Entrainment of Premotor Cortex Activity by Ambiguity in...

Entrainment of Premotor Cortex Activity by Ambiguity in Musical Metre Daniel Cameron,*1 Job Lindsen,#2 Marcus Pearce,+3 Geraint Wiggins,+4 Keith Potter,^5 Joydeep Bhattacharya,#6

*Brain and Mind Institute, University of Western Ontario, Canada #Dept. of Psychology, Goldsmiths, University of London, UK

^Dept. of Music, Goldsmiths, University of London, UK +Centre for Digital Music, Queen Mary, University of London, UK

[email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

Background Musical rhythm tends to be organized in a metric

structure, usually consisting of periodic salient positions (beats). Metric beats are anticipated by neural processing, as demonstrated by previous research showing that metric beats coincide with increased attention (Jones, et al., 2002) and impact of movements (e.g. foot-tapping).

Motor areas of the brain, including basal ganglia, premotor area, and supplementary motor area, have been implicated in the perception of musical rhythms (e.g. Grahn and Brett, 2007). Previous electroencephalographic (EEG) research has shown modulation of gamma band (24-60 Hz) activity with the metric beat during listening to rhythmic tone sequences (Snyder and Large, 2005; Fujioka, et al., 2009). These findings reflect anticipatory brain activity in response to auditory rhythms. Other research has shown that for isochronous auditory sequences, amplitude is increased for frequencies that match the endogenously generated metric beat (Nozaradan, et al., 2011), and inter-trial phase coherence (ITC) is increased at the frequency of the stimulus sequence (Will and Berg, 2007). One limitation of previous EEG studies on rhythm and metre processing is the use of isochronous tones as stimuli, which does not capture the full extent of metre processing of non-isochronous sequences, as in musical rhythm.

Aims This study aims to investigate how neural processing of

rhythm, as measured by amplitude and ITC of EEG, varies as a function of the metrical ambiguity of the stimulus.

Method Twenty musicians underwent 32 channel EEG recording

as they listened to two versions of Steve Reich’s Clapping Music, a piece of rhythmic music consisting of two performers clapping 12 distinct rhythms, each repeated 12 times. The rhythms in this music are either metrically ambiguous (fitting equally well to metres of 3 or 4 beats) or unambiguous (fitting best to a metre of 3 beats), according to a model of metre induction (Povel and Essens, 1985).

Using a fast Fourier transform, amplitude and ITC values were extracted from EEG, aligned with each repetition of each rhythmic figure. Amplitude and ITC values were analyzed for main effects and interactions of stimulus version, frequency band (delta, theta, alpha, beta, and gamma), metric ambiguity, and scalp distribution (4 quadrants), using repeated measures analyses of variance (ANOVA). Source analysis was used to investigate the likely neural sources of effects of interest, using the BESA® software package.

Further analyses investigated differences at narrow frequency bands centred on the specific frequencies of the 2 possible metric beats of the stimulus (1.33 Hz and 1.77 Hz).

For all analyses, ANOVA were Huynh-Feldt corrected and follow up contrasts used Tukey corrected paired t tests.

Results Results showed significant 3 way interactions of scalp

quadrant, frequency band, and metric ambiguity for both amplitude (F (4,76) = 2.46, p < .05), and ITC (F (4,76) = 14.10, p < .001). Follow up contrasts (Tukey corrected) show that ambiguous rhythms generate significantly greater amplitude compared to unambiguous rhythms, in frontal electrodes in the delta band (1-4 Hz) (t (19) = 3.13, p < .05). ITC is also greater for ambiguous rhythms in frontal electrodes in the delta band (t (19) = 4.92, p < .001). In the alpha band (8-12 Hz), ambiguous rhythms elicit lower amplitude responses than unambiguous rhythms (t (19) = -3.47, p < .05). Based on examination of topographical plots of the scalp distribution of effects, we also performed a paired t test on gamma band amplitude over electrodes located most laterally on both sides, on the central line. Gamma amplitude is greater in these electrodes for ambiguous compared to unambiguous rhythms (t(19) = 2.49, p < .05).

Results of ANOVA for metre-specific frequencies (1.33 Hz and 1.77 Hz) shows significant interactions of scalp quadrant and ambiguity (F(3,57) = 8.98, p < .001), and of scalp quadrant and frequency (F(3,57) = 4.15, p < .05) for ITC, and significant interaction of scalp quadrant and ambiguity (F (3,57) = 4.00, p < .05). Follow up contrasts show that ITC is significantly greater in frontal electrodes for ambiguous rhythms at 1.33 Hz (t(19) = 3.83, p < .01) and at 1.77 Hz (t(19) = 3.29, p < .01). Amplitude is also greater in frontal electrodes for ambiguous rhythms at 1.33 Hz (t(19) = 1.76, p = .09) and at 1.77 Hz (t(19) = 2.99, p < .01).*

Topographical plots of ambiguity-based differences (ambiguous - unambiguous) in delta amplitude, delta ITC, alpha amplitude, and gamma amplitude are shown in Fig 1.

Results of the source analysis for differences in ITC in the 1.25-1.75 Hz band indicate that dipoles may be located in right inferior frontal gyrus (x=57, y=10, z=22) and left ventral premotor cortex (x=-62, y=4, z=27). Images showing the location of sources associated with this difference are found in Fig 2.

*Though results for amplitude of one frequency (1.33 Hz) is marginally above the nominal threshold of statistical significance, we take this as reflecting a meaningful difference, since it is consistent with other effects.

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Keywords: Rhythm processing; EEG; Inter-trial phase coherence; Metric ambiguity

1. INTRODUCTION

Metre is the hierarchical organisation of rhythm, corresonding to dynamic perceived strength of positions in rhythmic sequences, and tactus a frequency conventionally perceived as the ‘beat’ to which listeners can most readily synchronise movements. Metre perception and the endogenous process by which it emerges from rhythm are essential cognitive aspects of music, yet its underlying dynamical neural responses remain largely uncharacterized. While previous research has investigated event-related EEG aspects of metre perception[1,3], we explore spectral power (characterizing local neuronal synchronization), and inter-trial coherence (ITC) (characterizing the stimulus-locked, trial-to-trial phase consistency of brain responses) as they vary with musical rhythms which differ in metric information. We investigate a range of EEG frequency bands with special emphasis on low frequencies corresponding to the tactus of plausible metres of stimulus rhythms.

2. METHOD

Multivariate (32 channels) EEG signals were recorded from twenty musicians listening to two versions of Steve Reich’s Clapping Music (1972), a piece consisting of two performers clapping a rhythm and systematically transforming it to produce 12 unique rhythms, each repeated 12 times. Rhythms are categorized according to a model of metre-induction[4] as having either an unambiguous metre of 3 tactus beats (1.33 Hz) or ambiguously plausible metres of 3 or 4 tactus beats (1.77 Hz for the latter), respectively. These different rhythm categories are compared in terms of their mean spectral power and ITC values of standard EEG frequency bands and also for specific low frequencies.

3. RESULTS

Metrically ambiguous rhythms, as compared to unambiguous rhythms, are associated with significantly greater power at 1.77 Hz and in the broader delta band (1-4 Hz). These low frequency effects are most conspicuous over bilateral frontal areas (see Fig. 1a). Ambiguous figures also show greater power in the gamma band (24-60 Hz) over bilateral temporal regions (see Fig. 1d). On the other hand, unambiguous rhythms show greater power in the alpha band (8-12 Hz) (see Fig. 1c).

Ambiguous figures are also associated with greater ITC at 1.33 Hz, 1.77 Hz, and the broader delta band, also over bilateral frontal areas (see Fig. 1b).

Figure 1: Topographical scalp distribution of the differences between metrically ambiguous and unambiguous rhythmic figures in a) delta power, b) delta inter trial phase-coherence, c) alpha power, and d) gamma power.a) b)

1.33 Hz 1.77 Hz Delta (1-4 Hz) Alpha (8-12 Hz) Gamma (24-60 Hz)

Amplitude

ITC


Differences: Ambiguous - UnambiguousAll differences significant (p<.05) for frontal electrodes, unless otherwise labeled

All electrodes, p = .058

n.s.All electrodes, p = .10

Dec 13, 2011Daniel Cameron

update on Clapping Music - EEG project*colobars added Dec 17 2011

n.s.

Saturday, December 17, 2011


Amplitude

ITC







n.s.


c) d)


Amplitude

ITC







n.s.


4. DISCUSSION

Differences in EEG power and phase (ITC) at the frequencies corresponding to plausible metre/tactus frequencies may reflect aspects of the endogenous perception/processes of metre. Localisation over premotor areas is consistent with past research on the role of motor systems in rhythm/metre perception and cognition[2]. Gamma band power differences over auditory cortex may correspond to metre-based differences found previously[1].

Rhythm structure may play a role, as effects correlate linearly with number of rests in rhythms. (i.e. rests may provide metrical information in the context of this rhythmic music, influencing EEG correlates of metre perception/processing).

5. REFERENCES

[1] Fujioka, T., Trainor, L.J., Large, E.W., & Ross, B. (2009). Beta and gamma rhythms in human auditory cortex during musical beat processing. Annals of the New York Academy of Sciences, 1169: 89-92.

[2] Grahn, J.A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. Journal of Cognitive Neuroscience, 19(5): 893-906.

[3] Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. (2011). Tagging the neuronal entrainment to beat and meter. Journal of Neuroscience, 31(28): 110234-10240.

[4] Povel, D., & Essens, P. (1985). Perception of temporal patterns. Music Perception, 2(4): 411-440.

Metrical Ambiguity in a Piece of Rhythmic MusicDaniel Cameron1, Job Lindsen2, Marcus Pearce3, Geraint Wiggins3, Keith Potter2 and Joydeep Bhattacharya2

1University of Western Ontario, 2Goldsmiths, University of London, 3Queen Mary University of [email protected]

Format: oral presentation Wish to be considered for publication in an edited volume: yes

Figure 1. Topographical scalp distributions for differences between metrically ambiguous and unambiguous rhythms in a) delta amplitude, b) delta ITC, c) alpha amplitude, and d) gamma amplitude.

Figure 2: Images constructed source analysis, showing the most likely dipole locations for ambiguity-based differences in ITC in the 1.25-1.75 Hz band. These locations are right inferior frontal gyrus (BA45) and left ventral premotor cortex (BA6).

Conclusions Our results show characteristic changes in EEG responses

during musical listening, corresponding to differences in the metric information present in musical rhythms. We find effects in amplitude and ITC at the frequency of the metric beat of rhythmic sequences (1.33 Hz and 1.77 Hz), consistent with previous research (Nozaradan, et al., 2011; Will and Berg, 2007; for amplitude and ITC, respectively) and modulation of gamma amplitude with metric differences in rhythms, also in agreement with previous research (Snyder and Large, 2005; Fujioka, et al., 2009). Furthermore, the apparent brain and scalp distributions of these effects are consistent with previous research showing effects of metrical structure in gamma amplitude response generated in primary auditory cortex (Fujioka, et al., 2009). Changes in amplitude and ITC of metre-specific frequencies appear to be generated bilaterally in ventral premotor area and inferior frontal gyri. This is consistent with previous research showing the ventral premotor area as a site of activity during the aesthetic appreciation of musical rhythmic sequences (Kornysheva, et al., 2010). This area has also been linked to domain-general anticipatory processing for hierarchical sequences (Fiebach and Schubotz, 2006). Our findings are consistent with this, since we show

effects related to the metric structure of rhythmic sequences (hierarchical organization of anticipated salient time points in a temporal sequence).

These results represent a significant advance over previous research because the stimuli used were real-world non-isochronous musical rhythms, rather than artificial isochronous sequences, as well as showing changes in both amplitude and ITC of metre-specific frequencies in motor areas of the brain.

Amplitude in the alpha band showed the opposite pattern to other frequency bands, in that it was lower for metrically ambiguous rhythms than unambiguous. This may reflect anticipatory processing due to greater metric information of rhythmic sequences, since reduction of alpha power has been observed during temporal anticipation (Rohenkohl and Nobre, 2011).

Future research should investigate similar effects of metre on EEG using perceptual and behavioural methods (i.e. a synchronized tapping task) to correlate individuals’ explicit metre-perception with amplitude and ITC of EEG at particular frequency bands. Moreover, rhythmic figures should be presented in random order rather than the same order for both versions and all participants. This will eliminate any possible order effects of rhythms on EEG. It is also important to investigate how neural responses vary to systematically manipulated metrical strength, since metric rhythms differ not only in which metre fits, but how strongly a metric beat is induced.

Keywords Metre processing; Rhythm; Entrainment;

REFERENCES Fiebach, C.J., & Schubotz, R.I. (2006). Dynamic anticipatory

processing of hierarchical sequential events: a common role for Broca’s area and ventral premotor cortex across domains? Cortex, 42(4): 499-502.

Fujioka, T., Trainor, L.J., Large, E.W., & Ross, B. (2009). Beta and gamma rhythms in human auditory cortex during musical beat processing. Ann. NY Acad. Sci., 1169: 89-92

Grahn, J.A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. J. Cog. Neurosci., 19(5): 893-906.

Jones, M.R., Moynihan, H., MacKenzie, N., & Puente, J. (2002). Temporal aspects of stimulus-driven attending in dynamic arrays. Psychological Science, 13(4): 313-319.

Kornysheva, K., von Cramon, D.Y., Jacobsen, T., & Schubotz, R.I. (2010). Tuning-in to the beat: aesthetic appreciation of musical rhythms correlates with a premotor activity boost. Human Brain Mapping, 31:48-64.

Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. (2011). Tagging the neuronal entrainment to beat and meter. J. Neurosci, 31(28): 10234-10240.

Povel, D., & Essens, P. (1985). Perception of temporal patterns. Music Perception, 2(4): 411-440.

Rohenkohl, G., & Nobre, A.C. (2011). Alpha oscillations related to anticipatory attention follow temporal expectations. J. Neurosci., 31(40): 14076-14084.

Snyder, J.S., & Large, E.W. (2005). Gamma-band activity reflects the metric structure of rhythmic tone sequences. Cognitive Brain Research, 24: 117-126.

Will, U., & Berg, E. (2007). Brain wave synchronization and entrainment to periodic acoustic stimuli. Neuroscience Letters, 424: 55-60.

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