Sage Journals: Discover world-class research

Abstract

Background/Objectives

Harp music has long been used for comfort and healing, yet its neural mechanisms remain unclear. This study examined how major and minor harp sounds influence frontal alpha activity, mood, and reaction times, with a focus on depression-related individual differences.

Methods

Participants listened to 3-min major and minor harp excerpts while EEG alpha activity was recorded. Subjective ratings of arousal, stress, comfort, and mood were assessed, followed by auditory and visual reaction-time tasks.

Results

Individuals with lower depression scores showed marked frontal alpha suppression during harp listening, which was associated with faster auditory reaction times. Those with higher depression scores exhibited consistently lower frontal alpha power with minimal modulation by harp sounds.

Conclusions

Frontal alpha suppression during harp listening may reflect enhanced auditory attentional processing in individuals with lower depression levels. These findings suggest that depression-related traits shape neural responsiveness to harp music and should be considered when evaluating its therapeutic potential.

Keywords

harp music therapy EEG alpha oscillation depression frontal cortex

Introduction

Music therapy has been recognized as a viable alternative to conventional clinical treatments across various clinical settings.^1,2 Music therapy is provided by a trained music therapist for a single patient or a group of patients and involves the use of music or musical elements.³ Meta-analyses have shown that musical interventions are effective in reducing pain, anxiety, and distress in patients with traumatic brain injury,⁴ those undergoing chemotherapy,⁵ and pregnant women.⁶ In addition, during music therapy, it is not only subjective measures but also objective physiological measures, such as heart rate, blood pressure,⁷ and electroencephalogram (EEG),⁸ that have been demonstrated to exert a relaxation response associated with the reduction in stress and anxiety. However, despite these effects, few studies have confirmed a link between the subjective outcomes and physiological responses of music therapy.

Among the many modalities of music therapy, the harp sound has been shown to decrease blood pressure and heart rate in postoperative thoracic patients.⁹ Furthermore, harp sound intervention for pain control significantly decreases pain perception in patients in the intensive care unit.¹⁰ It is thought that harp sounds influence brain rhythms (particularly 8-13 Hz alpha-band oscillations, which are defined as the relaxation index) because they comprise a wide range of frequency components. Importantly, alpha oscillations in the frontal cortex are closely linked to attentional control, emotional regulation, and cognitive flexibility, and suppression of frontal alpha power has been associated with enhanced information processing.^11,12 In addition, the alpha band is commonly subdivided into alpha-1 (8-10 Hz) and alpha-2 (10-13 Hz), which are thought to reflect different neural processes¹¹ Alpha-1 power has been associated with general cortical inhibition and relaxation, whereas alpha-2 power has been more strongly linked to higher-order cognitive functions, such as semantic processing, working memory, and task-related attentional control.¹³ Distinguishing between alpha-1 and alpha-2 power therefore allows for a more precise interpretation of how music influences both emotional and cognitive brain mechanisms. However, the relationship between alpha-band oscillations while listening to harp sounds and comfort, anxiety, and depression remains uncertain. Furthermore, it is unclear how alpha rhythm affects cognitive processing and behavioral responses after the presentation of harp sounds.

In this study, we explored the effect of harp sounds on alpha-band oscillations and its relationship with anxiety and depression in healthy individuals. Specifically, we focused on how major (positive affect sounds) and minor keys (negative affect sounds) influence alpha frequency patterns and their relationship with behavioral responses to sound and visual stimuli. Our aim was to deepen our understanding of the neurophysiological effects of harp music therapy and the potential benefits for patients experiencing pain, anxiety, and depression.

Materials and Methods

Participants

We recruited 28 healthy Japanese participants (12 men and 16 women, mean age 35.7 ± 10.1 years) for the study. A sample size of 28 was determined using G Power based on a previous study on harp music therapy¹⁰ with a standard α value (ie, 95% confidence limits or α = 0.05). All participants completed Spielberger's state and trait anxiety scale (STAI)¹⁴ and the Self-rating Depression Scale (SDS).¹⁵ The study was approved by the Institutional Review Board of Showa University Hospital and the Ethical Committees of Showa University School of Medicine (clinical trial identifier 21-218-A). All participants provided written informed consent. All procedures were aligned with the principles of the Declaration of Helsinki.

Experimental Design

The experiment was performed in a soundproofed and lit room. Subjects were seated in a chair and were informed about the study procedure. The subjects first completed the STAI and SDS. The EEG electrodes were then placed on the subject's head, and impedances were checked. Subjects were asked to keep their eyes closed when the EEG recording was started. Once the stability of the EEG signal was confirmed, we continuously recorded EEG for 3 min per condition, at rest (pre-stimulus resting condition) and during the presentation of the positive affect (major harp sound condition) and negative affect (minor harp sound condition) harp sounds. Each harp sound was tested twice to examine the reliability of EEG changes and subjective scale scores. The order of sound stimulus presentation was randomly alternated between participants. The sound stimuli were presented to subjects using a surround high-resolution audio stereo system (SRS RA5000, Sony, Japan) placed 1 meter in front of the participants. After each sound presentation, we measured the reaction time to auditory and visual stimuli.

Sound Stimuli

To avoid memory retrieval of the music, we limited the sound presentation to 3 min and created sound samples of major and minor chord harp sounds (sounds are available in the supplemental materials). In addition to the key structure (major vs minor), we provide further acoustic descriptions of the stimuli. Each excerpt consisted of arpeggiated harp chords spanning approximately 196–2093 Hz (mean octave ≈ 815 Hz). The spectral envelope of the harp tones included rich harmonic overtones characteristic of plucked-string timbre, with a rapid attack and natural decay. All recordings were captured at a 192-kHz sampling rate (PCM-D10, Sony, Tokyo, Japan) and preserved the full overtone structure, including high-frequency components above the audible range. Because the major and minor stimuli were matched in duration, loudness (<85 dB),⁸ and overall temporal structure, differences in neural response may reflect the combination of timbral characteristics and harmonic mode.

Subjective Scale

Subjective assessments of arousal and stress levels during the pre-stimulus resting condition and sound presentation were performed using a visual analog scale (VAS). Additionally, subjective scores for arousal level, stress level, comfort, mood elevation, and favorability toward harp sounds were also measured using the VAS, immediately after each condition. The VAS consisted of a 10-cm horizontal line with the extreme left representing “not at all” and the extreme right indicating “very much.”

EEG Recordings

We used a telemetric system for the EEG recording to minimize the restraining of the subject. EEG and eye blink movements were continuously recorded at a sampling frequency of 1 KHz (LiveAMP, STE-101411-0374, Brain Product, Gilching, Germany) from 32 scalp electrodes according to the International 10–20 system.¹⁶ The linked right earlobe electrodes were used as the reference. Eye blinks were detected by the bipolar vertical electrooculogram channel, which was generated by subtracting the activity of the infraorbitally placed electrode from the activity of the superorbitally placed electrode. The channel was 0.5-Hz high-pass filtered to remove direct current offsets. Impedances were maintained below 5 kΩ. All data were saved by the EEG software installed on a tablet computer via a USB connected to the amplifier.

Measurement of Reaction Time to Auditory and Visual Stimuli

Reaction times to auditory and visual stimuli were tested during the pre-stimulus resting condition and after the presentation of each sound. The reaction time measurements to auditory and visual stimuli are available online (https://wtetsu.github.io/reaction-time/).

Auditory and visual stimuli were delivered via a computer stereo and screen, respectively (Lenovo, IBM, Japan), placed 50 cm in front of the subjects. For auditory stimuli (70 db sound pressure level), subjects were asked to click the mouse button when an auditory stimulus was presented. For visual stimuli, subjects were asked to click the mouse button when a square presented on the screen (10 × 2.5 cm) changed from gray to yellow. The order of presentation was randomly assigned. Reaction times were automatically measured as the time between the presentation of the stimuli and the mouse button click.

Statistical analysis

Subjective scores for arousal level, stress level, comfortableness, mood elevation, and favorability of harp sounds were first assessed using the Shapiro-Wilk test. Because none of the scores for any of the conditions were normally distributed (P < 0.05), we used the Kruskal-Wallis non-parametric one-way analysis of variance (ANOVA) in SPSS Statistics (IBM SPSS Statistics, version 23.0, IBM Corp., Armonk, NY). Post-hoc tests with Bonferroni correction (P < 0.005) were performed for comparisons that yielded significant differences in the Kruskal-Wallis test. Reaction times to auditory and visual stimuli were compared among the four conditions (ie, pre-stimulus resting condition and the two major and two minor sounds) using a one-way ANOVA.

EEG Data Analysis

Offline pre-processing of EEG data was carried out using EEG Lab toolbox for MATLAB (2020a).¹⁷ Data were first band-pass filtered between 1 and 40 Hz to minimize slow drifts and remove high-frequency components and notch filtered at 50 Hz to remove the power line noise. Visual inspection was performed on continuous data, where EEG channels affected by major noise sources throughout the whole experiment were identified and temporarily removed from the analyses. Prominent artifacts affecting all the recording channels were also removed from the data. Data were then re-referenced to the common average reference and decomposed using Independent Component Analysis (ICA) with the extended Infomax algorithm as implemented in EEGLab.^18,19 Continuous data were then segmented into epochs of 180 s for each condition. A final visual inspection was performed to check the quality of the cleaned data and remove any noisy epochs retained.

The sensor level of EEG analysis was conducted using non-parametric cluster-based permutation tests in FieldTrip²⁰ to correct for multiple comparisons, as described previously.²¹ Briefly, each electrode was subjected to a paired sample t-test, and t-values over an a priori threshold (cluster alpha = 0.05) were grouped into clusters according to a predefined neighboring channel template (EEG 1010 system). Cluster-based statistics were computed by taking the sum of the t-values within every cluster. Statistical comparisons were performed for the maximum values of the summed t-values. A permutation test was applied to test the statistic for the distribution of the maximum of the summed cluster t-values. Cluster-based permutation tests were performed for each frequency band (ie, theta [4-8 Hz], alpha [8-13 Hz], and beta waves [13-30 Hz]) for the four conditions (ie, two major and two minor harp sounds). Correction for multiple comparisons was performed using the Bonferroni method. Data from all channels were entered into the analysis. We performed multiple comparisons between sensor level (electrodes) for each frequency band and the four conditions: two major harp sound conditions and two minor harp sound conditions.

All statistical analyses for power spectral density were performed using SPSS Statistics.

Each condition was considered continuous because there were no external triggers, and the duration was the same across all conditions and electrodes. Given the findings of a previous EEG study²² and the significant result of the FieldTrip multiple comparisons, our primary focus was on prefrontal alpha-band oscillations (electrodes F7 and F8) during the presentation of harp sounds and depression level.

Power spectral density was measured for each epoch using Welch's overlapped segment averaging estimator. We extracted the power spectral density of the two alpha bands (8-13 Hz), alpha 1 (8-10 Hz) and alpha 2 (10-13 Hz), from the F7 and F8 electrodes of each subject during the four conditions. These were compared among the four conditions using a one-way ANOVA after confirming that the data were normally distributed via the Shapiro-Wilk test (P > 0.05). The unit for power spectral density is arbitrary unit (a.u.), which represents the power distribution of the signal per frequency component.

Before performing the multiple regression analysis, we calculated Pearson's correlation coefficients for the relationships among the STAI and SDS scores and F7 and F8 alpha (both alpha 1 and alpha 2) power. We used a false discovery rate (FDR) of P < 0.05 to correct for multiple comparisons.²³ We then conducted multiple regression analysis with interactions to compare the SDS score-alpha power slopes between the pre-stimulus resting state and the major sound condition and between the pre-stimulus resting state and the minor sound condition using nominal dummy variables (dummy 1 as the major sound and dummy 2 as the minor sound). For each regression model, we entered alpha 1 or alpha 2 power as the dependent variable, and alpha 1 and alpha 2 were analyzed separately for F7 and F8 electrodes. We performed exploratory Pearson correlation analysis between the suppression level of F7 alpha power and reaction time. P-values were corrected using FDR correction.

Results

The demographic data are presented in Table 1, and the subjective scale scores are presented in Table 2.

Table 1.

Demographic Data.

	Subjects
Total(M/F), No.	28(Female, 16/Male, 12)
Age, y	35.7 ± 10.1
Years of education	17.1 ± 2.5
SDS	32.1 ± 7.8
STAI(TRAIT)	45.4 ± 4.6
STAI(STATE)	42 ± 6.3

M, male; F, female; SDS, Self-rating Depression Scale; STAI, Spielberger's state and trait anxiety scale.

Table 2.

Subjective Scale Scores for the Resting and Harp Sound Conditions.

	Rest	Major1	Major2	Minor1	Minor2
Arousal	0.49 ± 0.32	0.56 ± 0.31	0.55 ± 0.28	0.51 ± 0.30	0.56 ± 0.30
Stress	0.22 ± 0.22	0.14 ± 0.17	0.13 ± 0.12	0.14 ± 0.16	0.16 ± 0.18

	Major1	Major2	Minor1	Minor2
Comfortable	0.75 ± 0.19	0.76 ± 0.17	0.73 ± 0.21	0.72 ± 0.22
Mood elevation	0.47 ± 0.30	0.46 ± 0.28	0.49 ± 0.28	0.50 ± 0.28
Favorability	0.64 ± 0.24	0.66 ± 0.19	0.68 ± 0.20	0.65 ± 0.22

*P < 0.05.

There were no significant differences in arousal level (chi-square = 0.61, P = 0.96). However, there was a significant difference in stress level (chi-square = 19.26, P < 0.001). Post-hoc comparisons indicated that stress level differed significantly between the pre-stimulus resting condition and all harp sound conditions (pre-stimulus rest vs major 1, P = 0.001; pre-stimulus rest vs major 2, P < 0.001; pre-stimulus rest vs minor 1, P = 0.001; pre-stimulus rest vs minor 2, P = 0.001). There were no significant differences in stress level among the harp sound conditions (all P > 0.005). Because there were no differences in subjective scale scores between major 1 and 2 conditions and between minor 1 and 2 conditions, we averaged the two values to obtain one score each for the major and minor harp sound conditions. These were then used for the subsequent correlation analyses with alpha 1 and alpha 2 power density.

EEG Cluster-Based Permutation Test

The cluster-based permutation tests of the t-values showed significantly different topological maps among the four conditions (Figure 1A). A significantly lower alpha band (8-13 Hz) was observed in the left frontal regions (F7, FT9, FC5, and T7) during major chord harp music than during minor chord harp music. Figure 1B shows a topological representation of the grand-average alpha power across all subjects for each condition. Because there were no differences in alpha values between major 1 and 2 conditions and between minor 1 and 2 conditions (all P > 0.05, Table 1 and Supplemental Figures 1 and 2), we averaged the two alpha values to obtain a single value for each sound condition.

Figure 1.

(A) Topographical representations of grand-average power spectral density (PSD) across conditions and subjects (N = 28). The cluster-based permutation test revealed a significant decrease during major harp condition in the alpha band (8–13 Hz) in the left frontal region (F7, FT9, FC5, and T7). (B) Topological representation of the grand-average alpha band across all subjects for the major and minor harp sound conditions.

Correlation Analysis and Multiple Regressions with Interaction Analysis

We additionally examined whether subjective ratings (stress, comfort, arousal, mood elevation, favorability) were associated with frontal alpha suppression.

Although some correlations were present in the resting condition, no robust or significant correlations were observed between subjective ratings and alpha suppression during harp listening after false-discovery-rate correction (Supplemental Table 2a–c). Therefore, subjective–physiological coupling could not be confirmed in this dataset.

Figure 2A–D shows the alpha power and SDS score slopes between the pre-stimulus resting and major sound conditions and between the pre-stimulus resting and minor sound conditions. We found a significant interaction between the major harp condition and the pre-stimulus resting condition and between the minor harp condition and the pre-stimulus resting condition for the relationship between SDS score and F7 alpha 1 power (both P < 0.05). There were significant main effects of SDS score for F7 alpha 2 power, F8 alpha 1 power, and F8 alpha 2 power. However, there were no interactions between each sound condition and the pre-stimulus resting condition. Results are presented in Table 3.

Figure 2.

Slopes of the relationship between Self-rating Depression Scale (SDS) score and alpha power (F7 and F8 alpha 1 and 2 power) for pre-stimulus resting condition vs. major sound condition and pre-stimulus resting condition vs. minor sound condition. The regression lines of the major harp (red line) and minor harp (blue line) conditions were compared with that of the pre-stimulus resting condition (gray line) to test the interaction between the slopes. Power (a.u), arbitrary units.

Table 3.

Statistical Details of the Regression with Interaction Analysis.

	Standardized Coefficients Beta	t	Sig.
(a) F7 Alpha 1
SDS	−0.1	−0.78	0.433
Major harp sound*SDS	−0.36	−2.7	0.009
Minor harp sound*SDS	−0.28	−2.1	0.03
F. statistic	F(2,60) = 4.17	P = 0.02
(b) F8 Alpha 1
SDS	−0.28	−2.3	0.02
Major harp sound*SDS	−0.2	−1.5	0.13
Minor harp sound*SDS	−0.17	−1.29	0.2
F. statistic	F(3,64) = 3.71	P = 0.01
(c) F7 Alpha 2
SDS	−0.32	−2.7	0.008
Major harp sound*SDS	−0.2	−1.41	0.65
Minor harp sound*SDS	−0.11	−0.77	0.44
F. statistic	F(3,60) = 3.39	P = 0.01
(d) F8 Alpha 2
SDS	−0.41	−3.4	0.001
Major harp sound*SDS	−0.16	−1.22	0.22
Minor harp sound*SDS	−0.11	−0.8	0.39
F. statistic	F(3,61) = 5.79	P = 0.002

Reaction Time and F7 Power

There were no differences in reaction times to auditory or visual stimuli between the major and minor conditions (auditory: F = 0.22, P = 0.87; visual: F = 0.49, P = 0.68). Therefore, reaction time was averaged across the major and minor conditions.

We conducted an exploratory analysis to understand whether suppression of F7 alpha 1 power during major and minor sound presentation is associated with reaction time. The F7 alpha power value during each condition was subtracted from that of the pre-stimulus resting condition, and a higher resulting value indicated higher suppression of F7 alpha 1 power. There was a significant negative correlation between suppression of F7 alpha 1 and auditory reaction time for major sound presentation (r = −0.58, P < 0.02, FDR correction, P = 0.04; Figure 3). This indicated that subjects with higher suppression of F7 alpha 1 power had a shorter reaction time, especially following major sound presentation. There was no relationship between F7 alpha 1 suppression level and reaction time for minor sound presentation (r = 0.41, P = 0.11, FDR correction, P = 0.22). Moreover, there was no significant correlation between F7 suppression level and reaction time for the visual stimuli of major or minor sound stimuli (major: r = −0.3, P = 0.18; minor: r = −0.03, P = 0.91, FDR correction, P = 0.24 and P = 0.91, respectively).

Figure 3.

The relationship between reaction time to auditory stimuli and suppression level of F7 alpha 1 activity. The suppression level was determined by subtracting the F7 alpha power of the pre-stimulus resting condition from that of the major harp condition. (a.u), arbitrary units.

Discussion

The current study aimed to investigate the effect of major and minor harp music on frontal alpha EEG activity and its relationship with anxiety, depression, and subjective scale scores of arousal, stress level, comfort, mood elevation, and favorability of harp sounds. Alpha power for the F7 electrode differed significantly among all electrodes and conditions. In addition, the power spectral densities of alpha 1 and alpha 2 extracted from F7 and F8 electrodes were significantly associated with depression level during major and minor harp conditions. Subjects with a low level of depression had higher alpha frontal alpha power during the pre-stimulus resting condition, and this frontal alpha power was suppressed during harp sound conditions. This alpha suppression was not observed in subjects with a higher depression level. Additionally, high F7 alpha 1 suppression was associated with shorter reaction time to auditory stimuli; however, this association should be interpreted as exploratory.

F7 Alpha 1 Power Suppression of the Left Frontal cortex in low Depression Score Individuals During the Harp Sound Conditions

There was a significant interaction between the pre-stimulus resting condition and the sound conditions (both major and minor) for the slope of the relationship between F7 alpha 1 power and depression level. As shown in Figure 2A, harp sounds (both major and minor) had a particularly strong effect on individuals with a low level of depression (ie, alpha 1 power was suppressed). Previous EEG research has suggested that high alpha amplitudes reflect a state of reduced information processing, which is a concept based on the idling or relaxed state.²⁴ Alpha power suppression is observed when individuals open their eyes even in a completely dark room, which suggests that alpha suppression is triggered by top-down processes.²⁵ Furthermore, high perception performance has been shown to be associated with low alpha power.¹³ Klimesh et al¹¹ suggest that alpha suppression, interpreted as desynchronization, reflects a state of comparatively high excitability, whereas high alpha amplitudes reflect a state of inhibition. Therefore, the alpha power suppression observed during the harp sound conditions in individuals with low depression scores may indicate the involvement of higher cognitive processes of the frontal cortex. Indeed, this higher cognitive process is consistent with the results of the reaction time to auditory stimuli. The suppression in individuals with lower depression scores in our study was associated with shorter reaction time to auditory stimuli, but not visual stimuli. The major harp sound share of the auditory processing may lead to efficient auditory response processing, as reflected in the faster behavioral responses. However, we note that the observed association between frontal alpha suppression and auditory reaction times is exploratory and correlational in nature. Future studies with larger samples and more targeted experimental designs are needed to further clarify this relationship.

Laterality of the F7 Alpha Power

The suppression of the alpha 1 power was particularly notable in the left hemisphere.

Alpha power has been shown to decrease in the left frontal cortex while listening to familiar music,²⁵ which suggests that alpha suppression reflects increased attention or arousal. Although we found no relationship between arousal level and alpha power, left frontal alpha 1 suppression was exhibited by those with low depression scores.

Malekemohammadai et al¹² suggest that musical sequences are encoded into and retrieved from long-term memory, and decreases in alpha power may reflect such long-term memory processes. We speculate that the alpha 1 power suppression observed in the present study was attributed to the encoding of musical sequences into memory. Notably, this suppression was specific to those with lower depression levels.

Intriguingly, this alpha 1 power phenomenon was observed only in the left frontal F7 region. Platel et al^26,27 suggest that nonverbal semantic processes help sustain semantic musical representations, and the left hemisphere is primarily responsible for such processes. Furthermore, the left frontal cortex may be associated with semantic musical memory processes of encoding and retrieval, particularly left middle temporal cortex regions, such as the hippocampus. These processes may be crucial for desynchronization (alpha rhythm suppression) of the left frontal cortex.

F7 Alpha 1 Power in Individuals with High Depression Scores During the Harp Sound Conditions

Suppression of F7 alpha 1 power was not observed in subjects with high depression scores during the harp sound conditions. High depression individuals showed lower alpha power even during the pre-stimulus resting condition, and this low alpha power was sustained during the harp sound presentation. Previous study report that individuals with depressive disorder exhibit lower alpha band-spectral power, reflecting reduced synchronization of the thalamocortical networks.²⁸ lower alpha activity has been suggested as a biomarker for depressive episodes.²⁹ Although our subjects were healthy individuals, the variety of depression levels had a significant effect across the alpha frequency range. This finding is consistent with the previous results^28,29 showing an association between depression level and alpha-band oscillations in individuals with depression. The magnitude of the alpha rhythm is associated with intercortical and thalamocortical neural networks, and synchronization and desynchronization of this rhythm determines information processing and working memory.³⁰ We suggest that a lower level of depression support a higher alpha rhythm magnitude, facilitating suppression during cognitive engagement and leading to improved auditory processing. However, it is important to note that the simple auditory and visual reaction time tasks used in this study primarily reflect general auditory attention and basic processing speed, rather than higher-order cognitive processes. Thus, the observed correlation between frontal alpha suppression and auditory reaction time should be interpreted as exploratoryand limited to low-level attentional mechanisms. Future studies employing more complex, theory-driven behavioral paradigms—such as emotional Stroop tasks (emotional interference), dot-probe paradigms (attentional bias), or self-referential encoding tasks (negative self-processing)—will be necessary to clarify the functional significance of music-evoked neural modulation in relation to cognitive mechanisms relevant to depression. Although alpha suppression in low-depression individuals was accompanied by faster auditory reaction times, we did not find significant correlations between alpha suppression and subjective ratings of comfort or stress. This suggests that, within this experimental design, neural modulation induced by harp sounds did not translate directly into self-reported affective changes. Future studies employing more sensitive, continuous, or physiological affective measures may better capture subjective relevance.

A key finding of our study was that harp sounds were more effective for low-depression individuals than for high-depression individuals. This raises the question of whether harp music has a potential therapeutic use for those with a high level of depression. A meta-analysis of clinical studies showed that music therapy, as an addition to conventional treatment, offers greater therapeutic benefit for depressive symptoms than conventional treatment alone.³¹ Preclinical evidence further supports the biological relevance of music: recent animal models³² have shown that daily music exposure reduces depression- and anxiety-like behaviors, normalizes oxidative stress and inflammatory markers, and preserves synaptic integrity in the hippocampus and prefrontal cortex. Such findings suggest that music may modulate neural and immune pathways, offering a potential mechanistic basis for its therapeutic effects.

In future studies, we plan to test the effect of harp sounds on brain rhythms in both healthy subjects and those with varied levels of depression including those with major depressive disorder. The effect of harp sounds on the EEG rhythm may be even clearer in patients with a high level of depression. Additionally, this study used recorded harp sounds, real harp sounds may induce stronger physiological changes when applied in clinical settings.^9,10 Indeed, harp sounds contain hypersonic sounds (those that contain high-frequency components above 20 kHz), which have been shown to induce strong physiological effects.³³ Therefore, the effect of live harp sounds on EEG alpha power in depression patients may be worth investigating in future research.

Limitations

Several limitations of the present study must be noted. Although the sample size was determined a priori using G*Power based on previous harp music studies, the sample of 28 participants offers limited statistical power for complex regression and interaction analyses involving continuous individual-difference variables. Thus, slope estimates and EEG–behavior associations should be interpreted with caution. Larger and more diverse samples, including clinically diagnosed populations, will be necessary to replicate and extend these findings. The methodological design also presents several constraints. The study used short, 3-min recorded harp excerpts. Such brief exposures may not capture the full temporal dynamics of music-induced neural modulation. Longer or continuous exposure—and especially live harp performance—may produce different or stronger effects on alpha-band activity. Future studies should examine duration-dependent and ecologically valid responses. Another important limitation is that harp music was compared only against a pre-stimulus resting baseline. This design allows us to conclude that listening to harp sounds differs from sitting quietly with eyes closed, but cannot establish whether the observed effects are specific to harp timbre. Similar neural responses might arise with other instruments, other musical genres, or even acoustically structured non-musical sounds. Therefore, our conclusions should be restricted to the particular auditory stimuli used in this study. Future research should incorporate more rigorous control conditions such as (a) other relaxing music (eg, piano), (b) neutral acoustic stimuli matched in tempo, pitch range, or spectral envelope, or (c) emotionally contrasting music.

Such comparisons will be essential to determine stimulus specificity. In addition, because both major and minor conditions used the same instrument, the present design does not allow us to disentangle effects of harp timbre from effects of musical mode. Control stimuli such as synthetic tones matched in acoustic features, music from different instruments, or controlled spectral-envelope sounds will be necessary to separate timbre-related and structural contributions. Finally, although the study distinguished between major and minor harp sounds, the two conditions did not produce markedly different effects on frontal alpha activity or reaction time. This null effect suggests that for an instrument such as the harp, intrinsic timbral properties and spectral richness may exert a dominant relaxing or attentional influence that overrides emotional nuances typically conveyed by musical mode. Future research with broader musical controls will be required to clarify the relative influence of timbre, mode, and acoustic structure.

Conclusion

In this exploratory study, we investigated the effects of major and minor harp music on frontal alpha EEG activity and their relationship with anxiety, depression, and subjective states. Alpha power in frontal regions was strongly dependent on depression level: individuals with low depression scores exhibited higher baseline alpha and showed alpha 1 suppression during harp sound presentation. This suppression was associated with shorter auditory reaction times. These neural and behavioral patterns may reflect desynchronization mechanisms of the left frontal cortex, although the findings remain preliminary and require replication with larger samples and more rigorous controls.

Supplemental Material

sj-doc-1-chp-10.1177_2515690X261418393 - Supplemental material for Neural and Behavioral Effects of Harp Music: Frontal Alpha Suppression and Reaction Times in Relation to Depression-Related Traits

Supplemental material, sj-doc-1-chp-10.1177_2515690X261418393 for Neural and Behavioral Effects of Harp Music: Frontal Alpha Suppression and Reaction Times in Relation to Depression-Related Traits by Misako Matsui, Yuri Masaoka, Nobuyoshi Koiwa, Motoyasu Honma, Akira Yoshikawa, Shota Kosuge, Miku Kosuge, Daiki Shoji, Shunsuke Sakakura, Katsunori Inagaki and Masahiko Izumizaki in Journal of Evidence-Based Integrative Medicine

Footnotes

Acknowledgments

The authors would like to thank Mr Shouta Ogasawara (Physio-Tech, Co. LTD., Tokyo, Japan) for technical advice for the EEG measurements. We thank Sarina Iwabuchi, PhD, from Edanz () for editing a draft of this manuscript.

ORCID iD

Yuri Masaoka

Ethics Approval and Consent to Participate

The study was approved by the Institutional Review Board of Showa University Hospital and the Ethical Committees of Showa University School of Medicine (clinical trial identifier 21-218-A). All participants provided written informed consent.

Author Contributions

Conceptualization, Misako Matsui and Yuri Masaoka; Data curation, Yuri Masaoka and Nobuyoshi Koiwa; Formal analysis, Misako Matsui, Yuri Masaoka and Nobuyoshi Koiwa; Funding acquisition, Yuri Masaoka; Investigation, Misako Matsui, Yuri Masaoka, Nobuyoshi Koiwa and Akira Yoshikawa; Methodology, Misako Matsui, Yuri Masaoka, Nobuyoshi Koiwa and Motoyasu Honma; Project administration, Yuri Masaoka; Resources, Akira Yoshikawa, Shota Kosuge, Miku Kosuge, Daiki Shoji and Shunsuke Sakakura; Supervision, Yuri Masaoka, Katsunori Inagaki and Masahiko Izumizaki; Visualization, Yuri Masaoka and Motoyasu Honma; Writing – original draft, Misako Matsui and Yuri Masaoka; Writing – review & editing, Yuri Masaoka, Motoyasu Honma and Masahiko Izumizaki.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a Grant-in-Aid for Scientific Research from KAKENHI (grant number 24K14231) and the Showa University Research Fund.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data used in this study are available from the corresponding author upon request.

Supplemental Material

Supplemental material for this article is available online.

References

Dileo

Bradt

. Medical Music Therapy: A Meta-analysis and Agenda for Future Research. Jeffrey Books; 2005.

Naylor

Kingsnorth

Lamont

McKeever

Macarthur

. The effectiveness of music in pediatric healthcare: A systematic review of randomized controlled trials. Evid Based Complement Alternat Med. 2011;2011:464759. doi:10.1155/2011/464759

Hilliard

. Music therapy in hospice and palliative care: A review of the empirical data. Evid Based Complement Alternat Med. 2005;2(2):173‐178. doi:10.1093/ecam/neh076

Ormston

Howard

Gallagher

Mitra

Jaschke

. The role of music therapy with infants with perinatal brain injury. Brain Sci. 2022;12(5):578. doi:10.3390/brainsci12050578

Lee

Bhattacharya

Sohn

Verres

. Monochord sounds and progressive muscle relaxation reduce anxiety and improve relaxation during chemotherapy: A pilot EEG study. Complement Ther Med. 2012;20(6):409‐416. doi:10.1016/j.ctim.2012.07.002

Shin

Kim

. Music therapy on anxiety, stress and maternal-fetal attachment in pregnant women during transvaginal ultrasound. Asian Nurs Res (Korean Soc Nurs Sci. 2011;5(1):19‐27.

Bradt

Dileo

Myers-Coffman

Biond

. Music interventions for improving psychological and physical outcomes in people with cancer. Cochrane Database Syst Rev. 2021;10(10):CD006911.

Geffen

Braun

. Effects of geometric sound on brainwave activity patterns, autonomic nervous system markers, emotional response, and Faraday wave pattern morphology. Evid Based Complement Alternat Med. 2024;2024:9844809. doi:10.1155/2024/9844809

Aragon

Farris

Byers

. The effects of harp music in vascular and thoracic surgical patients. Altern Ther Health Med. 2002;8(5):52‐60.

10.

Chiasson

Baldwin

McLaughlin

Cook

Sethi

. The effect of live spontaneous harp music on patients in the intensive care unit. Evid Based Complement Alternat Med. 2013;2013:428731. doi:10.1155/2013/428731

11.

Klimesch

Sauseng

Hanslmayr

. EEG Alpha oscillations: The inhibition-timing hypothesis. Brain Res Rev. 2007;53(1):63‐88. doi:10.1016/j.brainresrev.2006.06.003

12.

Malekmohammadi

Ehrlich

Rauschecker

Cheng

. Listening to familiar music induced continuous inhibition of alpha and low-beta power. J Neurophysiol. 2023;129(4):1344‐1358. doi:10.1152/jn.00269.2022

13.

Ergenoglu

Demiralp

Bayraktaroglu

Ergen

Beydagi

Uresin

. Alpha rhythm of the EEG modulates visual detection performance in humans. Cogn Brain Res. 2004;20(3):376‐383. doi:10.1016/j.cogbrainres.2004.03.009

14.

Spielberger

Gorsuch

Lushene

Vagg

Jacobs

. Manual for the State-Trait Anxiety Inventory (Form Y1-Y2). Consulting Psychologists Press; 1983.

15.

Zung

. A self-rating depression scale. Arch Gen Psychiatry. 1965;12:63‐70. doi:10.1001/archpsyc.1965.01720310065008

16.

Jasper

. The ten-twenty electrode system of the international federation. Electroencephalogr Clin Neurophysiol. 1958;10:371‐375.

17.

Delorme

Makeig

. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9‐21. doi:10.1016/j.jneumeth.2003.10.009

18.

Cardoso

. Infomax and maximum likelihood for blind source separation. IEEE Signal Process Lett. 1997;4(4):112‐114. doi:10.1109/97.566704

19.

Delorme

Sejnowski

Makeig

. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage. 2007;34(4):1443‐1449. doi:10.1016/j.neuroimage.2006.11.004

20.

Maris

Oostenveld

. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007;164(1):177‐190. doi:10.1016/j.jneumeth.2007.03.024

21.

Pizzamiglio

Naeem

Abdalla

Turner

. Neural correlates of single- and dual-task walking in the real world. Front Hum Neurosci. 2017;11:460. doi:10.3389/fnhum.2017.00460

22.

Gollan

Hoxha

Chihade

Pflieger

Rosebrock

Cacioppo

. Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression. Biol Psychol. 2014;99:198‐208. doi:10.1016/j.biopsycho.2014.03.003

23.

Benjamini

Hochberg

. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289‐300. doi:10.1111/j.2517-6161.1995.tb02031.x

24.

Adrian

Matthews

. The Berger rhythm: Potential changes from the occipital lobes in man. Brain. 1934;57(4):355‐385. doi:10.1093/brain/57.4.355

25.

Moosmann

Ritter

Krastel

, et al. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage. 2003;20(1):145‐158. doi:10.1016/S1053-8119(03)00344-6

26.

Platel

Price

Baron

, et al. The structural components of music perception: A functional anatomical study. Brain. 1997;120(2):229‐243. doi:10.1093/brain/120.2.229

27.

Platel

Baron

Desgranges

, et al. Semantic and episodic memory of music are subserved by distinct neural networks. Neuroimage. 2003;20(1):244‐256. doi:10.1016/S1053-8119(03)00287-8

28.

Kuno

. The physical basis of alpha waves in the electroencephalogram and the origin of the ‘Berger effect’. Biol Cybern. 2005;92(3):92‐100.

29.

Bokhan

Galkin

Vasilyeva

. EEG Alpha band characteristics in patients with a depressive episode within recurrent and bipolar depression. Consort Psychiatr. 2023;4(3):5‐12.

30.

Basar

. Alpha oscillations shed new light on relation between EEG and single neurons. Neurosci Res. 1999;33(2):79‐80.

31.

Aalbers

Fusar-Poli

Freeman

, et al. Music therapy for depression. Cochrane Database Syst Rev. 2017;11:CD004517. doi:10.1002/14651858.CD004517.pub3

32.

Qiu

Chen

Cheng

. Music prevents stress-induced depression and anxiety-like behavior in mice. Transl Psychiatry. 2023;13(1):317. doi:10.1038/s41398-023-02606-z

33.

Fukushima

Yagi

Kawai

Honda

Nishina

Oohashi

. Frequencies of inaudible high-frequency sounds differentially affect brain activity: Positive and negative hypersonic effects. PLoS One. 2014;9(4):e95464. doi:10.1371/journal.pone.0095464

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.23 MB