fNIRS Responses in Professional Violinists While Playing Duets: Evidence for Distinct Leader and Follower Roles at the Brain Level

Abstract

Music played in ensembles is a naturalistic model to study joint action and leader-follower relationships. Recently, the investigation of the brain underpinnings of joint musical actions has gained attention; however, the cerebral correlates underlying the roles of leader and follower in music performance remain elusive. The present study addressed this question by simultaneously measuring the hemodynamic correlates of functional neural activity elicited during naturalistic violin duet performance using fNIRS. Findings revealed distinct patterns of functional brain activation when musicians played the Violin 2 (follower) than the Violin 1 part (leader) in duets, both compared to solo performance. More specifically, results indicated that musicians playing the Violin 2 part had greater oxy-Hb activation in temporo-parietal (p = 0.02) and somatomotor (p = 0.04) regions during the duo condition in relation to the solo. On the other hand, there were no significant differences in the activation of these areas between duo/solo conditions during the execution of the Violin 1 part (p’s > 0.05). These findings suggest that ensemble cohesion during a musical performance may impose particular demands when musicians play the follower position, especially in brain areas associated with the processing of dynamic social information and motor simulation. This study is the first to use fNIRS hyperscanning technology to simultaneously measure the brain activity of two musicians during naturalistic music ensemble performance, opening new avenues for the investigation of brain correlates underlying joint musical actions with multiple subjects in a naturalistic environment.

Capturing Human Interaction in the Virtual Age: A Perspective on the Future of fNIRS Hyperscanning

“…As shown in Figure 1 , the distribution of hyperscan conditions dedicated to each of the nine categories defined by our framework is highly unequal. Over half of all reported hyperscan conditions (57.8%, N = 87) were conducted when the interacting dyad were in the same room without any means of digital interaction (i.e., Analog ToI) (Funane et al, 2011 ; Holper et al, 2012 , 2013 ; Jiang et al, 2012 , 2015 ; Osaka et al, 2014 , 2015 ; Duan et al, 2015 ; Liu N et al, 2016 ; Nozawa et al, 2016 , 2019 ; Hirsch et al, 2017 , 2018 ; Ikeda et al, 2017 ; Zhang et al, 2017a , b ; Zhao et al, 2017 ; Dai et al, 2018a , b ; Fishburn et al, 2018 ; Pan et al, 2018 , 2020a ; Xue et al, 2018 ; Zhang Y et al, 2018 ; Lu et al, 2019 , 2020 ; Mayseless et al, 2019 ; Niu et al, 2019 ; Vanzella et al, 2019 ; Noah et al, 2020 ), while (42.4%, N = 64) included some element of technology (e.g., playing a computer game) while participants were in the same room (Cui et al, 2012 ; Dommer et al, 2012 ; Duan et al, 2013 ; Cheng et al, 2015 , 2019 ; Liu T et al, 2015 , 2016 , 2017 ; Baker et al, 2016 ; Tang et al, 2016 ; Balconi and Vanutelli, 2017a , b ; Hu et al, 2017 ; Pan et al, 2017 , 2020b ; Piva et al, 2017 ; Takeuchi et al, 2017 ; Fishburn et al, 2018 ; Zhang M et al, 2018 ; Zheng et al, 2018 , 2020 ; Balconi et al, 2019 ; Liu et al, 2019 ; Nozawa et al, 2019 ; Sarinasadat et al, 2019a …”

“…Only 3.3% of all scans ( N = 5) focused on inter-brain synchrony during open-ended communication (Liu N et al, 2016 ; Ikeda et al, 2017 ; Fishburn et al, 2018 ; Zhang Y et al, 2018 ; Yang et al, 2020 ), whereas 77.5% ( N = 117) focused on Joint goal-driven interactions. The remaining 19.2% ( N = 29) tasks contained elements of both communication types (Cui et al, 2012 ; Jiang et al, 2012 ; Holper et al, 2013 ; Osaka et al, 2014 , 2015 ; Cheng et al, 2015 ; Liu T et al, 2015 , 2016 , 2017 ; Fishburn et al, 2018 ; Hirsch et al, 2018 ; Niu et al, 2019 ; Vanzella et al, 2019 ).…”

“…However, the classification of the comparisons reported were limited to Analog vs. Mixed and Inter-ToI comparisons. The studies comprised comparisons between Mixed ToI/Joint goal-driven ToC and Mixed ToI/Mixed ToC ( N = 5) (Cui et al, 2012 ; Cheng et al, 2015 ; Liu T et al, 2015 , 2016 , 2017 ), Analog ToI/Mixed ToC and Analog ToI/Joint goal-driven ToC ( N = 7) (Jiang et al, 2012 ; Holper et al, 2013 ; Osaka et al, 2014 , 2015 ; Hirsch et al, 2018 ; Niu et al, 2019 ; Vanzella et al, 2019 ), Analog ToI/Joint open-ended ToC and Analog ToI/Joint goal-driven ToC ( N = 3) (Liu N et al, 2016 ; Ikeda et al, 2017 ; Zhang Y et al, 2018 ), and Analog ToI/Mixed ToC and Mixed ToI/Joint goal-driven ToC ( N = 2) (Nozawa et al, 2019 ; Noah et al, 2020 ). Two studies included three cross-condition comparisons, including comparisons between Analog ToI/Mixed ToC, Analog ToI/Joint goal-driven ToC, and Mixed ToI/Joint open-ended ToC ( N = 1) (Fishburn et al, 2018 ), as well as between Analog ToI/Joint goal-driven ToC, Analog ToI/Joint open-ended ToC, and Mixed ToI/Joint goal-driven ToC ( N = 1) (Yang et al, 2020 ).…”

<p>Shedding Light On The Human Auditory Cortex: A Review Of The Advances In Near Infrared Spectroscopy (NIRS)</p>

“…Hyperscanning is a technique whereby the brain activity of two individual's is recorded in unison as they complete a task or are exposed to particular stimuli. The first fNIRS study using this technique is believed to be Funane et al, 95 and its current popularity stems from an influx of work around social interaction (for a review see Koike, Tanabe & Sadato), 96 including research into cooperation, 97 sensorimotor synchronization, 98 leader-follower relationships during music performance 99 and communication. 100 However, as of yet no research has explored this technique when exploring the auditory cortex.…”

Optimization of wavelet coherence analysis as a measure of neural synchrony during hyperscanning using functional near-infrared spectroscopy

“…In particular, cross-brain neural synchrony measured with wavelet coherence analysis has been applied to investigate interactive behaviors, such as cooperative and competitive gameplay, 2-4 synchronized finger tapping, 5 unstructured conversation, 6 dyadic singing and humming, 7 button-pressing, 8 creative problem solving, 9 face-to-face interaction, 10 structured talking and listening, 11 playing poker against a human or computer opponent, 12 judging intentions and fairness in economic exchanges, 13 and following and leading. 14,15 Although the wavelet coherence computations have been applied previously in these and other applications, the computational factors that affect the power of the analysis have not been explored for fNIRS signals. Here we use a method of actual acquired signals with a known wavelet coherence in order to determine optimal approaches for wavelet coherence analysis applied specifically to fNIRS data.…”