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Rhythmic brain activities related to singing in humans
www.elsevier.com/locate/ynimg NeuroImage 34 (2007) 426 – 434
Rhythmic brain activities related to singing in humans Atsuko Gunji, a,b,c,⁎ Ryouhei Ishii, a,d Wilkin Chau, a Ryusuke Kakigi, b and Christo Pantev a,e a
The Rotman Research Institute for Neuroscience, Baycrest Centre for Geriatric Care, Toronto, Ontario, Canada M6A 2E1 Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan c Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan d Department of Post-Genomics and Diseases, Division of Psychiatry and Behavioral Proteomics, Osaka University, Suita, Osaka 565-0871, Japan e Institute for Biomagnetism and Biosignalanalysis, Münster University Hospital, University of Münster, Kardinal-von-Galen Ring 10, Münster, D-48129, Germany b
Received 4 June 2005; revised 22 June 2006; accepted 6 July 2006 Available online 16 October 2006 To investigate the motor control related to sound production, we studied cortical rhythmic changes during continuous vocalization such as singing. Magnetoencephalographic (MEG) responses were recorded while subjects spoke in the usual way (speaking), sang (singing), hummed (humming) and imagined (imagining) a popular song. The power of alpha (8–15 Hz), beta (15–30 Hz) and low-gamma (30–60 Hz) frequency bands was changed during and after vocalization (singing, speaking and humming). In the alpha band, the oscillatory changes for singing were most pronounced in the right premotor, bilateral sensorimotor, right secondary somatosensory and bilateral superior parietal areas. The beta oscillation for the singing was also confirmed in the premotor, primary and secondary sensorimotor and superior parietal areas in the left and right hemispheres where were partly activated even for imagined a song (imaging). These regions have been traditionally described as vocalization-related sites. The cortical rhythmic changes were distinct in the singing condition compared with the other vocalizing conditions (speaking and humming) and thus we considered that more concentrated control of the vocal tract, diaphragm and abdominal muscles is responsible. Furthermore, characteristic oscillation in the high-gamma (60–200 Hz) frequency band was found in Broca’s area only in the imaging condition and might occur singing rehearsal and storage process in Broca’s area. © 2006 Elsevier Inc. All rights reserved. Keywords: Singing; Speech; Motor control; Magnetoencephalography; Synthetic aperture magnetometry
Introduction Vocal communication, such as speech and song, engages a number of cognitive and linguistic processes and motor control of ⁎ Corresponding author. Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8553, Japan. Fax: +81 42 346 2158. E-mail address: [email protected] (A. Gunji). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2006.07.018
the facial muscles, tongue, larynx and respiration. Recent neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (f MRI) have attracted increased attention to the brain regions underlying vocal communication in humans. The supplementary motor area (SMA), sensorimotor area, premotor area, Broca’s area, Wernicke’s area, auditory area and insula have been reported as vocalizationassociated brain regions (McGuire et al., 1996; Paus et al., 1996; Hirano et al., 1996, 1997; Wildgruber et al., 1996; Murphy et al., 1997; Perry et al., 1999; Huang et al., 2001; Sakurai et al., 2001; Blank et al., 2002). Simple vocalization drives both hemispheres almost symmetrically, whereas speech production usually manifests with left hemispheric dominance (Wildgruber et al., 1996; Hirano et al., 1996, 1997; Huang et al., 2001; Sakurai et al., 2001; Blank et al., 2002). Wildgruber et al. measured brain activities related to naming the months of year, tongue movement and syllable singing using f MRI. In naming condition, the left lower motor cortex was activated lager than right hemisphere whereas tongue movement did not show any significant lateralization difference. On the other hand, the right motor cortex, insula and inferior-frontal lobe are dominantly activated in the right hemisphere when people are singing (Wildgruber et al., 1996; Perry et al., 1999). Previous electroencephalographic (EEG) and magnetoencephalographic (MEG) studies have reported cortical activities associated with simple vocalization. Vocalization of an obscure vowel sound elicits mainly a motor-related cortical response in the motor area just before and after vocalization onset and the auditoryevoked response to one’s own voice after vocalization onset (Deecke et al., 1986; Wohlert, 1993; Gunji et al., 2000a,b, 2001; Curio et al., 2000; Houde et al., 2002). Furthermore, planning the articulation and control of the vocal cords activates the insula and the inferior-frontal lobe prior to vocalization onset (Salmelin et al., 1994, 2000; Sasaki et al., 1995; Kuriki et al., 1999, Gunji et al., 2003). They are consistent with the findings of the previous neuroimaging studies mentioned above, cortical responses to naming and counting tasks showing left hemispheric dominancy but not to simple movement, repeating a vowel. The studies have
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been suggested that vocalization-related cortical activities, even in the case of simple vocalization without articulation and semantic processes, might temporally overlap in several cortical regions. Here we considered two disadvantages when recording eventrelated responses during vocalization. One is the movement artifacts of the facial muscles, jaw and eye blinks, which are frequently time-locked to vocalization onset, making it difficult to separate real cortical activity from artifacts. Another disadvantage is the trigger for analysis, that is, whether at the onset of movement or the onset of vocalized sound. When we utter a word, electromyographic activity (EMG) of the pharyngeal, lingual, facial and mandibular muscles appears usually with a latency of approximately 400 ms to 200 ms prior to the onset of vocalized sound (Szirtes and Vaughan, 1977; Brooker and Donald, 1980; Kuriki et al., 1999). This timing is not fixed; it is variable and depends upon the word uttered. Therefore, no suitable trigger can be pinpointed easily in vocalization tasks. A new approach has to be considered to investigate neural activity for complicated and continuous vocalization, e.g. singing and speaking. In this study, we focused on spontaneous rhythmic activity, which might reflect thalamo-cortical network activation and show certain frequency oscillation related to internal and/or external events (Lopes da Silva, 1991; Pfurtscheller and Lopes da Silva, 1999). Previous studies have suggested that oscillatory power in alpha and beta frequency bands was suppressed during movement (e.g. palmar pinch, tongue protrusion, foot dorsiflexion) showing rebound after the movement. This type of behavior is usually described as eventrelated desynchronization (ERD) and event-related synchronization (ERS) (Crone et al., 1998a,b; Pfurtscheller and Lopes da Silva, 1999; Neuper and Pfurtscheller, 2001), representing sensorimotor cortical areas correlated with movement. Thus, by investigating specific oscillations in the MEG response, we might be able to detect cognitive brain activities related to continuous vocalization with complicated articulation. Synthetic aperture magnetometry (SAM) is a spatial filtering technique based on a non-linear constrained minimum variance beam former that can detect task-related cortical synchronization within a specific frequency band with three-dimensional source mapping (Robinson and Rose, 1992; Ishii et al., 2002; Dziewas et al., 2003; Herdman et al., 2003, 2006; Schulz et al., 2004). It could help to evaluate the cortical activities associated with continuous vocalization but not by motion artifacts of the subjects’ mouth movements. Therefore, we measured the steady-state responses of vocalization using a box-designed paradigm and analyzed them using SAM in order to explore the cortical processes related to continuous vocalization. A further objective of this study was to determine the current source density distributions associated with the controls of voice key, respiration and articulation from the MEG recordings. For this reason, we designed a simple paradigm minimizing the linguistic and cognitive processes as word retrieval and consisting of singing, speaking, humming and imagining a popular song. Methods Subjects Eleven healthy volunteers with normal hearing (seven males and four females; mean age 29 years, range 22–36 years) participated in this study. All subjects were right-handed. None were currently training or had previously formally trained their voice. Informed consent to participate in the experiment was
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obtained and the study was approved by the Ethics Commission of the University of Toronto. Experimental protocol The purpose of this experiment was to separate neural activities associated with vocalization into the following three factors: modulation of the fundamental frequency, respiration control and articulation of one’s own voice. The experiment paradigm was designed to minimize linguistic and cognitive processes such as word retrieval. Subjects were instructed to carry out the vocalization or imagine the vocalization in the following four conditions (Table 1): (1) singing: subjects sang the first two phrases of a popular song “Happy birthday to you”, (2) speaking: subjects were instructed to speak the same phrases used in the singing condition, with no modulation of their fundamental frequency, (3) humming: subjects were instructed to sing the same song with a fixed obscure vowel similar to the vowel / /, not real humming, and they sang it with no articulation of their mouths and jaws, and (4) imagining: subjects imagined singing the same phrases. The visual cues were presented on a screen inside a magnetically shielded room from a projection system outside the room. All cues, white letters on a gray screen, 44 mm in width and 10 mm in length (visual angle: 3°), were presented at the center of the screen for 250 ms. The distance between the screen and the subject was 880 mm. One trial consisted of four time stages (Fig. 1). To prepare for vocalization, the subjects were required to remain relaxed with their mouth slightly open in the first stage. After 7 s, one of four conditions was revealed by a visual cue and the subjects performed one of three vocalization conditions or imagining. The subjects were instructed to stop their vocalization-related movement after seeing the instruction “stop”. When the last instruction “rest” was shown on the screen, subjects could rest for 10 s. Each condition had eight trials. Thirty-two trials in total (eight trials × four conditions) were randomly performed in each subject. During the task, the subjects were seated comfortably on a chair and were instructed to fix their eyes on the center of the screen and minimize muscle artifacts. They also were instructed to minimize their head movement due to facial muscle activity and jaw movements during MEG measurement. Before the recordings were made, they practiced to sing, speak and hum with sound pressure of 55 dB SPL approximately while an experimenter checked it by a sound level meter. They also practiced to start to imagine the corresponding singing task just after a visual cue of task-instruction.
Table 1 Paradigm of the experiment Condition
Singing Speaking Humming Imagining
Factor Melody
Respiration
Articulation
+ − +
+ + +
+ + −
This paradigm defines the brain rhythmic activities affected strongly by the three factors: Melody (modulation of subjects' voice key), Respiration control and Articulation associated with vocalization. See the text.
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Fig. 1. Experimental procedure and definition of the analyzed stages. See the text.
EMG recording Surface EMG was measured using two pairs of bipolar gold electrodes from the orbicularis oris, submental and infrahyoid muscles. It was effectual to detect muscle activities in the singing and speaking conditions so that an experimenter could check if the subject could perform the tasks with appropriate timings to start and stop behaviors. The electrodes were connected to a bipolar amplifier (DSQ 800 EOG/EMG system, CTF Systems Inc., Canada), and a high-pass filtering of 0.16 Hz was set. MEG recording MEG data were recorded in a magnetically shielded room using a 151-channel whole head biomagnetometer system (Omega 151, CTF Systems Inc., Canada) with detection coils spaced by 31 mm. Each coil was 20 mm in diameter and was configured as a firstorder axial gradiometer with a baseline of 50 mm and connected to a superconducting quantum interference device (SQUID). The spectral density of the intrinsic noise of each channel was between 3 and 6 fT (rms)/√Hz in the frequency range above 1 Hz. The magnetic responses were filtered with a 60 Hz notch filter and 200 Hz low pass filter and digitized at 625 Hz. Periods in which signal variations were larger than 3 pT in the MEG were excluded from the analysis. The head position was measured at the beginning and end of each session and data with a mean head movement < 6 mm were allowed for the analysis. Anatomical MRI To convert the sources of MEG responses into subjects’ brain images, magnetic resonance imaging (MRI) scans (1.5 T Sigma Scanner, GE Medical Systems, Milwaukee, WI) were obtained for all subjects. T1-weighted axial anatomical images with an in-plane resolution of 256 × 192 and 128 slices (1.4 mm thickness) were recorded using spoiled gradient echo imaging. Anatomical landmarks (nasion and center points of the entrance to the bilateral ear canals) were used to create an MEG head-based three-dimensional coordinate system. The same landmarks were visualized on the MRI by affixing Gadulinium markers to these points.
(motionless) stage after stopping the vocalization or imagining task: 7.0 s in each trial. These two stages were filtered within the frequency bands of 8–15 Hz, 15–30 Hz, 30–60 Hz and 60–200 Hz. For the filtered MEG responses, Student’s t values were calculated from the oscillatory changes between the Task stages and the Stop stages with each 5 mm voxel resolution for each hemisphere of a real head model, which was produced from each subject’s MRI (Robinson and Vrba, 1999). Using SAM analysis, furthermore, oscillation powers subtracted Task stage from Stop stage in the singing condition were compared with the speaking condition to define the effect of modulation of the fundamental frequency and respiration control of one’s own voice. The singing condition was also compared with the humming condition to detect the effect of articulation. To analyze the group data, the distribution of each individual’s SAM image was transformed into a common anatomical space, the SPM T1 template space, as follows (Chau et al., 2002, 2004): the SAM images of each subject were first co-registered with his/her 3D anatomical MRI based on landmarks (nasion and ear canals). They were spatially normalized and averaged across subjects to provide a group SAM image in the same template space as in the group of f MRI data. Transformation parameters to the template space were then determined by SPM99 software (Wellcome Department of Cognitive Neurology, London, UK). A nonparametric permutation test was applied to the normalized SAM results to determine which voxels were significant by comparing the grand mean pseudo t value of a voxel to the distribution of permuted pseudo t values. This distribution was computed by randomly rearranging two periods (between Task and Stop stages in each task, singing and speaking conditions, singing and humming conditions) and averaging the newly calculated pseudo t values. The null hypothesis of SAM permutation was that the original grand mean pseudo t value falls within the permuted distribution of pseudo t values. Thus, the omnibus null hypothesis of “no activation” anywhere in the brain was rejected if at least one t value was above the critical threshold for p < 0.05 determined by 1024 permutations. Voxels with grand mean pseudo t values above the critical 0.05-threshold were considered as a region of activation and the corresponding voxels were then overlaid on a normalized structural MRI. Results
Data analysis The MEG data were analyzed using synthetic aperture magnetometry (SAM). To detect the vocalization-specific cortical states, the following time intervals were defined: (1) Task stage during the vocalization task: 7.0 s in each trial, and (2) Stop
All subjects performed acceptably as instructed in the vocalization tasks (Fig. 1). No consistent EMG response was observed after presenting the Stop instruction and thereby the Control stage should be regarded as a motionless period after the vocalization task. In the imagining task, the subjects reported that
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Table 2 Regions showing event-related desynchronization (ERD) in alpha (8–15 Hz), beta (15–30 Hz) and low-gamma frequency bands (30–60 Hz) Regions
Alpha: 8–15 Hz
Beta: 15–30 Hz
Low gamma: 30–60 Hz
Singing Speaking Humming Imagining Singing Speaking Humming Imagining Singing Speaking Humming Imagining – R Superior L/R Inferior L/R BA 3, 1, 2 Superior L/R Inferior L/R BA 40 R BA 43 L/R BA 7 L/R BA 9 BA 6 BA 4
– L/R L/R L/R L L/R L L/R R
R R R L/R R L/R L/R –
– – L – – – – – –
L/R L/R L/R L/R L/R L/R L/R L L/R
L/R L/R L/R L/R L/R L/R L/R L –
L/R L/R – L/R – L/R L/R –
– L R L – L – – –
– L/R L/R
– R L/R
L/R – – – –
L – – – –
R R R R – – – – –
– – – – – – – – –
BA: Brodmann's area.
they imagined their singing as instructed, although there was no objective evidence in the sound or an EMG response. Magnetic oscillations in the singing, speaking, humming and imagining conditions The task stage in each condition resulted in multiple eventrelated desynchronization (ERD) of rhythmic brain activity at 8–15 Hz, 15–30 Hz and 30–60 Hz frequency bands. The ERD was denoted as the suppression of rhythmic brain activity during vocalizing or imagining tasks as compared with the rebound after the tasks. Table 2 summarizes the cortical distribution of significant ERDs (p < 0.05) obtained by the SAM-permutation test (Chau et al., 2002, 2004) for all subjects comprising the ERDs in each frequency band in any vocalizing and imagining conditions. We obtained the following topographic results: In the alpha frequency band (8–15 Hz), ERD was bilaterally confirmed in the middle frontal region, pre- and post-central regions (premotor, sensorimotor and secondary somatosensory areas), inferior parietal lobe-operculum and superior parietal cortex in the singing, speaking and humming conditions (Fig. 2). The ERD of the
sensorimotor cortex was separated into superior and inferior parts. During the imagining condition, slight ERD was obtained in the superior part of the left sensorimotor cortex. Vocalizing ERDs were found bilaterally in the inferior primary somatosensory cortex (BA 3, 1, 2) and the inferior primary motor cortex (BA 4), they extended to bilateral primary gustatory area (BA 43). In the superior side, singing ERD was found bilaterally although the other ones were found in left hemisphere for speaking and in right hemisphere for humming. The secondary sensorimotor cortex in right hemisphere (BA 40) showed ERD for singing and the left secondary sensorimotor cortex showed ERD for speaking. In addition, bilateral ERD in the sensorimotor integration areas (BA 7) was obtained in the singing condition but not in the other conditions. Comparing the singing condition with the speaking or humming condition, the cortical distribution of alpha-ERD was quite different. In the right sensorimotor cortex and the left parietal cortex (Fig. 3), it was significantly larger in the singing than in the speaking and humming conditions. The area showing significant difference between the singing and the speaking conditions was widespread in the right sensorimotor cortex, similar to the ERD difference between the singing and the humming conditions overlapping the same areas, but by a smaller extent.
Fig. 2. Group results for all subjects (n = 11) of relative event-related desynchronization (ERD) in alpha (8–15 Hz), beta (15–30 Hz) and low-gamma (30–60 Hz) frequency bands associated with the vocalization and imagining tasks. Voxels with grand mean pseudo t values (represented in the color bar) above the critical 0.05 threshold were considered the region of activation and the corresponding voxels were then overlaid on a normalized structural MRI.
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Fig. 3. Comparison of event-related desynchronization (ERD) between singing, speaking or humming conditions in alpha (8–15 Hz) and beta (15–30 Hz) frequency bands for all subjects (n = 11).
In the beta frequency band (15–30 Hz), the ERD was also confirmed in the premotor, primary and secondary sensorimotor cortices in the left and right hemispheres for the singing, speaking and humming conditions and even in the imagining condition (Fig. 2). Comparisons of beta-band oscillations between the singing and speaking, and between the singing and humming conditions confirmed significant differences in the sensorimotor cortex of the right hemisphere with ERD power larger in the singing condition than in the other vocalizing conditions (Fig. 3). ERD in the low-gamma frequency band (30–60 Hz) was found in two vocalization tasks, singing and speaking, in the premotor and sensorimotor cortices in both hemispheres (cf. Fig. 2). In contrast, in the humming condition, a dominant low-gamma ERD was observed in the premotor and motor areas of the right hemisphere. However, the ERD did not show any significant differences between singing and speaking conditions and between singing and humming conditions. For the imagining condition, we found no significant low-gamma ERD. Comparisons in the high-gamma frequency band (60–200 Hz) in the imagining condition showed a strongly induced ERD around the posterior inferior frontal region (Broca’s area; BA 44 and 45) (Fig. 4), but not in the singing, speaking and humming conditions. On the other hand, the event-related synchronization (ERS) of rhythmic brain activity appeared remarkably in a very deep area, almost in the brainstem in three vocalization conditions (cf. Fig. 5) that did not appear in the imagining condition. Discussion This study suggested that characteristic rhythmic-brain activities were induced by continuous vocalization (singing, speaking and humming), which was associated with cognitive neural activities
Fig. 4. Group results (n = 11) of relative event-related desynchronization (ERD) in a high-gamma (60–200 Hz) frequency band associated with imaginary singing.
familiar in human communication. We focused on the identification of cortical rhythmic changes and their cortical distribution as contributed by the control of one’s own voice key (pitch) and the respiration. The frequency-specific analysis might identify factors, melody retrieval and articulation, related to vocalization. In this study, we suggested that continuous vocalization in humans mainly caused the bilateral suppression of cortical oscillations in alpha and beta frequency bands in the sensorimotor and premotor areas. Interestingly, even imaginary singing could elicit rhythmic changes in of the left and/or right motor cortices in alpha and beta bands partly. The characteristic oscillation was added in Broca’s area only in the imagining condition. On the other hand, spatial filtering analysis might separate facial muscles’ artifacts such as undesired rhythmic activities from cortical oscillations related to real vocalization, because oscillation range of the artifact should be different to the cortical one. Since strong high-gamma ERS occurred around the brainstem in real vocalization conditions and was distributed in more caudal regions, i.e. the jaw and neck, it can be assumed to describe artifacts from face and jaw movements. Rhythmic changes and cortical distribution induced by vocalization ERDs of alpha and beta frequency bands in the premotor, sensorimotor and secondary somatosensory areas and superior parietal cortex were consistently obtained in all vocalization tasks. The attenuated and enhanced ERD power was also found in previous studies on mu-rhythm (Pfurtscheller and Aranibar, 1979; Pfurtscheller, 1981; Salmelin et al., 1995; Crone et al., 1998b; Pfurtscheller et al., 2003), who found sources of these oscillations around middle of the motor strip in a finger-tapping task. The present observation might show by reason of the motor control of vocalization-related organs, mouth, larynx, etc. Interestingly, the cortical regions displaying alpha ERD were separated into superior and inferior parts of the bilateral sensorimotor cortices, which seems consistent with the trunk and face (including larynx) areas of ‘homunculus’. The previous studies also reported these two areas as vocalization-related areas in the sensorimotor cortex (Penfield and Roberts, 1959. Salmelin et al., 1994; Wildgruber et al., 1996; McGuire et al., 1996, 1997; Hirano et al., 1997, Murphy et al., 1997; Perry et al., 1999; Gunji et al., 2000b; Sakurai et al., 2001; Blank et al., 2002; Huang et al., 2001). In this study, alpha-ERD was thought to occur by control of the facial muscles, larynx and respiration. Furthermore, the power change between during and after singing was larger in the right sensorimotor cortex and in the left parietal cortex compared with speaking and humming. When
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Fig. 5. Group results (n = 11) of relative event-related synchronization (ERS) in a high-gamma (60–200 Hz) frequency band associated with the vocalization and imaging tasks.
we sing, it is necessary to control our own voice-key and maintain the rhythm of several musical processes when compared to speaking the words of a song. Recent studies reported that the right hemisphere was dominant in musical ability (Zatorre et al., 1994; Pantev et al., 1998; Halpern and Zatorre, 1999; Hirata et al., 1999; Hashimoto et al., 2000). The results of our study indicated that singing requires fine and delicate control of the vocal cords, abdominal muscles and diaphragm, and that the right sensorimotor cortex is more dominant for singing. Processing the pitch of tone shows right hemispheric dominancy, and melody control following articulation may also be dominantly driven by the right hemisphere. In addition, the cortical area of alpha-ERD difference as measured by subtracting the speaking from the singing condition in the right sensorimotor cortex was more widely distributed than the subtraction of humming and singing. Thus, this difference of alpha-ERD distribution might be elicited by controlling the laryngeal muscle, which is necessary just for singing, although it is difficult to precisely separate the activity of the facial area (related to articulation) from that of the laryngeal area (related to melody control). On the other hand, specific ERD for singing in the parietal cortex was showing left hemispheric dominancy. Thus the cortex assume the sensorimotor integration area, the significant difference might be caused by complex operation to control vocalization-related organs, too. It is well known that the ERD accompanying movement is found for the beta frequency band (15–30 Hz) as well as for the alpha frequency band although they reflect each functional role (Salmelin et al., 1995, Crone et al., 1998b). In this study, the cortical distribution of beta-ERD spreads from the superior to the inferior part of the sensorimotor cortex without interruption and was also found in the premotor area and the parietal cortex during singing, speaking and humming conditions (cf. Fig. 3). These regions were consistent with cortical areas related to vocalization and tongue movement reported in the previous studies (Penfield and Roberts, 1959; Salmelin and Sams, 2002; Salmelin et al., 1994, 1995, 2000; Wildgruber et al., 1996; McGuire et al., 1996, 1997; Hirano et al., 1997, Murphy et al., 1997; Crone et al., 1998b; Perry et al., 1999; Gunji et al., 2000b; Nakasato et al., 2001; Sakurai et al., 2001; Blank
et al., 2002; Huang et al., 2002; Pfurtscheller et al., 2003). Some have reported beta-oscillations in the left and right sensorimotor cortices, as found in this study (Salmelin and Sams, 2002; Salmelin et al., 1995, 2000; Crone et al., 1998b; Pfurtscheller et al., 2003). Considering the obtained ERD distribution, our results are suggested to represent the voluntary control of facial muscles, larynx and respiration in vocalization. As the ERD in the facial area of the sensorimotor cortex in the right hemisphere in the singing condition was significantly larger than in the speaking and the humming conditions, this might indicate an inter-hemispheric difference caused by the combination of articulation with the processing of a musical action. The left sensorimotor area may be dominantly activated in vocalization tasks involving the language process (Deecke et al., 1986), although this study did not investigate this issue. A previous neuroimaging study assumed that the movement related to utterance was controlled bilaterally but hemispheric dominancy in the sensorimotor area was hardly described (Perry et al., 1999). We hypothesized that the activity of the sensorimotor area for music-related action was more dominant in the right hemisphere. Such hemispheric dominancy may be found particularly in the human brain, since we probably execute melody and language separately. In addition, a very small superior-middle part of the right sensorimotor cortex showed enhanced ERD related to singing compared with speaking. This region was thought to include the truncal area of the sensorimotor cortex. Therefore, this result suggested that voluntary control of respiration might be required more for singing than for speaking. The ERD in the low-gamma frequency band was found in the superior (truncal) part of the left and right sensorimotor cortices, whereas alpha- and beta-ERD was identified in superior and inferior parts. The low-gamma band distribution in our results is in line with previous reports, in which gamma-band activities correlated to areas showing alpha- and beta-band activities (Crone et al., 1998a; Mima et al., 1999; Kopell et al., 2000). However, most were usually described as ERS during motor tasks, suggesting that cortical synchronization of low gamma-band activity might be interpreted by the following: If the ERD in our study consist of that rhythmic brain activity during vocalization tasks and subsequent rebound, the
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activity during vocalization tasks in the low gamma band might be smaller than after vocalization. The ERD difference between singing and humming conditions was localized in the left superior part of the sensorimotor cortex. If the area is in truncal area of ‘homunculus’, the result supposes that subjects are required much resonance to control their respiration following articulated vocalization in the singing condition, while the humming condition had to control only respiration. Rhythmic changes in the sensorimotor cortex induced by imagining a song Rhythmic brain activities in alpha- and beta-frequency bands were found in the sensorimotor cortices during singing task while some of the cortices (alpha: superior part of motor cortex in left hemisphere; beta: inferior part of sensorimotor cortex in left hemisphere, superior part of motor cortex in right hemisphere) were changed the oscillation power even by imagining a song unaccompanied by movement or sound. Recent EEG studies also reported that the topography of alpha and beta oscillations while imagining hand movement was distributed close to the handrepresentation area as identified by execution of the same movement (Pfurtscheller and Neuper, 1997; Pfurtscheller et al., 1999; McFarland et al., 2000). In addition, some previous PET studies revealed that activity in the sensorimotor cortex corresponds with finger and tongue movements during motor imagery (Lotze et al., 1999; Porro et al., 1996, 2000; Hanakawa et al., 2003, Ehrsson et al., 2003). Thus, imaginary singing might also activate the vocalization-related area of the sensorimotor cortex partly. Movement imagery can be applied to the motor system and is often used to help train or learn suitable movement. Although our findings did not clarify whether neural activities take place between executing and imagining movements, they would indicate that oscillatory representation caused by imaginary singing corresponds to actual singing. Rhythmic changes in Broca’s area Broca’s area has been considered one of the most important language-related cortices. Many neuroimaging studies have investigated the relationship between Broca’s area and the processes of speech planning involving articulation and mouth movement. In this study, we could recognize significant ERD in the high-gamma band in Broca’s area in only the imagining condition. Surprisingly, Broca’s area showed no remarkable oscillations in any other frequency bands or in any other vocalizing conditions in this study, whereas many studies were showing specific oscillation changes, 20-Hz rhythm, related to naming aloud and mouth movement there (Salmelin and Sams, 2002; Salmelin et al., 1995). We could ask the question: why were there no significant cortical oscillations in Broca’s area during the vocalization conditions? Previous PET studies showed no remarkable activity in Broca’s area when subjects were repeating a fixed sentence (Murphy et al., 1997; Wildgruber et al., 1996). Similarly, since our study used “the repeated vocalization of a simple and familiar sentence” as a task, cortical oscillations might not be significantly identified in Broca’s area. Furthermore, many neuroimaging studies provided that Broca’s area processed imitation, imagery and perception of action (Binkofski et al., 2000; Nishitani et al., 2005 review) and working-memory contents (Smith and Jonides, 1998, 1999).
Gerardin et al. (2000) noted that Broca’s area was remarkably activated for imagination compared with execution of handaction. Thus, the area supposes to comprise rehearsal process and short-term maintenance of body-movements including speech production. In addition, rhythm processing during piano playing was also associated with Broca’s area especially, which might help to understand cortical functions during covert singing. Our findings recognized the processes as specific oscillations in Broca’s area. In conclusion, the present study can be summarized as follows: (1) The rhythmic brain activities associated with continuous vocalization were suppressed during vocalization and subsequent strong rebound was identified after vocalization; (2) Imaginary singing could elicit rhythmic changes in the left premotor and left or right sensorimotor cortices partly which were also activated in the vocalization conditions; (3) The rhythmic change in singing was more remarkable in the right motor cortex compared with the other vocalization conditions, indicating hemispheric dominancy in the cortical regions corresponding to melody generation and motor control of singing. These findings may be found especially in the human brain since we probably perform a cognitive role for music and language, respectively. Thus, musical output induces specific activities in the motor cortices continuously which may be added to processes reflected by simple vocalization and speech. Acknowledgments This study was supported by the CIHR, the Ontario Innovation Trust and the Canadian Foundation for Innovation, and JSPS, Grants-in-Aid for JSPS Fellows (1507335, 1661602) and a Grantin-Aid for Young Scientists (B) (17700324). We are grateful to Dr. Sachiko Koyama, Dr. Hidehiko Okamoto and Mr. Wollbrink for their suggestions and technical assistance. References Binkofski, F., Amunts, K., Stephan, K.M., Posse, S., Schormann, T., Freund, H.J., Zilles, K., Seitz, R.J., 2000. Broca’s region subserves imagery of motion: a combined cytoarchitectonic and f MRI study. Hum. Brain Mapp. 11, 273–285. Blank, S.C., Scott, S.K., Murphy, K., Warburton, E., Wise, R.J., 2002. Speech production: Wernicke, Broca and beyond. Brain 125, 1829–1838. Brooker, B.H., Donald, M.W., 1980. Contribution of the speech musculature to apparent human EEG asymmetries prior to vocalization. Brain Lang. 9, 226–245. Chau, W., Ishii, R., Ross, B., McIntosh, A.R., Pantev, C., 2002. Group analysis for the synthetic aperture magnetometry (SAM) data. In: Nowak, H., Haueisen, J., Giessler, F., Hounker, R. (Eds.), Proceedings of the 13th International Conference on Biomagnetism. VDE Verlag, Berlin, pp. 1009–1011. Chau, W., McIntosh, A.R., Robinson, S.E., Schulz, M., Pantev, C., 2004. Improving permutation test power for group analysis of spatially filtered MEG data. NeuroImage 23, 983–996. Crone, N.E., Miglioretti, D.L., Gordon, B., Lesser, R.P., 1998a. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis: II. Event-related synchronization in the gamma band. Brain 121, 2301–2315. Crone, N.E., Miglioretti, D.L., Gordon, B., Sieracki, J.M., Wilson, M.T., Uematsu, S., Lesser, R.P., 1998b. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis: I. Alpha and beta event-related desynchronization. Brain 121, 2271–2299. Curio, G., Neuloh, G., Numminen, J., Jousmaki, V., Hari, R., 2000.
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Rhythmic brain activities related to singing in humans Atsuko Gunji, a,b,c,⁎ Ryouhei Ishii, a,d Wilkin Chau, a Ryusuke Kakigi, b and Christo Pantev a,e a
The Rotman Research Institute for Neuroscience, Baycrest Centre for Geriatric Care, Toronto, Ontario, Canada M6A 2E1 Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan c Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan d Department of Post-Genomics and Diseases, Division of Psychiatry and Behavioral Proteomics, Osaka University, Suita, Osaka 565-0871, Japan e Institute for Biomagnetism and Biosignalanalysis, Münster University Hospital, University of Münster, Kardinal-von-Galen Ring 10, Münster, D-48129, Germany b
Received 4 June 2005; revised 22 June 2006; accepted 6 July 2006 Available online 16 October 2006 To investigate the motor control related to sound production, we studied cortical rhythmic changes during continuous vocalization such as singing. Magnetoencephalographic (MEG) responses were recorded while subjects spoke in the usual way (speaking), sang (singing), hummed (humming) and imagined (imagining) a popular song. The power of alpha (8–15 Hz), beta (15–30 Hz) and low-gamma (30–60 Hz) frequency bands was changed during and after vocalization (singing, speaking and humming). In the alpha band, the oscillatory changes for singing were most pronounced in the right premotor, bilateral sensorimotor, right secondary somatosensory and bilateral superior parietal areas. The beta oscillation for the singing was also confirmed in the premotor, primary and secondary sensorimotor and superior parietal areas in the left and right hemispheres where were partly activated even for imagined a song (imaging). These regions have been traditionally described as vocalization-related sites. The cortical rhythmic changes were distinct in the singing condition compared with the other vocalizing conditions (speaking and humming) and thus we considered that more concentrated control of the vocal tract, diaphragm and abdominal muscles is responsible. Furthermore, characteristic oscillation in the high-gamma (60–200 Hz) frequency band was found in Broca’s area only in the imaging condition and might occur singing rehearsal and storage process in Broca’s area. © 2006 Elsevier Inc. All rights reserved. Keywords: Singing; Speech; Motor control; Magnetoencephalography; Synthetic aperture magnetometry
Introduction Vocal communication, such as speech and song, engages a number of cognitive and linguistic processes and motor control of ⁎ Corresponding author. Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8553, Japan. Fax: +81 42 346 2158. E-mail address: [email protected] (A. Gunji). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2006.07.018
the facial muscles, tongue, larynx and respiration. Recent neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (f MRI) have attracted increased attention to the brain regions underlying vocal communication in humans. The supplementary motor area (SMA), sensorimotor area, premotor area, Broca’s area, Wernicke’s area, auditory area and insula have been reported as vocalizationassociated brain regions (McGuire et al., 1996; Paus et al., 1996; Hirano et al., 1996, 1997; Wildgruber et al., 1996; Murphy et al., 1997; Perry et al., 1999; Huang et al., 2001; Sakurai et al., 2001; Blank et al., 2002). Simple vocalization drives both hemispheres almost symmetrically, whereas speech production usually manifests with left hemispheric dominance (Wildgruber et al., 1996; Hirano et al., 1996, 1997; Huang et al., 2001; Sakurai et al., 2001; Blank et al., 2002). Wildgruber et al. measured brain activities related to naming the months of year, tongue movement and syllable singing using f MRI. In naming condition, the left lower motor cortex was activated lager than right hemisphere whereas tongue movement did not show any significant lateralization difference. On the other hand, the right motor cortex, insula and inferior-frontal lobe are dominantly activated in the right hemisphere when people are singing (Wildgruber et al., 1996; Perry et al., 1999). Previous electroencephalographic (EEG) and magnetoencephalographic (MEG) studies have reported cortical activities associated with simple vocalization. Vocalization of an obscure vowel sound elicits mainly a motor-related cortical response in the motor area just before and after vocalization onset and the auditoryevoked response to one’s own voice after vocalization onset (Deecke et al., 1986; Wohlert, 1993; Gunji et al., 2000a,b, 2001; Curio et al., 2000; Houde et al., 2002). Furthermore, planning the articulation and control of the vocal cords activates the insula and the inferior-frontal lobe prior to vocalization onset (Salmelin et al., 1994, 2000; Sasaki et al., 1995; Kuriki et al., 1999, Gunji et al., 2003). They are consistent with the findings of the previous neuroimaging studies mentioned above, cortical responses to naming and counting tasks showing left hemispheric dominancy but not to simple movement, repeating a vowel. The studies have
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been suggested that vocalization-related cortical activities, even in the case of simple vocalization without articulation and semantic processes, might temporally overlap in several cortical regions. Here we considered two disadvantages when recording eventrelated responses during vocalization. One is the movement artifacts of the facial muscles, jaw and eye blinks, which are frequently time-locked to vocalization onset, making it difficult to separate real cortical activity from artifacts. Another disadvantage is the trigger for analysis, that is, whether at the onset of movement or the onset of vocalized sound. When we utter a word, electromyographic activity (EMG) of the pharyngeal, lingual, facial and mandibular muscles appears usually with a latency of approximately 400 ms to 200 ms prior to the onset of vocalized sound (Szirtes and Vaughan, 1977; Brooker and Donald, 1980; Kuriki et al., 1999). This timing is not fixed; it is variable and depends upon the word uttered. Therefore, no suitable trigger can be pinpointed easily in vocalization tasks. A new approach has to be considered to investigate neural activity for complicated and continuous vocalization, e.g. singing and speaking. In this study, we focused on spontaneous rhythmic activity, which might reflect thalamo-cortical network activation and show certain frequency oscillation related to internal and/or external events (Lopes da Silva, 1991; Pfurtscheller and Lopes da Silva, 1999). Previous studies have suggested that oscillatory power in alpha and beta frequency bands was suppressed during movement (e.g. palmar pinch, tongue protrusion, foot dorsiflexion) showing rebound after the movement. This type of behavior is usually described as eventrelated desynchronization (ERD) and event-related synchronization (ERS) (Crone et al., 1998a,b; Pfurtscheller and Lopes da Silva, 1999; Neuper and Pfurtscheller, 2001), representing sensorimotor cortical areas correlated with movement. Thus, by investigating specific oscillations in the MEG response, we might be able to detect cognitive brain activities related to continuous vocalization with complicated articulation. Synthetic aperture magnetometry (SAM) is a spatial filtering technique based on a non-linear constrained minimum variance beam former that can detect task-related cortical synchronization within a specific frequency band with three-dimensional source mapping (Robinson and Rose, 1992; Ishii et al., 2002; Dziewas et al., 2003; Herdman et al., 2003, 2006; Schulz et al., 2004). It could help to evaluate the cortical activities associated with continuous vocalization but not by motion artifacts of the subjects’ mouth movements. Therefore, we measured the steady-state responses of vocalization using a box-designed paradigm and analyzed them using SAM in order to explore the cortical processes related to continuous vocalization. A further objective of this study was to determine the current source density distributions associated with the controls of voice key, respiration and articulation from the MEG recordings. For this reason, we designed a simple paradigm minimizing the linguistic and cognitive processes as word retrieval and consisting of singing, speaking, humming and imagining a popular song. Methods Subjects Eleven healthy volunteers with normal hearing (seven males and four females; mean age 29 years, range 22–36 years) participated in this study. All subjects were right-handed. None were currently training or had previously formally trained their voice. Informed consent to participate in the experiment was
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obtained and the study was approved by the Ethics Commission of the University of Toronto. Experimental protocol The purpose of this experiment was to separate neural activities associated with vocalization into the following three factors: modulation of the fundamental frequency, respiration control and articulation of one’s own voice. The experiment paradigm was designed to minimize linguistic and cognitive processes such as word retrieval. Subjects were instructed to carry out the vocalization or imagine the vocalization in the following four conditions (Table 1): (1) singing: subjects sang the first two phrases of a popular song “Happy birthday to you”, (2) speaking: subjects were instructed to speak the same phrases used in the singing condition, with no modulation of their fundamental frequency, (3) humming: subjects were instructed to sing the same song with a fixed obscure vowel similar to the vowel / /, not real humming, and they sang it with no articulation of their mouths and jaws, and (4) imagining: subjects imagined singing the same phrases. The visual cues were presented on a screen inside a magnetically shielded room from a projection system outside the room. All cues, white letters on a gray screen, 44 mm in width and 10 mm in length (visual angle: 3°), were presented at the center of the screen for 250 ms. The distance between the screen and the subject was 880 mm. One trial consisted of four time stages (Fig. 1). To prepare for vocalization, the subjects were required to remain relaxed with their mouth slightly open in the first stage. After 7 s, one of four conditions was revealed by a visual cue and the subjects performed one of three vocalization conditions or imagining. The subjects were instructed to stop their vocalization-related movement after seeing the instruction “stop”. When the last instruction “rest” was shown on the screen, subjects could rest for 10 s. Each condition had eight trials. Thirty-two trials in total (eight trials × four conditions) were randomly performed in each subject. During the task, the subjects were seated comfortably on a chair and were instructed to fix their eyes on the center of the screen and minimize muscle artifacts. They also were instructed to minimize their head movement due to facial muscle activity and jaw movements during MEG measurement. Before the recordings were made, they practiced to sing, speak and hum with sound pressure of 55 dB SPL approximately while an experimenter checked it by a sound level meter. They also practiced to start to imagine the corresponding singing task just after a visual cue of task-instruction.
Table 1 Paradigm of the experiment Condition
Singing Speaking Humming Imagining
Factor Melody
Respiration
Articulation
+ − +
+ + +
+ + −
This paradigm defines the brain rhythmic activities affected strongly by the three factors: Melody (modulation of subjects' voice key), Respiration control and Articulation associated with vocalization. See the text.
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Fig. 1. Experimental procedure and definition of the analyzed stages. See the text.
EMG recording Surface EMG was measured using two pairs of bipolar gold electrodes from the orbicularis oris, submental and infrahyoid muscles. It was effectual to detect muscle activities in the singing and speaking conditions so that an experimenter could check if the subject could perform the tasks with appropriate timings to start and stop behaviors. The electrodes were connected to a bipolar amplifier (DSQ 800 EOG/EMG system, CTF Systems Inc., Canada), and a high-pass filtering of 0.16 Hz was set. MEG recording MEG data were recorded in a magnetically shielded room using a 151-channel whole head biomagnetometer system (Omega 151, CTF Systems Inc., Canada) with detection coils spaced by 31 mm. Each coil was 20 mm in diameter and was configured as a firstorder axial gradiometer with a baseline of 50 mm and connected to a superconducting quantum interference device (SQUID). The spectral density of the intrinsic noise of each channel was between 3 and 6 fT (rms)/√Hz in the frequency range above 1 Hz. The magnetic responses were filtered with a 60 Hz notch filter and 200 Hz low pass filter and digitized at 625 Hz. Periods in which signal variations were larger than 3 pT in the MEG were excluded from the analysis. The head position was measured at the beginning and end of each session and data with a mean head movement < 6 mm were allowed for the analysis. Anatomical MRI To convert the sources of MEG responses into subjects’ brain images, magnetic resonance imaging (MRI) scans (1.5 T Sigma Scanner, GE Medical Systems, Milwaukee, WI) were obtained for all subjects. T1-weighted axial anatomical images with an in-plane resolution of 256 × 192 and 128 slices (1.4 mm thickness) were recorded using spoiled gradient echo imaging. Anatomical landmarks (nasion and center points of the entrance to the bilateral ear canals) were used to create an MEG head-based three-dimensional coordinate system. The same landmarks were visualized on the MRI by affixing Gadulinium markers to these points.
(motionless) stage after stopping the vocalization or imagining task: 7.0 s in each trial. These two stages were filtered within the frequency bands of 8–15 Hz, 15–30 Hz, 30–60 Hz and 60–200 Hz. For the filtered MEG responses, Student’s t values were calculated from the oscillatory changes between the Task stages and the Stop stages with each 5 mm voxel resolution for each hemisphere of a real head model, which was produced from each subject’s MRI (Robinson and Vrba, 1999). Using SAM analysis, furthermore, oscillation powers subtracted Task stage from Stop stage in the singing condition were compared with the speaking condition to define the effect of modulation of the fundamental frequency and respiration control of one’s own voice. The singing condition was also compared with the humming condition to detect the effect of articulation. To analyze the group data, the distribution of each individual’s SAM image was transformed into a common anatomical space, the SPM T1 template space, as follows (Chau et al., 2002, 2004): the SAM images of each subject were first co-registered with his/her 3D anatomical MRI based on landmarks (nasion and ear canals). They were spatially normalized and averaged across subjects to provide a group SAM image in the same template space as in the group of f MRI data. Transformation parameters to the template space were then determined by SPM99 software (Wellcome Department of Cognitive Neurology, London, UK). A nonparametric permutation test was applied to the normalized SAM results to determine which voxels were significant by comparing the grand mean pseudo t value of a voxel to the distribution of permuted pseudo t values. This distribution was computed by randomly rearranging two periods (between Task and Stop stages in each task, singing and speaking conditions, singing and humming conditions) and averaging the newly calculated pseudo t values. The null hypothesis of SAM permutation was that the original grand mean pseudo t value falls within the permuted distribution of pseudo t values. Thus, the omnibus null hypothesis of “no activation” anywhere in the brain was rejected if at least one t value was above the critical threshold for p < 0.05 determined by 1024 permutations. Voxels with grand mean pseudo t values above the critical 0.05-threshold were considered as a region of activation and the corresponding voxels were then overlaid on a normalized structural MRI. Results
Data analysis The MEG data were analyzed using synthetic aperture magnetometry (SAM). To detect the vocalization-specific cortical states, the following time intervals were defined: (1) Task stage during the vocalization task: 7.0 s in each trial, and (2) Stop
All subjects performed acceptably as instructed in the vocalization tasks (Fig. 1). No consistent EMG response was observed after presenting the Stop instruction and thereby the Control stage should be regarded as a motionless period after the vocalization task. In the imagining task, the subjects reported that
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Table 2 Regions showing event-related desynchronization (ERD) in alpha (8–15 Hz), beta (15–30 Hz) and low-gamma frequency bands (30–60 Hz) Regions
Alpha: 8–15 Hz
Beta: 15–30 Hz
Low gamma: 30–60 Hz
Singing Speaking Humming Imagining Singing Speaking Humming Imagining Singing Speaking Humming Imagining – R Superior L/R Inferior L/R BA 3, 1, 2 Superior L/R Inferior L/R BA 40 R BA 43 L/R BA 7 L/R BA 9 BA 6 BA 4
– L/R L/R L/R L L/R L L/R R
R R R L/R R L/R L/R –
– – L – – – – – –
L/R L/R L/R L/R L/R L/R L/R L L/R
L/R L/R L/R L/R L/R L/R L/R L –
L/R L/R – L/R – L/R L/R –
– L R L – L – – –
– L/R L/R
– R L/R
L/R – – – –
L – – – –
R R R R – – – – –
– – – – – – – – –
BA: Brodmann's area.
they imagined their singing as instructed, although there was no objective evidence in the sound or an EMG response. Magnetic oscillations in the singing, speaking, humming and imagining conditions The task stage in each condition resulted in multiple eventrelated desynchronization (ERD) of rhythmic brain activity at 8–15 Hz, 15–30 Hz and 30–60 Hz frequency bands. The ERD was denoted as the suppression of rhythmic brain activity during vocalizing or imagining tasks as compared with the rebound after the tasks. Table 2 summarizes the cortical distribution of significant ERDs (p < 0.05) obtained by the SAM-permutation test (Chau et al., 2002, 2004) for all subjects comprising the ERDs in each frequency band in any vocalizing and imagining conditions. We obtained the following topographic results: In the alpha frequency band (8–15 Hz), ERD was bilaterally confirmed in the middle frontal region, pre- and post-central regions (premotor, sensorimotor and secondary somatosensory areas), inferior parietal lobe-operculum and superior parietal cortex in the singing, speaking and humming conditions (Fig. 2). The ERD of the
sensorimotor cortex was separated into superior and inferior parts. During the imagining condition, slight ERD was obtained in the superior part of the left sensorimotor cortex. Vocalizing ERDs were found bilaterally in the inferior primary somatosensory cortex (BA 3, 1, 2) and the inferior primary motor cortex (BA 4), they extended to bilateral primary gustatory area (BA 43). In the superior side, singing ERD was found bilaterally although the other ones were found in left hemisphere for speaking and in right hemisphere for humming. The secondary sensorimotor cortex in right hemisphere (BA 40) showed ERD for singing and the left secondary sensorimotor cortex showed ERD for speaking. In addition, bilateral ERD in the sensorimotor integration areas (BA 7) was obtained in the singing condition but not in the other conditions. Comparing the singing condition with the speaking or humming condition, the cortical distribution of alpha-ERD was quite different. In the right sensorimotor cortex and the left parietal cortex (Fig. 3), it was significantly larger in the singing than in the speaking and humming conditions. The area showing significant difference between the singing and the speaking conditions was widespread in the right sensorimotor cortex, similar to the ERD difference between the singing and the humming conditions overlapping the same areas, but by a smaller extent.
Fig. 2. Group results for all subjects (n = 11) of relative event-related desynchronization (ERD) in alpha (8–15 Hz), beta (15–30 Hz) and low-gamma (30–60 Hz) frequency bands associated with the vocalization and imagining tasks. Voxels with grand mean pseudo t values (represented in the color bar) above the critical 0.05 threshold were considered the region of activation and the corresponding voxels were then overlaid on a normalized structural MRI.
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Fig. 3. Comparison of event-related desynchronization (ERD) between singing, speaking or humming conditions in alpha (8–15 Hz) and beta (15–30 Hz) frequency bands for all subjects (n = 11).
In the beta frequency band (15–30 Hz), the ERD was also confirmed in the premotor, primary and secondary sensorimotor cortices in the left and right hemispheres for the singing, speaking and humming conditions and even in the imagining condition (Fig. 2). Comparisons of beta-band oscillations between the singing and speaking, and between the singing and humming conditions confirmed significant differences in the sensorimotor cortex of the right hemisphere with ERD power larger in the singing condition than in the other vocalizing conditions (Fig. 3). ERD in the low-gamma frequency band (30–60 Hz) was found in two vocalization tasks, singing and speaking, in the premotor and sensorimotor cortices in both hemispheres (cf. Fig. 2). In contrast, in the humming condition, a dominant low-gamma ERD was observed in the premotor and motor areas of the right hemisphere. However, the ERD did not show any significant differences between singing and speaking conditions and between singing and humming conditions. For the imagining condition, we found no significant low-gamma ERD. Comparisons in the high-gamma frequency band (60–200 Hz) in the imagining condition showed a strongly induced ERD around the posterior inferior frontal region (Broca’s area; BA 44 and 45) (Fig. 4), but not in the singing, speaking and humming conditions. On the other hand, the event-related synchronization (ERS) of rhythmic brain activity appeared remarkably in a very deep area, almost in the brainstem in three vocalization conditions (cf. Fig. 5) that did not appear in the imagining condition. Discussion This study suggested that characteristic rhythmic-brain activities were induced by continuous vocalization (singing, speaking and humming), which was associated with cognitive neural activities
Fig. 4. Group results (n = 11) of relative event-related desynchronization (ERD) in a high-gamma (60–200 Hz) frequency band associated with imaginary singing.
familiar in human communication. We focused on the identification of cortical rhythmic changes and their cortical distribution as contributed by the control of one’s own voice key (pitch) and the respiration. The frequency-specific analysis might identify factors, melody retrieval and articulation, related to vocalization. In this study, we suggested that continuous vocalization in humans mainly caused the bilateral suppression of cortical oscillations in alpha and beta frequency bands in the sensorimotor and premotor areas. Interestingly, even imaginary singing could elicit rhythmic changes in of the left and/or right motor cortices in alpha and beta bands partly. The characteristic oscillation was added in Broca’s area only in the imagining condition. On the other hand, spatial filtering analysis might separate facial muscles’ artifacts such as undesired rhythmic activities from cortical oscillations related to real vocalization, because oscillation range of the artifact should be different to the cortical one. Since strong high-gamma ERS occurred around the brainstem in real vocalization conditions and was distributed in more caudal regions, i.e. the jaw and neck, it can be assumed to describe artifacts from face and jaw movements. Rhythmic changes and cortical distribution induced by vocalization ERDs of alpha and beta frequency bands in the premotor, sensorimotor and secondary somatosensory areas and superior parietal cortex were consistently obtained in all vocalization tasks. The attenuated and enhanced ERD power was also found in previous studies on mu-rhythm (Pfurtscheller and Aranibar, 1979; Pfurtscheller, 1981; Salmelin et al., 1995; Crone et al., 1998b; Pfurtscheller et al., 2003), who found sources of these oscillations around middle of the motor strip in a finger-tapping task. The present observation might show by reason of the motor control of vocalization-related organs, mouth, larynx, etc. Interestingly, the cortical regions displaying alpha ERD were separated into superior and inferior parts of the bilateral sensorimotor cortices, which seems consistent with the trunk and face (including larynx) areas of ‘homunculus’. The previous studies also reported these two areas as vocalization-related areas in the sensorimotor cortex (Penfield and Roberts, 1959. Salmelin et al., 1994; Wildgruber et al., 1996; McGuire et al., 1996, 1997; Hirano et al., 1997, Murphy et al., 1997; Perry et al., 1999; Gunji et al., 2000b; Sakurai et al., 2001; Blank et al., 2002; Huang et al., 2001). In this study, alpha-ERD was thought to occur by control of the facial muscles, larynx and respiration. Furthermore, the power change between during and after singing was larger in the right sensorimotor cortex and in the left parietal cortex compared with speaking and humming. When
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Fig. 5. Group results (n = 11) of relative event-related synchronization (ERS) in a high-gamma (60–200 Hz) frequency band associated with the vocalization and imaging tasks.
we sing, it is necessary to control our own voice-key and maintain the rhythm of several musical processes when compared to speaking the words of a song. Recent studies reported that the right hemisphere was dominant in musical ability (Zatorre et al., 1994; Pantev et al., 1998; Halpern and Zatorre, 1999; Hirata et al., 1999; Hashimoto et al., 2000). The results of our study indicated that singing requires fine and delicate control of the vocal cords, abdominal muscles and diaphragm, and that the right sensorimotor cortex is more dominant for singing. Processing the pitch of tone shows right hemispheric dominancy, and melody control following articulation may also be dominantly driven by the right hemisphere. In addition, the cortical area of alpha-ERD difference as measured by subtracting the speaking from the singing condition in the right sensorimotor cortex was more widely distributed than the subtraction of humming and singing. Thus, this difference of alpha-ERD distribution might be elicited by controlling the laryngeal muscle, which is necessary just for singing, although it is difficult to precisely separate the activity of the facial area (related to articulation) from that of the laryngeal area (related to melody control). On the other hand, specific ERD for singing in the parietal cortex was showing left hemispheric dominancy. Thus the cortex assume the sensorimotor integration area, the significant difference might be caused by complex operation to control vocalization-related organs, too. It is well known that the ERD accompanying movement is found for the beta frequency band (15–30 Hz) as well as for the alpha frequency band although they reflect each functional role (Salmelin et al., 1995, Crone et al., 1998b). In this study, the cortical distribution of beta-ERD spreads from the superior to the inferior part of the sensorimotor cortex without interruption and was also found in the premotor area and the parietal cortex during singing, speaking and humming conditions (cf. Fig. 3). These regions were consistent with cortical areas related to vocalization and tongue movement reported in the previous studies (Penfield and Roberts, 1959; Salmelin and Sams, 2002; Salmelin et al., 1994, 1995, 2000; Wildgruber et al., 1996; McGuire et al., 1996, 1997; Hirano et al., 1997, Murphy et al., 1997; Crone et al., 1998b; Perry et al., 1999; Gunji et al., 2000b; Nakasato et al., 2001; Sakurai et al., 2001; Blank
et al., 2002; Huang et al., 2002; Pfurtscheller et al., 2003). Some have reported beta-oscillations in the left and right sensorimotor cortices, as found in this study (Salmelin and Sams, 2002; Salmelin et al., 1995, 2000; Crone et al., 1998b; Pfurtscheller et al., 2003). Considering the obtained ERD distribution, our results are suggested to represent the voluntary control of facial muscles, larynx and respiration in vocalization. As the ERD in the facial area of the sensorimotor cortex in the right hemisphere in the singing condition was significantly larger than in the speaking and the humming conditions, this might indicate an inter-hemispheric difference caused by the combination of articulation with the processing of a musical action. The left sensorimotor area may be dominantly activated in vocalization tasks involving the language process (Deecke et al., 1986), although this study did not investigate this issue. A previous neuroimaging study assumed that the movement related to utterance was controlled bilaterally but hemispheric dominancy in the sensorimotor area was hardly described (Perry et al., 1999). We hypothesized that the activity of the sensorimotor area for music-related action was more dominant in the right hemisphere. Such hemispheric dominancy may be found particularly in the human brain, since we probably execute melody and language separately. In addition, a very small superior-middle part of the right sensorimotor cortex showed enhanced ERD related to singing compared with speaking. This region was thought to include the truncal area of the sensorimotor cortex. Therefore, this result suggested that voluntary control of respiration might be required more for singing than for speaking. The ERD in the low-gamma frequency band was found in the superior (truncal) part of the left and right sensorimotor cortices, whereas alpha- and beta-ERD was identified in superior and inferior parts. The low-gamma band distribution in our results is in line with previous reports, in which gamma-band activities correlated to areas showing alpha- and beta-band activities (Crone et al., 1998a; Mima et al., 1999; Kopell et al., 2000). However, most were usually described as ERS during motor tasks, suggesting that cortical synchronization of low gamma-band activity might be interpreted by the following: If the ERD in our study consist of that rhythmic brain activity during vocalization tasks and subsequent rebound, the
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activity during vocalization tasks in the low gamma band might be smaller than after vocalization. The ERD difference between singing and humming conditions was localized in the left superior part of the sensorimotor cortex. If the area is in truncal area of ‘homunculus’, the result supposes that subjects are required much resonance to control their respiration following articulated vocalization in the singing condition, while the humming condition had to control only respiration. Rhythmic changes in the sensorimotor cortex induced by imagining a song Rhythmic brain activities in alpha- and beta-frequency bands were found in the sensorimotor cortices during singing task while some of the cortices (alpha: superior part of motor cortex in left hemisphere; beta: inferior part of sensorimotor cortex in left hemisphere, superior part of motor cortex in right hemisphere) were changed the oscillation power even by imagining a song unaccompanied by movement or sound. Recent EEG studies also reported that the topography of alpha and beta oscillations while imagining hand movement was distributed close to the handrepresentation area as identified by execution of the same movement (Pfurtscheller and Neuper, 1997; Pfurtscheller et al., 1999; McFarland et al., 2000). In addition, some previous PET studies revealed that activity in the sensorimotor cortex corresponds with finger and tongue movements during motor imagery (Lotze et al., 1999; Porro et al., 1996, 2000; Hanakawa et al., 2003, Ehrsson et al., 2003). Thus, imaginary singing might also activate the vocalization-related area of the sensorimotor cortex partly. Movement imagery can be applied to the motor system and is often used to help train or learn suitable movement. Although our findings did not clarify whether neural activities take place between executing and imagining movements, they would indicate that oscillatory representation caused by imaginary singing corresponds to actual singing. Rhythmic changes in Broca’s area Broca’s area has been considered one of the most important language-related cortices. Many neuroimaging studies have investigated the relationship between Broca’s area and the processes of speech planning involving articulation and mouth movement. In this study, we could recognize significant ERD in the high-gamma band in Broca’s area in only the imagining condition. Surprisingly, Broca’s area showed no remarkable oscillations in any other frequency bands or in any other vocalizing conditions in this study, whereas many studies were showing specific oscillation changes, 20-Hz rhythm, related to naming aloud and mouth movement there (Salmelin and Sams, 2002; Salmelin et al., 1995). We could ask the question: why were there no significant cortical oscillations in Broca’s area during the vocalization conditions? Previous PET studies showed no remarkable activity in Broca’s area when subjects were repeating a fixed sentence (Murphy et al., 1997; Wildgruber et al., 1996). Similarly, since our study used “the repeated vocalization of a simple and familiar sentence” as a task, cortical oscillations might not be significantly identified in Broca’s area. Furthermore, many neuroimaging studies provided that Broca’s area processed imitation, imagery and perception of action (Binkofski et al., 2000; Nishitani et al., 2005 review) and working-memory contents (Smith and Jonides, 1998, 1999).
Gerardin et al. (2000) noted that Broca’s area was remarkably activated for imagination compared with execution of handaction. Thus, the area supposes to comprise rehearsal process and short-term maintenance of body-movements including speech production. In addition, rhythm processing during piano playing was also associated with Broca’s area especially, which might help to understand cortical functions during covert singing. Our findings recognized the processes as specific oscillations in Broca’s area. In conclusion, the present study can be summarized as follows: (1) The rhythmic brain activities associated with continuous vocalization were suppressed during vocalization and subsequent strong rebound was identified after vocalization; (2) Imaginary singing could elicit rhythmic changes in the left premotor and left or right sensorimotor cortices partly which were also activated in the vocalization conditions; (3) The rhythmic change in singing was more remarkable in the right motor cortex compared with the other vocalization conditions, indicating hemispheric dominancy in the cortical regions corresponding to melody generation and motor control of singing. These findings may be found especially in the human brain since we probably perform a cognitive role for music and language, respectively. Thus, musical output induces specific activities in the motor cortices continuously which may be added to processes reflected by simple vocalization and speech. Acknowledgments This study was supported by the CIHR, the Ontario Innovation Trust and the Canadian Foundation for Innovation, and JSPS, Grants-in-Aid for JSPS Fellows (1507335, 1661602) and a Grantin-Aid for Young Scientists (B) (17700324). We are grateful to Dr. Sachiko Koyama, Dr. Hidehiko Okamoto and Mr. Wollbrink for their suggestions and technical assistance. References Binkofski, F., Amunts, K., Stephan, K.M., Posse, S., Schormann, T., Freund, H.J., Zilles, K., Seitz, R.J., 2000. Broca’s region subserves imagery of motion: a combined cytoarchitectonic and f MRI study. Hum. Brain Mapp. 11, 273–285. Blank, S.C., Scott, S.K., Murphy, K., Warburton, E., Wise, R.J., 2002. Speech production: Wernicke, Broca and beyond. Brain 125, 1829–1838. Brooker, B.H., Donald, M.W., 1980. Contribution of the speech musculature to apparent human EEG asymmetries prior to vocalization. Brain Lang. 9, 226–245. Chau, W., Ishii, R., Ross, B., McIntosh, A.R., Pantev, C., 2002. Group analysis for the synthetic aperture magnetometry (SAM) data. In: Nowak, H., Haueisen, J., Giessler, F., Hounker, R. (Eds.), Proceedings of the 13th International Conference on Biomagnetism. VDE Verlag, Berlin, pp. 1009–1011. Chau, W., McIntosh, A.R., Robinson, S.E., Schulz, M., Pantev, C., 2004. Improving permutation test power for group analysis of spatially filtered MEG data. NeuroImage 23, 983–996. Crone, N.E., Miglioretti, D.L., Gordon, B., Lesser, R.P., 1998a. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis: II. Event-related synchronization in the gamma band. Brain 121, 2301–2315. Crone, N.E., Miglioretti, D.L., Gordon, B., Sieracki, J.M., Wilson, M.T., Uematsu, S., Lesser, R.P., 1998b. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis: I. Alpha and beta event-related desynchronization. Brain 121, 2271–2299. Curio, G., Neuloh, G., Numminen, J., Jousmaki, V., Hari, R., 2000.
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