Effects of a 10-week musical instrument training on cognitive function in healthy older adults: implications for desirable tests and period of training

1. Introduction

Active lifestyle, characterized by engagement in leisure activities, has been associated with slower cognitive decline in healthy older adults (Scarmeas and Stern, 2003). Longitudinally, healthy older adults are reported to improve their cognition after participating in short-term interventions; for example, physical activity training such as dancing (Kosmat and Vranic, 2017; Esmail et al., 2020) and computerized cognitive training (Simon et al., 2018; ten Brinke et al., 2018, 2020). Playing musical instrument, having both cognitive and motor training aspects, is also reported as one of the leisure activities which reduces the risk of dementia in an epidemiological study (Verghese et al., 2003). There is increasing attention to the impact of music on brain and cognitive function in older adults in recent years (Cuddy et al., 2020). In cross-sectional studies, compared with age-matched non-musicians, older musicians have been found to have not only superior music performance skills but also reserve related cognitive functions. Their life-long musicianship has been associated with better reserved auditory processing (Parbery-Clark et al., 2011; Zendel and Alain, 2012; Amer et al., 2013), executive function (Hanna-Pladdy and MacKay, 2011; Moussard et al., 2016; Yamashita et al., 2022), and verbal memory (Hanna-Pladdy and Gajewski, 2012; Fauvel et al., 2014). Although cross-sectional studies have associated music playing with positive outcomes in cognitive ability (Schneider et al., 2019), the advantages in older musicians are not always found over non-musicians (Dittinger et al., 2019). Moreover, the causal relationship remains unclear; thus, intervention studies are needed.

The effects of music training interventions on cognitive function have been studied primarily in children. These studies have shown that music training interventions can promote the development of auditory processing ability (Habibi et al., 2016; Barbaroux et al., 2019), language ability (Ho et al., 2003; Linnavalli et al., 2018; Nan et al., 2018; Barbaroux et al., 2019), executive function (Roden et al., 2014a,b; Habibi et al., 2018; Hennessy et al., 2019; Bugos et al., 2022), and verbal working memory (Rickard et al., 2010; Guo et al., 2018). Contrarily, studies on music training interventions in healthy older adults are still limited, although several studies have examined music therapy for pathological aging (Clair and Memmott, 2008; Ridder et al., 2013; Chu et al., 2014) and rehabilitation after brain injury (Sihvonen et al., 2017; Vik et al., 2018, 2019).

One pioneering intervention study investigated the effects of musical instrument training program in healthy older adults with no formal music training (Bugos et al., 2007). In that randomized controlled trial (RCT) study, participants in the intervention group (IG) received individualized piano instructions for six months and were compared with participants in the control group (CG), who had received no training. The IG significantly improved their performance on Trail Making Test (TMT) Part B and Digit Symbol Coding Test (DSCT) compared with the CG, which suggests an effect of participation in musical instrument training on executive function and processing speed. In a non-RCT study, participants in a four-month group piano training significantly improved their performance on the Stroop task (Stroop Color subtest and Stroop Color Word subtest) compared with those engaged in other leisure activities, reflecting enhanced selective processing and inhibitory control following the training program (Seinfeld et al., 2013).

Our research team also tested healthy older adults in an RCT study to investigate the effects of instrument (keyboard harmonica: melodica) training on cognitive function. The IG received a 16-week grouped training program and significantly improved their delayed story recall (in Logical Memory Test of Wechsler Memory Scale: LMT) compared with the CG participants who were instructed to maintain their usual leisure activities (Guo et al., 2021). However, unlike training by playing the piano (Bugos et al., 2007; Seinfeld et al., 2013), our melodica program did not detect improvement in executive function or processing speed, but only in delayed story recall.

In sum, RCT studies of musical instrument training programs of 16 weeks or more (1 session per week) have shown improvement on DSCT, TMT Part B, and LMT delayed recall in healthy older adults. This study sought to determine whether a shorter period of instrument intervention could also improve cognitive function in healthy older adults. Such information is useful for planning intervention programs in local governments where the whole program should be completed in a fiscal year. Additionally, prior studies have shown that other types of general mental stimulation interventions improve healthy older adults’ cognitive functions after a shorter period (Kelly et al., 2014). Theater art interventions improved the cognition of healthy older adults within 8 sessions in 4 weeks (Noice et al., 2004; Noice and Noice, 2008). Cognitively stimulating leisure activities showed effects after 10 training sessions in 4 months (Tesky et al., 2011). Healthy older adults also improved their reasoning ability after 10 sessions of intervention (Margrett and Willis, 2006). Taken together, healthy older adults showed their potential to improve the cognition through general mental stimulation intervention within 10 sessions. Thus, this study investigated whether a 10-week melodica training intervention could improve healthy older adults’ cognitive function similarly to longer-term programs.

We aimed to conduct a randomized controlled study with pre-test/post-test measurements to (1) determine if a 10-week intervention can replicate the improvements in cognitive functions reported in previous longer-term instrument training programs in healthy older adults and (2) determine if the effect of the training can be detected using other assessments, such as reaction time (RT) measurements which may be more sensitive due to wider variation among different participants, when the effect is subtle.

2. Materials and methods

2.1. Participants and setting

Participants were recruited in Yao, Osaka, Japan using the following criteria: aged 60 years and older, community-dwelling, without previous formal musical training, willing to participate in group melodica training for 10 weeks, and two cognitive test sessions pre- and post-intervention. The recruitment activities, which took place between June and September of 2019, included distributing a printed brochure to every household in the community, holding briefings for the project, and passing on information via word of mouth. Eighty Japanese older adults (67 females) between 61 and 87 years of age participated in this study.

To investigate only healthy older adult novices, we used a self-report daily condition questionnaire and neuropsychological assessments to exclude individuals who met the exclusion criteria of having any of the following: a history of head injury (n = 3); use of psychotropic medication (n = 3); Mini-Mental State Examination (MMSE) of less than 26 points (n = 0); LMT delayed recall of more than 2 SD less than the age-appropriate mean (Kawano, 2012) (n = 0); experience of musical instrument training for at least 5 years (not including musical training as part of elementary and secondary education) (n = 9) (Bugos et al., 2007; Sadakata and Sekiyama, 2011; Zhang et al., 2020). Additionally, five participants quit the study after the baseline assessment. As a result, 60 participants were included in data analysis.

2.2. Procedures

A self-report questionnaire and the baseline cognitive function pre-tests were administered over 2 weeks in September 2019. Cognitive function was measured by using software that we developed in-house, the presentation of pre-recorded instructions and time measurements were computed for the test procedure. All the instructions, read by a female native Japanese speaker, were recorded in advance using a linear pulse code modulation recorder. During the test, the examiner played each instruction through a loudspeaker connected to a personal computer. The entire test process was recorded using a digital voice recorder (IC recorder) for scoring and future confirmation with the permission of each participant. Each participant took the tests individually in a quiet room for approximately 70 min after answering the questionnaires. Participants were permitted to take a brief midway rest, if necessary. The test battery to measure cognitive function is described in section “2.4. Cognitive function tests and questionnaires.”

After excluding those deemed ineligible, the IG and CG were pseudo-randomly assigned by checking that the two groups were not significantly different in each cognitive test at baseline (see section “3. Results”). As Figure 1 shows, 60 participants met the inclusion criteria and were assigned to either the IG (n = 30) or CG (n = 30). We informed the participants of the assignment results through telephoning. The IG received the 10 weekly sessions of melodica group training intervention, while the CG maintained their usual life habits and practices. The post-tests were administered following the last intervention session in December. The study design was approved by the ethics committee of Kyoto University (29-P-7). All participants provided written informed consent.

FIGURE 1

Figure 1. Flow chart of distribution of participants throughout the study.

2.3. Intervention program

The intervention consisted of 10 weekly sessions of 70-min melodica group training supplemented by at-home practice. The training session was shown in Figure 2. The time taken for at-home practice was monitored through participants’ practice diaries. The CG received the same melodica training after the cognitive function tests post a 10-week waiting period for ethical and motivational reasons.

FIGURE 2

Figure 2. Musical instrument training session. The melodica (keyboard harmonica) was used for the training session.

The instructor group for the melodica training included one main instructor (lecturer) and seven assistants. The lecturer was a professional music instructor with expertise in piano and chorus and experienced in teaching melodica to older adults. She delivered lectures on basic music theory and guided melodic playing. The assistant instructors observed the performance of the participants and provided individual help when they could not catch up with the lecture or asked questions in person.

The program began with instructions on how to produce sound in the melodica by pressing the key while blowing the mouthpiece; over the 10 weeks, the participants practiced melodies with increasing complexity. Each session started with a finger warm-up by playing a series of tunes in a prescribed order according to the lecturer’s instructions. Participants practiced the melody they learned the week before in the first half of the training session. There was a half-time break for about 5 min, where some relaxing hand and finger exercises were introduced. After the intermission, the participants first learned new melodies by singing with the lyrics, and then with the names of the notes, while clapping their hands to the rhythm. After reviewing the melody, the participants played the melody using the melodica. Group practice was followed by the individuals practicing independently in the same facility and one-on-ones with the instructors. Over the course of the 10 intervention sessions, participants practiced a total of 16 songs and mastered a minimum of eight.

2.4. Cognitive function tests and questionnaires

2.4.1. Daily condition questionnaire

The daily condition questionnaire was administered to participants prior to the baseline screening. It included inquiries about their history of head injury, use of psychotropic medication, and past surgeries. The questionnaire also assessed participants’ musical experience, including the type of music training, duration of formal training, and current practice frequency. In addition, participants’ engagement in social activities was assessed using a cumulative score based on three items: (a) participation in a paid job, (b) involvement in volunteer activity, and (c) not frequently being alone during the daytime. Each item was assigned a value of 1 for “yes” and 0 for “no.” The questionnaire also collected information on participants’ current monthly frequency of engagement in cognitive activities such as handcrafting, handwriting, and chorus.

2.4.2. Neuropsychological assessments

To verify the intervention effects reported in previous studies, we conducted the same neuropsychological tests as in our previous melodica intervention: MMSE for screening, LMT to assess verbal memory, and DSCT, TMT, and VFTs to assess executive functions. Because these tests’ procedures were described elsewhere (Guo et al., 2021), we focus here on tests newly adopted.

Different from Guo’s study, participants were instructed to name as many vegetable (not animal) words as they could in 60 s for semantic VFT (no modification for phonological VFT, with “ka” as word-initial syllable). In addition to LMT, we administered Rey Auditory Verbal Learning Test (RAVLT) (Rey, 1964; Schmidt, 1996; Lezak et al., 2004) to determine if this study could yield similar enhancement of verbal memory as the previous 16-week melodica intervention. To minimize the practice effects (McCaffrey et al., 2001), we developed two alternative Japanese word lists for the assessment in this study (Lezak et al., 2004). We made a Japanese translation of Rey’s original word list and another equivalent English word list (Rey, 1964; Geffen et al., 1994). Some words from the English list were frequently used in English, but not in Japanese, owing to cultural and linguistic differences. We replaced these words with highly familiar semantically or phonetically similar words. Familiarity information was referenced in the Nippon Telegraph and Telephone Corporation (NTT) psycho-linguistic database “Lexical Properties of Japanese” (Amano et al., 2008). Word length and auditory familiarity were matched in the two lists (Amano et al., 2006). Combined with these 15 words in the learning list, 15 words semantically or phonetically like them were included in the 30-word list for recognition task in 2 alternative lists.

The study included five learning trials, one delayed recall trial, and one delayed recognition task. In learning trials, 15 words were given at 1 s interval. Participants were instructed to remember as many of the words as possible and recall them in any order within 1 min immediately after learning and after a 20-min interval without forewarning (delayed recall). The number of correctly recalled words was counted as the recall trial score, with a maximum possible score of 15. The total score of all five learning trials was calculated as the learning-capacity measurement. Subsequently, a recognition task was performed. Thirty words were presented verbally in random order. Participants were instructed to indicate whether the word was in the learning list by pressing the “Y” for “yes” and “N” for “no” (not from the learning list). The number of words correctly recognized was the recognition task score.

2.4.3. Experimental tasks

We prepared new tests with higher sensitivity—RT tasks (Rose and Ebmeier, 2006; Wilhelm and Oberauer, 2006)—to detect subtle improvements potentially caused by the shorter training period. Compared to paper-pencil-based neuropsychological tests, RT was considered more sensitive in detecting subtle changes due to wider variation in participants and less ceiling effect.

2.4.3.1. Sternberg task

To evaluate working memory, we administered a modified Sternberg task (Sternberg, 1966; Desmond et al., 2003). Figure 3 shows the procedure of one trial in the Sternberg task. All stimuli were presented on a 13-inch digital screen. One trial started from four fixation points (dots) presented for 500 ms. Next, in each of the four points, one Arabic numeral was presented for 1,500 ms. A single star mark was presented at the center for 3,000 ms followingly. Next, a probe (a Japanese kanji numeral) replaced the star mark. Participants were to indicate whether the probe number presented by Japanese kanji had been included in the set of four numbers presented as Arabic numerals (see Figure 3). They were instructed to indicate their answer by pressing the left key for “yes” (positive) or the right key for “no” (negative) as accurately and quickly as possible. A new trial began once the participant responded after 1,000 ms interval (pause). Participants completed 64 trials (two blocks of 32 trials each). Accuracy (% correct) and RT (ms) for every single trial was recorded.

FIGURE 3

Figure 3. Procedures of Sternberg task in one trial.

2.4.3.2. Timing task

We administered this rhythm entrainment task to measure the accuracy of participants’ timing processing and auditory-motor transformation (Wing and Kristofferson, 1973; Bernard et al., 2013). Participants were instructed to listen to tones and tap in sync with the tones. A beat of 12 tones (2,000 Hz, 20 ms) was presented with 980 ms intervals in each trial. In the first practice trial, participants were instructed to press the reaction key with their dominant hand in sync with periodic auditory tones. In the second practice trials, participants were instructed to keep tapping as consistently as possible after the auditory beat ceased, based on their memory of the beat, until they had tapped 30 times after the accompanying auditory beat had stopped. The accuracy of their “tapping based on beat memory” was measured twice; the procedure used was that of the second practice trial.

The coefficient of variance (CV) was used to quantify the performance of the rhythm entrainment task. The CV was calculated by dividing the standard deviation of the inter-tap intervals by the mean duration of the inter-tap intervals. The analyses focused on the CV for the paced continuation phase (30 taps without auditory beats).

2.4.4. Additional tests

We also conducted the Grooved Pegboard Test to measure effects on complex motor coordination (Kløve, 1963),¹ Geriatric depression scale 15 (Matsubayashi and Osawa, 1994), and a 10-item scale to investigate subjective memory (Osada et al., 1997; Kawagoe et al., 2019). These measures were mainly intended as screening or filler items before delayed recall tests and therefore, were not included in analysis. The focus of this study was on cognitive measures.

2.5. Data analysis

Pre-processing was conducted on the RT data in the Sternberg task. For each participant, we excluded RTs of incorrect responses and those that were longer or shorter than the individual’s mean by standard deviations (SD) of at least two. Data from 4.42% of the trials were excluded at baseline, and from 4.67% of trials were excluded in the post-intervention test. One participant was excluded from the analysis of Sternberg task performance for the low correct rate (50%), suggesting a failure in understanding the instruction.

We compared the IG and CG on demographic characteristics, cognitive functions, involvement in social and cognitive activities at baseline using two-sample t-test and chi-squared test to evaluate whether the pseudorandom grouping resulted in homogeneous groups using baseline data. To examine an intervention effect, we conducted a two-way mixed ANOVA with group (IG and CG) as a between-subject factor and time (pre- and post-intervention) as a within-subject factor. This ANOVA was conducted only for cognitive measures in which the IG significantly improved after training (by paired t-test), and an intervention effect was defined by a significant “group × time” interaction. For multiple comparison correction, we applied the Benjamini and Hochberg (1995) procedure for controlling the false discovery rate (FDR). For measures which showed intervention effects, a Pearson’s correlation analysis was performed to investigate the relation between cognitive changes and at-home melodica practice time in the IG. Furthermore, to assess the practice effect, the pre-post improvement within the CG was analyzed by paired t-test.

Data were analyzed using SPSS Windows version 26.0.0 (IBM Cort, 2019). Statistical significance was set at p < 0.05. Missing values were not filled in the analyses, but participants with missing data were discarded from the analysis of that measure (e.g., Timing task, RAVLT recognition).

3. Results

3.1. Characteristics of participants

Demographic data for the IG and CG are shown in Table 1. The data of the 30 participants (female/male = 26/4) in both the IG and CG (female/male = 24/6) were analyzed. Two-sample t-tests detected no significant difference between the two groups in age [IG: 73.17 (SD = 6.10); CG: 73.40 (SD = 6.05)], years of education [IG: 11.97 (SD = 2.46); CG: 12.53 (SD = 2.11)], MMSE score [IG: 28.07 (SD = 1.62); CG: 28.17 (SD = 1.91)] (see Table 1). Additionally, the two groups showed no significant differences in outcome behavioral measures, indicating that the groups were equivalent at baseline. Furthermore, no significant difference was detected between the two groups in the engagement of social or cognitive activities.

TABLE 1

Table 1. Demographic data and engagement in social and cognitive activities for intervention and control groups at baseline.

3.2. Effects of intervention and repetition

The paired t-test detected significant improvement in the IG on LMT delayed recall [t(29) = 4.930, p < 0.001], DSCT [t(29) = 3.291, p = 0.003], phonological VFT [t(29) = 2.241, p = 0.033], and RT in the Sternberg task [t(25) = −3.992, p < 0.001]. Other cognitive measures showed no significant improvement in the IG. Among these four measures, the “group × time” ANOVA revealed a significant interaction on the phonological VFT [F(1,58) = 6.942, p = 0.011, partial η² = 0.107] (see Figure 4). It remained significant after FDR correction (corrected p = 0.044). The “group × time” interaction was also significant on the RT in the Sternberg task [F(1,51) = 5.292, p = 0.026, partial η² = 0.094] (see Figure 5). It was marginally significant after FDR correction (corrected p = 0.052). A significant “group × time” interaction was not detected in the LMT delayed recall or the DSCT.

FIGURE 4

Figure 4. Number of words correctly named in phonological verbal fluency task. A two-way ANOVA found a significant group × time interaction: [F(1,58) = 6.942, p = 0.011, partial η² = 0.107]. The main effect of time was significant only in the intervention group [F(1,29) = 5.023, p = 0.033, partial η² = 0.148]. *A significant simple main effect of time in the IG (p < 0.05).

FIGURE 5

Figure 5. Reaction time (ms) for the Sternberg task in intervention group and control group. A two-way ANOVA found a significant group × time interaction: [F(1,51) = 5.292, p = 0.026, partial η² = 0.094]. The main effect of time was only significant in the intervention group [F(1,25) = 15.936, p = 0.001, partial η² = 0.389]. ***A significant simple main effect of time in the IG (p < 0.001).

As Table 2 shows, the IG improved 1.87 words in the phonological VFT and shortened their RT in the Sternberg task for 97.87 ms after the intervention. The IG practiced melodica at home for 1,692.86 min on average (SD = 1,312.79). Correlation between the at-home practice time and cognition improvement was not significant with either the phonological VFT (r = 0.209, p = 0.286) or the RT in the Sternberg task (r = −0.014, p = 0.949).

TABLE 2

Table 2. Cognitive outcomes of intervention and control group.

The improvement in the CG, indicative of the test-retest practice effect, was significant in several tests, including the LMT delayed recall [t(29) = 3.942, p < 0.001], DSCT [t(29) = 2.819, p = 0.009], TMT Part B [t(29) = −3.118, p = 0.004]. The practice effect was not significant on other measures.

4. Discussion

This study was designed to examine the effects of 10-week melodica training on cognitive function in healthy older adults. The IG and CG were matched on demographic characteristics, cognitive functions, and engagement in social and cognitive activities at baseline. A comparison was made between these two homogenous groups, with one receiving the intervention and the other serving as the control. On the one hand, it did not replicate intervention effects on the LMT delayed recall, DSCT, or TMT Part B reported in previous longer-term musical instrument RCTs. On the other hand, improvement was significant in the phonological VFT and marginally significant in the Sternberg task.

4.1. Improved verbal working memory

In this study, the intervention effect was significant on VFT in the phonological fluency condition and marginally significant on RT in the working memory (Sternberg) task, suggesting that verbal working memory was improved through the 10-weekly session musical instrument training intervention.

The VFT detected intervention effects with no significant practice effect, suggesting its usefulness in detecting intervention effects. To date, no music intervention studies have shown a clear improvement in the VFT. A 3-h concentrated group piano practice in older adults was reported to enhance both phonological and semantic VFT in a pilot study (Bugos and Kochar, 2017). However, the subsequent 16-week piano training intervention program did not improve the participants’ performance on the VFT compared with participants who received percussion instruction or music listening instructions (Bugos, 2019). Additionally, a 16-week melodica training did not improve participants’ performance on VFT in a previous study by our research team (Guo et al., 2021).

In children and adolescents, previous longitudinal studies have provided evidence showing the positive effects of musical training on working memory (Bergman Nutley et al., 2014; Roden et al., 2014a; Guo et al., 2018). However, non-significant improvements on working memory were also reported in music training interventions in children (Barbaroux et al., 2019). Thus, short-term musical instrument training may modestly improve working memory, if any. Consistently, our study detected working memory-related improvement, with the significant intervention effect on the phonological VFT and marginally significant intervention effect on RT in the Sternberg task. Our results suggest that healthy older adults maintain the ability to improve working memory through musical instrument training.

How can musical instrument training improve verbal working memory? Association between musical training experience and language processing has been interpreted as common acoustic processing (Besson et al., 2011, 2017). A recent study reported that music and language rely on similar working memory resources: musical stimuli produce similar working memory interference as linguistic stimuli (Fennell et al., 2021). It is also reported that both music and language processing elicited similar variations in brain electrical potentials with overall shorter onset latency for musicians than for non-musicians (Schön et al., 2004). Enhanced language processing ability was detected in dyslexic children following 18 h of musical training (Habib et al., 2016). In this study, the melodica practice improved older adults’ working memory which was presumably used in common for music processing and verbal processing. The improvement of verbal processing ability is important because it could mediate the improvement in verbal memory and executive function reported in previous studies.

4.2. Comparison with our Kyoto study: VFT and verbal memory measures

In our previous melodica intervention with a longer duration (16 weeks), the intervention effects were significant on verbal memory (LMT delayed recall) but not on verbal fluency (Guo et al., 2021).

For the inconsistency in VFT results, we examined differences in participants. While the mean age of participants was virtually identical in these two studies, one possible factor emerged: the participants had approximately one less year of education, based on the mean, in this study than those in our prior study. Considering that education affects phonological VF performance (Moraes et al., 2013), the 1-year difference in education may be crucial. In this study, the IG improved 2.87 words from 9.63 at baseline and the CG improved – 0.83 words from 10.23. In Guo’s study, the number correctly named changed less than one word in two groups from 12.30 (IG) and 11.63 (CG), respectively. Thus, we suggest that there may be a limit of 12 words for phonological VFT (for “ka” in Japanese), on average, for healthy older adults of approximately 73 years of age; the baseline score of participants in Guo et al. (2021) was almost the limit. Presumably, owing to a ceiling effect, the group-by-time interaction was not detected in Guo’s study. In this study, because fewer words were named at baseline (perhaps owing to the lower level of education), participants had more room for improvement. Hence, we suggest that the effect of musical instrument training on phonological VFT is more detectable for a sample with fewer years of education, although this supposition requires further investigation.

This inference is partly supported by the correlation between education and phonological VFT in this study. It is widely recognized that phonological VFT is more strongly correlated with education than age, whereas semantic VFT is more strongly correlated with age (Abe et al., 2004; Mitrushina et al., 2005). Consistent with these reports, the present results indicate that our participants with more education performed better in the phonological VFT at baseline (r = 0.487, p < 0.001). Note that the “group × time” interaction stayed significant in an ANCOVA with the years of education as a covariate [F(1, 57) = 5.949, p = 0.018, partial η2 = 0.095] in this study.

For the LMT delayed recall, both groups improved significantly after the training period, which might be due to repeating the test. These results were different from the previous study showing a significant intervention effect with a significant main effect of time only in the IG (Guo et al., 2021). One possible explanation for the inconsistency is that the interval between the pre-and post-cognitive function test was much shorter in this study than in the previous study—12 weeks on average, at least 8 weeks shorter than the previous study. As a typical memory assessment, the observed practice effects were expected in the LMT (Theisen et al., 1998; Lo et al., 2012), especially for short test-retest intervals. Practice effect was significant on story recall tasks in healthy older adults (mean age = 71.67 years old) even after a 5-year interval (Gavett et al., 2016). Hence, we recommend using alternative stories or test intervals in longitudinal studies.

The RAVLT also did not show any significant intervention effects. Unlike in the LMT, neither group improved their RAVLT performance in the post-intervention test. In previous studies, the effects of a theater art intervention for less than 10 weeks were detected on word list recall (and VFT) (Noice et al., 2004; Noice and Noice, 2008). Healthy older adult participants were trained to recall their short lines during the intervention for verbal memory training. Therefore, the word list recall (and VFT) measured a near transfer of the training (i.e., an improvement in tests, which is similar to training). In our study, verbal ability was not directly trained, and RAVLT (word list recall, i.e., context-free verbal memory) was perhaps too far from musical instrument training. Combined with the LMT results, if the melodica training intervention improves verbal memory in a longer intervention period, the improvements would be only for context-based memory (story recall), not word list recall or rote memorization.

4.3. Comparison with previous piano training interventions

Six months of individual piano practice improved older adults’ performance on the DSCT and TMT Part B (Bugos et al., 2007). However, the reported intervention effects were not replicated in this study. Our previous melodica intervention study with participants of similar demographic characteristics (means: 73 years of age, 13.0 years of education) did not improve their performance in TMT Part B either (Guo et al., 2021, DSCT not conducted). Therefore, one possible cause of this inconsistency is the kind of musical instrument that was used. Previous piano interventions require bimanual coordination (Bugos et al., 2007), whereas our melodica intervention only requires using the right hand. Additionally, participants’ demographic characteristics also may have affected the outcomes. The participants in the piano training intervention were approximately 3 years younger in age and had, on average, higher levels of education compared to those in this study [Bugos et al. (2007): 69.6 years old, 16.5 years of education; this study: 73.3 years old, 12.3 years of education]. It remained to be investigated which factor (education, age, or the kind of musical instrument) played the main role in the variances of intervention effects.

4.4. Limitation in study design: lack of active control

During the intervention period in this study, the IG gathered to undergo the musical instrument training together once per week, while the passive CG maintained their usual habits and routines. Although the two groups did not show significant differences in their engagement in social, intellectual and cognitive activities at baseline, it remained challenging to attribute the improvements solely to musical training rather than a general novice stimulation. Therefore, it is yet to be confirmed whether we can replicate the improvement in verbal working memory in the musical instrument training group compared with the active CG.

In previous music intervention studies with active controls, participants assigned to the CG listened to music during the intervention period, and no significant group-by-time interaction was detected on VFT, TMT Part A and Part B, or Stroop tasks (Bugos, 2010, 2019). Compared to musical interventions, a larger number of studies have investigated the effects of physical exercise in older adults. For the aerobic exercise IG, non-endurance programs such as light stretching have been considered as a control condition (Kramer et al., 1999; Voss et al., 2013; Tarumi et al., 2022). However, the effect of stretching was later reported on memory functions (Ruscheweyh et al., 2011; Hötting et al., 2012). Thus, physical exercise related effects might have been underestimated when compared to an active CG (for a review, see Hötting and Röder, 2013). Therefore, when implementing an active control, a three-group design may be desirable to compare the change in cognition between the IG and two CGs (active CG and passive CG).

4.5. Other limitations and future work

First, the intervention effects detected in this study might have been affected by the participants’ characteristics. Participants improved their phonological VFT performance, but the same-age and longer education sample did not in our previous melodica intervention (Guo et al., 2021). Although this inconsistency suggests that the intervention on the phonological VFT depends on education, in our more recent longitudinal study of over 4 years, participants returned from our Kyoto Study (more highly educated than they did in the current study). In these returned participants, those who had continued melodica practice for more than 3 years better maintained their phonological verbal fluency compared to those who had quit practice (Wang et al., in preparation)². Therefore, the phonological VFT may be promising to detect effects of musical instrument practice on cognitive function in advanced aging. Another participant-related limitation is that our participants were mostly women (50 women and 10 men). Therefore, future studies should clarify whether improved verbal working memory can be generalized to men.

Second, the high correct-response rate in the Sternberg task indicated a ceiling effect, which suggests that the task was too easy for healthy older adults. We used memory sequences of only four digits (Arabic numerals); therefore, increasing the difficulty of the task by increasing the number of digits may enable a more precise assessment of the improvement of working memory.

Third, the intervention period seemed too short, which was suggested by two aspects: (1) the test-retest practice effect was significant in several measures. (2) The IG showed no significant improvement on the timing task. The latter suggests that the 10-week melodical training was insufficient for improving auditory-motor transformation in musically untrained older adults. We conducted this study in collaboration with the Osaka local government. We set an intervention period for 10 weeks to finish all procedures of this project (participant recruitment, pre-and post-intervention/waiting test, intervention, and providing the same melodica lessons to the waiting group after the post-test) within one financial year. However, the results suggested the need for an extended interval between screenings to capture more rigorous intervention effects, which could be justified by implementing a longer training period in the same study. A longer training period might also be beneficial for participants to enhance their music performance skills and improve their related cognitive functions, especially for those who had difficulty keeping up with group lessons (see Kakihara et al., in revision)³. Fortunately, our informal assessment found the participants’ high motivation for continuing making music after the research program.

5. Conclusion

This study did not find a clear-cut intervention effect on LMT delayed recall, DSCT, and TMT Part B which were found in previous studies. The shorter intervention period may yield a (1) subtler intervention effect and (2) stronger test-retest effect which may disrupt detecting intervention specific-effects. A longer training period may be less subject to the test-retest practice effect and likely show a more clear-cut intervention effect, although it will be warranted by a direct manipulation of the period of training.

Nevertheless, the intervention effects were detected significant on the phonological VFT and marginally significant on RT in the Sternberg task, indicating the improvement in verbal working memory after this 10-week musical instrument training program. To the best of our knowledge, this study is the first to demonstrate the improved phonological verbal fluency and the shortened RTs for the Sternberg task following musical intervention. The improvement of verbal working memory may be an early and essential cognitive improvement induced by participation in the instrumental training program because it has the potential to mediate further improvement of verbal memory, processing speed, and executive function.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Kyoto University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

XW performed the research, analyzed the data, writing—original draft, and writing—review, and editing. TS, MY, and MK performed the research, review, and editing. TT programmed the cognitive function tests. SI performed the research. KS designed the research and supervision, recruited participants, and wrote, reviewed, and edited the manuscript. KS agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved. All authors contributed to the article and approved the submitted version.

Funding

This study was conducted as a contract between the Osaka Prefecture and Kyoto University. Writing this manuscript was supported by Grants-in-Aid (Nos. 16H06325 and 21H04422 to KS) for Scientific Research from the Japan Society for the Promotion of Science (JSPS).

Acknowledgments

We thank Satsuki Shioyama, Haruna Omura, Mariko Higuchi, Yumiko Mano, and Hisami Koike for their assistance in data collection and music intervention, Hikaru Hirasawa and Ayaka Mukainoge for their assistance in data collection, Chiaki Tan, Masae Ikuma, and Chieko Sadakata for their support in the music intervention, Nobuhito Abe for his valuable comments on statistical analyses, Yasuharu Tabara for his support in programming cognitive function tests, Kyohouji Community Center, Koma Shrine for arranging and providing a place for data collection, and Yao City Machinami Center for providing the place for intervention.

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^ https://lafayetteevaluation.com/products/grooved-pegboard
2. ^ Wang, X., Yamashita, M., Guo, X., Osawa, C., and Sekiyama, K. (in preparation). Effects of musical instrument practice on cognitive reserve in older adults: A three-year follow-up study.
3. ^ Kakihara, M., Wang, X., Iwasaki, S., Soshi, T., Yamashita, M., and Sekiyama, K. (in revision). The association between performance skills and cognitive improvement in a 10-week musical instrument training for older adults. Psychol. Music. Rev.

References

Abe, M., Suzuki, K., Okada, K., Miura, R., Fujii, T., Etsurou, M., et al. (2004). Normative data on tests for frontal lobe functions: Trail making test, verbal fluency, Wisconsin card sorting test (Keio version). Brain Nerve 56, 567–574.

Google Scholar

Amano, S., Kondo, K., and Kasahara, K. (2008). NTT database series nihongo-no goitokusei (Lexical properties of Japanese) Vol. 9 - word familiality. Tokyo: Sandeido publishing company.

Google Scholar

Amano, S., Kondo, T., Sakamoto, S., and Suzuki, Y. (2006). Influence of word familiarity on spoken word recognition. J. Acoust. Soc. Am. 120, 3322–3322.

Google Scholar

Amer, T., Kalender, B., Hasher, L., Trehub, S., and Wong, Y. (2013). Do older professional musicians have cognitive advantages? PLoS One 8:e71630. doi: 10.1371/journal.pone.0071630

CrossRef Full Text | Google Scholar

Barbaroux, M., Dittinger, E., and Besson, M. (2019). Music training with démos program positively influences cognitive functions in children from low socio-economic backgrounds. PLoS One 14:e0216874. doi: 10.1371/journal.pone.0216874

CrossRef Full Text | Google Scholar

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300.

Google Scholar

Bergman Nutley, S., Darki, F., and Klingberg, T. (2014). Music practice is associated with development of working memory during childhood and adolescence. Front. Hum. Neurosci. 7:926. doi: 10.3389/fnhum.2013.00926

CrossRef Full Text | Google Scholar

Bernard, J. A., Peltier, S., Wiggins, J., Jaeggi, S., Buschkuehl, M., Fling, B., et al. (2013). Disrupted cortico-cerebellar connectivity in older adults. NeuroImage 83, 103–119. doi: 10.1016/j.neuroimage.2013.06.042

CrossRef Full Text | Google Scholar

Besson, M., Barbaroux, M., and Dittinger, E. (2017). “Music in the brain: Music and language processing,” in The routledge companion to music cognition, eds R. Ashley and R. Timmers (New York, NY: Routledge).

Google Scholar

Besson, M., Chobert, J., and Marie, C. (2011). Transfer of training between music and speech: Common processing, attention, and memory. Front. Psychol. 2:94. doi: 10.3389/fpsyg.2011.00094

CrossRef Full Text | Google Scholar

Bugos, J., and Kochar, S. (2017). Efficacy of a short-term intense piano training program for cognitive aging: A pilot study. Musicae Sci. 21, 137–150. doi: 10.1177/1029864917690020

CrossRef Full Text | Google Scholar

Bugos, J. A. (2010). The benefits of music instruction on processing speed, verbal fluency, and cognitive control in aging. Music Educ. Res. Int. 4, 1–9.

Google Scholar

Bugos, J. A. (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

CrossRef Full Text | Google Scholar

Bugos, J. A., DeMarie, D., Stokes, C., and Power, P. (2022). Multimodal music training enhances executive functions in children: Results of a randomized controlled trial. Ann. N. Y. Acad. Sci. 1516, 95–105. doi: 10.1111/nyas.14857

CrossRef Full Text | Google Scholar

Bugos, J. A., Perlstein, W., McCrae, C., Brophy, T., and Bedenbaugh, P. (2007). Individualized piano instruction enhances executive functioning and working memory in older adults. Aging Ment. Health 11, 464–471. doi: 10.1080/13607860601086504

CrossRef Full Text | Google Scholar

Chu, H., Yang, C., Lin, Y., Ou, K., Lee, T., O’Brien, A., et al. (2014). The impact of group music therapy on depression and cognition in elderly persons with dementia: A randomized controlled study. Biol. Res. For Nurs. 16, 209–217. doi: 10.1177/1099800413485410

CrossRef Full Text | Google Scholar

Clair, A. A., and Memmott, J. (2008). Therapeutic uses of music with older adults, 2nd Edn. Silver Spring, MD: American Music Therapy Association.

Google Scholar

Cuddy, L., Belleville, S., and Moussard, A. (2020). Music and the aging brain. Cambridge, MA: Academic Press.

Google Scholar

Desmond, J. E., Chen, S., DeRosa, E., Pryor, M., Pfefferbaum, A., and Sullivan, E. (2003). Increased frontocerebellar activation in alcoholics during verbal working memory: An fMRI study. NeuroImage 19, 1510–1520. doi: 10.1016/S1053-8119(03)00102-2

CrossRef Full Text | Google Scholar

Dittinger, E., Scherer, J., Jäncke, L., Besson, M., and Elmer, S. (2019). Testing the influence of musical expertise on novel word learning across the lifespan using a cross-sectional approach in children, young adults and older adults. Brain Lang. 198:104678. doi: 10.1016/j.bandl.2019.104678

CrossRef Full Text | Google Scholar

Esmail, A., Vrinceanu, T., Lussier, M., Predovan, D., Berryman, N., Houle, J., et al. (2020). Effects of dance/movement training vs. aerobic exercise training on cognition, physical fitness and quality of life in older adults: A randomized controlled trial. J. Bodywork Movement Ther. 24, 212–220. doi: 10.1016/j.jbmt.2019.05.004

CrossRef Full Text | Google Scholar

Fauvel, B., Groussard, M., Mutlu, J., Arenaza-Urquijo, E., Eustache, F., Desgranges, B., et al. (2014). Musical practice and cognitive aging: Two cross-sectional studies point to phonemic fluency as a potential candidate for a use-dependent adaptation. Front. Aging Neurosci. 6:227. doi: 10.3389/fnagi.2014.00227

CrossRef Full Text | Google Scholar

Fennell, A. M., Bugos, J., Payne, B., and Schotter, E. (2021). Music is similar to language in terms of working memory interference. Psychon. Bull. Rev. 28, 512–525. doi: 10.3758/s13423-020-01833-5

CrossRef Full Text | Google Scholar

Gavett, B. E., Gurnani, A., Saurman, J., Chapman, K., Steinberg, E., Martin, B., et al. (2016). Practice effects on story memory and list learning tests in the neuropsychological assessment of older adults. PLoS One 11:e0164492. doi: 10.1371/journal.pone.0164492

CrossRef Full Text | Google Scholar

Geffen, G. M., Butterworth, P., and Geffen, L. B. (1994). Test-retest reliability of a new form of the auditory verbal learning test (AVLT). Arch. Clin. Neuropsychol. 9, 303–316. doi: 10.1016/0887-6177(94)90018-3

CrossRef Full Text | Google Scholar

Guo, X., Ohsawa, C., Suzuki, A., and Sekiyama, K. (2018). Improved digit span in children after a 6-week intervention of playing a musical instrument: An exploratory randomized controlled trial. Front. Psychol. 8:2303. doi: 10.3389/fpsyg.2017.02303

CrossRef Full Text | Google Scholar

Guo, X., Yamashita, M., Suzuki, M., Ohsawa, C., Asano, K., Abe, N., et al. (2021). Musical instrument training program improves verbal memory and neural efficiency in novice older adults. Hum. Brain Mapp. 42, 1359–1375. doi: 10.1002/hbm.25298

CrossRef Full Text | Google Scholar

Habib, M., Lardy, C., Desiles, T., Commeiras, C., Chobert, J., and Besson, M. (2016). Music and dyslexia: A new musical training method to improve reading and related disorders. Front. Psychol. 7:26. doi: 10.3389/fpsyg.2016.00026

CrossRef Full Text | Google Scholar

Habibi, A., Cahn, B., Damasio, A., and Damasio, H. (2016). Neural correlates of accelerated auditory processing in children engaged in music training. Dev. Cogn. Neurosci. 21, 1–14. doi: 10.1016/j.dcn.2016.04.003

CrossRef Full Text | Google Scholar

Habibi, A., Damasio, A., Ilari, B., Elliott Sachs, M., and Damasio, H. (2018). Music training and child development: A review of recent findings from a longitudinal study. Ann. N. Y. Acad. Sci. 1423, 73–81. doi: 10.1111/nyas.13606

CrossRef Full Text | Google Scholar

Hanna-Pladdy, B., and Gajewski, B. (2012). Recent and past musical activity predicts cognitive aging variability: Direct comparison with general lifestyle activities. Front. Hum. Neurosci. 6:198. doi: 10.3389/fnhum.2012.00198

CrossRef Full Text | Google Scholar

Hanna-Pladdy, B., and MacKay, A. (2011). The relation between instrumental musical activity and cognitive aging. Neuropsychology 25, 378–386. doi: 10.1037/a0021895

CrossRef Full Text | Google Scholar

Hennessy, S. L., Sachs, M., Ilari, B., and Habibi, A. (2019). Effects of music training on inhibitory control and associated neural networks in school-aged children: A longitudinal study. Front. Neurosci. 13:1080. doi: 10.3389/fnins.2019.01080

CrossRef Full Text | Google Scholar

Ho, Y.-C., Cheung, M.-C., and Chan, A. S. (2003). Music training improves verbal but not visual memory: Cross-sectional and longitudinal explorations in children. Neuropsychology 17, 439–450. doi: 10.1037/0894-4105.17.3.439

CrossRef Full Text | Google Scholar

Hötting, K., and Röder, B. (2013). Beneficial effects of physical exercise on neuroplasticity and cognition. Neurosci. Biobehav. Rev. 37, 2243–2257.

Google Scholar

Hötting, K., Reich, B., Holzschneider, K., Kauschke, K., Schmidt, T., Reer, R., et al. (2012). Differential cognitive effects of cycling versus stretching/coordination training in middle-aged adults. Health Psychol. 31:145. doi: 10.1037/a0025371

CrossRef Full Text | Google Scholar

IBM Cort. (2019). IBM SPSS statistics for mac (version 26.0) [computer software]. Armonk, NY: IBM Corp.

Google Scholar

Kawagoe, T., Onoda, K., and Yamaguchi, S. (2019). Subjective memory complaints are associated with altered resting-state functional connectivity but not structural atrophy. NeuroImage Clin. 21:101675. doi: 10.1016/j.nicl.2019.101675

CrossRef Full Text | Google Scholar

Kawano, N. (2012). A pilot study of standardization of WMS-R logical memory for Japanese old-old people: Differences between the story A and story B. Otsuma J. Soc. Inform. Stud. 21, 223–231.

Google Scholar

Kelly, M. E., Loughrey, D., Lawlor, B., Robertson, I., Walsh, C., and Brennan, S. (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Ageing Res. Rev. 15, 28–43. doi: 10.1016/j.arr.2014.02.004

CrossRef Full Text | Google Scholar

Kløve, H. (1963). Clinical Neuropsychology. Med. Clin. North Am. 47, 1647–1658. doi: 10.1016/S0025-7125(16)33515-5

CrossRef Full Text | Google Scholar

Kosmat, H., and Vranic, A. (2017). The efficacy of a dance intervention as cognitive training for the old-old. J. Aging Phys. Act. 25, 32–40. doi: 10.1123/japa.2015-0264

CrossRef Full Text | Google Scholar

Kramer, A. F., Hahn, S., Cohen, N., Banich, M., McAuley, E., Harrison, C., et al. (1999). Ageing, fitness and neurocognitive function. Nature 400, 418–419. doi: 10.1038/22682

CrossRef Full Text | Google Scholar

Lezak, M. D., Howieson, D. B., and Loring, D. W. (2004). Neuropsychological assessment, 5th Edn. New York, NY: Oxford University Press.

Google Scholar

Linnavalli, T., Putkinen, V., Lipsanen, J., Huotilainen, M., and Tervaniemi, M. (2018). Music playschool enhances children’s linguistic skills. Sci. Rep. 8:8767. doi: 10.1038/s41598-018-27126-5

CrossRef Full Text | Google Scholar

Lo, A. H. Y., Humphreys, M., Byrne, G., and Pachana, N. (2012). Test–retest reliability and practice effects of the Wechsler Memory Scale-III. J. Neuropsychol. 6, 212–231. doi: 10.1111/j.1748-6653.2011.02023.x

CrossRef Full Text | Google Scholar

Margrett, J. A., and Willis, S. L. (2006). In-home cognitive training with older married couples: Individual versus collaborative learning. Aging Neuropsychol. Cogn. 13, 173–195. doi: 10.1080/138255890969285

CrossRef Full Text | Google Scholar

Matsubayashi, K., and Osawa, T. (1994). Special issue: Synthetic daily life function evaluation methods. 1. Methods of evaluation. D. Evaluation on emotion of the elderly. Geriatr. Med. 32, 541–546.

Google Scholar

McCaffrey, R. J., Duff, K., and Westervelt, H. J. (2001). Practitioners guide to evaluating change with intellectual instruments. J. Clin. Exp. Neuropsychol. 23, 407–408. doi: 10.1076/jcen.23.3.407.1187

CrossRef Full Text | Google Scholar

Mitrushina, M., Boone, K. B., Razani, J., and D’Elia, L. F. (2005). Handbook of normative data for neuropsychological assessment. New York, NY: Oxford University Press.

Google Scholar

Moraes, A. L., Guimarães, L. S. P., Joanette, Y., de Mattos Pimenta Parente, M. A., Fonseca, R. P., and Almeida, R. M. M. (2013). Effect of aging, education, reading and writing, semantic processing and depression symptoms on verbal fluency. Psicologia 26, 680–690. doi: 10.1590/S0102-79722013000400008

CrossRef Full Text | Google Scholar

Moussard, A., Bermudez, P., Alain, C., Tays, W., and Moreno, S. (2016). Life-long music practice and executive control in older adults: An event-related potential study. Brain Res. 1642, 146–153. doi: 10.1016/j.brainres.2016.03.028

CrossRef Full Text | Google Scholar

Nan, Y., Liu, L., Geiser, E., Shu, H., Gong, C., Dong, Q., et al. (2018). Piano training enhances the neural processing of pitch and improves speech perception in Mandarin-speaking children. Proc. Natl. Acad. Sci. U.S.A. 115, E6630–E6639. doi: 10.1073/pnas.1808412115

CrossRef Full Text | Google Scholar

Noice, H., and Noice, T. (2008). An arts intervention for older adults living in subsidized retirement homes. Aging Neuropsychol. Cogn. 16, 56–79. doi: 10.1080/13825580802233400

CrossRef Full Text | Google Scholar

Noice, H., Noice, T., and Staines, G. (2004). A short-term intervention to enhance cognitive and affective functioning in older adults. J. Aging Health 16, 562–585. doi: 10.1177/0898264304265819

CrossRef Full Text | Google Scholar

Osada, Y., Shimonaka, Y., Nakazato, K., Kawaai, C., and Kikuchi, Y. (1997). The development of self-rating scales in the elderly. Jpn. J. Gerontol. 18, 123–133.

Google Scholar

Parbery-Clark, A., Strait, D., Anderson, S., Hittner, E., and Kraus, N. (2011). Musical experience and the aging auditory system: Implications for cognitive abilities and hearing speech in noise. PLoS One 6:e18082. doi: 10.1371/journal.pone.0018082

CrossRef Full Text | Google Scholar

Rey, A. (1964). L’examen clinique en psychologie, 2e éd Edn. Paris: Presses universitaires de France (Psychologue).

Google Scholar

Rickard, N. S., Vasquez, J. T., Murphy, F., Gill, A., and Toukhsati, S. R. (2010). Benefits of a classroom based instrumental music program on verbal memory of primary school children: A longitudinal study. Aust. J. Music Educ. 1, 36–47.

Google Scholar

Ridder, H. M. O., Stige, B., Qvale, L., and Gold, C. (2013). Individual music therapy for agitation in dementia: An exploratory randomized controlled trial. Aging Ment. Health 17, 667–678. doi: 10.1080/13607863.2013.790926

CrossRef Full Text | Google Scholar

Roden, I., Grube, D., Bongard, S., and Kreutz, G. (2014a). Does music training enhance working memory performance? Findings from a quasi-experimental longitudinal study. Psychol. Music 42, 284–298. doi: 10.1177/0305735612471239

CrossRef Full Text | Google Scholar

Roden, I., Könen, T., Bongard, S., and Frankenberg, E. (2014b). Effects of music training on attention, processing speed and cognitive music abilities—findings from a longitudinal study. Appl. Cogn. Psychol. 28, 545–557. doi: 10.1002/acp.3034

CrossRef Full Text | Google Scholar

Rose, E. J., and Ebmeier, K. P. (2006). Pattern of impaired working memory during major depression. J. Affect. Disord. 90, 149–161. doi: 10.1016/j.jad.2005.11.003

CrossRef Full Text | Google Scholar

Ruscheweyh, R., Willemer, C., Krüger, K., Duning, T., Warnecke, T., Sommer, J., et al. (2011). Physical activity and memory functions: An interventional study. Neurobiol. Aging 32, 1304–1319. doi: 10.1016/j.neurobiolaging.2009.08.001

CrossRef Full Text | Google Scholar

Sadakata, M., and Sekiyama, K. (2011). Enhanced perception of various linguistic features by musicians: A cross-linguistic study. Acta Psychol. 138, 1–10. doi: 10.1016/j.actpsy.2011.03.007

CrossRef Full Text | Google Scholar

Scarmeas, N., and Stern, Y. (2003). Cognitive reserve and lifestyle. J. Clin. Exp. Neuropsychol. 25, 625–633. doi: 10.1076/jcen.25.5.625.14576

CrossRef Full Text | Google Scholar

Schmidt, M. (1996). Rey auditory verbal learning test: RAVLT: A handbook. Los Angeles, CA: Western Psychological Services.

Google Scholar

Schneider, C. E., Hunter, E. G., and Bardach, S. H. (2019). Potential cognitive benefits from playing music among cognitively intact older adults: A scoping review. J. Appl. Gerontol. 38, 1763–1783. doi: 10.1177/0733464817751198

CrossRef Full Text | Google Scholar

Schön, D., Magne, C., and Besson, M. (2004). The music of speech: Music training facilitates pitch processing in both music and language. Psychophysiology 41, 341–349. doi: 10.1111/1469-8986.00172.x

CrossRef Full Text | Google Scholar

Seinfeld, S., Figueroa, H., Ortiz-Gil, J., and Sanchez-Vives, M. (2013). Effects of music learning and piano practice on cognitive function, mood and quality of life in older adults. Front. Psychol. 4:810. doi: 10.3389/fpsyg.2013.00810

CrossRef Full Text | Google Scholar

Sihvonen, A. J., Särkämö, T., Leo, V., Tervaniemi, M., Altenmüller, E., and Soinila, S. (2017). Music-based interventions in neurological rehabilitation. Lancet Neurol. 16, 648–660. doi: 10.1016/S1474-4422(17)30168-0

CrossRef Full Text | Google Scholar

Simon, S. S., Tusch, E., Feng, N., Håkansson, K., Mohammed, A., and Daffner, K. (2018). Is computerized working memory training effective in healthy older adults? Evidence from a multi-site, randomized controlled trial. J. Alzheimers Dis. 65, 931–949. doi: 10.3233/JAD-180455

CrossRef Full Text | Google Scholar

Sternberg, S. (1966). High-speed scanning in human memory. Science 153, 652–654. doi: 10.1126/science.153.3736.652

CrossRef Full Text | Google Scholar

Tarumi, T., Patel, N., Tomoto, T., Pasha, E., Khan, A., Kostroske, K., et al. (2022). Aerobic exercise training and neurocognitive function in cognitively normal older adults: A one-year randomized controlled trial. J. Intern. Med. 292, 788–803. doi: 10.1111/joim.13534

CrossRef Full Text | Google Scholar

ten Brinke, L. F., Best, J., Chan, J., Ghag, C., Erickson, K., Handy, T., et al. (2020). The effects of computerized cognitive training with and without physical exercise on cognitive function in older adults: An 8-week randomized controlled trial. J. Gerontol. Ser. A 75, 755–763. doi: 10.1093/gerona/glz115

CrossRef Full Text | Google Scholar

ten Brinke, L. F., Best, J., Crockett, R., and Liu-Ambrose, T. (2018). The effects of an 8-week computerized cognitive training program in older adults: A study protocol for a randomized controlled trial. BMC Geriatr. 18:31. doi: 10.1186/s12877-018-0730-6

CrossRef Full Text | Google Scholar

Tesky, V., Thiel, C., Banzer, W. E., and Pantel, J. (2011). Effects of a group program to increase cognitive performance through cognitively stimulating leisure activities in healthy older subjects: The AKTIVA study. GeroPsych 24, 83–92. doi: 10.1024/1662-9647/a000035

CrossRef Full Text | Google Scholar

Theisen, M. E., Rapport, L., Axelrod, B., and Brines, D. (1998). Effects of practice in repeated administrations of the Wechsler memory scale-revised in normal adults. Assessment 5, 85–92. doi: 10.1177/107319119800500110

CrossRef Full Text | Google Scholar

Verghese, J., Lipton, R., Katz, M., Hall, C., Derby, C., Kuslansky, G., et al. (2003). Leisure activities and the risk of dementia in the elderly. N. Engl. J. Med. 348, 2508–2516. doi: 10.1056/NEJMoa022252

CrossRef Full Text | Google Scholar

Vik, B. M. D., Skeie, G. O., and Specht, K. (2019). Neuroplastic effects in patients with traumatic brain injury after music-supported therapy. Front. Hum. Neurosci. 13:177. doi: 10.3389/fnhum.2019.00177

CrossRef Full Text | Google Scholar

Vik, B. M. D., Skeie, G., Vikane, E., and Specht, K. (2018). Effects of music production on cortical plasticity within cognitive rehabilitation of patients with mild traumatic brain injury. Brain Injury 32, 634–643. doi: 10.1080/02699052.2018.1431842

CrossRef Full Text | Google Scholar

Voss, M. W., Heo, S., Prakash, R., Erickson, K., Alves, H., Chaddock, L., et al. (2013). The influence of aerobic fitness on cerebral white matter integrity and cognitive function in older adults: Results of a one-year exercise intervention. Hum. Brain Mapp. 34, 2972–2985. doi: 10.1002/hbm.22119

CrossRef Full Text | Google Scholar

Wilhelm, O., and Oberauer, K. (2006). Why are reasoning ability and working memory capacity related to mental speed? An investigation of stimulus–response compatibility in choice reaction time tasks. Eur. J. Cogn. Psychol. 18, 18–50. doi: 10.1080/09541440500215921

CrossRef Full Text | Google Scholar

Wing, A. M., and Kristofferson, A. B. (1973). The timing of interresponse intervals. Percept. Psychophys. 13, 455–460. doi: 10.3758/BF03205802

CrossRef Full Text | Google Scholar

Yamashita, M., Ohsawa, C., Suzuki, M., Guo, X., Sadakata, M., Otsuka, Y., et al. (2022). Neural advantages of older musicians involve the cerebellum: Implications for healthy aging through lifelong musical instrument training. Front. Hum. Neurosci. 15:784026. doi: 10.3389/fnhum.2021.784026

CrossRef Full Text | Google Scholar

Zendel, B. R., and Alain, C. (2012). Musicians experience less age-related decline in central auditory processing. Psychol. Aging 27, 410–417. doi: 10.1037/a0024816

CrossRef Full Text | Google Scholar

Zhang, J. D., Susino, M., McPherson, G. E., and Schubert, E. (2020). The definition of a musician in music psychology: A literature review and the six-year rule. Psychol. Music 48, 389–409. doi: 10.1177/0305735618804038

CrossRef Full Text | Google Scholar