The Trail-Making Test is usually used to measure executive functions, and neuroimaging and lesion studies have identified that TMT recruits large-scale fronto-parietal brain networks related to these functions (Varjacic et al., 2018). However, there is evidence that performance in the TMT is related primarily to processing speed and working memory ability, as well as fluid intelligence (Sánchez-Cubillo et al., 2009; Satterthwaite et al., 2013). These findings are supported by evidence of genetic correlations between trail-making performance, reasoning ability and general cognitive ability, processing speed, and memory (Hagenaars et al., 2018). Research has also found differences between the cognitive processes underlying TMT-A and B performance. TMT-A is thought to rely mainly upon processing speed, and TMT-B to additionally require working memory and switching ability (Arbuthnott and Frank, 2000; Sánchez-Cubillo et al., 2009). According to a validation study of a computerized version of the TMT, TMT-B performance was explained to a large degree by inhibition and visual working memory skills (Fellows et al., 2017). Similar results were obtained in a factor analysis of TMT performance and several other neurocognitive measures in older individuals, where TMT-B performance was connected to measures of working memory and inhibition, and TMT-A to processing speed (Llinàs-Reglà et al., 2017).

There is also significant overlap between the cognitive processes that the TMT recruits. For instance, working memory skills and working memory capacity are tightly related to fluid intelligence (Kane et al., 2005; Kail, 2007; Demetriou et al., 2014; Salthouse, 2014; Heinzel et al., 2016). It has also been found that working memory predicts switching (Blackwell et al., 2009), presumably through supporting the maintenance of switching rules. Inhibitory control, in turn, may have a role in supporting working memory maintenance (Jonides et al., 1998; Zanto and Gazzaley, 2009; Getzmann et al., 2018).

In behavioral studies, DS backwards performance has been found to predict TMT-B performance, suggesting a partial overlap between the cognitive requirements of these tasks (Sánchez-Cubillo et al., 2009). Both DS backwards and TMT-B engage cognitive control more than DS forwards and TMT-A, but there are also obvious differences between test requirements. TMT-B requires switching attention from one rule and sequence of information in memory to another (letters or numbers). It also requires continuous updating of information about the respondent's position along the series of letters or numbers they are connecting. DS backwards requires recoding a string of digits in mind into reverse order, or updating the representation of the acquired information, but does not require switching between rules or response patterns during responding. The TMT-A and B also engage specifically working memory maintenance, by requiring the participant to keep the response rule and progression along the sequence of letters and numbers in mind. As in this study, we observed a musician advantage in DS forwards but not backwards, and both the TMT-A and B, our results point towards enhancement of skills that are required by these tests but not by the DS backwards test. These include working memory maintenance for DS forwards, as well as TMT-A and B. TMT-B also requires switching ability, not required by the DS backwards test. In addition to working memory maintenance, the musician advantage in TMT-B can therefore also be explained by enhancement of switching ability.

In sum, while the Digit Span and the Trail-Making Test are routinely used to assess and connected to working memory ability, the task impurity problem complicates reaching conclusions about specifically which cognitive functions are measured and to what extent. Our results are best explained by enhancement of working memory maintenance, required by the TMT-A, B and the DS forward test. In addition, enhancement of switching ability may explain the musician advantage in TMT-B.

## How Musical Training Could Exert Selective Effects on the Development of Working Memory

Learning to play a musical instrument or sing requires working memory in a multitude of ways. For example, memorizing and producing sequences of tones when learning music by heart, and responding to changes in music when playing together with others both require working memory. It is possible that musical training during childhood could enhance working memory to the extent that this could be seen as faster development of these skills.

Augmentation of memory skills has been obtained by working memory programs (Melby-Lervåg and Hulme, 2013; Sala and Gobet, 2017b). It has been suggested that programs focusing on core working memory skills are most effective (Morrison and Chein, 2011). These programs are characterized by tasks that contain stimuli in more than one modality, require working memory maintenance and interference control, quick memory encoding and retrieval, change according to the individual's skill level and require high engagement and focus (Morrison and Chein, 2011). Musical training matches these characteristics of core working memory training programs well. For instance, learning to play sheet music requires transformation of visual stimuli into motor actions, which produce sound stimuli. Playing from notes requires concurrent working memory maintenance and updating of visual information from notation and auditory information produced by the musician. Playing from memory adds to working memory updating and maintenance demands through requiring monitoring of the sounds and movements produced and matching them to the model of the musical piece in memory. In ensemble playing, interference control is needed in order to be able to segregate the stream of sound produced by the individuals from those produced by others. Ensemble playing also requires rapid working memory encoding and retrieval, as musicians need to follow not only their own stream of sound but also that of others, and respond to changes in others' output. In joint improvisation, these rapid working memory encoding and retrieval requirements are accentuated. Musical training increases in challenge according to the proficiency of the individual, and successful learning and playing of music requires great engagement and focus. It is therefore feasible that musical training might influence working memory processes.

The results on selective enhancement of the participants' working memory maintenance, but not working memory updating skills, would mean that musical training selectively engages these mechanisms and perhaps selectively supports development of one more than the other. This explanation resonates with findings of different patterns of brain activation during memory encoding and rehearsal, reflecting differences in memory processes in musicians compared to nonmusicians (Schulze et al., 2011). It is feasible that musical training might exert powerful effects specifically on working memory maintenance. Learning to play by ear relies heavily on an individual's capability of acquiring and storing auditory information, melodies, and then reproducing this information immediately. Learning to play from notes, in turn, hones working memory maintenance in the visual domain. Conversely, classical musical training may not as much emphasize the ability to augment the presented information in mind, but rather reproduce it exactly as presented.

An alternative explanation for selective enhancement of working memory maintenance is that musical training improves selective attention. Indeed, there is evidence that selective attention underlies working memory maintenance (Sreenivasan and Jha, 2007; Berry et al., 2009; Gazzaley and Nobre, 2012). Selective attention seems to support encoding and maintenance of information in memory by shielding it from distracting information. This notion is supported by neuroimaging evidence of attenuated processing of distracting information during a working memory maintenance task (Sreenivasan and Jha, 2007). There is also tentative evidence of a musician advantage selective attention, indexed by decreased variability of frontal brain responses to attended stimuli (Strait and Kraus, 2011; Strait et al., 2015). It is possible that music training, for instance through playing in ensembles, and learning to focus on only the sound produced by one's instrument, could develop selective attention, which is of benefit in tasks requiring memorization of aurally presented information. Selective attention may also be required and therefore trained in learning to play sheet music. For instance, in learning to play piano, there are two notations to follow—one for the right and one for the left hand. Selectively attending to this visual information is required for successful production of sound. Trail-Making Test performance has been connected to selective attention skills, as measured by the ability to recognize speech in noise (Ellis et al., 2016), but to the knowledge of the authors, similar results on a connection between specifically selective attention and Digit Span performance have not been obtained. In future studies investigating working memory in musically trained individuals, including measures for selective attention would help further elucidate this possible connection.

#### Augmented Developmental Trajectories in Trained and Nontrained Participants

The difference in performance between the Music and Control group observed in this study diminished over time. It is possible that musical training enhances the development of working memory maintenance or selective attention, which can be seen as faster maturation in the Music group, but with time the Control group children attain the same level of performance. This explanation is contrasted by studies that have found enhanced working memory still in musically trained adults (Chan et al., 1998; Bialystok and Depape, 2009; George and Coch, 2011; Zuk et al., 2014; Talamini et al., 2016; Ding et al., 2018). There are, however, also contrary findings. In one study, adult nonmusicians were found to outperform musicians in tests requiring immediate as well as delayed recall of newly acquired information, with no significant group differences in performance in the TMT-A or B or DS (Virtala et al., 2014). Thus, the existence of working memory benefits associated with musical expertise in adulthood should be considered with caution.

As stated before, the task impurity problem complicates understanding of which cognitive functions are putatively most affected by musical training. Furthermore, the maturation of other executive functions may influence the maturation of subprocesses of working memory. For instance, the protracted development of inhibitory control and shifting ability influence performance in complex working memory span tasks that require these skills in addition to maintaining information in working memory (Jonides et al., 1998; Schleepen and Jonkman, 2009). Future longitudinal studies investigating working memory development should include measures that allow for disentangling the unique contributions of development in these cognitive skills to the development of working memory.

Since our study lacks baseline measurement of working memory skills prior to musical training, our results may also be explained by pre-existing differences between the two groups, instead of developmental causal explanation (for a study pointing towards pre-existing differences in intelligence, which may explain better performance in executive functions, see Schellenberg, 2015). The lack of the baseline measurement is caused by our choice to minimize the length of the experimental session when the children were only 7 years old and about to start their instrumental training. We added more behavioral and ERP paradigms gradually when the children became older and could then better cope with longer sessions. By this arrangement, we were able to minimize the number of drop-out participants—a serious problem in all longitudinal studies (for discussion, see Tervaniemi et al., 2018; Barbaroux et al., 2019).

One might consider the lack of random group allocation also as a caveat of our study. However, in our view, it is not feasible to plan a longitudinal study for several years on children and adolescents, at least if a control group is included. If the participants are not motivated, they either quit the training, do not participate in the investigations, or both. Even in shorter longitudinal studies, it has been a challenge to maintain the motivation of the participants unless the study is conducted in special circumstances such as summer camp like the study environment in the innovative study by Moreno et al. (2011). Thus, the current choice of having a longitudinal study on children who chose their music training based on their own and their family's initiative, gives solid evidence about the development of cognitive functions of music-oriented and control children obtained in an ecologically valid context.

## SUMMARY AND CONCLUSIONS

In this study, we investigated the maturation of working memory in musically trained and nontrained children and adolescents. We found different patterns of development for different subcomponents of working memory in the trained and nontrained participants. Musically trained individuals had better performance in tests tapping working memory maintenance, but not updating, than musically nontrained individuals. However, the difference lessened over time, as nontrained participants attained a similar level of performance as trained participants. Our results extend previous findings of a musician advantage in tests for working memory by specifying which subcomponents of working memory may be most affected, and by clarifying the trajectory of enhancement from childhood into adolescence.

## DATA AVAILABILITY STATEMENT

The datasets for this manuscript are not publicly available because the consent form signed by participants did not include

## REFERENCES

permission for distribution of data outside the research group. Requests to access the datasets should be directed to Katri Saarikivi<EMAIL_ADDRESS>
#### ETHICS STATEMENT

The studies involving human participants were reviewed and approved by The University of Helsinki Ethical Review Board in the Humanities and Social and Behavioural Sciences. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

#### AUTHOR CONTRIBUTIONS

KS and VP participated in planning the experiment, conducting the measurements, analyzing the data and writing the manuscript. MT and MH were responsible for establishing the longitudinal study, planning the experiments and measurement paradigms included, and reviewing and writing the manuscript.

#### FUNDING

This research has been funded by The Jenny and Antti Wihuri Foundation, The Finnish Cultural Foundation and The Academy of Finland.

**Conflict of Interest**: 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.

Copyright © 2019 Saarikivi, Huotilainen, Tervaniemi and Putkinen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# New Perspectives on Music in Rehabilitation of Executive and Attention Functions

#### Yuko Koshimori\* and Michael H. Thaut

Music and Health Research Collaboratory, Faculty of Music, University of Toronto, Toronto, ON, Canada

Modern music therapy, starting around the middle of the twentieth century was primarily conceived to promote emotional well-being and to facilitate social group association and integration. Therefore, it was rooted mostly in social science concepts. More recently, music as therapy began to move decidedly toward perspectives of neuroscience. This has been facilitated by the advent of neuroimaging techniques that help uncover the therapeutic mechanisms for non-musical goals in the brain processes underlying music perception, cognition, and production. In this paper, we focus on executive function (EF) and attentional processes (AP) that are central for cognitive rehabilitation efforts. To this end, we summarize existing behavioral as well as neuroimaging and neurophysiological studies in musicians, non-musicians, and clinical populations. Musical improvisation and instrumental playing may have some potential for EF/AP stimulation and neurorehabilitation. However, more neuroimaging studies are needed to investigate the neural mechanisms for the active musical performance. Furthermore, more randomized clinical trials combined with neuroimaging techniques are warranted to demonstrate the specific efficacy and neuroplasticity induced by music-based interventions.

#### Edited by:

Paul J. Colombo, Tulane University, United States

#### Reviewed by:

Aline Moussard, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Canada Alfredo Raglio, Scientific Clinical Institutes Maugeri IRCCS (ICS Maugeri), Italy

\*Correspondence:

Yuko Koshimori<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience

Received: 03 June 2019 Accepted: 05 November 2019 Published: 19 November 2019

#### Citation:

Koshimori Y and Thaut MH (2019) New Perspectives on Music in Rehabilitation of Executive and Attention Functions. Front. Neurosci. 13:1245. doi: 10.3389/fnins.2019.01245

Therapy

## INTRODUCTION

Brain and biomedical research involving music has shown that music is a highly structured auditory language engaging complex perception, cognition, and motor control in the brain (Peretz and Zatorre, 2005; Stewart et al., 2006; Alluri et al., 2011; Lee et al., 2011; Thaut et al., 2014) and has a distinct influence on the brain by stimulating complex cognitive (Leggieri et al., 2019), affective (Koelsch, 2014), and sensorimotor processes (Thaut et al., 2015; Crasta et al., 2018; Schaffert et al., 2019).

Keywords: executive function, attention processes, music neuroscience, music improvisation, Neurologic Music

As musical sound patterns are dynamic, continuously unfold, and change in time, external auditory input may serve well for cognitive rehabilitation, in particular for the domains of executive function (EF) and attentional processes (AP) by helping to adapt to a changing environment. Music by its nature drives attention exogenously (Klein and Lawrence, 2011; Thaut and Gardiner, 2014). Furthermore, music is a multidimensional stimulus consisting of multiple acoustical elements (e.g., pitch, loudness, tempo, rhythms, timbre, melody, harmony) that exist in temporally patterned simultaneity and sequentiality to drive AP (Thaut and Gardiner, 2014). In addition, active music performance involves moment-to-moment feedback systems, which facilitates monitoring, adjusting, and updating (Gardiner and Thaut, 2014). Further, there is some evidence that musicians

perform better on EF/AP (e.g., Hanna-Pladdy and Mackay, 2011) as well as there are prefrontal structural and functional differences between musicians and non-musicians (e.g., Fauvel et al., 2014; Groussard et al., 2014). Furthermore, research shows that active musical performance extensively modulates the prefrontal activity in musicians (Erkkinen and Berkowitz, 2018).

Therefore, this sensory language may effectively be used as a therapy to induce neuroplasticity in the brain affected by disorders, diseases, and injuries (Särkämö et al., 2014). With this neuroscientific knowledge, music may be able to yield more specific therapeutic and stimulation outcomes for targeted functions. In this paper, we first briefly summarize existing studies investigating the effects of formal musical training on the EF/AP performance as well as on brain structure and function. Second, we discuss the neuroimaging/neurophysiological studies demonstrating the prefrontal neural correlates and neuroplasticity of active musical performance. Third, we present the body of literature in music-based interventions in healthy and clinical populations, focusing on the EF/AP findings. Finally, we present the future directions of research in this field to move forward the neuroscientific approach, which facilitates further advancement of music-based interventions for the EF/AP stimulation and rehabilitation.

## MUSIC TRAINING ON EF/AP AND BRAIN STRUCTURE AND FUNCTION IN MUSICIANS

Some correlational and cross sectional studies have supported the beneficial effects of music training on EF/AP in older musicians (Hanna-Pladdy and Mackay, 2011; Hanna-pladdy and Gajewski, 2012; Liu et al., 2012; Amer et al., 2013; Moussard et al., 2016; Strong and Mast, 2019), younger musicians (Moradzadeh and Blumenthal, 2015; Okada and Slevc, 2018; Medina and Barraza, 2019), and musically trained children (Zuk et al., 2014). In addition, several studies demonstrated structural and functional changes associated with formal musical training in the prefrontal regions. For example, greater gray matter volume was associated with increasing musical practice in the multiple regions including supplementary motor area (SMA), superior/middle and medial frontal cortex, and insula (Groussard et al., 2014; James et al., 2014). Furthermore, resting-state functional connectivity revealed significantly greater connectivity density in the medial and lateral prefrontal regions as well as temporoparietal junction in musicians (Fauvel et al., 2014; Luo et al., 2014; Klein et al., 2016; Tanaka and Kirino, 2016).

A few studies investigated the brain activity during an EF task in musicians. One EEG study using a visual go/no-go task demonstrated that a group of 17 older musicians exhibited a significantly larger difference in the N2 amplitude (reflecting a conflict detect signal or inhibition of a prepotent response) in the central midline sites between go and no-go conditions compared to a group of 17 older non-musicians (Moussard et al., 2016). The music group also performed significantly better on the task, demonstrated with fewer no-go errors. Further, the music group showed a significant correlation between the N2 amplitude and the no-go task performance. However, there were no significant correlations between the N2 amplitude and any measures of musical background (age of first instruction, years of musical instruction, and hours of current practice). On the other hand, the measures of musical background, but not the task performance were significantly associated with the P3 amplitude that showed no significant group difference in amplitude but differential topography between groups (anterior shift in musicians). Two other studies demonstrated differential brain activities during EF tasks in younger musicians (Moreno et al., 2014) and musically trained children (Zuk et al., 2014) compared to non-musician groups without showing task performance differences between groups.

In summary, the cross-sectional or correlational studies in musicians have shed light on the potential benefit of formal musical training on EF/AP and brain changes in the prefrontal area. The task-based studies consistently demonstrated that individuals with formal musical training have differential brain activity during EF tasks in relative to those without it. However, these studies do not allow to determine the direct and causal effects of music training on EF/AP and brain changes. Furthermore, in these musician studies, the specific effects of different types of musical training on the EF/AP as well as on brain structure and function in the prefrontal area are still unclear.

#### NEURAL MECHANISMS FOR ACTIVE MUSICAL PERFORMANCE

Several neuroimaging/neurophysiological studies investigated the brain activity during active musical performance in musician. One study using near-infrared spectroscopy (NIRS) reported that piano playing engaged the frontal activity and playing more complex musical piece activated significantly wider frontal areas in musicians (Hashimoto and Okamoto, 2006). Furthermore, the brain activity during musical improvisation was investigated in amateur and professional musicians with varying experience of improvisation. These studies suggest that this creative musical activity extensively engages the brain activity in the prefrontal regions such as dorsal premotor area, pre-SMA, SMA, medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), inferior frontal gyrus (IFG), and anterior insula (Brown et al., 2006; Pinho et al., 2014; Beaty, 2015; Erkkinen and Berkowitz, 2018; Loui, 2018). However, the directionality of improvisationinduced brain activity varied across the studies. The enhanced prefrontal brain activity and connectivity was interpreted as a reflection of goal-directed, top-down processing including motor planning, response selection, inhibition of competing stimuli, and conscious monitoring (Bengtsson, 2007; Villarreal et al., 2013). The dissociation of decreased lateral and increased medial prefrontal brain activity was interpreted as a reflection of a disconnection or disintegration of the lateral regions to suppress the top-down conscious control to generate spontaneous ideas, engaging the medial prefrontal regions, which was observed in more experienced improvisers (Limb and Braun, 2008;

Liu et al., 2012). Further, the decreased lateral prefrontal activity and functional connectivity was interpreted as indicating that experts could spare EF load for the highly automatized activity (de Manzano and Ullén, 2012; Pinho et al., 2014; Rosen et al., 2016). The observed activation or deactivation in the lateral prefrontal cortex may also depend on the degree of top-down control required by a given improvisation task (Berkowitz and Ansari, 2008; Pinho et al., 2014). For example, the deactivation in the fronto-parietal regions was greater as the freedom of improvisation task increased (Berkowitz and Ansari, 2008). On the other hand, the enhanced lateral prefrontal activity observed in experienced improvisers may be due to the constrains imposed on the improvisation task (Bengtsson, 2007). In addition, the existing studies demonstrated that musical improvisation induced greater brain activity in the prefrontal regions such as DLPFC compared to simple musical repetitive or patterned tasks (Berkowitz and Ansari, 2008; Villarreal et al., 2013) and reproducing music (de Manzano and Ullén, 2012).

In summary, piano playing and musical improvisation extensively engages the prefrontal activity. This is likely because these musical activities involve EF/AP (Hannon and Trainor, 2007; Beaty, 2015). In addition, musical improvisation induces greater activation of the prefrontal regions and functional connectivity in musicians with less experience of improvisation and compared to simple musical repetitive and patterned tasks. Therefore, these musical activities may have some potential for EP/AP stimulation and neurorehabilitation.

## MUSIC-BASED INTERVENTIONS FOR EF AND AP STIMULATION IN NON-MUSICIANS

There are a few studies that have demonstrated benefits of learning to play a musical instrument on EF in older healthy nonmusicians. In one randomized control trial (RCT), 16 older adults received individualized piano lessons for 6 months (Bugos et al., 2007). They exhibited improvement on the Trail Making Test-B (TMT-B) over time during the period. This was not observed in a control group consisting of 15 older adults. In another study, a 4-month weekly group piano lessons designed and implemented by a professional music teacher and pianist resulted in improved performance on the Color-Word Stroop test in 13 older adults. This cognitive improvement was not observed in 16 older adults of the control group (Seinfeld et al., 2013). Another study further supported the benefit of group piano lessons on EF such as verbal flexibility and inhibition control in 24 older adults (Bugos, 2010). The EF improvement was significantly greater compared to a music appreciation group consisting of 22 older adults who had learned musical elements while listening to music. However, both music groups significantly improved the EF performance.

In addition, one EEG study with a pseudo-randomized design investigated the effects of music making on inhibition control and interference (Alain et al., 2019). Sixty healthy older non-musicians received either 3-month musical, visual art, or no training. The music-based intervention included music making using body percussion, and voice and non-pitched musical instruments, as well as learning basic music theory, and melody and harmony concepts by singing simple canons. It was provided by a professional music teacher. Transient differential neural activities were observed in the fronto-central sites in both intervention groups without showing improvement on the task performance.

In summary, learning to play the piano may have beneficial effects on EF in healthy older non-musicians. This may be because it is a complex process, requiring processes for the coordination of multiple sensory modalities, motor control, monitoring, working memory, inhibition, and attentional shifting (Hannon and Trainor, 2007; Seinfeld et al., 2013). However, the sample sizes of these studies are small with additional methodological limitations in some of the studies such as no randomization, no blind assessors, no active control group, and unknown intervention compliance.

## MUSIC-BASED INTERVENTIONS FOR EF AND AP NEUROREHABILITATION IN CLINICAL POPULATIONS

Several clinical studies incorporated musical improvisation and instrument playing as their intervention techniques. A few pilot studies investigated the effects of Neurologic Music Therapy (NMT) training for EF/AP. It is guided by a NMT-certified music therapist and is based on group improvisation projects with a special emphasis on attention switching that requires participants to musically respond to specific musical cues (Gardiner and Thaut, 2014; Thaut and Gardiner, 2014). In a pseudo-experimental design (due to institutional constraints of U.S. Veterans Administration), 31 persons with acquired brain dysfunction received one 30-min NMT training session (Thaut et al., 2009). A paired t-test analysis showed that the music group significantly improved cognitive flexibility assessed on TMT-B with a large effect size (d = 1.21), but not memory functions. A control group (N = 23) without any intervention did not show any change on the test. In addition, the music group significantly increased their confidence in the EF skill. Other NMT studies included children/adolescents with neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder (Abrahams and van Dooren, 2018) and Autism Spectrum Disorder (Pasiali et al., 2014). In both studies, the interventions consisted of a 45-min weekly training session over a period of 6 weeks. Both studies reported some improvement on AP. However, both studies included small sample sizes (2 participants in the NMT group in the former study and 9 participants with varying severity in the latter study). Due to the methodological limitations, these NMT preliminary findings need to replicate in RCTs with large sample sizes.

In addition, one RCT included 35 older adults with Mini Mental State Examination scores ≥18 and employed a 12 biweek group music cognitive training (Biasutti and Mangiacotti, 2018). The training was delivered by a specialist with music and neuropsychology background and consisted of voice and instrumental improvisation, which was created based on the

framework of different music-based interventions including NMT (Thaut, 2005). Repeated ANOVA showed that the music group (N = 18) showed significant improvement in mental flexibility assessed on verbal fluency and showed a trend toward significance in selective attention assessed on Attentional Matrices test compared to an active control group (gymnastic activities; N = 17).

In another RCT, 28 chronic stroke patients without extensive musical experience received either 30-h of music-supported therapy (MST) or conventional physical training over a 10-week period (Fujioka et al., 2018). The MST protocol included mapping functional movements on playing musical instruments, which was based on the NMT technique (Thaut, 2005), as well as music-making with a therapist. The music group showed significant improvement in cognitive flexibility measured by TMT-B in the mid-intervention whereas the active control group showed the improvement post-intervention.

In one RCT combined with functional near-infrared spectroscopy (fNIRS), 39 persons with mild cognitive impairment were assigned either to an instructor-guided 12-week movement music therapy (MMT) or a comparable active non-musical control therapy (Shimizu et al., 2018). MMT included light exercises synchronized to the background music and playing a percussion instrument accompanied with familiar songs. The MMT group showed enhanced mPFC activity and increased functional connectivity within the prefrontal area compared to the control group. ANOVA did not show a significant group effect on the Frontal Assessment Battery score. This is likely because both groups showed some improvement post-intervention in which the MMT group showed a significant improvement while the active control group showed a trend toward significance, shown using a paired t-test. A major limitation of this study was that the number of participants in each group was imbalanced (MMT: N = 30 vs. control: N = 9).

There are also RCTs that demonstrated the beneficial effects of other music-based interventions on EF/AP. For example, a 12-week singing (four different familiar songs) intervention guided by a professional choir instructor led to a significant improvement on inhibitory control in 31 persons with mild Alzheimer's disease (AD; Pongan et al., 2017). This positive effect was also observed in the active control group (painting) of 28 mild AD participants. Another RCT employed Sound Training for Attention and Memory in Dementia (STAM-Dem; Ceccato et al., 2012). STAM-Dem is a 12-week musicbased manualized protocol delivered by a music therapist in which the participants perform specific movements to instructed sound stimuli that requires selective attention. It consists of multiple phases with increasing difficulty. Test score changes on selective attention were significantly greater (improved test score) in the music group of 27 participants compared to the control group of 23 participants who received standard care. In addition, 1-h daily listening to participant-selected favorite music guided by a music therapist for 2 months significantly improved AP recovery in 17 persons with acute post-stroke compared to both active and passive control groups of 19 and 17 participants, respectively (Särkämö et al., 2008). Furthermore, the AP improvement was also associated with a significant increase in the prefrontal gray matter volume in those with left hemisphere stroke (Särkämö et al., 2014).

In summary, clinical studies showed some potential of active music-based interventions such as musical improvisation and instrument playing-based activities to enhance EF/AP. However, their specific effectiveness is still unclear as active control interventions are often effective as well. In addition, there is a paucity of research literature with various clinical conditions. With few neuroimaging studies, it is still to be determined whether the music-based interventions have engaged executive control areas or whether neuroplasticity had occurred in those areas. Further, some studies show critical limitations in the research design such as a small sample size, no randomization, no active control condition, no follow-up assessment, no analysis of a group × time interaction effect, and no trained therapist.

## FUTURE DIRECTIONS AND PERSPECTIVES FOR EF/AP STIMULATION AND NEUROREHABILITATION

The current paper summarizes the effects of formal musical training, active musical performance, and music-based interventions on EF/AP and associated brain activity in musicians, non-musicians, and clinical populations to present the future directions and perspectives of music-based interventions for the EF/AP stimulation and neurorehabilitation. Active musical performance such as piano playing and musical improvisation engages the prefrontal activity and some intervention studies showed the potential of these musical activities for a basis of the EF/AP stimulation and neurorehabilitation techniques. One study showed that enhanced inhibition control was associated with the extent of involvement of the musical activity in the motor system via the cognitive control system in musicians (Slater et al., 2017), suggesting that instrument playing may be more effective to enhance inhibition control compared to singing, for example. However, neuroimaging studies are needed to uncover the neural mechanisms for piano playing and musical improvisation in non-musicians and to demonstrate the direct link between these musical activities and the EF/AP performance. It is also useful for such studies to contrast images between musical activities and the resting state activity as well as between the two musical activities to show what areas are involved in each musical activity and how similar or different in the brain activity induced by these musical activities. Further, more RCTs with a rigorous design are warranted to replicate the findings. In addition, it is important to address whether these interventions are feasible and effective for different age groups and clinical populations with different clinical characteristics (e.g., severity, acute vs. chronic).

Furthermore, it is crucial to design music-based interventions that can tap into targeted processing components of EF/AP. Some of the clinical studies specifically targeted an enhancement of cognitive flexibility and selective attention, utilizing acoustic elements of music as cues or target stimuli (Thaut et al., 2009;

Ceccato et al., 2012). However, as music is tightly connected to the attention system (Särkämö et al., 2008), it can temporally enhance cognitive performance associated with some of the AP as well as learning and memory via increased arousal (Hommel et al., 1990; Thaut et al., 2005; Thompson et al., 2006; Särkämö et al., 2008). Therefore, future RCTs should include music listening as an active control condition to determine the specific efficacy of active music-based interventions above the music-induced arousal effect on EF/AP with follow-up assessments.

Lastly, neuroimaging techniques should be combined to determine whether music-based interventions engage the brain activity of the executive control regions and networks as well as whether neuroplasticity has occurred in the expected regions and networks following the interventions. These studies are also useful to determine whether the observed patterns of hyper- or hypo-activations are due to compensation, neural efficiency, or attempted compensation (Grady, 2012). Such research studies can help growth in this domain, shedding

#### REFERENCES

an increasing light on the neural mechanisms of how musicbased intervention techniques can tap into higher cognition to facilitate maintenance, enhancement, as well as recovery of cognitive functions.

### AUTHOR CONTRIBUTIONS

YK conceived the concept of the manuscript. YK wrote the first draft of the manuscript and MT wrote the sections of the draft. Both authors contributed to manuscript revision, and read and approved the submitted version of the manuscript.

## FUNDING

This research was supported by the Canada Research Chair Program.

middle cerebral artery stroke. Brain 131(Pt 3), 866–876. doi: 10.1093/brain/ awn013

**Conflict of Interest:** 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.

Copyright © 2019 Koshimori and Thaut. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Short-Term Choir Singing Supports Speech-in-Noise Perception and Neural Pitch Strength in Older Adults With Age-Related Hearing Loss

Ella Dubinsky<sup>1</sup> \*, Emily A. Wood<sup>1</sup> , Gabriel Nespoli<sup>1</sup> and Frank A. Russo1,2

<sup>1</sup> Department of Psychology, Ryerson University, Toronto, ON, Canada, <sup>2</sup> Toronto Rehabilitation Institute, Toronto, ON, Canada

Prior studies have demonstrated musicianship enhancements of various aspects of auditory and cognitive processing in older adults, but musical training has rarely been examined as an intervention for mitigating age-related declines in these abilities. The current study investigates whether 10 weeks of choir participation can improve aspects of auditory processing in older adults, particularly speech-in-noise (SIN) perception. A choir-singing group and an age- and audiometrically-matched do-nothing control group underwent pre- and post-testing over a 10-week period. Linear mixed effects modeling in a regression analysis showed that choir participants demonstrated improvements in speech-in-noise perception, pitch discrimination ability, and the strength of the neural representation of speech fundamental frequency. Choir participants' gains in SIN perception were mediated by improvements in pitch discrimination, which was in turn predicted by the strength of the neural representation of speech stimuli (FFR), suggesting improvements in pitch processing as a possible mechanism for this SIN perceptual improvement. These findings support the hypothesis that short-term choir participation is an effective intervention for mitigating age-related hearing losses.

#### Keywords: aging, musical training, speech-in-noise, frequency, hearing

## INTRODUCTION

As the population ages, and the expectation of longevity increases, a growing interest in healthcare is the promotion of healthy aging – the maintenance of mental, social, and physical wellbeing as one ages, in order to retain independence and lead a high-quality life. Aging is associated with declines in cognitive functioning (e.g., decreased working memory and attentional control; for review, see Fabiani, 2012), and deteriorating sensory-perceptual processes (e.g., Fozard, 1990). Declines in hearing can make it difficult for aging individuals to maintain personal relationships and engage socially, and have been linked to feelings of isolation and depression (Arlinger, 2003; Djernes, 2006). Although assistive technologies (e.g., hearing aids) can target aspects of peripheral hearing loss, persistent perceptual deficits are widely reported (e.g., Killion, 1997). One prevalent example is the loss of the ability to perceive speech in a noisy environment (Salomon, 1986; Chmiel and Jerger, 1996; Gomez and Madey, 2001; Ricketts and Hornsby, 2005; Betlejewski, 2006). Counseling programs may improve communication outcomes associated with age-related auditory declines, but they do not appear to influence speech-in-noise problems (Hickson et al., 2007, 2019). While some auditory rehabilitation programs have been shown to be moderately effective in mitigating speech-in-noise problems (Kricos and Holmest, 1996;

#### Edited by:

Claude Alain, Rotman Research Institute (RRI), Canada

#### Reviewed by:

Jeanette Tamplin, University of Melbourne, Australia Emily B. J. Coffey, Concordia University, Canada

#### \*Correspondence:

Ella Dubinsky<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience

Received: 05 July 2019 Accepted: 11 October 2019 Published: 28 November 2019

#### Citation:

Dubinsky E, Wood EA, Nespoli G and Russo FA (2019) Short-Term Choir Singing Supports Speech-in-Noise Perception and Neural Pitch Strength in Older Adults With Age-Related Hearing Loss. Front. Neurosci. 13:1153. doi: 10.3389/fnins.2019.01153

**148**

Sweetow and Sabes, 2006; Song et al., 2012), they require a high level of motivation and are not appropriate for all cases (Sabes and Sweetow, 2007; Saunders et al., 2016). As such, there is presently a great demand for complementary interventions that target age-related auditory declines, particularly ones that are engaging and scalable, and that show efficacy with regard to speech-in-noise perception. Developing and evaluating an intervention – and its proposed mechanism(s) for change – involves consideration of biological and experiential contributors to these abilities, beginning with age-related hearing loss and the role it plays in speech-in-noise perception.

Hearing loss can occur at different stages in the auditory system. Peripheral hearing loss refers to the reduction in efficient sound transmission through the bones of the middle ear (conductive hearing loss), and the deterioration of the outer and inner hair cells (sensorineural hearing loss; Arlinger, 2003; Yueh et al., 2003; Wingfield et al., 2005). Central hearing loss refers to the degradation of neural mechanisms that relay sound information from the cochlea to the brain, resulting from longterm attenuation of neural input from the cochlea, as well as age-related changes in neuronal responses to sound (Syka, 2002; Frisina and Walton, 2006; Yamasoba et al., 2013). Although peripheral losses can be remediated to some degree through the use of assistive technologies such as hearing aids (or, in extreme cases, cochlear implants), central processing deficits seem to persist in spite of such interventions (Chmiel and Jerger, 1996; Killion, 1997). These central processing deficits – including age-related declines in the synchrony of neural firing (Pichora-Fuller and Schneider, 1992; Frisina and Frisina, 1997; Pichora-Fuller et al., 2007), length of recovery time (Walton et al., 1998), and numbers of neurons in auditory nuclei (Frisina and Walton, 2006) – have been associated with agerelated losses in key auditory perceptual abilities, such as sound localization (Abel et al., 2000), pitch discrimination (Raz et al., 1989), duration judgments (Fitzgibbons and Gordon-Salant, 1994; Schneider et al., 1994), mistuned harmonic detection (Alain et al., 2001), and speech-in-noise perception (Pichora-Fuller et al., 1995; Russo and Pichora-Fuller, 2008; Schneider et al., 2010). Of the perceptual deficits, the loss of speech-innoise perception seems to have the most severe impact on the aging adult's quality of life (e.g., Pichora-Fuller et al., 1995, 2007; Anderson et al., 2011).

Speech-in-noise perception refers to the ability to track a voice in a complex acoustic environment, such as a crowded room with many people talking. Vital in social settings and everyday interactions, the loss of this skill can immensely impact an individual's ability to maintain independence, emotional wellbeing, and quality of life as they age (Salomon, 1986; Gomez and Madey, 2001; Betlejewski, 2006). This age-related decline also appears to persist in spite of peripheral remediation, and can even occur in adults with normal audiometric thresholds (Cruickshanks et al., 1998; Schneider B. A. et al., 2002; Tremblay et al., 2003; Gordon-Salant, 2005; Souza et al., 2007; Vermiglio et al., 2012; Alain et al., 2014); in research studies involving older individuals, pure-tone thresholds tend to be a poor predictor of speech-in-noise perception (Dubno et al., 1984; Hargus and Gordon-Salant, 1995; Kim et al., 2006; Souza et al., 2007).

One way to elucidate the neural underpinnings of speech-innoise perception is through the use of electroencephalography (EEG recordings) to study cortical and subcortical responses to acoustic stimuli (Tremblay et al., 2003; Musacchia et al., 2008; Anderson et al., 2011). Of particular interest here, the auditory brainstem – a collection of nuclei involved in afferent and efferent auditory processing – has been shown to encode spectral and temporal acoustic information with a high degree of precision (Clinard et al., 2010; Skoe and Kraus, 2010).

One component of the auditory brainstem response (ABR; Skoe and Kraus, 2010) that has been implicated in perceptual deficits – in particular, speech-in-noise perception – is the frequency following response (Johnson et al., 2005; Skoe and Kraus, 2010). This response consists of phase-locked neural activation, wherein the inter-spike intervals correspond to the fundamental frequency (F0) of the sound input (Hoormann et al., 1992). On the basis of animal work involving ablations, the primary source of the FFR appears to be the inferior colliculus (Smith et al., 1975), however recent work also suggests cortical contributions (Lehmann and Schönwiesner, 2014; Coffey et al., 2016, 2017a).

The FFR provides a useful index of the auditory nervous system's representation of periodic sound – such as a vowel in speech – through sustained synchronous neural phase-locking. Spectral and temporal features of the FFR, obtained through signal analysis, are associated with different aspects of neural pitch encoding. A fast Fourier transform (FFT) of the signal yields a spectral analysis that can be used to assess the strength of the neural representation of periodic sound input (Skoe and Kraus, 2010). Another feature, the inter-trial phase coherence (ITPC), can be used to assess the extent of consistency in the neural response to periodic sound input – i.e., the extent of phase alignment (synchronization) in oscillatory responses (e.g., Delorme and Makeig, 2004).

In the perception of speech cues, the ability to discern and track changes in pitch over time gets significantly more difficult when the signal-to-noise ratio (SNR) decreases (e.g., Killion et al., 2004). By the time an acoustic signal reaches the auditory cortex of an aging adult, it is likely to have undergone both peripheral and neural distortion (due to age-related declines in sensorineural hearing, and neural noise introduced as the signal is relayed through the ascending auditory pathway, respectively), leading to diminished preservation of key temporal and spectral characteristics (e.g., Yueh et al., 2003; Clinard et al., 2010). This suggests a possible mechanism for age-related declines in speech-in-noise perception (and other auditory perceptual abilities which rely on pitch discrimination), whereby age-related central processing deficits (such as reduced FFR fidelity) result in downstream perceptual impairments which tend to persist in spite of peripheral remediation. In terms of mitigating and preventing these declines, one activity that appears to confer some benefits against certain age-related auditory losses is musical experience (e.g., Alain et al., 2014).

Musicianship is purported to have some benefits outside the musical domain, but the most convincing positive effects have been observed in regards to the auditory system (for review, see Herholz and Zatorre, 2012). Over the course of

training, musicians are taught to attend to fine-grained acoustic features – including pitch, timing, and timbre – that contribute to human perception of sound (Kraus et al., 2009; Kraus and Chandrasekaran, 2010). This trained sensitivity to minute acoustic changes is thought to promote the enhancement of auditory perceptual abilities, including those which decline throughout the aging process (Musacchia et al., 2007; Parbery-Clark et al., 2012; Zendel and Alain, 2012; Alain et al., 2014). In studies comparing auditory perception in musicians and non-musicians, musical experience has been associated with a relative advantage in processing some of the same features that have been linked to age-related declines.

Musicians also demonstrate structural and functional differences in the neural substrates of auditory, sensory-motor, and visuospatial processing (Musacchia et al., 2007; Kraus and Chandrasekaran, 2010; Schlaug, 2015). Among musicians, musical aptitude is correlated with an increase in gray matter volume in the primary auditory cortex, as well as somatosensory and motor areas, the inferior temporal gyrus, hippocampus, and corpus callosum regions (Schlaug et al., 1995; Schneider P. et al., 2002; Gaser and Schlaug, 2003; Herdener et al., 2010). In addition to structural changes in associated brain regions, musicians also demonstrate functional improvements in neural responses to sound, at cortical and subcortical levels in the auditory processing pathway.  Notably, musicians demonstrate improvements in both FFR strength (Musacchia et al., 2007, 2008; Bidelman et al., 2011b; Slater et al., 2017) and consistency (Parbery-Clark et al., 2009; Strait et al., 2009; Bidelman et al., 2011a,b; Skoe and Kraus, 2013; Slater et al., 2017); these benefits appear largely resistant to normal age-related declines (Parbery-Clark et al., 2012; White-Schwoch et al., 2013). Because of the importance of pitch processing across auditory perceptual domains, FFR improvements have been suggested as one of the mechanisms through which musicianship enhances auditory perceptual abilities (Kraus and Chandrasekaran, 2010; Bidelman et al., 2011b; Coffey et al., 2017a).

While the aforementioned studies suggest that musical training can improve speech-in-noise perception, they are cross-sectional studies which should preclude causal inferences (Schellenberg, 2019); further, not all studies have found an effect (Ruggles et al., 2014; Boebinger et al., 2015; Madsen et al., 2017, 2019). One way to resolve these inconsistencies is through the evaluation of musical training outcomes in a controlled experimental design (i.e., a longitudinal context), which a handful of studies have sought to do. These studies essentially provided musical training to non-musicians (with the extent and nature of musical training varying between studies), and administered pre- and post-training assessments to determine whether changes occurred in outcomes of interest. In terms of neural changes, musical training has been found to enhance both structure (Hyde et al., 2009) and function (Fujioka et al., 2006; Lappe et al., 2008; Lappe et al., 2011; Habibi et al., 2016) of the auditory cortex in young children and adults who received musical training, compared to those who did not (control participants). Consistency of the FFR has also been found to be enhanced in adolescents following musical training (Tierney et al., 2015). Children who took part in instrumental music training showed improvements in speech-in-noise perception following 2 years of training (Slater et al., 2015), and younger adults who participated in singing training demonstrated improvements in speech-innoise perception after only 8 days (Jain et al., 2015). Older adults with 6 months of piano training demonstrated improved cortical responses and speech-in-noise perception following training, suggesting that neural and perceptual benefits can be conferred to aging adults in an intervention context (Zendel et al., 2019).

In addition to improvements in auditory processing, musical training has been linked to enhancements in different aspects of cognitive functioning in older adults – including improvements in working memory and executive control processes – in both cross-sectional (Parbery-Clark et al., 2011; Slevc et al., 2016; Grassi et al., 2017; Mansens et al., 2017) and longitudinal studies (Bugos et al., 2007; Särkämö et al., 2014; Biasutti and Mangiacotti, 2018; Fu et al., 2018). Musical experience has also been shown to alter neural structure and function in regions associated with cognition (Schulze et al., 2011; West et al., 2017); improvements in shared neural substrates for music and nonmusic domains have been suggested as one possible mechanism for transfer from musical training to speech-in-noise perception (e.g., Kraus et al., 2012).

Taken together, cross-sectional and longitudinal findings suggest that musical training may be able to alter brain structure and function; moreover, it appears to have the capacity to promote enhancements in the same auditory abilities that decline as we age (Solé Resano et al., 2010; Hanna-Pladdy and MacKay, 2011; Zendel and Alain, 2012; Alain et al., 2014), suggesting its use as an intervention to mitigate declines in older adults. Of the forms of music making available, singing may be particularly suited to this purpose.

Singing emerges spontaneously in the first months of life (Papoušek, 1996), and appears to be a universal form of expression (Mithen et al., 2006). Although considerable variability in accuracy exists, the vast majority of adults appear to be able to carry a tune (Dalla Bella et al., 2007; Pfordresher and Brown, 2007; Dalla Bella and Berkowska, 2009). Group singing has been shown to lead to improvements in cooperation (Good and Russo, 2016), social and emotional wellbeing (Hillman, 2002; Bailey, 2005; Hays and Minichiello, 2005; Clift and Morrison, 2011; Creech et al., 2013), and physical and creative outcomes (Beck et al., 2000; Clift and Hancox, 2001; Cohen et al., 2006). Some of these benefits may be mediated by changes in hormonal levels that occur during choral singing: after choir practice,

choristers demonstrate decreased cortisol (Beck et al., 2000) and enhanced immune system functioning more generally (Kreutz et al., 2004). Singing in a group can also be highly motivating for older adults (Hillman, 2002; Creech et al., 2013), which may promote intervention adherence. This is of particular import when singing is contrasted with existing auditory rehabilitation programs, which tend to be plagued by low compliance rates and high attrition (Sweetow and Sabes, 2010; Tye-Murray et al., 2012).

In addition to the social, cognitive, and emotional benefits, singing appears better positioned to confer near-transfer benefits to speech. All forms of vocal production involve the rapid integration of auditory and vocal-motor systems (Hickok, 2001; Zatorre et al., 2007; Pfordresher and Dalla Bella, 2011; Pruitt and Pfordresher, 2015); this integration requires feedback loops along the auditory dorsal stream that allow for realtime monitoring and adjustments (Houde and Jordan, 1998; Brainard and Doupe, 2000; Zheng et al., 2010). In a recent study that compared temporal lobe activations across perception of singing, instrumental music, and speech, it was found that compared with instrumental music, singing and speech both led to greater bilateral activations of the superior temporal sulcus (STS; Whitehead and Armony, 2018), a critical node in the auditory dorsal stream (Hickok et al., 2003).

In terms of perceptual processes, there is greater reliance on the vocal-motor system in more challenging listening environments, such as understanding speech-in-noise (Du et al., 2014); older adults rely on this to an even greater degree (Du et al., 2016). Training the vocal-motor system through singing could theoretically improve the resources upon which older adults draw to perceive degraded speech signals. An emphasis on pitch training, feedback, repetition, and the rewarding nature of improvements have been implicated as key components of successful auditory training paradigms, and in the transfer of musical experience to speech perceptual benefits (Besson et al., 2011; David et al., 2012; Herholz and Zatorre, 2012; Shepard et al., 2013; Patel, 2014; Pruitt and Pfordresher, 2015).

The current study investigated whether short-term choir participation and musical training could improve speech-innoise perception in older adults, compared to an age- and audiometrically matched control group who were not taking part in musical training. Outcomes of interest included speech-innoise perception (SIN) and pitch discrimination (FDL), strength and consistency of the neural response to sound (as indexed by features of the frequency following response [FFR] to a repeated speech stimulus), and exploratory cognitive measures of working memory (LSpan) and inhibitory control of attention (Flanker task). We hypothesized that older adults who took part in 10 weeks of group choral practice (2 hours weekly) and individual online musical training (up to 1 hour weekly) would demonstrate improved speech-in-noise perceptual abilities following training, which may be driven in part by enhancements in pitch processing and perception, as indexed by enhanced neural responses to sound (features of the FFR) and improved pitch discrimination thresholds. Exploratory cognitive measures of working memory and attention were assessed in relation to training outcomes, as potential dependent variables. We hypothesized that choir participants would experience greater post-training gains than an age- and audiometrically matched do-nothing control group.

## MATERIALS AND METHODS

#### Participants

The process of recruitment and participation is shown in **Figure 1**. Participants were recruited from the first class in a 10-week group singing course run through the 50+ Program at Ryerson University; interested participants came into the lab to undergo eligibility testing that week. Fifty three participants were screened (8 didn't meet eligibility criteria), 45 participants enrolled (9 withdrew from the study), and 36 participants completed choral training and all test sessions. Two participants were rejected as audiometric outliers, so the final analysis included 34 choir singers.

Thirty-four choir participants (31 female), aged 54–79 (mean age = 67.6, standard deviation [SD] = 6.1 years) underwent pre-testing data collection during the first week of the choir and post-testing data collection following the final choir class. Peripheral hearing loss was measured by an in-lab audiometric assessment at standard test frequencies between 0.25 and 8 kHz; average peripheral hearing loss ranged from 9.7 to 45.3 dB HL (mean = 23.1, SD = 9.9 dB HL). Participants were prescreened to ensure that they did not have any neurological conditions and did not use assistive technology (e.g., hearing aids; see **Figure 1**). Twenty-nine age- and audiometrically matched do-nothing control participants (26 female) aged 60–76 (mean age = 67.7, SD = 4.9 years) were recruited through the Ryerson University Hearing Database. Control participants' average peripheral hearing loss ranged from 10.6 to 47.5 dB HL (mean = 24.1, SD = 10.3 dB HL); average pure-tone thresholds for both groups are shown in **Figure 2**. Groups were matched for the duration of time between test sessions, and there were no group differences in previous musical experience, as indexed by years of formal musical training. Informed consent was obtained from each volunteer prior to their participation in the study, in accordance with the Ryerson Research Ethics Board guidelines (REB 2013-128).

#### Study Design

Each choir participant (n = 34) visited the lab for a pretraining assessment that took approximately three hours, during which time they completed several questionnaires and auditory and cognitive assessments, and underwent an EEG during presentation of repeated auditory stimuli.

Choir-singing participants took part in weekly 2-hour group choral sessions over the course of 10 weeks, during which time they received pitch training and vocal direction in an open and encouraging environment. In addition to the weekly group choir sessions, participants were assigned weekly individual online musical and vocal training exercises (up to 1 hour weekly). This training consisted of pitch discrimination and vocal production exercises designed to target and improve the participants' abilities to perceive and produce small changes in pitch (Theta Music Trainer)<sup>1</sup> .

After 10 weeks of choir participation and online musical training, each choir participant returned to the lab for a

#### Experimental Procedure

Apart from the questionnaires, all assessments were completed in an Industrial Acoustics Company (IAC) double-walled soundattenuating booth. Computerized assessments were presented

post-training assessment that lasted approximately 2.5 hours. During this session, participants completed different versions of the original assessments, and underwent a post-training EEG of during auditory stimulus presentation. To account for possible differences in version difficulty within the matched behavioral tasks, participants were assigned one of four possible counterbalanced configurations of assessments.

The do-nothing control group (n = 29) underwent the same battery of pre- and post-testing, with 8–10 weeks between data collection sessions, but did not receive any active training during this time. The inclusion of this control group in the analysis intended to account for any practice effects within the repeated measures, enabling a controlled examination of the unique effects of the musical intervention on experimental outcomes.

<sup>1</sup>https://trainer.thetamusic.com

using a Mac mini (Apple, 2010), with visual components of the experiment presented on a 24<sup>00</sup> Acer LCD display (Acer X243w, 1920 × 1200) placed at eye level approximately 0.5 m in front of the participant. Audiometric testing and FFR auditory stimuli were administered through binaural foam insert headphones (Electro-Medical Instruments, 3A) connected to a GSI 61 Clinical Audiometer (VIASYS Healthcare). All other auditory assessments were administered binaurally through Koss SB40 headphones at approximately 70 dB SPL.

Before the experiment began, participants were familiarized with task requirements and response methods for each assessment. Participants were monitored throughout the data collection session.

#### Questionnaires

After signing the consent form and going over experimental expectations and volunteer rights, participants were given background and music history questionnaires. These elicited demographics and medical history, and years of formal musical training.

#### Auditory Measures

#### **Speech-in-noise perception: signal-to-noise ratio (SNR)**

Ability to track speech in a noisy environment was assessed using the QuickSIN test (Speech-In-Noise; Etymotic Research; Killion et al., 2004). Participants were presented with four sets of six pre-recorded sentences, with five key words per sentence embedded in four-talker babble noise. In this assessment, the sentences were presented binaurally with a decreasing SNR: the first sentence was presented with an SNR of 25 dB (i.e., the target sentence was twenty-five dB above the background noise; very easy), each subsequent sentence was presented with a −5 dB SNR reduction, to an SNR of 0 dB for the final sentence. Participants were asked to repeat back the target sentences as closely to what they heard as possible, and were awarded one point for each correctly repeated target word, for a possible total of 30 points per set. The sentences in the QuickSIN do not contain many semantic or contextual cues, despite being syntactically correct (Wilson et al., 2007). Out of the four sets of sentences presented, the first two lists were treated as practice sets, to familiarize participants with the task requirements, and the second two lists were scored as experimental data. Mean SNR loss (dB) for each list was calculated by subtracting the total number of correct words from 25.5; Mean SNR loss (dB) represents the increased SNR required to correctly repeat 50% of key words on the QuickSIN test (Etymotic), above 2 dB SNR (the level required for normal hearing individuals to achieve 50% test accuracy; Killion, 1997; Killion et al., 2004). Final scores were calculated by averaging the scores of the two experimental lists; since this is a threshold assessment, a more negative SNR score indicates better performance. Participants' responses were scored online by a researcher, and were also recorded using Audacity software in case response ambiguity necessitated further review. The pre- and post-testing lists consisted of different sentence sets in order to avoid practice effects, and participants' exposure to the sets were counterbalanced across experimental conditions.

#### **Pitch discrimination: frequency difference limens (FDL)**

Participants' ability to distinguish different frequencies was measured using a computerized assessment of FDL. In this task, participants were presented with 3 pure tones, each lasting 200 ms, with amplitude envelopes of 20 ms rise and delay times. A three-alternative forced choice paradigm was used, in which each presented set contained two pure tones at the standard 500 Hz frequency, and one stimulus at a randomly selected higher frequency (Schneider, 1997; Parbery-Clark et al., 2009; Russo et al., 2012). The participant was instructed to identify which tone was higher than the other two by pressing the corresponding number on a computer keyboard (i.e., 1 = first tone is higher; 2 = second tone is higher; 3 = third tone is higher). An adaptive staircase procedure was used to determine the pitch discrimination threshold, whereby the difference between standard and comparison frequencies was halved after three correct responses, or doubled after one incorrect response. After five reversals, the step was changed, so that the frequency difference was divided by 1.414 after 3 correct responses or multiplied by 1.414 after one incorrect response. FDL was determined from the mean of the last 10 reversals.

#### EEG Measure: The Frequency Following Response (FFR)

#### **Stimulus**

Auditory presentation of a repeated/dα/syllable (F0 = 100 Hz) was used to elicit the FFR, following methodological conventions described by Skoe and Kraus (2010). This stimulus was selected because it is a speech sound that has been extensively used in this area of research, and robustly elicits clear FFRs (Russo et al., 2005; Parbery-Clark et al., 2009, 2012; Skoe and Kraus, 2010). Each participant heard 6000 repetitions of this 170 ms sound, presented at alternating polarities. Stimuli were presented binaurally through insert headphones; stimulus volume was set to 60 dB SPL for normal hearers. For individuals with hearing loss above 25 dB, presentation volume was set to 60 dB + (dB HL – 25 dB), controlling stimulus levels for sensory loss across all participants.

#### **EEG administration and data collection**

EEG data were collected using a vertical one-channel montage configuration, using three electrodes, in which active and reference electrodes were placed on the mastoids, and a ground electrode was placed on the forehead. A researcher applied 1 <sup>00</sup> square cloth solid gel electrodes (EL504, BIOPAC Systems, Inc.) to the mastoids and forehead; electrodes were connected to a BIOPAC MP150 data acquisition system and ERS100C Evoked Response Amplifier (BIOPAC Systems, Inc.). Data were recorded at a sampling rate of 20 kHz, with an online lowpass filter of 10 kHz and a high-pass filter of 1 Hz; the signal was recorded using Acknowledge software (AcqKnowledge, version 4.1). Stimuli were presented for 25 minutes in total, during which time participants shown a silent film<sup>2</sup> , to promote relaxation and stillness during the EEG.

<sup>2</sup>http://www.openculture.com/free-silent-films

#### TABLE 1 | Summary of linear mixed effects models for choir-singing and do-nothing control groups across key auditory measures.

The Session × Group interaction accounted for significant unique variance in speech-in-noise perception (mean SNR loss; dB) and pitch discrimination (Frequency Difference Limens; FDL, log Hz), and marginally significant variance in the strength of the FFR at F0 (µV). Group analyses showed that the choir-singing class demonstrated significant improvements in SNR, FDL, and FFR strength following training, while the control group showed no changes. Bold values indicate statistical significance (p < 0.05).

TABLE 2 | Mean scores compared with post hoc pairwise t-tests and related effect sizes (Cohen's d) for the choir-singing class (n = 34) and do-nothing control group (n = 29) at pre- and post-testing sessions, across auditory measures of interest.

Bold values indicate statistical significance (p < 0.05).

#### **EEG data processing**

fnins-13-01153 November 26, 2019 Time: 18:16 # 8

EEG data were processed in MATLAB, using the PHZLAB toolbox (Nespoli, 2016). A 75 Hz high-pass filter was applied, and data were segmented according to individual stimulus responses (i.e., 6000 segments), with epoch windows extending 40 ms pre- and post-stimulus, and the steady-state component extending from 60 to 170 ms post-stimulus onset (Skoe and Kraus, 2010). The 40 ms signal preceding stimulus onset was used as a baseline of ambient EEG activity, against which to compare the response activation. Peak amplitudes in the response waveform were compared to the baseline; response peaks with absolute amplitudes that did not exceed the baseline were not considered 'reliable' (Skoe and Kraus, 2010). Myogenic artifacts, which are many times larger than the neural response, were accounted for by rejecting all trials with amplitudes that exceeded a threshold of 50 µV. Responses that remained after artifact rejection were averaged, using the addition method of inverse polarity processing in order to preserve the representation of the fundamental frequency while minimizing stimulus artifact in the signal. Peak amplitude of the fundamental frequency (a measure of the strength of the pitch representation in the signal, or FFR strength) was calculated by applying a FFT to the averaged signal, and inter-trial phase coherence (ITPC; a measure of response consistency or FFR consistency) was calculated by finding the latency variations across each participant's un-averaged signal.

#### Exploratory Cognitive Measures

Cognitive assessments were administered electronically on the stimulus computer (see section Experimental Procedure). Assessment scripts, coded in HTML-5, were retrieved from

the Millisecond online database<sup>3</sup> (2016), adapted to have fewer blocks and runtime, and administered using Inquisit software (version 5.0.6). Working memory was assessed using a computerized version of the listening span task (LSpan), an auditory adaptation of the reading span task developed by Daneman and Carpenter (1980). Inhibitory control of attention was assessed using a computerized version of an adapted Flanker task (Ridderinkhof et al., 1997).

## Statistical Analyses

Linear mixed effects analyses in a regression format were conducted on choir and control groups to examine the effects of choir participation on speech-in-noise perception (mean SNR loss; dB), pitch discrimination ability (FDL; Hz), and aspects of the FFR which represent the strength and consistency with which the speech fundamental frequency was represented. Exploratory cognitive measures of auditory working memory (LSpan) and inhibitory control of attention (Flanker effect) were also examined in the same format. In all models, measures were regressed on Session, Group, and the interaction between them (e.g., SNR ∼ Session × Group); contrasts were assigned such that the interaction effect represented the training effect of the choir group compared to the control group. Intercepts significantly varied across participants for all auditory measures; because the Session × Group interaction was the main effect of interest for each outcome measure, individual variability in baseline scores across all dependent variables were included as random effects in the multilevel models. Session × Group interactions are reported first in each section, and significant Session × Group interactions were plotted and broken down in separate multilevel models of the choir and control groups. In these separate regressions conducted on each group, the models specified were the same as the main model for each variable, but excluded the main effect and interaction term involving Group. This was done in order to elucidate differential effects of choir participation vs. do-nothing control participation, on all outcomes of interest. Statistical analyses were conducted in R; the nlme and lmer packages were used to conduct linear mixed effects modeling in a regression format (Bates et al., 2015; Pinheiro et al., 2019).

## RESULTS

Linear mixed effects models for key auditory measures are summarized in **Table 1**; pre- and post-testing group means, post hoc pairwise t-tests, and effect size calculations are reported in **Table 2**. Due to a computer error, FDL scores for 25 participants were spurious and removed from analyses.

## Speech-in-Noise Perception

Pre-training and post-training SNR loss (dB) for choir singing and do-nothing control groups is plotted in **Figure 3**. The Session × Group interaction accounted for significant variance in dB SNR loss [b = −1.26, t(61) = −2.57, p = 0.009]. Regressions conducted on each group showed that choir participants

<sup>3</sup>http://www.millisecond.com/download/library/

demonstrated improvements of 0.81 dB SNR following training [b = −0.81, t(33) = −2.68, p = 0.006], while control participants showed no change [b = 0.45, t(28) = 1.14, p = 0.247; see **Table 1**].

## Pitch Discrimination and Neural Representation of Frequency

fnins-13-01153 November 26, 2019 Time: 18:16 # 9

**Figure 4** shows mean pitch discrimination thresholds (FDL, log Hz) before and after 10 weeks of choir singing and do-nothing control participation. The Session × Group interaction accounted for significant variance in frequency discrimination thresholds [log Hz; b = −0.11, t(36) = −2.10, p = 0.036]; regressions conducted on each group showed that choir participants demonstrated improved pitch discrimination thresholds following training [b = −0.13, t(19) = −3.21, p = 0.0013], while control participants showed no changes [b = −0.03, t(17) = −0.99, p = 0.322; see **Table 1**].

**Figure 5** shows the strength of the neural representation of the fundamental frequency (F0) of the steady-state component of a complex sound (/da/; F0 = 100 Hz), before and after choir-singing or do-nothing control participation. The Session × Group interaction accounted for marginally significant variance in the FFR strength at F0 [µV; b = 0.0037, t(48) = 1.77, p = 0.0721]; regressions conducted on each group showed that following training, choir participants demonstrated improvements in the neural representation of pitch [b = 0.0033, t(29) = 2.60, p = 0.009], while control participants did not demonstrate significant changes [b = −0.00034, t(19) = −0.21, p = 0.8346; see **Table 1**]. The Session × Group interaction did not account

for significant variance in the inter-trial phase coherence of the FFR [µV; b = 0.0025, t(50) = 0.38, p = 0.7066].

power of the fundamental in participants' EEG signals (FFR strength at F0).

#### Exploratory Cognitive Measures

There were no Session × Group interaction effects on either listening span (auditory working memory) or the Flanker effect (inhibitory control of attention).

## Possible Contributors to Choir-Driven Improvements in Speech-in-Noise Perception

**Figure 6** shows the relationship between improvements in pitch discrimination ability, FFR strength at F0, and speech-in-noise perceptual gains. SNR scores of choir participants were analyzed in a multilevel model including Session (effect of training), FDL, and FFR strength as potential predictors of variance, in order to elucidate potential mechanisms for choir-related improvements in speech-in-noise perception. Predictors were added into the model hierarchically based on hypothesized contributions to speech-in-noise perception derived from previous research, and only predictors with significant Session × Group interactions were included in the model (i.e., FDL and FFR strength at F0). The final choir model is reported in **Table 3**; with Session included in the model, the interaction between FDL and FFR strength accounted significant unique variance in SNR scores [b = −239.74, t(21) = −3.18, p = 0.0045]; inclusion of FDL accounted for training-related variance in SNR loss previously accounted for by Session, suggesting a potential mediating effect of pitch discrimination on training-related improvements in speech-in-noise perception.

## Pitch Discrimination as a Potential Mechanism for Musicianship Improvements in Speech-in-Noise Perception: A Mediation Analysis

**Figure 7** shows the distribution of the indirect effect of choir training on SNR, which shows that choir-related improvements in speech-in-noise perception were significantly mediated by improvements in pitch discrimination. The regression of mean SNR loss (dB) on Session, ignoring the mediator, was significant (see **Table 1**); when mean SNR loss (dB) was regressed on the mediator while controlling for Session, FDL significantly predicted SNR [b = 3.57, t(18) = 3.22, p = 0.0047], but training-specific effect was no longer a significant predictor of SNR [b = −0.23, t(18) = −0.605, p = 0.553]. Aroian's test revealed a significant mediating effect of FDL on choir-related improvements in SNR (Aroian's test statistic = −2.20, p = 0.0280), and a Monte Carlo resampling approach (n = 20000) confirmed that FDL fully mediated the relationship between choir participation and SNR improvements, 95% CI [−0.775, −0.0894]; **Figure 7**.

The significant interaction effect of FDL × FFR strength on SNR (**Table 3**) suggested a potential moderating effect of FFR on the relationship between pitch discrimination and SNR, so a moderated mediation analysis was conducted to assess the statistical significance of this effect. This analysis revealed a marginally significant moderation of the mediation by changes in FFR strength [b = −277.14, t(14) = −1.885139, p = 0.0803]; the model of this relationship is shown in **Figure 8**.

Exploratory analyses considered whether the relationship between choir-related improvements in pitch discrimination and speech-in-noise perception could be predicted by the

TABLE 3 | Hierarchical regression of possible contributors to speech-in-noise perceptual gains following choir (vs. control group) participation; marginal R <sup>2</sup> = 0.325, conditional R <sup>2</sup> = 0.691.

Continuous predictor variables included in the moderation analysis (FDL and FFR strength at F0) were centered around the grand means for each variable before groups were subsetted. Bold values indicate statistical significance (p < 0.05).

strength of the representation of F0 in the FFR. Simple slopes analyses on low, average, and high FFR strength at F0 (centered variable ± SD) showed that the relationship between pitch discrimination and SNR was strongest in high FFR conditions [i.e., when F0 is strongly represented in the FFR; b = 4.28, t(14) = 3.29, p = 0.0054], weaker in average FFR conditions [b = 2.28, t(14) = 1.08, p = 0.0546], and non-significant in low FFR conditions [b = 0.28, t(14) = 0.16, p = 0.8714], suggesting that when controlling for session, the strength of the FFR at F0 is predictive of the strength of the relationship between pitch discrimination and speech-in-noise perception. These analyses suggest that neural and perceptual pitch processes play a role in speech-in-noise perceptual ability in older adults, and could mechanistically contribute to a potential musicianship advantage in this domain.

## Effects of Peripheral Hearing Loss on Perceptual and Neural Auditory Outcomes

**Figures 9–11** demonstrate the differential effects of peripheral hearing loss (dB HL) on the efficacy of the choir-singing intervention on: speech-in-noise perception (**Figure 9**); pitch

FIGURE 7 | A mediation analyses was conducted using the Monte Carlo technique (20000 samples); choir related gains in SNR were fully mediated by improvements in pitch discrimination ability; 95% CI [–0.775, –0.0894].

discrimination (**Figure 10**); and FFR strength at F0 (**Figure 11**). There were significant main effects of Audiometry on SNR [b = 0.07, t(60) = 2.66, p = 0.0101] and FDL [b = 0.008, t(45) = 2.52, p = 0. 0153]; across groups and sessions, worse peripheral impairments were predictive of worse performance on perceptual tasks. However, there were no significant effects of peripheral hearing loss on FFR strength at F0, indicating a potential differentiating effect of audiometry on neural vs. perceptual outcomes.

## DISCUSSION

This study demonstrated experimentally that short-term choir participation can be used as an intervention to target and improve speech-in-noise perception in older adults, supporting the hypotheses that: (1) the musicianship advantage in speechin-noise perception can be conferred to older adults through a relatively short training period, using choir singing and vocal training; and (2) enhancements in pitch processing contribute to improvements in this domain. This study lays the groundwork

FIGURE 9 | The relationship between peripheral hearing loss and pre-post changes in mean SNR loss (dB), for choir and control groups. Degree of impairment is categorized on clinical audiometric criteria, where normal to near normal hearing = 0–25 dB HL, and mild to moderate = 26–55 dB HL. Error bars are within subjects CIs (95%) plotted around Session × Group means.

groups. Degree of impairment is categorized on clinical audiometric criteria, where normal to near normal hearing = 0–25 dB HL, and mild to moderate = 26–55 dB HL. Error bars are within subjects CIs (95%) plotted around Session × Group means.

for a highly scalable, cost-effective, and engaging intervention that can be used to mitigate declines in speech-in-noise perception in older adults, and importantly provides insight into potential neural and perceptual mechanisms underlying these changes. In particular, the relationship between auditory processing, pitch discrimination, and speech-in-noise perception suggested by this study elucidates one way in which musical experience – and specifically, singing and vocal training – can transfer to improvements in speech processing, through enhanced representation of pitch.

FIGURE 11 | The relationship between peripheral hearing loss and pre-post changes in the strength of neural representation of fundamental frequency (FFR strength at 100 Hz) for choir and control groups. Degree of impairment is categorized on clinical audiometric criteria, where normal to near normal hearing = 0–25 dB HL, and mild to moderate = 26–55 dB HL. Error bars are within subjects CIs (95%) plotted around Session × Group means.

Compared with do-nothing control participants, choir singers demonstrated 1.26 dB improvements in mean SNR loss following training, a change that corresponds to a functional difference of 10–20% improvement in speech intelligibility (Middelweerd et al., 1990). Other forms of auditory rehabilitation for older adults yield similar improvements (1.5 dB with LACE training), require intensive practice in the target domain (30 min per day, 5 days per week for 4 weeks), which may account for the relatively poor compliance and high rates of attrition (Sweetow and Sabes, 2006; Song et al., 2012; Tye-Murray et al., 2012). In contrast, group singing is reported to be highly engaging and motivating, provides many benefits outside of the focus of training, and promotes ongoing social involvement and activity (Hillman, 2002; Creech et al., 2013). It is important to note that in the current study, while nine participants withdrew from data collection, almost all of the original 53 participants surveyed remained in the choir class (two withdrew due to health issues), and many participants reported joining other choirs and singing groups after the study ended. As a proof of concept, this makes a strong case for the engagement and enjoyment of participants in a group singing class, and the sustainability of this type of intervention, along with its efficacy at improving speech-innoise perception.

In terms of possible mechanisms accounting for changes in speech-in-noise perception, improvements appeared to be driven at least in part by enhancements in pitch processing. In addition to improved speech-in-noise perceptual abilities, choir singers demonstrated improved pitch discrimination thresholds (as indexed by lower FDL) and stronger neural representations of the speech fundamental frequency (F0) following training as (stronger FFR representation of F0 of the/da/stimulus; 100 Hz). Analyses showed that training-related improvements in

speech-in-noise perception were fully mediated by improvements in pitch discrimination, suggesting that the benefits afforded by choir-singing arose at least in part from enhancements in the perception of pitch. A moderated mediation analysis suggested that over the course of choir training, the strength of the neural representation of F0 was predictive of the strength of the relationship between pitch discrimination and speech-innoise perception. This suggests that neural indices of pitch processing influence the extent to which older adults rely on pitch cues to support speech-in-noise perception. Taken together, these findings suggest that older adults who take part in 10 weeks of choir singing and vocal training demonstrate enhanced neural responses to and perception of subtle frequency cues, which lead to improved perception of speech-in-noise following training.

A number of previous studies have findings that converge with our mediation account of speech-in-noise via pitch perception. For example, musical experience has been correlated with improvements in speech-in-noise perception (Parbery-Clark et al., 2009; Zendel and Alain, 2012; Swaminathan et al., 2015; for review see Coffey et al., 2017a), pitch discrimination ability (Micheyl et al., 2006; Parbery-Clark et al., 2009; Schellenberg and Moreno, 2009; Bidelman et al., 2011a,b; Fuller et al., 2014; Boebinger et al., 2015; Yates et al., 2018), and subcortical encoding of F0 (Parbery-Clark et al., 2009, 2011; Bidelman et al., 2011a,b; Musacchia et al., 2017); relationships have also been demonstrated between pitch perception and subcortical encoding of F0 (Carcagno and Plack, 2011; Coffey et al., 2016; Bianchi et al., 2017), as well as between speech-in-noise perception and pitch processing (e.g., Coffey et al., 2017a). On the other hand, a number of correlational studies have not been able to replicate the musicianship advantage for speech-in-noise perception (e.g., Ruggles et al., 2014; Madsen et al., 2017). Some of this discrepancy may be based on methodological or sampling differences across studies. More generally, limited experimental work in this field has left the nature of these relationships somewhat uncertain (excepting some recent work by Zendel et al., 2019). Zendel et al. (2019) found that older adults (non-musicians) who took part in 6 months of piano training showed improvements in speech-in-noise perception compared with control and video game intervention groups showing no improvements in this domain. Importantly, these individuals were randomly assigned to the interventions in this study, lending credence to the use of musical training to support auditory abilities in older adults.

As a musical intervention, choir singing may be uniquely suited to hone pitch perceptual processes, through activation of existing vocal-motor systems, rapid integration of perceptual and productive processes, and shared neural architecture activated by speech and vocal song. Choir singers have the benefit of both intrinsic auditory and sensorimotor feedback, and can harness existing feedback loops between auditory perception and vocal production – which allow humans to monitor and dynamically alter speech – to rapidly alter and hone vocal output, including production of pitch. These integrative feedback loops and fine-tuned changes may allow choir singers to undergo rapid improvements in both productive and perceptual processes, in a short period of time. Singing is also an intuitive and innate form of music-making, and may be learned (and improved upon) more quickly than learning to play an instrument. This innate quality, along with intrinsic auditory and vocal motor feedback loops, and extrinsic feedback (from the choir director and other singers) create the ideal circumstances to quickly and effectively improve pitch processing and downstream perceptual abilities such as speechin-noise perception.

While the current study found that improvements in pitch processing fully mediated choir enhancements in speech-innoise perception, this does not preclude the role of other neural, perceptual, and cognitive contributors to this ability. Previous work suggests that musical training also leads to enhancements in attentional processes involved with speech encoding (e.g., Zendel et al., 2019); auditory working memory has also been implicated in this ability (e.g., Kraus et al., 2012). Further experimental research is necessary to determine the unique contributions of various auditory, cognitive, and neural processes to music-related improvements in speech-innoise perception in older adults. In addition, the contribution of productive musical training (i.e., singing practice) vs. perceptual training (i.e., learning to listen to differences in pitch) to auditory processing improvements is not clear from the current study. Notably, a recent study found that non-musicians who received targeted pitch perceptual training achieved pitch discrimination thresholds comparable to musicians in 4–8 hours (Micheyl et al., 2006); it is unknown whether these pitch improvements would be sustained over time, or transfer to speech-in-noise perceptual benefits; it is also unclear whether the mechanism by which pitch discrimination is improved – i.e., through targeted psychoacoustic training, vs. through a more naturalistic singing or music listening paradigm – would alter the degree to which pitch perception mediates speech perceptual processes. This further underscores the need for targeted experimental study of musical (and non-musical) perceptual and productive training on auditory abilities, to elucidate the roles and contributions of each. It is also unclear from the current study whether the auditory benefits of choir participation would persist after cessation of training, and whether these benefits would accumulate with additional/long-term choir or musical involvement. These are rich avenues for future research projects, especially in an experimental/longitudinal context.

In addition to the role of pitch, the degree of peripheral hearing loss appeared to influence the amount of gains choir participants experienced as a function of training, whereby participants with lower levels of peripheral hearing loss appeared to experience greater training-related improvements in speechin-noise perception. This could be suggestive of a possible limit on the efficacy of this intervention at improving perceptual processes in individuals with levels of hearing loss approaching the need for peripheral assistance (i.e., 26–55 dB HL). One potential explanation is that these individuals may not have been able to hear well enough in the classes to pitch-match with other voices, and thus did not receive equivalent experiential benefits from this activity.

Another explanation is that greater peripheral impairments may have led to more substantial central deficits that were recalcitrant to a behavioral intervention in this capacity, at least in the current dose of 10 weeks of choir singing (2 hours/week of group singing, plus 1 hour/week of individual online exercises). However, there were no effects of peripheral hearing loss on the strength of FFR responses, as individuals within the upper range of peripheral hearing loss still showed improvements in the FFR representation of F0. This suggests that while individuals with greater peripheral hearing loss may not receive perceptual benefits from 10 weeks of choir participation, their neural responses may still be enhanced through this experience. An interesting line of inquiry for a future study would be to address whether individuals with greater hearing loss may be able to obtain similar benefits by participating in choir training in conjunction with the use of hearing aids.

Overall, group choral singing appears to be uniquely well suited for this training paradigm, as it encourages singers to produce (and hopefully, perceive) fine-tuned adjustments in pitch structure, which seem to play a major role in improving speech-in-noise perceptual outcomes in this population. The intrinsic relationship with speech, rapid sensory-motor integration, instantaneous feedback afforded by vocal production and auditory perception, and the innate nature of this ability suggest singing as an ideal candidate for improving a speechrelated perceptual issue. Group singing is highly motivating, social, and emotionally fulfilling; this is of immense import in developing interventions that will encourage engagement and promote active involvement, especially with older adults. Overall, running a choir is an immensely scalable intervention, requiring minimal cost and equipment (and which could be implemented anywhere), and this study demonstrated that training-related improvements in auditory perception can appear after a very short intervention period (10 weeks of choir singing). The efficacy of this intervention can easily be assessed through experimental manipulations of dose or duration (e.g., using longer periods of choir singing, or assessing persistence of effects post-training), different study populations (e.g., hearing aid users vs. unaided individuals), and with different emphases during the class (e.g., focusing on pitch matching/singing in unison vs. attending to different melodic or harmonic lines). The ease of implementation and scalability of the choir singing paradigm, efficacy at improving auditory abilities in aging adults, and rich opportunity for further investigation suggest choir singing as an ideal framework for examining musical training as an auditory rehabilitation for aging adults.

#### CONCLUSION

Group singing is an intuitive, engaging, and motivating form of music making, that has in previous studies been shown to contribute to social, emotional, cognitive, and physical well-being. The current findings suggest that choir singing can be used as an effective intervention to mitigate age-related losses in auditory perceptual abilities, in as short a time as 10 weeks. Importantly, these findings showed that this intervention improved older adults' abilities to perceive speech in noisy environments, a key concern in promoting healthy aging. This work provides an empirical basis for a highly scalable and effective intervention that could significantly improve quality of life in older adults.

## DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

## ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ryerson University Ethics Board (REB). The patients/participants provided their written informed consent to participate in this study.

## AUTHOR CONTRIBUTIONS

ED and FR conceived and designed the study. ED recruited the participants, collected the data, analyzed the results, and wrote up findings as Master's thesis with FR as supervisor. EW collected the data, revised the literature review, and contributed to the theoretical interpretation of findings. GN developed MATLAB toolbox for processing EEG data. All authors were involved in discussions about the interpretation of the results and the writing of the manuscript.

## FUNDING

This work was supported by a Research Chair sponsored by Sonova Holding AG awarded to FR, as well as a grant from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-06969).

## ACKNOWLEDGMENTS

We would like to thank Saul Moshe-Steinberg, James McGrath, and Paolo Ammirante for their work toward establishing a subset of the protocols implemented in the current investigation. We are also indebted to Sina Fallah for leading the singing classes and to the 50+ Program of the Chang School of Continuing Education (Ryerson University) for their partnership in all aspects of this research.

## REFERENCES

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**Conflict of Interest:** 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.

Copyright © 2019 Dubinsky, Wood, Nespoli and Russo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Effects of Bimanual Coordination in Music Interventions on Executive Functions in Aging Adults

#### Jennifer A. Bugos\*

School of Music, Center for Music Education Research, University of South Florida, Tampa, Tampa, FL, United States

Music training programs have been shown to enhance executive functions in aging adults; however, little is known regarding the extent to which different types of bimanual coordination (i.e., fine and gross motor) in music instruction contribute to these outcomes. The aim of this study was to examine the effects of bimanual coordination in music interventions on cognitive performance in healthy older adults (60–80 years). Participants (N = 135) completed motor measures and battery of standardized cognitive measures, before and after a 16-week music training program with a 3 h practice requirement. All participants were matched by age, education, and estimate of intelligence to one of three training programs: piano training (fine motor); percussion instruction (gross motor), and music listening instruction (MLI) (no motor control condition). Results of a Repeated Measures ANOVA revealed significant enhancements in bimanual synchronization and visual scanning/working memory abilities for fine and gross motor training groups as compared to MLI. Pairwise comparisons revealed that piano training significantly improved motor synchronization skills as compared to percussion instruction or music listening. Results suggest that active music performance may benefit working memory, the extent of these benefits may depend upon coordination demands.

#### Edited by:

Assal Habibi, University of Southern California, United States

#### Reviewed by:

Eckart Altenmüller, Hanover University of Music Drama and Media, Germany Nils Henrik Pixa, Chemnitz University of Technology, Germany

#### \*Correspondence:

Jennifer A. Bugos<EMAIL_ADDRESS>
Received: 05 July 2019 Accepted: 05 November 2019 Published: 05 December 2019

#### Citation:

Bugos JA (2019) The Effects of Bimanual Coordination in Music Interventions on Executive Functions in Aging Adults. Front. Integr. Neurosci. 13:68. doi: 10.3389/fnint.2019.00068 Keywords: music training, executive functions, piano training, cognitive intervention, older adult

## INTRODUCTION

Bimanual coordination is a common element of instrumental music performance that requires collaboration between the hands. Often bimanual coordination is measured based upon in-phase, antiphase, or out- of phase movements calculated by phase angles of each limb at discrete timepoints. However, in music performance, bimanual coordination can also be accounted for in hand synchronization, pattern accuracy, and rhythmic accuracy while performing hand movements in parallel, contrasting, and oblique motion. This research employs this musical definition of bimanual coordination with respect to pattern, synchronization, and rhythmic accuracy and examines its relationship to music training and cognitive performance.

Bimanually coordinated movements can be varied by level of task complexity, task difficulty, absence or presence of feedback, and level/amount of training or task practice (Maes et al., 2017). According to information processing theory, bimanual coordination is an example of a dual-task situation that includes interference between simultaneously performed tasks, placing demands upon cognitive and neural resources (Swinnen and Gooijers, 2015). Practicing a musical instrument can offer a level of complexity for novice instrumentalists using a dual-task situation of bimanual motor movements

coupled with auditory feedback which may exercise areas of cognition. Our hypothesis is that bimanual coordination tasks such as performance on a musical instrument, may contribute to improving and maintaining cognitive skills in aging adults.

The purpose of this study was to examine the effects of bimanual coordination in musical tasks with graded dual-task load on cognitive and motor outcomes in healthy older adults (60–80 years) as compared to a non-motor music training intervention, music listening instruction (MLI). Specifically, we evaluated the effects of music training with fine motor (i.e., group piano training), gross motor (i.e., group percussion ensemble), and no motor (i.e., music listening instruction) requirements on bimanual coordination skills and executive functions. We also examined the relationship between bimanual coordination performance as measured by pattern, synchronization, and rhythmic accuracy, and performance on measures of executive functions. Based upon the Supply and Demand framework in aging adults, fine motor training may contribute to corticocerebellar networks (Seidler et al., 2010). Therefore, we hypothesized that those allocated to group piano training who practice fine motor skills in piano training may exhibit greater enhancements in executive functions than the other two interventions.

Musicians devote many practice hours to the development of complex bimanual sensorimotor skills which vary based upon instrumentation (Krampe and Ericsson, 1996; Krampe, 2002; Kim et al., 2017). Gross motor skills refer to movements that require coordination of arms, legs, other large muscle groups. For example, percussionists with hand drums or mallets utilize gross motor skills. While fine motor skills such as those used by wind players and pianists, require the coordination of small muscles such as the digits of the hand. While all musical instruments require precise timed movements, differentiation in task requirements by instrument and types of motor coordination may impact transfer to motor skills and the degree of transfer to cognitive performance. For instance, Kim et al. (2017) showed correlations between synchronization errors committed on an electric drum task by older adults and errors committed on cognitive tasks of executive functions. This research explored relationships between motor performance and executive functions in the context of a training study.

#### Expert Musicians and Motor Control

Few studies examine the emergence of skilled bimanual coordination in novice musicians, however, studies of professional musicians can provide insight into the neural mechanisms implicated through intense musical training. In professional musicians with extensive practice, neuroplastic changes have been found that correspond to the trained instrument. For instance, in professional string players, larger cortical representations for fingers of the left hand were found as compared to non-musicians (Elbert et al., 1995). Expert musicians were shown to have a larger anterior corpus callosum as compared to non-musicians, suggesting more efficient interhemispheric communication (Schlaug et al., 1995).

Many examples of experience-dependent plasticity have been found in musicians who engage in fine motor control. For example, wind players were found to have showed enlarged cortical thickness in lip-and tongue related brain areas when compared to non-musicians (Choi et al., 2015). Similar to these findings, research in professional pianists showed differential cortical activation patterns (Koeneke et al., 2004), structural and functional activity in auditory and motor areas (Krings et al., 2000), and changes to white matter integrity and gray matter density (Han et al., 2009). If neurological evidence indicates neural changes resulting from instrumental training (Palomar-García et al., 2017), what are the functional differences in cognitive outcomes from engaging in different types of bimanual coordination in adults?

## Bimanual Coordination and Cognition in Aging

Age-related cognitive decline is associated with deficits in executive functions and motor coordination (Seidler et al., 2010; Fujiyama et al., 2013; Bernard and Seidler, 2014; Rueda-Delgado et al., 2019). Motor deficits may be due to the gradual degeneration of the neuromuscular system, contributing to sensory and motor challenges. However, older adults can learn new motor skills despite the availability of fewer cognitive resources (Seidler, 2007).

Research suggests that older adults recruit additional neural resources when compared with younger adults (Heuninckx et al., 2005; Rueda-Delgado et al., 2019). For instance, neuroimaging research has shown that older adults completing coordination tasks, demonstrate increased activation patterns in the posterior cerebellum, an area associated with coordinated movements, when compared to younger adults (Heuninckx et al., 2005). Additional research by Rueda-Delgado et al. (2019) found functional reorganization after training in older adults as compared to younger adults who completed a novel bimanual gross motor task. Specifically, beta power was reduced after task training in older adults suggesting higher neural activity.

The "Supply and Demand" Framework (Seidler et al., 2010) provides a potential explanation for age-related changes in the neural control of movement. This framework accounts for structural and functional declines (demand), shown to increase with age, in the motor cortices, cerebellum, and basal ganglia pathways. Thus, older adults rely more heavily upon cognitive resources for motor control. In addition, aging adults demonstrate reductions in attentional capacity and cognitive resources (supply) due to the degradation of the prefrontal cortex and anterior corpus callosum. The framework suggests that the dopaminergic system acts on the corticocerebellar neural pathways projecting from the cerebellum to the frontal cortices, areas associated with higher cognitive processes. By engaging neural pathways associated with cerebellar activity in fine motor tasks such as piano training, the corticocerebellar pathway may be strengthened. Activations in the corticerebellar pathway can influence neural architecture such as the dorsolateral prefrontal cortex, an area associated with executive functions (Watson et al., 2014). While Seidler et al. (2010) have suggested that training bimanual coordination skills may reduce the

potential for cognitive decline, few cognitive training programs consider bimanual motor coordination in the context of a music intervention.

Some interventions for aging adults include bimanual coordination tasks such as multisensory exercise programs (Moreira et al., 2018); and juggling interventions (Draganski et al., 2004; Scholz et al., 2009). Results of a randomized controlled trial showed benefits from multisensory exercise training with bimanual hand movements to cognition and motor outcomes in institutionalized older adults (Moreira et al., 2018). In addition, neuroimaging data in adults who participated in a 90-day juggling intervention showed increased gray matter volume in the mid-temporal area bilaterally and in the left posterior intraparietal sulcus (Draganski et al., 2004). Boyke et al. (2008) replicated the juggling intervention in a sample of older adults in which similar neuroplastic changes were found posttraining. A significant increase in gray matter in the hippocampus and nucleus accumbens was found for older adult jugglers. Training on a musical instrument includes bimanual motor control in a temporal context, similar to complex motor activities such as juggling.

### Piano Training and Cognition

Piano performance requires complex fine motor control and integrates auditory feedback in a temporal context. Research studies have found that piano training enhances several executive functions such as working memory (Bugos et al., 2007; Guo et al., 2018), spatial reasoning (Rauscher and Zupan, 2000), verbal fluency (Bugos and Kochar, 2017), and cognitive control (Seinfeld et al., 2013) in adults and children. Preschool children who received piano training programs were shown to increase spatial-temporal reasoning (Rauscher and Zupan, 2000). Results of a 6-week keyboard harmonica intervention improved working memory in young children as compared to no treatment controls (Guo et al., 2018). In addition, piano training was shown to significantly increase auditory word discrimination skills in young children as compared to a reading training program and no treatment controls (Nan et al., 2018). Collectively, studies in young children showed the impact of piano training on working memory, verbal skills, and reasoning abilities as compared to control conditions.

Additional research has been conducted in aging adults on the effects of piano training on cognitive performance in novice adult musicians. Bugos et al. (2007) found that older adults who were randomly assigned to a 6-month individualized piano training program outperformed those assigned to a no treatment control group on a series of standardized measures of executive functions and working memory. Data suggested that some areas of executive functions such as perceptual speed and visualscanning, were maintained after a 3-month delay period.

In another research study, Seinfeld et al. (2013) examined the impact of a four-month piano intervention on cognitive control as measured by the Spanish version of the Stroop task, visual spatial performance by the Trail Making Card A, and wellbeing as measured by the Beck Depression Inventory, Profile of Mood States, and World Health Questionnaire Quality of Life Brief Questionnaire. While adults in Seinfeld et al. (2013) were not randomly assigned, post-training data showed enhanced performance on measures of cognition, particularly in response inhibition for those engaged in group piano training compared to a group of older adults engaged in recreational activities. There is a need for additional experimental research on the impact of piano training on cognitive outcomes in older adults and to compare and contrast with outcomes in other percussion based interventions.

## Gross Motor Coordination in Percussion Programs

Research in the music therapy literature showed that gross motor performance on non-pitched percussion instruments (e.g., drums, congas, or djembes) in drum circles can assist with health-related outcomes such as stress and anxiety. For example, patients with dementia who received a drumming program for 6 weeks (twice weekly) demonstrated significantly reduced anxiety compared to no treatment controls (Sung et al., 2012). Other variables affecting health such as blood pressure have been shown to change after drumming. Researchers in Africa found that older adults with high blood pressure who partook of three, 40 min djembe drumming sessions demonstrated reduced systolic blood pressure post-training (Smith et al., 2014).

Research has also found that drumming can impact psychosocial and cognitive outcomes. For example, patients with Parkinson's disease who received a 6-week West African drumming class (twice per week), self-reported higher quality of life than controls (Pantelyat et al., 2016). In another study, nine participants with Huntington's disease demonstrated increased performance on measures of executive functions after 2 months of a drumming intervention (Metzler-Baddeley et al., 2014). Neurological data further suggested that the training may have contributed to changes in the genu of the corpus callosum. While there are only a few studies that actively implement percussion performance in the music therapy literature, we seek to evaluate a different kind of percussion training program, mallet training in healthy older adults.

Few studies have researched the impact of percussion training on cognitive outcomes in healthy older adults. Dege and Kerkovius (2018) showed 15 weeks of drumming (60 min, weekly) enhanced working memory in aging females compared to a literature group and no treatment group. Another study found that beginning adult musicians who completed an 8 week mallet training program demonstrated no significant differences in cognitive outcomes, though a non-significant trend was noted for processing speed. Researchers found increased self-efficacy compared to an autobiographical writing group (Bugos and Cooper, 2019). While both studies acknowledged limitations in sample size, these studies included a progressively difficult curriculum and a social learning environment, factors positively associated with musical cognitive training programs (Bugos, 2014). Therefore, this research included training groups with a social learning environment (i.e., groupbased format) and a progressively difficulty curriculum (see **Supplementary Material**).

The impact of fine motor and gross motor music training on cognitive and motor performance in aging adults remains unclear. The purpose of this study was to investigate the effects of bimanual coordination in fine motor training (Group Piano Training; GPI), gross motor training (Group Percussion Ensemble; GPeI), and a no motor control regimen (Music Listening Instruction; MLI) on cognitive performance and bimanual coordination in healthy adults. It was hypothesized that older adults assigned to instrumental interventions, group piano training, and group percussion instruction (GPeI), would outperform matched controls enrolled in MLI on measures of executive functions. Since fine motor activities such as piano training have been more closely linked to corticerebellar pathways associated with bimanual coordination (Herrojo Ruiz et al., 2017; Jirenhed et al., 2017), it was hypothesized that those in group piano instruction would demonstrate increased bimanual coordination as compared to GPeI. However, we predicted that those in GPeI would demonstrate increased rhythmic coordination.

## MATERIALS AND METHODS

#### Participants

One hundred eighty non-musicians (60–80 years) were recruited from a mid-size city in the Southeastern United States and a series of surrounding rural communities. Recruitment included flyers and presentations to local Councils on Aging, church groups, and media coverage of the research. Criteria for participation consisted of those between the ages of 60–80, a native English speaker, no history of colorblindness, no prior history of neurological impairment (e.g., dementia or stroke), no difficulty with the movement of the hands or persistent tremor, not currently taking any psycho-reactive medications or those affecting memory performance (e.g., sleep medications, antidepressants), less than 3 years of prior musical training, and not currently engaged in music reading or musical performance. Participants were screened for cognitive impairment (scores ≥ 30) with the Telephone Interview for Cognitive Status (TICS, Brandt et al., 1988). The TICS is a short reliable screening for cognitive impairment. Those scores, ≥30, suggest no cognitive impairment and correspond to those found on the Mini-Mental State Exam (Fong et al., 2009). Informed written consent was obtained from all participants in accordance with the procedures of the University Institutional Review Board.

One hundred thirty-five participants completed the research study (**Table 1**). While each music training intervention began with 60 adults, the final sample included: group piano instruction (n = 49), MLI (n = 48), or GPeI (n = 38). Attrition was due to lack of assignment to preferred group, personal illness, family illness, caregiver responsibilities, or a financial need to return to the workplace. The attrition rate was 24%, similar to other research employing interventions for older adults (Jancey et al., 2007).

#### Procedure

All participants completed baseline measures of music aptitude and estimate of intelligence. A series of dependent measures

VIQ, Verbal Intelligence Quotient; PIQ, Performance Intelligence Quotient; FSIQ, Estimate of Full Scale Intelligence Quotient scores; AMMA, Advanced Measures of Music Audiation.

of executive functions (i.e., visual scanning/working memory, processing speed, verbal fluency, and cognitive control) and motor measures (i.e., finger dexterity and bimanual coordination) were repeated at post-testing. Alternate forms of measures (e.g., Form A and B for the Verbal Fluency subtest) were used whenever possible to avoid the potential for practice effects. All measures were administered by two highly trained research assistants with experience in psychological testing for aging adults. Both research assistants were blind to the participants' assigned condition. Participants were matched by age, education, and intelligence to three separate training interventions; however, placement of three individuals who matched these criteria were randomly assigned to one of three interventions. Sixty participants were assigned to each of three training interventions. Fifteen participants were allocated to each class session within each training intervention (i.e., music listening, group piano training, and group percussion ensemble). Trainers remained the same for the study duration and each conducted four separate classes for their prescribed intervention. All participants received 16 weeks of training sessions, which met for 45 min weekly.

Since success in music programs depends upon access to practice and consolidation of learning, all participants were required to practice training exercises for 30 min per day or 3 h per week. All practice homework was checked and practice times logged. The format of the training programs were structured similarly with 10 min of review (e.g., homework or practice log checks and review of concepts), 25 min of group performance of new skills/concepts/repertoire, and 10 min for self-practice and assessment. Training fidelity checks were conducted bi-weekly in each intervention by two separate research assistants. These checks insured that time was allocated equivalently between interventions and that the trainer adhered to the research protocols which included the training curriculum. A separate research assistant was solely responsible for collecting homework and checking practice logs for completion.

#### Description of Programs

Group Piano Instruction (GPI) consisted of a basic piano course which included technical exercises (e.g., major scales), finger dexterity exercises (e.g., Hanon exercises), basic piano repertoire selected from the Alfred All-in-One text (Palmer et al., 1995).

Classes were taught in a Yamaha Clavinova electronic piano lab by a certified music educator with 20 + years of piano teaching experience. Participants either borrowed keyboards for practice from the university or completed practice sessions at a local church. Weekly practice assignments were provided that consisted of finger exercises, written theory assignments, and two practice pieces.

Group Percussion Instruction (GPeI) consisted of a basic mallet program which included technical exercises (e.g., major scales), music theory, ostinati exercises (e.g., Music For Children: Volume 1, Murray, 1976) and repertoire performed was chosen from Get America Singing Again vol. 2 (Hal Leonard Publishing Corporation, 1997) and Hot Marimba (Hampton, 1995). Each participant borrowed a soprano or alto Peripole-Bergerault Orff rosewood xylophone that was brought to each class held at the local senior center. Classes were taught by the same music educator. Weekly homework assignments were provided that consisted of mallet exercises (e.g., scales, ostinati, and patterning), written music theory assignments, and two percussion ensemble pieces.

Music Listening Instruction (MLI) consisted of a basic music appreciation course with information about genre, composers, and forms in classical and world music based upon the Music Listening Today text (Hoffer, 2005). All music listening classes were held at a local senior center and were taught by the same music educator. Participants were loaned copies of the texts and accompanying enhanced CDs for the duration of the program. All enhanced CDs, not only provided audio practice, but CDs could also be inserted into computers (either at home or at the senior center) to view a listening map containing illustrated formal elements of the pieces unfolding over time. Weekly homework assignments included listening assignments and written questions that required written responses.

#### Description of Measures Baseline Measures

The Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999) was used to generate a Verbal Intelligence Quotient (VIQ), Performance Intelligence Quotient (IQ), and an estimate of Full-Scale Intelligence Quotient (FSIQ) (**Table 1**). The WASI consists of four subtests: Vocabulary, Block Design, Similarities, and Matrix Reasoning. The Vocabulary subtest is an untimed measure in which participants orally define 42 words based upon visual stimuli. The Similarities subtest involves 26 sets of paired words in which participants respond as to how the two words are similar. The Block Design subtest measures visuospatial skills with 13 timed block patterns constructed from nine cubes. Items of the Matrix Reasoning subtest measure non-verbal reasoning skills with the selection of a missing piece to complete a visual pattern from five potential items. The Vocabulary and Similarities subtests contribute to an estimate of Verbal Intelligence Quotient (VIQ), while the Block Design and Matrix Reasoning subtests may be used to estimate one's Performance Intelligence Quotient (PIQ). The WASI scores correspond to intelligence scores from the longer Wechsler Adult Intelligence Scale III (Axelrod, 2002); and provide norms for a broad age range (6–89 years).