Edited by: Claude Alain, Rotman Research Institute (RRI), Canada
Reviewed by: Boris Kleber, Aarhus University, Denmark; Dan Zhang, Tsinghua University, China
*Correspondence: Dezhong Yao
This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience
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The multiple-demand (MD) system has proven to be associated with creating structured mental programs in comprehensive behaviors, but the functional mechanisms of this system have not been clarified in the musical domain. In this study, we explored the hypothesis that the MD system is involved in a comprehensive music-related behavior known as musical improvisation. Under a functional magnetic resonance imaging (fMRI) paradigm, 29 composers were recruited to improvise melodies through visual imagery tasks according to familiar and unfamiliar cues. We found that the main regions of the MD system were significantly activated during both musical improvisation conditions. However, only a greater involvement of the intraparietal sulcus (IPS) within the MD system was shown when improvising with unfamiliar cues. Our results revealed that the MD system strongly participated in musical improvisation through processing the novelty of melodies, working memory, and attention. In particular, improvising with unfamiliar cues required more musical transposition manipulations. Moreover, both functional and structural analyses indicated evidence of neuroplasticity in MD regions that could be associated with musical improvisation training. These findings can help unveil the functional mechanisms of the MD system in musical cognition, as well as improve our understanding of musical improvisation.
The understanding of the mechanisms of complex tasks is far from clear. A major difficulty in the investigation of complex actions is decomposing the components responsible for different aspects of a behavior (Coffey and Herholz,
The MD system consists of several areas in the prefrontal and parietal regions, including the posterior part of the inferior frontal sulcus (IFS), the anterior insula and adjacent frontal operculum (AI/FO), the presupplementary motor area and adjacent dorsal anterior cingulate (pre-SMA/ACC), and the intraparietal sulcus (IPS). Occasionally, activity can also be seen in the rostrolateral prefrontal cortex (RPFC) (Duncan,
Music is a universal human activity involving perceptually discrete elements organized into hierarchically structured sequences (Patel,
Until now, studies on musical improvisation mainly assessed the role of the frontal regions. A functional magnetic resonance imaging (fMRI) study found that the dorsal premotor area, the rostral cingulate region and the inferior frontal gyrus are recruited for the invention of novel motor sequences in musical improvisation (Berkowitz and Ansari,
Here, we used fMRI to study neural activity during imagery improvisations based on two different cues. One was a familiar cue, which was mainly considered to be involve working memory. The other was an unfamiliar cue, which was thought to be highly involved in creative novelty. We recruited 29 composers who had systematic knowledge of how to conceive a novel piece of music as our participants. General linear model (GLM) analysis was conducted to investigate the neural activity involved in improvisation under different conditions (Woolrich et al.,
Twenty-nine composers (14 males, aged 18–23 years) selected through a musical background questionnaire from the Department of Composition at Sichuan Conservatory of Music participated in the experiment. All composers had experience playing piano, which was regarded as the fundamental skill for studying musical improvisation. They all had training in musical improvisation for at least three years. All participants passed the MIL exam, which is considered an objective assessment of improvisation level. Scores of the exam were decided by the committee consisting of ten professors from the Department of Composition at Sichuan Conservatory of Music. Thirty-one non-musicians without a musical training background from the University of Electronic Science and Technology of China were recruited as the control group. Participants were all right-handed according to the Edinburgh Inventory (Oldfield,
Firstly, we need to clarify the use of this phrase “improvisation.” It is commonly accepted that the notions of composition and improvisation in making music are almost overlapped. Thus, we adopted “improvisation” in our manuscript to be in consistent with previous studies.
The familiar vs. unfamiliar design was widely used in musical-related research (Halpern and Zatorre,
Illustration of the trials of all conditions in the experimental paradigm. For each trial, participants were asked to imagine improvising a melody piece according to different cues after a cross was shown for 2 s. The
The tasks were designed and presented with E-prime 2.0 software. For each trial, participants were asked to imagine improvising a melody piece according to different cues after a cross was shown for 2 s. The
First, each participant performed a behavioral pilot on a computer outside the magnetic resonance imaging (MRI) scanner. Participants were instructed to follow the instructions on the screen, perform imagery improvisation and complete the evaluation by pressing the keyboard with their right hand. After the pilot, an interview was conducted to confirm familiarity with the paradigm and the ability to imagine improvisations. Thus, we ensured the eligibility of participants for MRI scanning.
Images were acquired on a 3T magnetic resonance imaging (MRI) scanner (GE Discovery MR750, USA) at the MRI Research Center of UESTC using a standard GE whole head coil.
During scanning, we used foam padding and ear plugs to reduce head motion and scanning noise, respectively. For the group of composers, the task fMRI scanning was conducted with the same paradigm as the pilot. Importantly, participants were asked to follow the instructions on the screen and to move as little as possible when pressing a button on the keyboard. Functional images were acquired using echo-planar imaging (EPI) sequences, and the parameters of both resting-state and task scanning with an eight-channel phased array head coil were as follows: repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, matrix = 64 × 64, field of view (FOV) = 240 × 240 mm, and slice thickness = 4 mm (with a gap of 0.4 mm). The first five volumes were discarded due to magnetization equilibrium. During the first and second functional image runs, anatomical T1-weighted images were recorded between the first acquired using a 3-dimensional fast spoiled gradient echo (T1-3D FSPGR) sequence [TR = 5.948 ms, TE = 1.964 ms, FA = 9°, matrix = 256 × 256, FOV = 204 × 163 mm, slice thickness 1 mm (no gap), 154 slices]. For the control group, only anatomical T1-weighted images were collected, with the same parameters above.
The mean value and the standard deviation (SD) of the evaluation scores by each composer in an fMRI session were calculated to assess their improvisation status during scanning. Additional demographic properties such as age, years of musical improvisation training and MIL scores were also analyzed by statistical methods.
fMRI data were preprocessed using the SPM8 software package (statistical parametric mapping,
We conducted the standard second-level analysis embedded in the SPM software. Three main contrasts were specified per single-participant analysis: (1)
To investigate the participation of the MD system in musical improvisation, we defined six 6-mm radius spherical ROIs based on Duncan's report (Duncan,
To study the relationship between functional imaging and levels of improvisation variables, we chose regions that showed significant changes and calculated the average value of the regression coefficients as a
Based on the results of abovementioned analysis of ROIs, the IPS was found have a higher activation under the
We are curious about whether long-term training on musical improvisation can affect the structure of brain, thus we did the structural covariance analysis.
For both composers and controls, T1-weighted images were processed by the CIVET pipeline (version 2.0) developed at the Montreal Neurological Institute (Ad-Dab'bagh et al.,
We calculated structural covariance by correlating the cortical thickness of each seed (IPS, IFS, AI/FO, RPFC, preSMA, ACC) with the thickness of all other surface points of the entire cortex in the composer group and the control group (Suh et al.,
The mean value and the standard deviation (SD) of the evaluation scores by each composer in the fMRI session are shown in Figure
The evaluation score by each participant in the fMRI session.
Demographics of all participants.
Age (years) | 19.79 ± 1.45 | 20.16 ± 2.38 |
Gender | 14 males/15 females | 18 males/13 females |
Years of improvisation training | 3.36 ± 0.67 | – |
MIL score | 79.37 ± 4.97 | – |
We compared the activation maps across different improvisation conditions and baseline to assess basic task-related activation.
Compared with
Activation under the
Supplementary motor area | L | 6 | −3 | 3 | 69 | 11.77 | 13,999 |
Supplementary motor area | R | 6 | 1 | 3 | 69 | 8.61 | |
Precentral gyrus | L | 6 | −48 | −3 | 51 | 8.54 | |
Precentral gyrus | R | 6 | 54 | 0 | 48 | 5.45 | |
Postcentral gyrus | L | 6 | −60 | 0 | 16 | 4.21 | |
Inferior parietal lobule | L | 40 | −39 | −45 | 42 | 5.79 | |
Inferior parietal lobule | R | 40 | 48 | −39 | 48 | 4.04 | |
Superior parietal lobule | L | 7 | −15 | −75 | 51 | 7.72 | |
Superior parietal lobule | R | 7 | 21 | −70 | 51 | 5.11 | |
Inferior frontal gyrus | L | 44 | −54 | 9 | 18 | 6.70 | |
Inferior frontal gyrus | R | 9 | 63 | 15 | 30 | 3.53 | |
Superior frontal gyrus | L | 6 | −21 | 3 | 65 | 4.71 | |
Superior frontal gyrus | R | 6 | 24 | 0 | 54 | 4.54 | |
Middle frontal gyrus | L | 6 | −27 | 5 | 57 | 3.73 | |
Middle frontal gyrus | R | 6 | 32 | 1 | 58 | 3.34 | |
Middle occipital gyrus | L | 7 | −27 | −66 | 39 | 5.72 | |
Middle occipital gyrus | R | 39 | 30 | −63 | 36 | 5.34 | |
Superior occipital gyrus | L | 7 | −23 | −74 | 39 | 4.57 | |
Superior occipital gyrus | R | 7 | 26 | −68 | 39 | 4.15 | |
Superior temporal gyrus | L | 22 | −52 | 12 | −3 | 6.50 | |
Superior temporal gyrus | R | 42 | 60 | −33 | 12 | 3.45 | 118 |
Activated regions under different contrasts.
Activation under the
Supplementary motor area | L | 6 | −3 | 3 | 69 | 9.65 | 17,337 |
Supplementary motor area | R | 6 | 2 | 3 | 69 | 7.22 | |
Precentral gyrus | L | 6 | −48 | 0 | 54 | 8.89 | |
Precentral gyrus | R | 6 | 57 | 6 | 45 | 5.50 | |
Postcentral gyrus | L | 6 | −56 | −1 | 41 | 5.71 | |
Inferior parietal lobule | L | 40 | −39 | −45 | 42 | 7.17 | |
Inferior parietal lobule | R | 40 | 36 | −49 | 42 | 4.08 | |
Superior parietal lobule | L | 7 | −21 | −72 | 48 | 7.50 | |
Superior parietal lobule | R | 7 | 22 | −68 | 55 | 6.32 | |
Inferior frontal gyrus | L | 44 | −51 | 9 | 18 | 7.63 | |
Inferior frontal gyrus | R | 44 | 50 | 12 | 18 | 3.65 | |
Superior frontal gyrus | L | 6 | −24 | −2 | 65 | 6.04 | |
Superior frontal gyrus | R | 6 | 22 | 6 | 55 | 3.80 | |
Middle frontal gyrus | L | 6 | −30 | 4 | 55 | 4.89 | |
Middle frontal gyrus | R | 6 | 33 | 3 | 58 | 4.40 | |
Middle occipital gyrus | L | 19 | −30 | −71 | 40 | 4.95 | |
Middle occipital gyrus | R | 19 | 33 | −73 | 40 | 5.04 | |
Superior occipital gyrus | L | 7 | −18 | −76 | 41 | 5.48 | |
Superior occipital gyrus | R | 7 | 26 | −73 | 41 | 5.59 | |
Superior temporal gyrus | L | 22 | −53 | 13 | −8 | 5.15 |
Afterwards, the contrast between
Contrast between
Precentral gyrus | L | 9 | −54 | 10 | 36 | 4.43 | 142 |
Inferior parietal lobule | L | 40 | −42 | −39 | 45 | 3.70 | 23 |
Inferior parietal lobule | R | 40 | 32 | −52 | 44 | 3.77 | |
Superior parietal lobule | L | 7 | −23 | −62 | 44 | 3.72 | |
Superior parietal lobule | R | 7 | 26 | −65 | 51 | 4.67 | |
Inferior frontal gyrus | L | 9 | −57 | 12 | 27 | 3.55 | |
Superior frontal gyrus | L | 6 | −23 | −3 | 53 | 4.28 | 96 |
Inferior occipital gyrus | L | 18 | −33 | −84 | −4 | 4.49 | |
Inferior occipital gyrus | R | 18 | 32 | −86 | −3 | 5.97 | |
Middle occipital gyrus | L | 18 | −35 | −87 | −3 | 5.38 | |
Middle occipital gyrus | R | 19 | 31 | −86 | 4 | 4.50 | |
Superior occipital gyrus | L | 17 | −14 | −92 | 3 | 5.06 | |
Superior occipital gyrus | R | 7 | 27 | −67 | 42 | 5.35 | |
Superior temporal gyrus | R | 42 | 57 | −29 | 17 | −3.97 | 41 |
We compared six ROIs between the different conditions using one sample
The comparisons of six ROIs between different conditions (FDR-corrected,
Unfamiliar-baseline | ||||||
Familiar-baseline | ||||||
Unfamiliar-familiar | ||||||
Linear partial correlation coefficients (
We calculated linear partial correlation coefficients between the average
The results for the functional connectivity between the left IPS and the whole brain are shown in Table
Results of functional connectivity assessments based on the seed of the left IPS.
Lingual_L/Occipital_Sup_L | L | 19 | −9 | −87 | 45 | 5.01 | 3,788 |
Occipital_Mid_R | R | 19 | 36 | −78 | 18 | 2.67 | 61 |
Supp_Motor_Area_L | L | 6 | −6 | 0 | 66 | 2.63 | 34 |
Angular_R | R | 39 | 48 | −54 | 36 | −3.96 | 301 |
Frontal_Sup_Medial_R | R | 6 | 51 | 51 | −3.16 | 199 | |
Temporal_Inf_R | R | 20 | 51 | 12 | −36 | −3.49 | 133 |
Frontal_Mid_R | R | 42 | 9 | 60 | −3.10 | 107 | |
Frontal_Inf_Tri_L | L | 44 | −57 | 15 | 18 | −3.27 | 96 |
Temporal_Inf_L | L | 20 | −42 | −3 | −33 | −3.66 | 94 |
Frontal_Inf_Orb_R | R | 38 | 42 | 27 | −24 | −3.44 | 74 |
Functional connectivity based on the seed of the left IPS (FDR-corrected
Results of the functional connectivity assessments based on the seed of the right IPS.
Occipital_Sup_L/Lingual_L | L | 18 | −12 | −66 | −15 | 3.66 | 2,327 |
Precuneus_R/Parietal_Sup_R | R | 27 | −39 | 42 | 4.85 | 1,048 | |
Temporal_Mid_L | L | 21 | −45 | −42 | 0 | 3.82 | 448 |
Putamen_R | R | 30 | −9 | −3 | 2.93 | 250 | |
Putamen_L | L | −18 | 9 | 3 | 2.93 | 225 | |
Frontal_Mid_L | L | 45 | −48 | 45 | 15 | 3.46 | 107 |
Temporal_Pole_Sup_R | R | 21 | 63 | 6 | −9 | 3.10 | 105 |
Frontal_Inf_Orb_L | L | 47 | −30 | 36 | −9 | 2.81 | 56 |
Supp_Motor_Area_L | L | 6 | −3 | 0 | 69 | 2.75 | 29 |
Functional connectivity based on the seed of the right IPS (FDR-corrected
The structural covariance results of the composer group and the control group are shown in Figure
Because improvisation with familiar cues involves more working memories of music, and unfamiliar cues involve more creative novelty, we used these stimuli to examine the specific brain regions involved in each condition of musical improvisation. The results show that the involvement of the MD system can be found during improvisation with both familiar and unfamiliar cues. Combined with previous findings, we can infer that the main role of the MD system is dealing with the novelty of a task while participating in working memory and attentional control during musical improvisation.
First, musical improvisation needs the creative competency of novelty (Gross and Seashore,
Second, the activation of the MD system in both conditions could also be explained by the function of working memory during improvisation. Improvisation needs a rich musical background including musical appreciation, knowledge of theory, and performance experience (Gross and Seashore,
Responses given by the participants illustrated that they attentively improvised music; thus, the involvement of the MD system could also be interpreted by the requirements of attention. Activation of the dorsolateral superior frontal gyrus, which is part of the MD system, is involved in attentional control (Desimone and Duncan,
In addition, we noticed that the IPS within the MD system had a higher activation under the
Finally, the influence of the experimental tasks should also be addressed. In our study, we used mental imagery tasks to simulate improvisational activities due to the lack of an MRI-compatible keyboard. However, previous studies have shown the involvement of some parts of the MD system (such as the SMA and IPS) during imagery tasks (Herholz et al.,
Our functional results showed that some areas within the MD system were positively correlated with the level of musical improvisation. These areas included the left precentral gyrus under the
The auditory cortex showed activation in all conditions and a significantly stronger activation when improvising with familiar cues. First, these results support the notion that auditory regions can be activated with music-related tasks via auditory-motor interactions regardless of auditory stimuli (Zatorre et al.,
To our knowledge, auditory cortices can be activated whether or not there are real motor activities when referring to musical activities (Halpern and Zatorre,
In this study, we provided evidence that the MD system strongly participated in musical improvisation. Our results suggested that musical improvisation was an activity with complex demands in which the MD system mainly contributed to the novelty of melodies, working memory, and attentional control. In particular, the higher IPS recruitment indicated that musical transposition manipulation was highly involved in improvising unfamiliar melodies. Both functional and structural analyses indicated evidence of neuroplasticity in MD regions that could be associated with musical improvisation training. These findings can help unveil the functional mechanisms of the MD system in musical cognition, as well as improve our understanding musical improvisation.
Nevertheless, this study still has some limitations that should be addressed here. Firstly, although strong evidence has been found that the MD system is involved in musical improvisation, a longitudinal study is still needed in the future. Secondly, in order to do a better manipulation check, an MRI-compatible keyboard for recording realtime music is indispensable in the following study. Besides, since we infer that MD system participated in musical improvisation through processing the novelty of melodies, working memory, and attention, it is also necessary to inspect how these functions interact with each other within the MD system. Moreover, improvisation is highly dependent on musical genre, which means that differences between improvising in a classical style and improvising in a jazz style should also be investigated. At last, investigations on how the auditory cortex relates to the MD system should be completed as well, as this could help find other cognitive components of musical improvisation.
JL, HY, AE, and DY: Substantial contributions to the conception or design of the work; JL, HY, and CH: Data acquisition; JL, HH, and SJ: Data analysis; JL, and DY: Drafting the work and revising it critically for important intellectual content; JL, AE, and DY: Final approval of the version to be published.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank Dr. Robert Zatorre for insightful comments on this work. We thank Yi Du, Emily Coffey, Gleb Bezgin, Sisi Jiang, and Cheng Luo for helpful discussion. We also thank all the participants who joined in the study. This study was supported by grants from the National Natural Science Foundation of China (No. 31600798, 91232725, 81330032) and the 111 Project from Ministry of Education of the People's Republic of China (B12027).