism. The different location of the different effects was verified statistically. Individual coordinates for the deviance, sensory, and cognitive effects were entered into a repeated measures one-way analysis of variance (ANOVA), separately for x, y, and z coordinates. In some subjects, coordinates could not be determined in some conditions (left hemisphere: tones cognitive/ deviance/sensory: 5, 5, 0 subjects; phonemes: 3, 4, 0;

Fig. 2. MRI results (A) Group analysis results for the phoneme and sine-wave tone experiments. Activations corresponding to the deviance effect, the sensory and the cognitive mechanism are rendered onto horizontal slice of the template brain created by averaging the T1 images of all 13 subjects. (B) projection of the individual centers of mass and of the mean center of mass of the different contrasts.

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Table 1. Group level analysis for phoneme and sine-wave stimuli Experimental condition

Hemisphere

x Phoneme stimuli Deviance effect Cognitive mechanism Sensory mechanism Sine-wave tone stimuli Deviance effect Cognitive mechanism Sensory mechanism

Volume (mm3)

Talairach center of mass y

z

L R L R L R

47 52 58 49 48 47

24 14 18 9 20 12

14 12 12 9 12 10

5760 5328 64 504 2232 1584

L R L R L R

52 55 47 52 52 54

19 14 5 4 17 12

13 11 5 7 12 10

5296 8208 616 1176 6480 5144

right hemisphere: tones: 5, 4, 2; phonemes: 4, 5, 0). These missing values were replaced by the group mean. The ANOVA was followed up by pair-wise post hoc Tukey’s tests. For the x-coordinate, a significant main effect was obtained (deviance/cognitive/sensory: 54.0 [SEM 0.96]/ 52.6 [0.94]/50.0 [1.2]; F(2,24) = 6.69, p < 0.005) with the pairwise comparison between the sensory and the deviance mechanisms being significant as well (q = 5.09). For the y-coordinate, a significant difference emerged as well (deviance/cognitive/sensory: 16.6 [0.72]/ 7.8 [1.2]/-12.0 [1.3]; F(2,24) = 20.35, p < 0.001). All pairwise comparisons were significant for the y-coordinate (all q > 4.2). Finally, a significant difference was also seen for the z-coordinate(deviance/ cognitive/sensory: 11.3 [0.79]/7.6 [1.3]/10.6 [0.9]; F(2,24) = 5.32, p < 0.02). Only the pairwise comparison between the deviance and cognitive effect was significant in this case (q = 4.34). A similar analysis was conducted for the phoneme experiment (Fig. 2, Table 1). The deviance and sensory effects again were associated with extended activations in bilateral auditory cortex, whereas the activated zone for the cognitive effect was considerably smaller. In the single-subject data we found significant activation in almost all subjects and the projection of the centers of mass of the activated zones revealed distinct locations for the sensory and cognitive effects, even though this difference was not as prominent as in the tone experiment (Table 2). As in the tone experiment, the different location of the different effects was assessed statistically. For the x-coordinate, no significant main effect was obtained (deviance/cognitive/ sensory: 50.8 [SEM 0.71]/52.44 [0.46]/52.0 [1.1]; F(2,24) = 1.48, n.s.). For the y-coordinate, a marginally significant difference emerged (deviance/cognitive/ sensory: 16.6 [0.72]/ 7.8 [1.2]/ 12.0 [1.3]; F(2,24) = 2.66, p = 0.09). None of the pairwise comparisons were significant for the y-coordinate (all q < 2.96). Finally, no significant difference was seen for

the z-coordinate (deviance/cognitive/sensory: 10.7 [1.0]/ 9.2 [0.78]/9.4 [0.93]; F(2,24) = 1.48). To allow the reader to appreciate the individual data patterns, we present the single-subject activation data for the different contrasts for both experiments in Fig. 3.

DISCUSSION The purpose of this study was to study the anatomical basis of two different mechanisms of preattentive auditory change detection using high-field 7 T fMRI. Independent of the type of stimulus (phonemes or sine wave tones) contrasts uncovering the sensory and cognitive mechanisms revealed widespread activations for the deviant infrequent stimuli in the auditory areas on the dorsal surface of the temporal lobe including HG and the PT. The PT involvement in the present study dovetails nicely with results of a recent meta-analysis by Alho et al. (2013) who found that median loci of activations due to the processing of infrequent pitch changes in a tone stream were centered in the STG or PT and thus significantly posterior to the activations for other types of pitch processing. The activation clusters for the sensory mechanism were more extended than those for the cognitive mechanism (Fig. 2). Also, analyses of single subjects showed that the sensory mechanism reached significance in more subjects than the cognitive mechanism (Fig. 3). More importantly, both mechanisms seem to be located in different parts of the auditory cortex. The centers of mass of both effects were located in – or in the vicinity of – HG. Our results agree with previous findings suggesting the existence of two separate mechanisms of irregularity detection (Opitz et al., 2005; Maess et al., 2007; Laufer et al., 2009). The sensory mechanism is based on the assumption that the repeating standard stimulus in oddball sequences and the deviant stimulus are associated with different refractory states of the respective neural subpopulations (Ja¨a¨skela¨inen et al.,

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G. R. Szycik et al. / Neuroscience 253 (2013) 100–109 Table 2. Results of the single-subject analysis Subject

T-score

Left hemisphere x

Sine-wave tone stimuli Deviance effect #01 47 #02 – #03 53 #04 53 #05 – #06 40 #07 58 #08 – #09 – #10 54 #11 – #12 – #13 49 Cognitive mechanism #01 – #02 – #03 51 #04 – #05 61 #06 – #07 53 #08 48 #09 46 #10 54 #11 52 #12 – #13 –

y

Phoneme stimuli Deviance effect #01 44 #02 – #03 42 #04 47 #05 52 #06 45 #07 49 #08 – #09 – #10 53 #11 – #12 39 #13 51

Vol (mm )

10 – 7 16 – 14 14 – – 17 – – 15

416 – 4656 6120 – 120 96 – – 12,360 – – 504

4.57 – 4.34 4.72 – 4.08 3.88 – – 4.67 – – 4.15

57 – 54 52 – 59 60 48 50 50 – – 56

– – 13 – 9 – 13 13 13 5 12 – –

– – 18,152 – 1976 – 6832 3744 14,048 960 440 – –

– – 4.63 – 4.67 – 4.50 4.29 4.03 3.95 3.52 – –

58 – 50 – 54 – 57 45 56 52 49 – –

24 34 14 20 23 15 21 12 8 30 24 14 22

10 15 11 14 15 14 12 14 13 14 15 6 14

800 13,444 42,080 7672 200 112 5824 19,656 18,288 9696 2056 408 232

4.53 4.42 5.09 5.40 4.07 4.15 4.50 4.24 4.83 4.95 5.57 3.48 4.48

57 46 45 52 44 46 57 46 54 51 52 – –

25

14 – 13 15 10 13 15 – – 16 – 13 7

680 – 14,612 6560 1900 5448 7580 – – 12,472 – 128 704

4.10 – 4.43 4.23 4.07 4.58 3.98 – – 5.00 – 4.22 4.17

52 – 51 47 46 55 55 – – 49 – – 52

23 21 22 – 18 26 – – 26 – – 20 – – 19 – 22 – 22 2 11 19 20 – –

– 34 22 35 18 29 – – 25 – 16 7

T-score

Right hemisphere

z

–

Sensory mechanism #01 47 #02 44 #03 47 #04 53 #05 44 #06 42 #07 54 #08 43 #09 44 #10 53 #11 46 #12 47 #13 53

3

x

y

3

z

Vol (mm )

17 – 10 13 – 12 11 9 10 15 – – 5

576 – 4532 8836 – 1364 1388 240 80 12,800 – – 184

4.35 – 4.46 5.04 – 3.95 4.27 4.51 3.96 4.68 – – 4.16

17 – 10 – 5 – 6 16 0 7 0 – –

2504 – 17,840 – 3176 – 7744 6276 15,048 1720 2544 – –

3.43 – 5.00 – 4.25 – 4.78 4.30 4.18 4.16 3.71 – –

15 11 8 10 20 17 8 2 17 16 8

16 9 7 11 11 15 6 13 5 11 13 – –

3756 4632 44,548 8768 432 96 9300 8504 15,320 10,080 2488 – –

4.38 4.03 5.43 5.31 4.25 3.78 4.64 4.23 5.28 5.38 5.54 – –

16

20 – 11 11 4 11 8 – – 14 – – 7

1176 – 9112 6452 4400 6124 9952 – – 9608 – – 96

4.18 – 4.53 4.47 4.13 4.34 4.20 – – 4.75 – – 4.00

15 – 23 13 – 19 16 13 18 18 – – 15 12 – 9 – 14 – 9 6 11 8 6 – –

– –

– 24 15 12 15 14 – – 20 – – 21

(continued on next page)

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Table 2 (continued) Subject

T-score

Left hemisphere x

Cognitive mechanism #01 60 #02 – #03 52 #04 56 #05 54 #06 – #07 52 #08 48 #09 54 #10 52 #11 – #12 49 #13 57 Sensory mechanism #01 55 #02 56 #03 47 #04 48 #05 53 #06 45 #07 51 #08 48 #09 56 #10 51 #11 54 #12 48 #13 46

y

3

z

Vol (mm )

17 37

11 – 11 10 5 – 14 12 14 12 – 9 15

2608 – 24,288 2064 3008 – 9048 868 4104 3344 – 4262 408

4.51 – 5.84 4.18 4.60 – 3.84 3.45 3.95 3.78 – 3.58 3.68

56 – 51 51 52 – 52 55 54 50 – – 51

22 19 24 22 18 19 26 16 25 22 15 22 29

13 11 11 15 8 13 15 16 12 17 10 10 14

8144 136 38,720 4184 9352 3760 12,100 4104 2788 15,936 784 5288 3368

4.38 3.82 6.14 4.92 4.87 3.60 5.48 3.77 3.71 4.44 3.44 3.57 3.52

56 56 47 49 49 54 54 53 53 44 51 60 50

20 – 21 18 9 – 23 11 35 19 –

2004). As the auditory cortex is organized tonotopically (Pantev et al., 1989) a sensory mechanism can be implemented in early cortical stages of sensory processing. This idea is supported by an early onset of the sensory effect in electroencephalography (EEG) and magnetoencephalography (MEG) measurements, i.e. 100 ms after the stimulus presentation (Maess et al., 2007; Laufer et al., 2009). The current study revealed a center of mass of the activations related to the sensory mechanism in the middle part of HG (Fig. 2). As HG extends from anterior lateral to posterior medial on the dorsal surface of the temporal lobe, the sensory mechanism of change detection can be located in primary auditory cortex (Rademacher et al., 1993). The cognitive (memory-based) comparison process has been shown to occur considerably later than the sensory mechanism (approximately 170–280 ms after stimulus onset) using EEG and MEG (Maess et al., 2007; Laufer et al., 2009). This mechanism is supposedly based implemented in a sensory memory system that compares incoming auditory input with a template generated from the previous input (Na¨a¨ta¨nen et al., 2001). Accordingly, this cognitive mechanism involves later stages of auditory processing and therefore should be based in secondary auditory areas. This is indeed what our results suggest, thus nicely

T-score

Right hemisphere x

y

3

z

Vol (mm )

23

14 – 8 8 6 – 14 12 5 6 – – 10

3824 – 29,908 5480 4704 – 18,758 3376 9496 2920 – – 2356

4.48 – 6.33 4.34 4.65 – 4.06 3.51 3.88 3.99 – – 3.42

17 8 16 16 13 15 15 16 15 19 4 13 19

14 5 9 10 4 14 9 13 5 12 9 8 11

9920 136 36,796 6584 10,796 3424 13,182 4900 12,314 13,680 1392 2408 8936

4.75 3.17 6.92 4.94 5.28 3.82 6.51 3.65 4.00 4.10 3.48 3.69 3.68

13 – 11 17 11 – 14 10 21 6 – –

corroborating previous findings (Opitz et al., 2005; Laufer et al., 2008). The second aim of this study was to check whether both deviance detection mechanisms are similarly engaged by simple (sine wave) and more complex phoneme stimuli. Previous research has shown that natural speech stimuli in the form of monosyllabic words and pseudowords gives rise to similar mismatch responses as sine-wave tones in oddball paradigms (Laufer et al., 2008). In the current study we have shown that the cognitive mechanism of change detection is also involved in the processing of nonlexical syllable stimuli and can be traced to the same regions as the cognitive mechanism for sine-wave tones. One difference between the tone and phoneme experiments is worth commenting on: While the experimental design in the two conditions was intended to be parallel, one difference should be pointed out. In the tone-condition the different frequencies can be aligned on the frequency dimension. To the extent to which neighboring frequencies exert a refractoriness effect on each other, this may result in higher refractoriness effects in the control than in the oddball blocks, because the control stimuli in the control blocks are more different in frequency. This, then, would result in an over-compensation of the refractoriness effect. As

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Fig. 3. Single-subject MRI results Shown are the single-subject data for the different contrasts of the phoneme and tone experiments.

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the phonemes used in the phoneme condition were uttered by the same speaker with the same F0, this argument does not apply in the phoneme condition. Taken together our results provide further support for the existence of two different mechanisms involved in preattentive auditory deviance detection. Both mechanisms differ in temporal (Maess et al., 2007; Laufer et al., 2009) and spatial aspects. The sensory mechanism is located more posterior on the HG and thus involves primary auditory areas. The cognitive mechanism occupies more anterior parts of the auditory cortex on the dorsal surface of the temporal lobe and thus is implemented in secondary auditory areas. Both mechanisms are engaged by simple sine-wave tones as well as more complex speech-related phonemes. Limitations of the present study The present study took advantage of very high-field fMRI to investigate the differences in the cortical locations of sensory and cognitive change detection mechanisms. While the study reached its main goal, i.e. the delineation of the cortical locations for the two different postulated mechanisms, it also suffers from a number of limitations. First, we used a continuous scanning protocol and thus exposed our volunteers to continuous auditory stimulation from the scanner. Previous studies, including one of our own, have shown that sparse sampling protocols might be advantageous for auditory neuroscience investigations (Zaehle et al., 2004; Schmidt et al., 2008; Szycik et al., 2008; Hurschler et al., 2012). We are unaware of any studies that have employed sparse sampling in conjunction with 7 Tesla high-field scanning, but this combination of techniques seems to be very promising to further improve on the current results. Second, our method to determine the center of gravity of activations is rather basic and could be improved. One way to test which voxels carry information necessary for the sensory and cognitive mechanisms would be via multivariate pattern analysis (Haynes and Rees, 2005; Hanke et al., 2009). Third, it seems necessary to further study the sensory mechanism with regard to the specific contribution of refractoriness and adaptation processes. This could be done by a systematic variation of the ISI. Acknowledgement—TFM was supported by the DFG (SFB TR31).

REFERENCES Alho K, Rinne T, Herron TJ, Woods DL (2013) Stimulus-dependent activations and attention-related modulations in the auditory cortex: a meta-analysis of fMRI studies. Hear Res [Epub ahead of print]. Baumgart F, Kaulisch T, Tempelmann C, Gaschler-Markefski B, Tegeler C, Schindler F, Stiller D, Scheich H (1998) Electrodynamic headphones and woofers for application in magnetic resonance imaging scanners. Med Phys 25:2068–2070. Berti S (2013) The role of auditory transient and deviance processing in distraction of task performance: a combined behavioral and event-related brain potential study. Front Hum Neurosci 7:352.

Boynton GM, Engel SA, Glover GH, Heeger DJ (1996) Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci 16:4207–4221. Cranford JL, Walker LJ, Stuart A, Elangovan S, Pravica D (2003) Potential contamination effects of neuronal refractoriness on the speech-evoked mismatch negativity response. J Am Acad Audiol 14:251–259. Elmer S, Meyer M, Ja¨ncke L (2012) Neurofunctional and behavioral correlates of phonetic and temporal categorization in musically trained and untrained subjects. Cereb Cortex 22:650–658. Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15:870–878. Hanke M, Halchenko YO, Sederberg PB, Hanson SJ, Haxby JV, Pollmann S (2009) PyMVPA: a python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7:37–53. Haynes JD, Rees G (2005) Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 8:686–691. Horva´th J, Winkler I, Bendixen A (2008) Do N1/MMN, P3a, and RON form a strongly coupled chain reflecting the three stages of auditory distraction? Biol Psychol 79:139–147. Hurschler MA, Liem F, Ja¨ncke L, Meyer M (2012) Right and left perisylvian cortex and left inferior frontal cortex mediate sentencelevel rhyme detection in spoken language as revealed by sparse fMRI. Hum Brain Mapp [Epub ahead of print]. Ja¨a¨skela¨inen IP, Ahveninen J, Bonmassar G, Dale AM, Ilmoniemi RJ, Levanen S, Lin FH, May P, Melcher J, Stufflebeam S, Tiitinen H, Belliveau JW (2004) Human posterior auditory cortex gates novel sounds to consciousness. Proc Natl Acad Sci U S A 101:6809–6814. Ja¨ncke L, Wu¨stenberg T, Scheich H, Heinze HJ (2002) Phonetic perception and the temporal cortex. Neuroimage 15:733–746. Laufer I, Negishi M, Rajeevan N, Lacadie CM, Constable RT (2008) Sensory and cognitive mechanisms of change detection in the context of speech. Brain Struct Funct 212:427–442. Laufer I, Negishi M, Constable RT (2009) Comparator and noncomparator mechanisms of change detection in the context of speech – an ERP study. Neuroimage 44:546–562. Maess B, Jacobsen T, Schro¨ger E, Friederici AD (2007) Localizing pre-attentive auditory memory-based comparison: magnetic mismatch negativity to pitch change. Neuroimage 37:561–571. Na¨a¨ta¨nen R (1990) The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. Behav Brain Sci 13:201–233. Na¨a¨ta¨nen R, Gaillard AW, Mantysalo S (1978) Early selectiveattention effect on evoked potential reinterpreted. Acta Psychol (Amst) 42:313–329. Na¨a¨ta¨nen R, Tervaniemi M, Sussman E, Paavilainen P, Winkler I (2001) ‘‘Primitive intelligence’’ in the auditory cortex. Trends Neurosci 24:283–288. Na¨a¨ta¨nen R, Paavilainen P, Rinne T, Alho K (2007) The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clin Neurophysiol 118:2544–2590. Nager W, Kohlmetz C, Altenmu¨ller E, Rodriguez-Fornells A, Mu¨nte TF (2003) The fate of sounds in conductors’ brains: an ERP study. Cogn Brain Res 17:83–93. Opitz B, Schro¨ger E, von Cramon DY (2005) Sensory and cognitive mechanisms for preattentive change detection in auditory cortex. Eur J Neurosci 21:531–535. Pantev C, Hoke M, Lutkenhoner B, Lehnertz K (1989) Tonotopic organization of the auditory cortex: pitch versus frequency representation. Science 246:486–488. Pettigrew CM, Murdoch BE, Ponton CW, Finnigan S, Alku P, Kei J, Sockalingam R, Chenery HJ (2004) Automatic auditory processing of english words as indexed by the mismatch negativity, using a multiple deviant paradigm. Ear Hear 25:284–301.

G. R. Szycik et al. / Neuroscience 253 (2013) 100–109 Pulvermu¨ller F, Shtyrov Y (2006) Language outside the focus of attention: the mismatch negativity as a tool for studying higher cognitive processes. Prog Neurobiol 79:49–71. Rademacher J, Caviness Jr VS, Steinmetz H, Galaburda AM (1993) Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb Cortex 3:313–329. Reiche M, Hartwigsen G, Widmann A, Saur D, Schro¨ger E, Bendixen A (2013) Involuntary attentional capture by speech and nonspeech deviations: a combined behavioral-event-related potential study. Brain Res 1490:153–160. Roeber U, Widmann A, Schro¨ger E (2003) Auditory distraction by duration and location deviants: a behavioral and event-related potential study. Cogn Brain Res 17:347–357. Romani GL, Williamson SJ, Kaufman L (1982) Tonotopic organisation of the human auditory cortex. Science 216:1339–1340. Schmidt CF, Zaehle T, Meyer M, Geiser E, Boesiger P, Ja¨ncke L (2008) Silent and continuous fMRI scanning differentially

109

modulate activation in an auditory language comprehension task. Hum Brain Mapp 29:46–56. Schro¨ger E (1997) On the detection of auditory deviations: a preattentive activation model. Psychophysiol 34:245–257. Schro¨ger E, Wolff C (1996) Mismatch response of the human brain to changes in sound location. Neuroreport 7:3005–3008. Szycik GR, Tausche P, Mu¨nte TF (2008) A novel approach to study audiovisual integration in speech perception: localizer fMRI and sparse sampling. Brain Res 1220:142–149. Ulanovsky N, Las L, Nelken I (2003) Processing of low-probability sounds by cortical neurons. Nat Neurosci 6:391–398. Ulanovsky N, Las L, Farkas D, Nelken I (2004) Multiple time scales of adaptation in auditory cortex neurons. J Neurosci 24:10440–10453. Zaehle T, Wu¨stenberg T, Meyer M, Ja¨ncke L (2004) Evidence for rapid auditory perception as the foundation of speech processing: a sparse temporal sampling fMRI study. Eur J Neurosci 20:2447–2456. Zaehle T, Jancke L, Herrmann CS, Meyer M (2009) Pre-attentive spectro-temporal feature processing in the human auditory system. Brain Topogr 22:97–108.

(Accepted 22 August 2013) (Available online 30 August 2013)