Professional musicians listen differently to music

Neuroscience 268 (2014) 102–111

PROFESSIONAL MUSICIANS LISTEN DIFFERENTLY TO MUSIC INTRODUCTION

C. A. MIKUTTA, a,c* G. MAISSEN, b A. ALTORFER, b W. STRIK a AND T. KOENIG b a

In recent years, the exploration of professional musicians has been shown to provide excellent access for investigating the influence of musical experience on emotional reactions (James et al., 2008). Further, the underlying functional (Ott et al., 2011; Elmer et al., 2012) and structural (Ja¨ncke, 2009; Moreno et al., 2009; Mu¨nte et al., 2002) changes may be well explored within professional musicians. Since alteration of emotional reaction due to musical expertise is not only restricted to musical stimuli but encompasses a large variety of auditory stimuli (including speech), this model is of general interest for neuroscientists. Therefore, professional musicians represent an ideal model in which to explore experience-driven changes (Schlaug et al., 1995a,b; Koelsch et al., 2002; Loui et al., 2011) with respect to the sensory-motor (Zatorre et al., 2007), cognitive (Ja¨ncke, 2009; Moreno et al., 2011), and emotional (James et al., 2008) processing domains. Compared with non-musicians, recent studies suggest that professional musicians have altered characteristics which may possibly influence emotional processing, such as better temporal discrimination (Agrillo and Piffer, 2012), enhanced auditory perception and related cortical organization (Francois and Schon, 2011; Marie et al., 2011; Elmer et al., 2012, 2013; Ku¨hnis et al., 2013), and improved working memory (George and Coch, 2011). However, superior musical expertise does not only encompass enhanced auditory and motor skills (Amir et al., 2003; Abdul-Kareem et al., 2011). It also involves altered emotional aspects of music perception. Although, principally, music is apt to modulate emotions in nearly everybody (Zatorre et al., 1994; Blood et al., 1999; Koelsch and Mulder, 2002; Baumgartner et al., 2006a,b; Sammler et al., 2007), it was hypothesized that the specific emotional reaction evoked by music is modulated by the degree of musical expertise (James et al., 2008). Music is a continuous stream of transient auditory events that people perceive and respond to in an affective manner (Steinbeis et al., 2006). Music is dynamic and changes over time (Grewe et al., 2005); therefore, it is preferable to dynamically evaluate the change of emotions. To evaluate the quality of those fluctuating emotions, Russel’s circumplex model (Russel, 1980) was used (Thayer and Faith, 2001; Hirokawa, 2004; Schubert, 2007). The valence axis describes the liking of the music and seems to be strongly dependent on consonant and dissonant tones (Dellacherie et al., 2011). The arousal axis was added

University Hospital of Psychiatry, University of Bern, Switzerland

b Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland c Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA

Abstract—Introduction: Experience-based adaptation of emotional responses is an important faculty for cognitive and emotional functioning. Professional musicians represent an ideal model in which to elicit experience-driven changes in the emotional processing domain. The changes of the central representation of emotional arousal due to musical expertise are still largely unknown. The aim of the present study was to investigate the electroencephalogram (EEG) correlates of experience-driven changes in the domain of emotional arousal. Therefore, the differences in perceived (subjective arousal via ratings) and physiologically measured (EEG) arousal between amateur and professional musicians were examined. Procedure: A total of 15 professional and 19 amateur musicians listened to the first movement of Ludwig van Beethoven’s 5th symphony (duration = 7.4 min), during which a continuous 76-channel EEG was recorded. In a second session, the participants evaluated their emotional arousal during listening. In a tonic analysis, we examined the average EEG data over the time course of the music piece. For a phasic analysis, a fast Fourier transform was performed and covariance maps of spectral power were computed in association with the subjective arousal ratings. Results: The subjective arousal ratings of the professional musicians were more consistent than those of the amateur musicians. In the tonic EEG analysis, a mid-frontal theta activity was observed in the professionals. In the phasic EEG, the professionals exhibited an increase of posterior alpha, central delta, and beta rhythm during high arousal. Discussion: Professionals exhibited different and/or more intense patterns of emotional activation when they listened to the music. The results of the present study underscore the impact of music experience on emotional reactions. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Key words: music, EEG, neuroplasticity, emotion, arousal.

*Correspondence to: C. Mikutta, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000 Bern, Switzerland. Tel: +41-031-930-9111; fax: +41-031-930-9404. E-mail address: [email protected] (C. A. Mikutta). Abbreviations: ACC, anterior cingulate cortex; BOLD, blood-oxygenlevel dependent; EEG, electroencephalogram; SPL, sound pressure level; TANCOVA, topographic analysis of covariance; TANOVA, topographic analysis of variance. http://dx.doi.org/10.1016/j.neuroscience.2014.03.007 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 102

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

to make connections with psychoacoustic parameters such as sound intensity (Dean et al., 2011; Mikutta et al., 2013) and timbre (Frego, 1999). Additional factors include tempo (Nyklicek et al., 1997; Frego, 1999), rhythm (Bernardi et al., 2006; Mikutta et al., 2013), and expectation (Koelsch et al., 2002, 2007, 2008; Maidhof et al., 2009). In addition to the aforementioned parameters, it was hypothesized that the degree of arousal is dependent on listener-specific variables such as musical experience (Chapin et al., 2010) and knowledge of the actual piece (Scherer, 1995). Specifically, it was hypothesized that musical expertise changes music-induced emotions due to altered expectations (Pearce and Wiggins, 2006). Because of the aforementioned altered neurophysiological representation, it is plausible that professional musicians have a different and/or more intense music-evoked experience of arousal. To investigate the experience-driven alteration of musicinduced emotional arousal, it is, thus, important to employ measures that are sensitive to transient arousalrelated neurophysiological changes. In this regard, the electroencephalogram (EEG) is particularly well suited because it has a high temporal resolution (Mikutta et al., 2012) and its sensitivity to arousal has been wellestablished (Sammler et al., 2007). In the EEG, music-evoked alterations of arousal have been linked especially to the delta, theta, and alpha frequency bands (Sammler et al., 2007). Interestingly, the theta correlate was at a mid-frontal location. This frontal midline theta was correlated with glucose metabolism in the anterior cingulate cortex (ACC) at rest (Pizzagalli et al., 2003). The ACC has generally been implicated in emotional control (Critchley et al., 2003). A blood-oxygen-level-dependent (BOLD) activation of the ACC was observed due to pleasant emotions evoked by music (Blood and Zatorre, 2001), demonstrating its possibly important role in emotional processing. Further, an asymmetrical frontal alpha distribution (Mikutta et al., 2012) and an asymmetrical parietotemporal alpha distribution (Fu et al., 2001) were related to a heightened arousal level due to music. An arousal network, comprising frontal, temporal, parietal and occipital structures was stipulated by Baumgartner et al. (2006b). The sample of nine women showed low alpha activity in these regions during combined presentation of music and affective pictures. In an analysis across participants, a heightened level of arousal due to music was related to delta waves (Lin et al., 2010). It was the aim of the present study to investigate the influence of musical experience on emotion-related EEG correlates. Therefore, the differences in the perceived (subjective arousal via ratings) and physiologically measured (EEG) arousal level between amateur and professional musicians were examined. We wanted to exclude novelty effects due to differences in the basic knowledge of the music stimulus. Thus, we chose the first movement of Beethoven’s 5th symphony, a musical piece that has proved to be known by all of the amateur musicians (Mikutta et al., 2012). In addition, we had previously shown that this stimulus was apt to modulate

103

arousal in amateur musicians (Mikutta et al., 2012). Based on previous EEG studies showing enhanced theta activity during pleasant music-induced emotional states in connection with arousal (Asada et al., 1999; Sammler et al., 2007), we expected to observe more intense central nervous system correlates of emotional activity, such as mid-frontal theta activation, in professional musicians. Since our previous study (Mikutta et al., 2012) showed frontal asymmetrical alpha oscillations in connection with changes in arousal in amateur musicians, we specifically expected to observe a more intense emotion-associated reaction, such as an alpha (frontal or parietal) asymmetry, in professional musicians (Mikutta et al., 2012). Therefore we use the amateur musicians (n = 17) from Mikutta et al., 2012 as control for comparison with the professional musicians for exploring differences in emotional reaction.

EXPERIMENTAL PROCEDURES Participants Fifteen professional musicians from the schools of fine arts in Berne, Basel, Feldkirch, and Vienna and 17 amateur musicians recruited from the in-house staff and the medical students took part in the present study. All 34 participants were right-handed. The mean age of the professional group was 25 years (range = 21–33). The professional group was comprised of seven males and eight females. The mean age of the amateur group was 24 (range = 17–33). The amateur group was comprised of 10 females and nine males. There was no significant between-groups difference in age or in gender. The amateur group was recruited at 2011 and the results were already used in the publication of Mikutta et al., 2012. The inclusion criteria for the professional musicians were as follows: (1) the musician currently studied at a school of fine arts; or (2) the musician had completed their study of music and obtained a concert diploma. The inclusion criterion for the amateur musicians was that they were currently receiving instruction in a musical instrument. The exclusion criteria for both groups were the presence of central neurological disease, amblyacousia, psychiatric disorders, or the use of psychotropic medication. Amblyacousia was ruled out by testing the auditory threshold of 5–10 dB at 2000 Hz. Tests were performed using a Diatec screening audiometer (model AS-608). Of the professional musicians, 7, 3, 4, and 1 played the piano, percussion instruments, wind instruments, and a string instrument, respectively. All of them had a Bachelor or Master of Arts degree. None of the professional musicians had absolute pitch. On average, they practiced for 30 h per week (range = 25–40 h). All of the amateur musicians played a musical instrument; 12, 5, 1, and 1 played the piano, string instruments, pan flute, and guitar, respectively. Fourteen of the amateur musicians played for more than 5 years. They all received music lessons as amateur musicians in different styles (e.g., classical,

104

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

jazz, modern music). On average, they practiced for 4 h per week (range = 0.5–9 h). Table 1 gives an overview over the music-specific biographical attributes of the two groups. The study was approved by the Ethics Committee of the State of Bern. Therefore, the study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its amendments. All of the participants gave informed consent prior to their inclusion in the study. Stimulus In the present study, the first movement of Ludwig van Beethoven’s 5th symphony (duration: 442 s) was used as the musical stimulus. It was apt to modulate arousal in amateur musicians (Mikutta et al., 2012). We specifically chose a very famous music piece to ensure all of the participants would be familiar with it. As in our prior study, (Mikutta et al., 2012) we used the 1987 recording of the Vienna Philharmonic Orchestra conducted by Carlos Kleiber. The music was presented via in-house software. We used a Technics SUV-306 M2 amplifier and a Technics SB-CS6 loudspeaker; the sound pressure was 51–83 dB. Procedure During the first presentation, a continuous EEG was recorded using an elastic cap containing 76 Ag–AgCl electrodes placed according to the international 10–20 system. Two electrodes served as electrooculograms (EOGs); they were placed below both outer canthi to control for artifacts caused by eye movements. The C3 and C4 average was used as the hardware-defined recording reference. The impedance level was fixed at 20 kOhm. The EEG was amplified band-pass filtered (10-s time constant, 120-Hz low pass), and sampled at 500 Hz using a Nihon Kohden, Neurofax 1100 system. A marker channel was used for the exact positioning of the onset of the music in the EEG. Moreover, the audio track was recorded with the EEG system. The participants’ eyes were closed during the whole procedure. The second presentation took place without EEG recording on the same day directly after the first session, wherein subjective arousal level was assessed (Mikutta et al., 2012). The participants sat at a small table with a computer screen and a mouse. They were asked to rate their subjective arousal level via movements of the mouse. Moving the mouse forward indicated a heightened inner tension due to the music, independent of their affective valence. When they felt a reduced inner tension, they moved the mouse

backward. The screen turned proportionally red when an increased arousal level was indicated, and proportionally blue when a decreased arousal level was indicated. The ratings were recorded with a 100-Hz sampling rate. Before the measurements, all of the participants participated in a 5-min training session to become familiar with the rating instrument. Since the study is based on gaining EEG correlates of spontaneous arousal, we set aside a randomized counterbalanced study design. Therefore, all participants underwent the EEG session before indicating their arousal ratings. After the second presentation, the participants completed a questionnaire concerning handedness, age, and gender. Further, musical education (instrument, duration of training, frequency of training, and lessons) and the degree to which they liked or disliked the piece were assessed with a Likert scale, ranging from 1 (maximally disliked) to 10 (maximally liked). Data analysis We conducted two main analyses to compare the group of professionals with the group of amateurs. The first was a so-called tonic analysis, which focused on the average EEG data throughout the course of the musical piece. The aim of this analysis was to evaluate general differences in electrophysiological representations when the participants listened to the music. The second was a so-called phasic analysis, which related the time-varying subjective arousal ratings to the time-varying EEG fluctuations. The purpose of this analysis was to identify the specific differences in the neurophysiological representation of arousal between professional and amateur musicians. These analyses were, thus, independent. While the first analysis focused on the mean across time and disregarded temporal fluctuations, the second analysis specifically excluded the mean and focused solely on the change over time. Behavioral data. First, the subjective arousal ratings of both groups were downsampled to 1 Hz using spline interpolation, such that they could be correlated to the EEG analysis. The ratings were normalized and mean and standard deviation was calculated. These indices were correlated (Pearson’s correlation coefficient) with the individual liked versus disliked ratings of the music (assessed using a 10-point Likert scale) and the reported hours of music practice per week for each of the two groups. These correlations were used to identify possible relationships between the arousal level when listening to the music, the valence rating after they had listened to the music, and to determine whether the

Table 1. Biographical attributes of the participants Groups

Sex (f/m)

Age

Age of commencement

Mean hours practice/week

Years of practice

Cumulative hours

Professionals Amateurs

7/8 10/9

25 (21–33) 24 (17–33)

7.6 (3–15) 13.7 (8–20)

27.6 (25–30) 4.8 (0.5–9)

16.5 (7–28) 9.2 (3–18)

29705 (13650–54600) 1530 (390–5200)

The range is indicated in brackets.

105

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

music-elicited arousal level habituated or increased with practice. In previous studies, arousal was found to be confounded by loudness (Krumhansl, 1995; Bigand et al., 2006; Granot and Eitian, 2011). Therefore, we computed the sound pressure level (SPL) of the music as a function of time using a Hilbert transformation as implemented in Matlab. We then computed the momentary mean arousal values corrected for SPL; this correction was done by subtracting the portion of arousal that could be explained by a linear effect of sound pressure. As a post hoc analysis we computed a t-test between the mean rating of the professionals and the amateurs. EEG preprocessing. The EEG data were analyzed with the Brain Vision Analyzer, version 1.05.0005. The eye movements were corrected using independent component analysis (Delorme et al., 2007). Thereafter, channels with excessive artifacts were interpolated (in the average four channels per recording). Additional artifacts were removed manually. The data was truncated according to the markers that indicated the onset and offset of the music. The data was parsed into 442 epochs of 1-s duration, recomputed to average reference, frequency transformed by means of a fast Fourier transform (FFT) with a 10% Hanning window, and spectral amplitude (defined as the square root of spectral power) was computed. Spectral amplitude was then averaged within the frequency bands based on a recent publication by Jann et al. (2010), which determined its EEG frequency band borders through correlations with functional magnetic resonance (fMRI) BOLD signals of resting states (delta, 1–3.5 Hz; theta 1, 3.5–6.25 Hz; theta 2, 6.25–8.2 Hz; alpha 1, 8.2–10.5 Hz; alpha 2, 10.5–14 Hz; beta 1, 14–18.75 Hz; beta 2, 18.75–21.88 Hz; and beta 3, 21.88–30 Hz). Statistical analysis. For the tonic analysis, the spectral amplitude values were averaged across epochs for each subject, and group comparisons were conducted using topographic analysis of variance (TANOVA) (Strik et al., 1998; Koenig et al., 2011). The calculated mean spectral amplitudes were displayed as topographic maps of t-values. Further, we used topographic analysis of covariance (TANCOVA; to relate the EEG features to a linear predictor) (Koenig et al., 2011) to correlate the EEG data to the degree that the participants liked or disliked the music, assessed by the Likert scale, the hours practiced, and the instrument played (as evaluated by the questionnaire). The phasic analysis was performed by computing covariance maps based on the individual arousal ratings. For the covariance analysis, for each electrode separately, the mean spectral amplitude across all epochs was removed from each epoch, as we were interested only in the phasic changes of the EEG spectral amplitude; we were not interested in the constant tonic part that is represented by the mean. The same analysis was applied to the mean dynamics of arousal level, as we were again only interested in the

changes of the arousal ratings over time. The covariance of the EEG spectral amplitude with the arousal ratings was then computed as a weighted sum of the epochs’ spectral amplitude, with the average arousal ratings for each epoch serving as weights. This covariance analysis was performed for each subject, excluding epochs with artifacts. The details of this covariance analysis have been given elsewhere (Koenig et al., 2008). In order to test if these covariance maps had common features across participants, topographic consistency tests were performed for each frequency band (Koenig and Melie-Garcia, 2010). This procedure tested the stability of a mean map across participants by comparing its spatial variance (i.e., the variance of the values across all of the electrodes) with the spatial variances of the maps obtained by averaging the individual maps, in which the measurements were randomly shuffled across the electrodes (i.e., randomized mean maps). The shuffling eliminates any spatial distribution consistencies among the individual maps. If the spatial variance of the initially measured mean map is significantly larger than that of most (usually 95%) of the randomized mean maps, the test indicates that it is unlikely that the measured mean map has been obtained by averaging individual maps that have nothing in common. If this global test was significant (p < 0.05 at 5000 randomization runs), tmaps (across participants and against zero) of the covariance maps were computed and displayed. To assess if there were significant differences in the EEG signatures of music-induced arousal, we compared the covariance maps of the professional and the amateur musicians using TANOVAs. Given the difficulty of recruiting a large sample of professional musicians, we anticipated to have a limited statistical power and refrained from corrections for multiple testing. Our results therefore have to be considered as exploratory and need further confirmation.

RESULTS Participants’ profile and subjective arousal ratings Each of the members of the group of professional musicians identified the music piece (5th symphony) and the composer (Ludwig van Beethoven) correctly. On the 10-point Likert scale, the mean liked versus disliked rating was 8 (maximum = 10, minimum = 6). Similarly, each of the members of the group of amateur musicians identified the piece and the composer correctly. The mean liked versus disliked rating was 5.8 (maximum = 9, minimum = 3) (see Fig. 1). For the members of the group of professional musicians, we observed a strong positive correlation between the trace of the mean arousal ratings and the individual arousal ratings (r = 0.758; explained variance, r2 = 58%). For the members of the group of amateur musicians, we observed a strong positive correlation between the trace of the individual arousal ratings across time and the mean arousal ratings across time, as indicated by the mean correlation coefficient,

106

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111 0.5 0.4

Normalized Values

0.3 0.2 0.1 0 -0.1 -0.2

Prof. Ama.

-0.3 -0.4 -0.5

0

1

2

3

4

5

6

7

Time (Min.) Fig. 1. The mean subjective arousal ratings of the professional musicians (black line) and the amateur musicians (gray line) (vertical axis, normalized values, maximal arousal = 0.5, minimal arousal = 0.5) as function of time (min) (horizontal axis) during music listening.

r = 0.600. The mean explained variance (r2) was 43%. There were inter-individual differences in r values; they ranged from r = 0.55 to r = 0.89 in the professional musicians and from r = 0.82 to r = 0.12 in the amateur musicians. There was no significant betweengroups difference in the mean ratings averaged over time (t = 0.663; p = 0.49). In a post hoc analysis (ttests between each point of the mean amateurs and mean professionals rating), we observed significant differences at time points (in seconds) 89–94, 175–180, 204–208, 267–270, 333–335, 364–367, 380–382, 409– 423. We were not able to observe a specific pattern or factor for the parts with significant differences. EEG results Tonic analysis. In the professional musicians, the overall tonic analysis revealed high delta and theta power in the fronto-central region. Further, a symmetrical frontal activation in the alpha 2 band and a frontal activation of the beta 1 and 2 bands were observed (Fig. 2, first line). In the amateur musicians, the overall tonic analysis revealed a high power in the alpha 1 and 2 bands symmetrically in the occipital regions (Fig. 2, second line). The group comparison of the tonic analysis over the whole piece revealed significant differences in the delta (p = 0.029), theta 1 (p = 0.0042), and theta 2 (p = 0.0278) bands. As shown in Fig. 2, the significant difference in the theta 1 and 2 bands were located in the mid-frontal area. Correlations between individual EEG, musical expertise and perceived valence. There were tendencies for positive correlations between the theta 1 (p = 0.072) and theta 2 (p = 0.095) bands and the Likert scale ratings across all of the participants (professional and amateur musicians). However, in the professional group

alone, this correlation was not significant (p > 0.7). In the group of amateur musicians, there was a tendency for a correlation in the alpha 2 band (p = 0.073). There were no significant differences in the frequency bands that were correlated to the number of hours per week spent practicing for the professional and the amateur musicians (p > 0.45, p > 0.64, respectively). For the professional musicians, none of the EEG frequency bands were significantly correlated with the hours of training (TANCOVAs, p > 0.7 for all of the frequency bands). However, when all of the participants (professional plus amateur musicians) were included in the analysis, a significant correlation in the delta (p = 0.042) and theta 1 bands (p = 0.05) was observed. There were also no positive correlations between the EEG frequency bands and the cumulative practice hours. Phasic analysis. The topographic consistency test for the professional musicians showed significant results for the delta (p = 0.0026), theta 2 (p = 0.0008), alpha 2 (p = 0.0008), beta 1 (p = 0.0002), and beta 2 bands (p = 0.003). The computed covariance maps of the professional musicians showed a central delta band increase and a right parieto-temporal alpha band decrease during high arousal (Fig. 3, first line). The topographic consistency test for the amateur musicians showed significant results for the alpha 1 (p = 0.0002), alpha 2 (p = 0.0002), and beta 2 bands (p = 0.0001). The computed covariance maps of the amateur musicians showed a bilateral parieto-temporal suppression of high theta band activity, a right frontal suppression of lower alpha band activity, a left parietotemporal suppression of lower alpha band activity, and a left temporal suppression of beta band activity during states of high arousal (Fig. 3, second line). The TANOVA used to compare the covariance maps of the amateur and professional musicians showed no significant differences in any frequency band. A detailed

107

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

Theta1

Theta2

Alpha1

Alpha2

Beta1

Beta2

Beta3

Difference

Amateurs

Professionals

Delta

L

R

Power Fig. 2. Depicts the t-maps of the averaged spectral amplitude values for all epochs. First line: professional musicians, second line: amateur musicians. The third line shows the results of the TANOVA comparing the t-maps of the professional musicians with those of the amateur musicians. The frequency bands from left to right: delta, theta 1, theta 2, alpha 1, alpha 2, beta 1, beta 2, and beta 3. L = left, R = right. Red areas indicate more spectral amplitude in the professional musicians, blue areas more amplitude in the amateur musicians. The color scale indicates t-values. Colors above the first color step (±2.1) correspond to p n 0:05, uncorrected. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Theta1

Theta2

Alpha1

Alpha2

Beta1

Beta2

Beta3

Difference

Amateurs

Professionals

Delta

L

R

Power Fig. 3. The results of the phasic analysis in both groups and the differences between the professional and the amateur musicians. The t-maps of the individual covariances for all of the frequency bands (first line: professional musicians, second line: amateur musicians, third line: difference between the professional musicians and the amateur musicians. The red areas indicate a power increase with arousal; the blue areas indicate a decrease (lines 1 and 2). In the third line, the red areas indicate a power increase during high arousal in the professionals; the blue areas indicate a higher power during low arousal. The color scale indicates t-values. Colors above the first color step (±2.1) correspond to p n 0:05, uncorrected. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

inspection revealed that the amateur musicians have a higher right frontal alpha power. The professional musicians seem to have a higher central activation in the beta 2 band (Fig. 3, third line).

DISCUSSION The present study investigated the differences between the emotion ratings and the central nervous system

108

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

reactions of professional and amateur musicians when listening to music. Participants’ profile and arousal rating There were no significant differences between the mean subjective arousal ratings of the professional and the amateur musicians. However, compared to the amateur musicians, there were smaller inter-individual differences in the ratings of the professional musicians. Consequently, the explained variance was higher for the professional musicians compared to the amateur musicians (58% in the group of professional musicians versus 49% in the group of amateur musicians). Perceived arousal depends on musical features and listeners’ attributes (Scherer, 1995). The most prominent musical features frequently mentioned were loudness (Krumhansl, 1997; Mikutta et al., 2013), agogics (Mikutta et al., 2013), melody (Pearce et al., 2010), and harmony (Lehne et al., 2013). In several studies arousal was found to be confounded by loudness (Krumhansl, 1995; Schubert, 2004; Bigand et al., 2006; Granot and Eitian, 2011; Mikutta et al., 2012). Lehne et al. (2013) found that discarding loudness preserves a large part of the tension-resolution patterns in music. However, recent investigations were not able to find a clear loudness pattern (Mikutta et al., 2013), even for inducing strong emotions (chill patterns). Therefore, in our investigation, we regressed out sound intensity (Mikutta et al., 2012). Consequently, we speculate that the more (but not significant) congruent ratings of the professionals may result from more precise expectations concerning the tonal aspects (e.g. harmonic and melodic structure). In addition to the factors mentioned above, it was speculated by Schubert (2004) that memory during the listening process (meaning that every moment in the listening experience is, to some extent, related to every other moment of listening to the same piece) may play a role in perceived arousal. Further, the arousal response was associated with the listener’s attributes such as predisposition, preferences and personality traits, and musical experience. In the present study we only controlled for musical experience. Similar results (with no difference in subjective arousal ratings between professional musicians and amateur musicians) were found be Fredrickson (1997, 1999, 2000). In contrast to Fredrickson (2000) we found a heightened standard deviation (StD). in professional musicians, which reflects, in our opinion, a more intense arousal reaction. Tonic EEG analysis In the tonic EEG analysis, we observed more mid-frontal theta band activity in the professional musicians. In general, a mid-frontal theta band activation was mostly observed during cognitive functions like error processing (Luu et al., 2004), mental calculation (Asada et al., 1999) and memorization tasks (Jensen and Tesche, 2002). In a combined EEG/positron emission tomography (PET) study, mid-frontal theta band activity had been linked to increased cerebral metabolism in the

ACC (Pizzagalli et al., 2003). Intracranial EEG recordings in humans have shown theta band oscillations in the ACC, the septo-hippocampal area, and other subcortical limbic structures (Meador et al., 1991; Vinogradova, 1995; Oddie and Bland, 1998). The ACC – as part of the limbic system – has often been associated with emotional processing (Bush et al., 2000). When professional musicians were compared with amateurs, there were experience-dependent changes in right hemisphere limbic areas (including the amygdala and hippocampal complex) and the right insula during the processing of chord violations (James et al., 2008). Musical experience may therefore be an important modulator of the professional musicians’ emotional evaluation. Thus, the higher mid-frontal theta band activity of the professional musicians may be connected with a more sophisticated musical experience (as seen in the strong positive correlation between theta band power and the number of hours of practice per week), influencing the professional musicians’ arousal reaction. Further, we found a significant difference in the theta band activity that was dependent on the valence in the Likert scale ratings. The connection between mid-frontal theta arousal and valence was also observed by Sammler et al. (2007), stating that there seems to be an interaction of valence and arousal (in the sense that a higher arousal level seems to be necessary for a midfrontal theta sign in positive valence). Summarizing our investigation added to the findings of Sammler et al. (2007) further evidence that mid-frontal theta is, not only connected to cognitive processes, but also modulated during emotional processes. Those emotion-related processes seem to be dependent on musical experience. Phasic EEG analysis In the phasic analysis of the EEG, the most prominent observation in the professional musicians was an increase of posterior alpha band rhythm during phases with less arousal. Essentially, posterior alpha band has been connected to a state of relaxed wakefulness (Pfurtscheller et al., 1994). Further, it was shown that high posterior alpha band amplitudes occur during imagination tasks (Klinger et al., 1973) and sentences/ arithmetic tasks (Ray and Cole, 1985). A heightened posterior alpha power was also found during auditory expectation (Fu et al., 2001). In an experiment investigating pitch memory, a left hemisphere lateralized parieto-occipital increase in alpha power was found (van Dijk et al., 2010). A heightened posterior alpha power occurred during music perception and during music imagery (Cooper et al., 2003). Notably, it was suggested that alpha band synchronization reflects an active inhibition process (Klimesch et al., 2007), therefore suggesting for our data an increased activity of the posterior areas during high arousal. In contrast to Babiloni et al. (2012) we were not able to find an alpha activity in the inferior prefrontal gyrus, which was associated with a heightened empathy of hearing the musicians own playing (Babiloni et al., 2012). Our findings would support the thesis of Heller (1993)

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

suggesting a right parieto-temporal location of arousal processing (Heller, 1993). In contrast to the professionals, the amateur musicians showed a right-frontal suppression of lower alpha band activity during high arousal, as described in detail in a previous study (Mikutta et al., 2012). In sum, a frontal alpha asymmetry has been connected to states of emotional arousal (Mikutta et al., 2012) as well as attention (Coan and Allen, 2003). Concerning music, different patterns of frontal asymmetry were found for specific changes in key, tempo and melody (Overman et al., 2003). In contrast to our findings Altenmu¨ller et al. (2002) reported similar asymmetrical frontal alpha patterns for positive and negative emotions on the valence axis (Altenmu¨ller et al., 2002). Similar to Altenmu¨ller et al. (2002) and Schmidt and Trainor (2001) reported frontal alpha asymmetry in connection with differences in valence (Schmidt and Trainor, 2001). Hence, we suggest a different representation of musicinduced arousal due to a different level of musical expertise. Though not statistically significant, there seemed to be an obvious difference in the activation scheme during high arousal (i.e., a right frontal suppression in the amateur musicians versus a decrease of symmetric posterior alpha rhythm in the professionals). In the beta 2 band, we observed a higher central activation in the professional musicians compared to the amateurs. Central beta activation wasinter alia-associated with motor activity. In general, music performance is an extremely complex process, which requires the integration of the auditory system, proprioceptive feedback, visual information, and motor control (Rodriguez-Fornells et al., 2012). In the audiomotor coupling hypothesis, it was suggested that music performance requires the creation of fast feed-forward and feedback loops to precisely coordinate auditory and motor information (Zatorre et al., 2007). Further, in professional musicians, motor and premotor cortical activity was elicited during and after passive listening to known melodies (Bangert et al., 2006; Baumann et al., 2007). Hence, we speculate that the central beta band activation may be a correlate of a more efficient audiomotor coupling. Finally, we observed a higher central delta power during high arousal in the professional musicians compared to the amateur musicians. So far, the delta spectrum has been mostly associated with sleep phases (e.g., Borbely, 1998). However, Lin and colleagues also observed an increased fronto-central delta power due to heightened subjective arousal during music listening (Lin et al., 2010). Bhattacharya and Petsche found – in contrast to our result – a heightened delta power in music processing in amateurs compared to professionals (Bhattacharya and Petsche, 2005). Khalfa et al. (2005) suggested that music perception and emotional perception involve some common brain areas.

LIMITATIONS Concerning the influences on perceived arousal, we did not prove for personality or actual mood. Since both

109

groups had (different) musical experiences, for further exploration of functional differences a control group consisting of non-musicians would be necessary. This would also be a necessary prerequisite for conclusions on neuroplasticity. Given the small sample, our results have to be considered as exploratory and need further confirmation.

CONCLUSION We observed a mid-frontal theta frequency in the professional musicians over the entire musical piece pointing to more intense emotional activity when listening to music. We further found different patterns of alpha (right frontal in the amateurs versus bi-occipital in the professionals), beta (central in the professionals), and delta activity (central in the professionals) during high arousal. The results of the present study underscore the influence of musical experience on emotional processes during music listening.

REFERENCES Abdul-Kareem IA, Stancak A, Parkes LM, Al-Ameen M, Alghamdi J, Aldhafeeri FM, Embleton K, Morris D, Sluming V (2011) Plasticity of the superior and middle cerebellar peduncles in musicians revealed by quantitative analysis of volume and number of streamlines based on diffusion tensor tractography. Cerebellum 10:611–623. Agrillo C, Piffer L (2012) Musicians outperform nonmusicians in magnitude estimation: evidence of a common processing mechanism for time, space and numbers. Q J Exp Psychol (Hove) 65:2321–2332. Altenmu¨ller E, Schurmann K, Lim VK, Parlitz D (2002) Hits to the left, flops to the right: different emotions during listening to music are reflected in cortical lateralisation patterns. Neuropsychologia 40:2242–2256. Amir O, Amir N, Kishon-Rabin L (2003) The effect of superior auditory skills on vocal accuracy. J Acoust Soc Am 113:1102–1108. Asada H, Fukuda Y, Tsunoda S, Yamaguchi M, Tonoike M (1999) Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulate cortex in humans. Neurosci Lett 274:29–32. Babiloni C, Buffo P, Vecchio F, Marzano N, Del Percio C, Spada D, Rossi S, Bruni I, Rossini PM, Perani D (2012) Brains ‘‘in concert’’: frontal oscillatory alpha rhythms and empathy in professional musicians. Neuroimage 60:105–116. Bangert M, Peschel T, Schlaug G, Rotte M, Drescher D, Hinrichs H, Heinze HJ, Altenmu¨ller E (2006) Shared networks for auditory and motor processing in professional pianists: evidence from fMRI conjunction. Neuroimage 30:917–926. Baumann S, Koeneke S, Schmidt CF, Meyer M, Lutz K, Ja¨ncke L (2007) A network for audio-motor coordination in skilled pianists and non-musicians. Brain Res 1161:65–78. Baumgartner T, Esslen M, Ja¨ncke L (2006a) From emotion perception to emotion experience: emotions evoked by pictures and classical music. Int J Psychophysiol 60:34–43. Baumgartner T, Lutz K, Schmidt CF, Ja¨ncke L (2006b) The emotional power of music: how music enhances the feeling of affective pictures. Brain Res 1075:151–164. Bernardi L, Porta C, Sleight P (2006) Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart 92:445–452. Bhattacharya J, Petsche H (2005) Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise. Signal Proc 85:2161–2177.

110

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111

Bigand E, Tillmann B, Poulin-Charronnat B (2006) A module for syntactic processing in music? Trends in Cognitive Science 10:195–196. Blood AJ, Zatorre RJ (2001) Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proc Natl Acad Sci U S A 98:11818–11823. Blood AJ, Zatorre RJ, Bermudez P, Evans AC (1999) Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nat Neurosci 2:382–387. Borbely AA (1998) Processes underlying sleep regulation. Horm Res 49:114–117. Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 4:215–222. Chapin H, Jantzen K, Kelso JA, Steinberg F, Large E (2010) Dynamic emotional and neural responses to music depend on performance expression and listener experience. PLoS One 5:e13812. Coan JA, Allen JJB (2003) The state and trait nature of frontal EEG asymmetry in emotion. In: Hugdahl K, Davidson RJ, editors. The Asymmetrical Brain. Cambridge, MA: MIT Press. p. 565–615. Cooper NR, Croft RJ, Dominey SJ, Burgess AP, Gruzelier JH (2003) Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int J Psychophysiol 47:65–74. Critchley HD, Mathias CJ, Josephs O, O’Doherty J, Zanini S, Dewar BK, Cipolotti L, Shallice T, Dolan RJ (2003) Human cingulate cortex and autonomic control: converging neuroimaging and clinical evidence. Brain 126:2139–2152. Dean RT, Bailes F, Schubert E (2011) Acoustic intensity causes perceived changes in arousal levels in music: an experimental investigation. PLoS One 6:e18591. Dellacherie D, Roy M, Hugueville L, Peretz I, Samson S (2011) The effect of musical experience on emotional self-reports and psychophysiological responses to dissonance. Psychophysiology 48:337–349. Delorme A, Sejnowski T, Makeig S (2007) Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 34:1443–1449. 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. Elmer S, Hanggi J, Meyer M, Ja¨ncke L (2013) Increased cortical surface area of the left planum temporale in musicians facilitates the categorization of phonetic and temporal speech sounds. Cortex 49:2812–2821. Francois C, Schon D (2011) Musical expertise boosts implicit learning of both musical and linguistic structures. Cereb Cortex 21:2357–2365. Fredrickson WE (1997) Elementary, middle, and high school perceptions of tension in music. J Res Music Edu 45:626–635. Fredrickson WE (1999) Effect of musical performance on perception of tension in Gustav Hoist’s First Suite in E-flat. J Res Music Edu 47:44–52. Fredrickson WE (2000) Perception of tension in music: musicians versus nonmusicians. J Music Ther 37:40–50. Frego R (1999) Effects of aural and visual conditions on response to perceived artistic tension in music and dance. J Res Music Edu 47:31–43. Fu KM, Foxe JJ, Murray MM, Higgins BA, Javitt DC, Schroeder CE (2001) Attention-dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parietooccipital alpha-band oscillations. Brain Res Cogn Brain Res 12:145–152. George EM, Coch D (2011) Music training and working memory: an ERP study. Neuropsychologia 49:1083–1094. Granot R, Eitian Z (2011) Musical tension and the interaction of dynamic auditory parameters. Music Percept 28:219–245. Grewe O, Nagel F, Kopiez R, Altenmu¨ller E (2005) How does music arouse ‘‘chills’’? Investigating strong emotions, combining psychological, physiological, and psychoacoustical methods. Ann N Y Acad Sci 1060:446–449.

Heller W (1993) Neuropsychological mechanisms of individual differences in emotion, personality, and arousal. Neuropsychology 7:476–489. Hirokawa E (2004) Effects of music listening and relaxation instructions on arousal changes and the working memory task in older adults. J Music Ther 41:107–127. James CE, Britz J, Vuilleimier P, Hauert CA, Michel CM (2008) Early neuronal responses in right limbic structures mediate harmony incongruity processing in musical experts. Neuroimage 42:1597–1608. Ja¨ncke L (2009) The plastic human brain. Restor Neurol Neurosci 27:521–538. Jann K, Kottlow M, Dierks T, Boesch C, Koenig T (2010) Topographic electrophysiological signatures of FMRI resting state networks. PLoS ONE 5:e12945. Jensen O, Tesche CD (2002) Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci 15:1395–1399. Khalfa S, Schon D, Anton JL, Liegeois-Chauvel C (2005) Brain regions involved in the recognition of happiness and sadness in music. Neuroreport 16:1981–1984. Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53:63–88. Klinger E, Gregoire KC, Barta SG (1973) Physiological correlates of mental activity: eye movements, alpha, and heart rate during imagining, suppression, concentration, search, and choice. Psychophysiology 10:471–477. Koelsch S, Mulder J (2002) Electric brain responses to inappropriate harmonies during listening to expressive music. Clin Neurophysiol 113:862–869. Koelsch S, Schmidt BH, Kansok J (2002) Effects of musical expertise on the early right anterior negativity: an event-related brain potential study. Psychophysiology 39:657–663. Koelsch S, Jentschke S, Sammler D, Mietchen D (2007) Untangling syntactic and sensory processing: an ERP study of music perception. Psychophysiology 44:476–490. Koelsch S, Fritz T, Schlaug G (2008) Amygdala activity can be modulated by unexpected chord functions during music listening. Neuroreport 19:1815–1819. Koenig T, Kottlow M, Stein M, Melie-Garcia L (2011) Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics. Comput Intell Neurosci 2011:938925. Krumhansl C (1995) Music psychology and music theory: problems and prospects. Music Theory Spectr 17(1):53–80. Krumhansl C (1997) An exploratory study of musical emotions and psychophysiology. Can J Exp Psychol 51:336–352. Ku¨hnis J, Elmer S, Meyer M, Ja¨ncke L (2013) The encoding of vowels and temporal speech cues in the auditory cortex of professional musicians: an EEG study. Neuropsychologia 51:1608–1618. Lehne M, Rohrmeier M, Gollmann D, Koelsch S (2013) The influence of different structural features on felt musical tension in two piano pieces by mozart and mendelssohn. Music Percep 31:171–185. Lin YP, Duann JR, Chen JH, Jung TP (2010) Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components. Neuroreport 21:410–415. Loui P, Li HC, Hohmann A, Schlaug G (2011) Enhanced cortical connectivity in absolute pitch musicians: a model for local hyperconnectivity. J Cogn Neurosci 23:1015–1026. Luu P, Tucker DM, Makeig S (2004) Frontal midline theta and the error-related negativity: neurophysiological mechanisms of action regulation. Clin Neurophysiol 115:1821–1835. Maidhof C, Vavatzanidis N, Prinz W, Rieger M, Koelsch S (2009) Processing expectancy violations during music performance and perception: an ERP study. J Cogn Neurosci 22:2401–2413. Marie C, Magne C, Besson M (2011) Musicians and the metric structure of words. J Cogn Neurosci 23:294–305. Meador KJ, Thompson JL, Loring DW, Murro AM, King DW, Gallagher BB, Lee GP, Smith JR, Flanigin HF (1991) Behavioral

C. A. Mikutta et al. / Neuroscience 268 (2014) 102–111 state-specific changes in human hippocampal theta activity. Neurology 41:869–872. Mikutta C, Altorfer A, Strik W, Koenig T (2012) Emotions, arousal, and frontal alpha rhythm asymmetry during Beethoven’s 5th symphony. Brain Topogr 25:423–430. Mikutta CA, Schwab S, Niederhauser S, Wu¨rmle O, Strik W, Altorfer A (2013) Music, perceived arousal, and intensity: psychophysiological reactions to Chopin’s ‘‘Tristesse’’. Psychophysiology 50:909–919. Mu¨nte TF, Altenmu¨ller E, Ja¨ncke L (2002) The musician’s brain as a model of neuroplasticity. Nat Rev Neurosci 3:473–478. Nyklicek I, Thayer JF, Van Doornen LJP (1997) Cardiorespiratory differentiation of musically-induced emotions. J Psychophysiol 11:304–321. Oddie SD, Bland BH (1998) Hippocampal formation theta activity and movement selection. Neurosci Biobehav Rev 22:221–231. Overman AA, Hoge J, Dale JA, Cross JD, Chien A (2003) EEG alpha desynchronization in musicians and nonmusicians in response to changes in melody, tempo, and key in classical music. Percept Mot Skills 97:519–532. Pearce MT, Wiggins GA (2006) Expectation in melody: the influence of context and learning. Music Percept 23:377–405. Pearce MT, Ruiz MH, Kapasi S, Wiggins GA, Bhattacharya J (2010) Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage 50:302–313. Pfurtscheller G, Neuper C, Berger J (1994) Source localization using event-related desynchronization (ERD) within the alpha band. Brain Topogr 6:269–275. Pizzagalli DA, Oakes TR, Davidson RJ (2003) Coupling of theta activity and glucose metabolism in the human rostral anterior cingulate cortex: an EEG/PET study of normal and depressed subjects. Psychophysiology 40:939–949. Ray WJ, Cole HW (1985) EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science 228:750–752. Rodriguez-Fornells A, Rojo N, Amengual JL, Ripolles P, Altenmu¨ller E, Mu¨nte TF (2012) The involvement of audio-motor coupling in the music-supported therapy applied to stroke patients. Ann N Y Acad Sci 1252:282–293. Russel J (1980) A circumplex model of affect. J Pers Soc Psychol 39:1161–1178.

111

Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44: 293–304. Scherer KR (1995) Expression of emotion in voice and music. J Voice 9:235–248. Schlaug G, Ja¨ncke L, Huang Y, Staiger JF, Steinmetz H (1995a) Increased corpus callosum size in musicians. Neuropsychologia 33:1047–1055. Schlaug G, Ja¨ncke L, Huang Y, Steinmetz H (1995b) In vivo evidence of structural brain asymmetry in musicians. Science 267:699–701. Schmidt LA, Trainor LJ (2001) Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn Emotion 15:487–500. Schubert E (2004) Modeling perceived emotion with continuous musical features. Music Percept 4:561–585. Schubert E (2007) Locus of emotion: the effect of task order and age on emotion perceived and emotion felt in response to music. J Music Ther 44:344–368. Steinbeis N, Koelsch S, Sloboda JA (2006) The role of harmonic expectancy violations in musical emotions: evidence from subjective, physiological, and neural responses. J Cogn Neurosci 18:1380–1393. Strik WK, Fallgatter AJ, Brandeis D (1998) Pascual-Marqui RD: Three-dimensional tomography of event-related potentials during response inhibition: evidence for phasic frontal lobe activation. Evoked Potential 108:406–413. Thayer JF, Faith ML (2001) A dynamic systems model of musically induced emotions. Physiological and self-report evidence. Ann N Y Acad Sci 930:452–456. van Dijk H, Nieuwenhuis IL, Jensen O (2010) Left temporal alpha band activity increases during working memory retention of pitches. Eur J Neurosci 31:1701–1707. Vinogradova OS (1995) Expression, control, and probable functional significance of the neuronal theta-rhythm. Prog Neurobiol 45:523–583. Zatorre RJ, Evans AC, Meyer E (1994) Neural mechanisms underlying melodic perception and memory for pitch. J Neurosci 14:1908–1919. Zatorre RJ, Chen JL, Penhune VB (2007) When the brain plays music: auditory-motor interactions in music perception and production. Nat Rev Neurosci 8:547–558.

(Accepted 6 March 2014) (Available online 15 March 2014)