Maria Agapaki
Elizabeth A. Pinkerton
Efthymios Papatzikis- 1Department of Early Childhood Education and Care, Oslo Metropolitan University, Oslo, Norway
- 2Independent Researcher, Dubai, United Arab Emirates
Introduction
The brain function on music has been a recently developing field of neuroscience that holds a position of great interest to neuroscientists, psychologists, health professionals, and musicians alike. The primary goal of investigating music and the brain has been to determine particular neural correlates that are involved in or altered by the engagement of humans with music. It can be said that the study of the neuroscience behind music is a discussion regarding human behavior, environmental stimuli, and how that can be represented in our physiology, as well as how our brain structure allows us to interact with such stimuli in a unique and functional way.
Music is a complex phenomenon that employs, from very early in life, widespread neural activity in interconnected regions of sensory perception (Papatzikis et al., 2019), and ranging from the auditory cortex (Brattico et al., 2006; Allen et al., 2017) to the motor system (Furukawa et al., 2017; Bashwiner and Bacon, 2019) during active and passive music listening or instrument training. By the same token, the music's impact has been greatly correlated to both ontogenetic and phylogenetic neuroplastic changes (Papatzikis and Rishoni, 2022), showcasing a strong link to human brain function proven through a plethora of neuroimaging modalities [please see for example (Lin et al., 2010; Cross and Fujioka, 2019) for EEG/ERP studies; (Donnay et al., 2014) for fMRI; (Chacon-Castano et al., 2017) for MEG; (Moore et al., 2014) for DTI; (Sluming et al., 2002) for Voxel-based Morphometry]. However, emerging data suggest that the association between music and the brain is markedly more intricate than simply the response to sensory stimuli. For instance, music has been implicated in contexts of emotional, social, cultural, and biological influence (Peretz, 2006; Koelsch, 2018; Savage, 2019; Savage et al., 2020). Developmental neuroscience has studied the processing and perception of music in the fetal and infant brain and its selective role in environmental enrichment and socioemotional development (Papatzikis and Papatziki, 2016; Chorna et al., 2019; Arrasmith, 2020; Papatzikis et al., 2021). Mental health research suggests the potential benefits of music in alleviating symptoms in a variety of neurological and affective disorders ranging from depression and schizophrenia to dementia (Van de Winckel et al., 2004; Talwar et al., 2006; Lin et al., 2011; Gustavson et al., 2021). Cognitive studies of attention have even observed executive system efficiency differences when comparing the attentional network test (ANT) scores of the alerting and orienting networks of musicians and non-musicians (Medina and Barraza, 2019).
Considering music research encompasses a plethora of fields in psychology and neuroscience, and that the current advancements in neuroimaging technologies have made research questions of interest in the field substantially more feasible and diversified, our investigations require a sufficient foundation of quality and validity that ensures the field to move in an effective progression. The assessment of the primary qualitative and quantitative studies that drive the field forward is a necessary systematic review to acknowledge and evade issues like bias, inadequate methodology, or a reproducibility crisis. Previous exemplary studies of the analysis of research quality in other various fields have summarized challenges and subsequent directions, as in the field of population neuroscience (Paus, 2010), or have even provided guidelines to address future studies, as in the field of the neuroscience of information systems (Brocke and Liang, 2014). Both approaches advocate such consideration to obtain and maximize the potential of neuroscience research.
Therefore, the quality and logistics of research are significant factors that must be adequately regulated to set a standardized precedent for future experimentation within the field. Without doing so, research in music and neuroscience enables the risk of error, bias, and deficient methodology which, in turn, impedes the progression of the field. For instance, in the field of behavioral neuroscience, Bespalov and Steckler (2018) suggest a current lack of quality control sparked by criticisms regarding poor design, misreporting, and lack of power. Recognizing that the alternative for inadequate research quality would be that which is credible and valuable, it can be implied there are two possible directions for the neuroscience of music. What current research indicates might give us an understanding of which direction that would be, as well as what to do to avoid such devaluation, further justifying the importance of such quality studies.
The current debate
A great and controversial discussion referring to whether and under what conditions music is involved in the intricate network of cognition, emotional regulation, autonomic activity, behavioral and psychophysiological responses, and ultimately in people's mental health (Lin et al., 2011) has emerged from researchers in the field of psychology and neuroscience (Swaminathan and Schellenberg, 2018). While most of the researchers have expressed optimism about the benefits of music on cognition (Schellenberg, 2004; Slater et al., 2015; Tierney et al., 2015; Jaschke et al., 2018; Nan et al., 2018; Barbaroux et al., 2019), treatment of psychiatric disorders (Ho et al., 2003; Degé and Schwarzer, 2011; Chacón-Moscoso et al., 2016; Fang et al., 2017) as well as on people's overall wellbeing (Hsu and Lai, 2004), others have found this enthusiasm unjustified (Sala and Gobet, 2020), trying to explain and delineate some research quality failures that arise in the neuroplasticity and music field.
More specifically, in regards to the optimistic point of view, music-based intervention approaches, and practices favor research designs in this domain in relation to implementation and utilitarianism (Reybrouck and Eerola, 2017). That is, music can be experienced without necessitating a dedicated sensory organ, the ears, which are its main perceptual apparatus, and after vibrations through the peripheral nervous system, epithelium, and bones induce neuroplastic changes in the human brain. An example of this conclusion is that fetuses and deaf individuals can perceive and respond to music (Chorna et al., 2019). Likewise, the infants respond to the rhythmic dimensions of their mothers' speech and emotional tone because of humans' innate ability to engage with the “communicative musicality” of conversation (Lin et al., 2011). Also, researchers have found evidence of far-transfer effects related to “therapeutic” traits, or biological and cognitive paths of development (Miendlarzewska and Trost, 2013; Carter and Panisch, 2020). Typically, this means that a wide range of complex cognitive, emotional, behavioral, and psychophysiological responses can be adjusted through music and, as a result, the mental health of patients with various psychiatric disorders can be improved (Lin et al., 2011; Clift, 2012; Gustavson et al., 2021). Indeed, Gustavson et al. (2021) have claimed that therapies with active music participation, and structured and multiple sessions have significant positive effects on mood disorders (e.g., depression). Furthermore, Sanfilippo et al. (2021) have supported that passive music listening reduces anxiety and pain during labor, anxiety symptoms during pregnancy, and postnatal depression.
On the contrary, evidence of systematic reviews or meta-analyses has shown a pessimistic point of view, as described by few, concerning the music's usage and possible direct link to neuroplasticity. According to this literature, there is limited understanding and no clear evidence of how music, directly and indirectly, contributes to mental health (Lin et al., 2011; Gustavson et al., 2021). Also, the potential causational role of music in cognitive or academic development is very weak (Schellenberg, 2020) and conclusions of causation are precluded (Swaminathan and Schellenberg, 2018). Therefore, music does not reliably ameliorate psychiatric disorders (Lin et al., 2011) and enhance cognitive or academic skills, and there are non-pragmatic neuroplastic changes due to the inability to be reported as a causational link between music and neuroplastic development (Schellenberg, 2020, p. 430). As a result, positive correlational findings are probably due to confounding (e.g., individual differences) or unidentified variables (i.e., genetic, or demographic factors) that contribute to the confounding ones (Schellenberg, 2020, p. 431). Besides, far transfer effects of music on development and mental health appear to be an extremely rare occurrence, an over-optimistic and incorrect view, as they stem from a misinterpretation of the empirical data and possibly confirmation bias (Sala and Gobet, 2020). For example, Swaminathan et al. (2017) showed that the correlation between fluid intelligence and engagement in music in a sample of adults was mediated by innate personality factors (i.e., music aptitude) and not trained music skills. Based on this finding, the hypothesis that music training boosts cognition or academic skills cannot be supported. In another example of reviews investigating the effect of listening to music on anxiety symptoms during pregnancy, Sanfilippo et al. (2021) have argued that the positive correlational effect comes from the predominant use of self-reported measures and, as a consequence, it is not evident the exact mechanism through which music achieves the reduction of pregnant women's anxiety symptoms.
Moreover, the results of far transfer studies seem to be inconclusive or contradictory, due to non-specific or occasional methods used, absence of proper and structured classification of far transfer, lack of a structured understanding of music and musicality, as well as differences in neural activation during the processing of the tasks (Jaschke et al., 2013; Fang et al., 2017). An example that depicts the lack of uniformity in the test methods used is when comparing two different results of far transfer studies: on the one hand, Ho et al. (2003) did not show a positive transfer effect of music on visual memory, but on the other hand, Schellenberg (2004) did show a positive effect of music on intelligence using two different and non-specific IQ measures (Raven's standard matrices and general intelligence). Although both studies analyzed intelligence, Schellenberg (2004) may have a stronger effect sensitivity because of the generalized measures used. Also, as far as the contrasting far transfer results, some researchers claim that musicians may be at higher risk for mental health problems (Wesseldijk et al., 2019), but others suggest the opposite (Teorell et al., 2014; Johnson et al., 2017). As a result, there are no strong generalizations from their findings because of the variability in outcome measures and music intervention used from one study to another (Lin et al., 2011). In the same vein, immature field implementation of methods used and many times non-replicable findings are present in studies investigating the effect of music therapy on Alzheimer's Disease (AD) (Fang et al., 2017). For that reason, Fang et al. (2017) have argued that there are not as many clinical trials as possible with cohort, randomized, blinded, and rigorous methodological investigations for music's therapeutic effect on the topic of AD.
Additionally, meta-analyses that examined the causal link between musical and non-musical abilities reported skeptical results as this link is not clear-cut or, in the case of correlational studies, these associations are not always evident (Swaminathan and Schellenberg, 2018). Indeed, there are studies that their findings are more liable to yield a positive effect of music on cognition because the researchers adopt non-standard pedagogies (e.g., training in music-listening skills rather than teaching participants to sing or play an instrument) (Swaminathan and Schellenberg, 2018) (for example please see Degé and Schwarzer, 2011). Apart from that, firm conclusions referring to the protective effect of music on various psychiatric conditions are difficult to be drawn due to the mixed quality regulation shown in many studies (i.e., small sample sizes, lack of appropriate control groups, few interventions with multiple sessions, omitted necessary information such as inclusion/exclusion criteria regarding the intervention, lack of masking of interviewers during post-test, and randomization concealment) (Wesseldijk et al., 2019). As a result, it seems that some researchers cannot clarify how music leads to greater people's health and wellbeing (Gustavson et al., 2021).
Discussing the ways forward
Many different neuroscientific and clinical studies have proven that music possesses a beneficial role in cognitive, behavioral, and emotional development (Miendlarzewska and Trost, 2013; Carter and Panisch, 2020), improving also the overall psychophysiological health of patients with various psychiatric disorders (Lin et al., 2011; Gustavson et al., 2021; Sanfilippo et al., 2021). Nonetheless, according to the aforementioned systematic reviews or meta-analyses (please see above for more details), various factors contribute to a blurred outcome due to non-unified research methods, inconsistent results, misinterpretation or distortion of empirical data, and low reproducibility and replicability of scientific findings, to name just a few. Whilst not all of these factors are necessarily problematic, we believe there is “room” for improvement and development in this research field. For this reason, we propose some state-of-the-art approaches to minimizing the frequency of commonly observed limitations, based on potential solutions that are observed to be universal in empirical research (Lin et al., 2011; Boutron and Ravaud, 2018; Brown et al., 2018; Jaschke et al., 2018; Sala and Gobet, 2020; Gustavson et al., 2021; Ganley et al., 2022), and likewise can be applied to this specific domain, too.
More specifically, as far as the limitation of non-unified research methods and heterogeneous results, research should perhaps be conducted through more longitudinal randomized controlled trials (RCTs) combined with both clinical and neuroscientific outcome measures, uniform methodological investigations (e.g., different kinds of control groups) and analysis of sub-groups of tasks proposed (Lin et al., 2011; Fang et al., 2017; Jaschke et al., 2018). Following this perspective, more reliable, accurate, and powerful results, and a consistent research protocol on far transfer from music to cognition/mental health studies can be produced. Besides, the interconnection between music, cognition, and mental health will be better disentangled, perhaps implementing new strategies for music therapy (Lin et al., 2011).
As far as the restriction of the results' ineffectiveness is concerned, some researchers suggest that a near transfer effect between music and personal development (e.g., self-esteem) could much easier be successful as it focuses on more technical and visible concepts (i.e., self-esteem) rather than far transfer one that represents a broader and less visible concept among music and cognitive development (e.g., music and cognitive development in mathematics) (Sala and Gobet, 2020). On the contrary, more demanding experimental intervention studies should be propitious on the limitations' resolution. That is, the use of larger samples, and improved reporting standards, combined with not only psychological, physiological, and neurochemical aspects, but also with genetic and environmental designs (e.g., longitudinal twin and family studies, for more details please see Purcell, 2002), neuroimaging methods (focusing on the reward's circuitry activation, and dynamic patterns of brain activity in health and disease due to music), and biobanks of electronic health record (EHRs) databases estimating music-mental health and other related health associations in large samples (Gustavson et al., 2021). As a consequence, the exact interactive mechanism across music and existing risk factors supporting mental health and overall wellbeing can be more easily explored by employing the above approaches.
Moreover, regarding the problem of data misinterpretation or distortion, we can suggest five different and essential solutions to resolve this limitation. Firstly, important information on the full experiment protocol, statistical analysis plan, or sequence of analytical choices and raw data for all designed research should be accessible, reducing the risk of confirmation bias (Boutron and Ravaud, 2018). Secondly, maximization of the effect-to-bias ratio in research through random group assignment, incorporation of blinding, and heterogeneity (if possible) into the design should be accomplished to enhance generalizability (Boutron and Ravaud, 2018). Thirdly, editors' journals should provide recommendations on how results should be interpreted (e.g., guidelines for the proper use and interpretation of statistical tools), how the conclusions should be reported, and how to avoid misrepresentation of data (Boutron and Ravaud, 2018). Fourth, researchers should not only raise evidence for systematic reviews or meta-analyses but also evaluate the quality of the papers themselves following standardized scoring systems (e.g., the “QualSyst” tool developed by Kmet et al., 2004) specialized for this kind of inquiry (Chacón-Moscoso et al., 2016). Lastly, additional training and tools for peer-reviewers and editors should be included in identifying misrepresentation in clinical research reports and outcome measures (Boutron and Ravaud, 2018).
Finally, in relation to the low reproducibility and replicability of scientific findings, in one way, universities should increase awareness of problems in research quality and teach their solutions by adding additional courses (e.g., courses on study design or statistical analysis) (Brown et al., 2018). In another way, proper funding and personnel for more pilot and feasibility studies should be important, minimizing many small, non-randomized studies with cross-sectional survey data, and a variety of non-validated questionnaires (Brown et al., 2018). Also, partnerships across researchers in academia and research organizations outside of academia (e.g., music industries) or private companies should result in the reassurance of data integrity and results in quality generated in this domain (Ganley et al., 2022).
Conclusion
Due to the implementation of non-unified and blurred quality methodological standards, more justification and work are needed to explain music's possible causal link to neuroplasticity. Support of future studies may be apprehended through comparable and inter-connected related outcomes, allowing the presentation of new knowledge content and processes. Therefore, given the variability in published results in the specific field and the difficulty of interpreting these results, the development of stronger, more thorough, and more uniform research methods in quality is urgent (Jaschke et al., 2013) as has already been stated in the past (Chacón-Moscoso et al., 2016). Solutions mentioned previously such as RCTs, larger sample size, the 5 methods of overcoming data bias and error, and more extensive incorporation of study-validity and feasibility assessment would be our suggested manner of approaching such objectives. Thus, future studies of far transfer from music to mental health and cognitive development could be more reliable and accurate (Jaschke et al., 2013). However, these solutions should not be considered a panacea, as science will certainly continue to evolve in the future. Instead, they should be approached as “food-for-thought” for researchers, publishers, regulators, and stakeholders (e.g., funding agencies) to move forward.
Author contributions
EPa and MA: conceptualization. EPa: methodology and supervision. MA and EPi: validation and resources. MA, EPi, and EPa: writing—original draft preparation and writing—review and editing. MA: project administration. All authors have read and agreed to the published version of the manuscript.
Funding
The article publication charge for this work was funded by Oslo Metropolitan University.
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.
References
Allen, E. J., Burton, P. C., Olman, C. A., and Oxenham, A. J. (2017). Representations of pitch and timbre variation in human auditory cortex. J. Neurosci. 37, 1284–1293. doi: 10.1523/JNEUROSCI.2336-16.2016
Arrasmith, K. (2020). Infant music development and music experiences: a literature review. Appl. Res. Music Educ. 38, 9–17. doi: 10.1177/8755123319889669
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
Bashwiner, D., and Bacon, D. (2019). Musical creativity and the motor system. Curr. Opin. Behav. Sci. 27, 146–153. doi: 10.1016/j.cobeha.2018.12.005
Bespalov, A., and Steckler, T. (2018). Lacking quality in research: Is behavioral neuroscience affected more than other areas of biomedical sciences? J. Neurosci. 300, 4–9. doi: 10.1016/j.jneumeth.2017.10.018
Boutron, I., and Ravaud, P. (2018). Misrepresentation and distortion of research in biomedical literature. Proc. Natl. Acad. Sci. U. S. A. 115, 2613–2619. doi: 10.1073/pnas.1710755115
Brattico, E., Tervaniemi, M., Näätänen, R., and Peretz, I. (2006). Musical scale properties are automatically processed in the human auditory cortex. Brain Res. 1117, 162–174. doi: 10.1016/j.brainres.2006.08.023
Brocke, J. V., and Liang, T. P. (2014). Guidelines for neuroscience studies in information systems research. J. Manag. Inf. Syst. 30, 211–234. doi: 10.2753/MIS0742-1222300408
Brown, A. W., Kaiser, K. A., and Allison, D. B. (2018). Issues with data and analyses: errors, underlying themes, and potential solutions. Proc. Natl. Acad. Sci. U. S. A. 115, 2563–2570. doi: 10.1073/pnas.1708279115
Carter, T. E., and Panisch, L. S. (2020). A systematic review of music therapy for psychosocial outcomes of substance use clients. Int. J. Ment. Health Addict. 19, 1–8. doi: 10.1007/s11469-020-00246-8
Chacon-Castano, J., Rathbone, D. R., Hoffman, R., Yang, H., Pantazis, D., Yang, J., et al. (2017). “Music and the brain-design of an MEG compatible piano,” in 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (Jeju Island: IEEE), 521–524.
Chacón-Moscoso, S., Sanduvete-Chaves, S., and Sánchez-Martín, M. (2016). The development of a checklist to enhance methodological quality in intervention programs. Front. Psychol. 7, 1811. doi: 10.3389/fpsyg.2016.01811
Chorna, O., Filippa, M., Sa De Almeida, J., Lordier, L., Monaci, M. G., Hüppi, P., et al. (2019). Neuroprocessing mechanisms of music during fetal and neonatal development: a role in neuroplasticity and neurodevelopment. Neural Plast. 5, 1–9. doi: 10.1155/2019/3972918
Clift, S. (2012). “Singing, wellbeing, and health,” in Music, Health, and Wellbeing, eds Macdonald, R. A. R., Kreutz, G., and Mitchell, L. A. (Oxford: Oxford University Press), 113–124.
Cross, K., and Fujioka, T. (2019). Auditory rhyme processing in expert freestyle rap lyricists and novices: an ERP study. Neuropsychologia 129, 223–235. doi: 10.1016/j.neuropsychologia.2019.03.022
Degé, F., and Schwarzer, G. (2011). The effect of a music program on phonological awareness in preschoolers. Front. Psychol. 2, 124. doi: 10.3389/fpsyg.2011.00124
Donnay, G. F., Rankin, S. K., Lopez-Gonzalez, M., Jiradejvong, P., and Limb, C. J. (2014). Neural substrates of interactive musical improvisation: an FMRI study of ‘trading fours' in jazz. PLoS ONE 9, e88665. doi: 10.1371/journal.pone.0088665
Fang, R., Ye, S., Huangfu, J., and Calimag, D. P. (2017). Music therapy is a potential intervention for cognition of Alzheimer's Disease: a mini-review. Transl. Neurodegener. 6, 1–8. doi: 10.1186/s40035-017-0073-9
Furukawa, Y., Uehara, K., and Furuya, S. (2017). Expertise-dependent motor somatotopy of music perception. Neurosci. Lett. 22, 650, 97–102. doi: 10.1016/j.neulet.2017.04.033
Ganley, E., Coriat, A. M., Shenow, S., and Prosser, D. (2022). Systemic problems require systemic solutions: the need for coordination and cooperation to improve research quality. BMC Res. Notes 15, 51. doi: 10.1186/s13104-022-05932-5
Gustavson, D. E., Coleman, P. L., Iversen, J. R., Maes, H. H., Gordon, R. L., and Lens, M. D. (2021). Mental health and music engagement: review, framework, and guidelines for future studies. Transl. Psychiatry 11, 1–13. doi: 10.1038/s41398-021-01483-8
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
Hsu, W. C., and Lai, H. L. (2004). Effects of music on major depression in psychiatric inpatients. Arch. Psychiatr. Nurs. 18, 193–199. doi: 10.1016/j.apnu.2004.07.007
Jaschke, A. C., Eggermont, L. H., Honing, H., and Scherder, E. J. (2013). Music education and its effect on intellectual abilities in children: a systematic review. Rev. Neurosci. 24, 665–675. doi: 10.1515/revneuro-2013-0023
Jaschke, A. C., Honing, H., and Scherder, E. J. A. (2018). Longitudinal analysis of music education on executive functions in primary school children. Front. Neurosci. 12, 103. doi: 10.3389/fnins.2018.00103
Johnson, J. K., Louhivuori, J., and Siljander, E. (2017). Comparison of well-being of older adult choir singers and the general population in Finland: a case-control study. Music Sci. 21, 178–194. doi: 10.1177/1029864916644486
Kmet, L. M., Lee, R. C., and Cook, L. S. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields. HTA Initiative 13, 1–31. Available online at: https://era.library.ualberta.ca/items/48b9b989-c221-4df6-9e35-af782082280e/view/a1cffdde-243e-41c3-be98-885f6d4dcb29/standard_quality_assessment_criteria_for_evaluating_primary_research_papers_from_a_variety_of_fields.pdf
Koelsch, S. (2018). Investigating the neural encoding of emotion with music. Neuron 98, 1075–1079. doi: 10.1016/j.neuron.2018.04.029
Lin, S. T., Yang, P., Lai, C. Y., Su, Y. Y., Yeh, Y. C., Huang, M. F., et al. (2011). Mental health implications of music: insight from neuroscientific and clinical studies. Harv. Rev. Psychiatry 19, 34–46. doi: 10.3109/10673229.2011.549769
Lin, Y. P., Wang, C. H., Jung, T. P., Wu, T. L., Jeng, S. K., Duann, J. R., et al. (2010). EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57, 1798–1806. doi: 10.1109/TBME.2010.2048568
Medina, D., and Barraza, P. (2019). Efficiency of attentional networks in musicians and non-musicians. Heliyon 5, 1–17. doi: 10.1016/j.heliyon.2019.e01315
Miendlarzewska, E. A., and Trost, W. J. (2013). How musical training affects cognitive development: rhythm, reward and other modulating variables. Front. Neurosci. 7, 279. doi: 10.3389/fnins.2013.00279
Moore, E., Schaefer, R. S., Bastin, M. E., Roberts, N., and Overy, K. (2014). Can musical training influence brain connectivity? Evidence from diffusion tensor MRI. Brain Sci. 4, 405–427. doi: 10.3390/brainsci4020405
Nan, Y., Liu, L., Geiser, E., Shu, H., Gong, C. C., Dong, Q., et al. (2018). Piano training enhances the neural processing of pitch and improves speech perception in Mandarin-speaking children. PNAS 115, 6630–6639. doi: 10.1073/pnas.1808412115
Papatzikis, E., Elhalik, M., Inocencio, S. A. M., Agapaki, M., Selvan, R. N., Muhammed, F. S., et al. (2021). Key challenges and future directions when running auditory brainstem response (ABR) research protocols with newborns: a music and language EEG feasibility study. Brain Sci. 11, 1–16. doi: 10.3390/brainsci11121562
Papatzikis, E., and Papatziki, S. (2016). Investigating heart rate and rhythm changes in an infant's music education course: a case study. Psychol. Music 44, 587–606. doi: 10.1177/0305735615584980
Papatzikis, E., and Rishoni, H. (2022). “What is music for neuroplasticity? Combined value on infant development and inclusion,” in Rethinking Inclusion and Transformation in Special Education, eds Efstratopoulou, M. (IGI Global). doi: 10.4018/978-1-6684-4680-5.ch010
Papatzikis, E., Svec, C., and Tsakmakidou, N. (2019). “Studying neural correlates of music features in the early years education and development process: a preliminary understanding based on a taxonomical classification and logistic regression analysis,” in Proceedings of the Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, Frontiers in Human Neuroscience. 10–11.
Paus, T. (2010). Population Neuroscience: why and How. Hum. Brain Mapp. 31, 891–903. doi: 10.1002/hbm.21069
Peretz, I. (2006). The nature of music from a biological perspective. Cognition 100, 1–32. doi: 10.1016/j.cognition.2005.11.004
Purcell, S. (2002). Variance components models for gene-environment interaction in twin analysis. Twin Res. 5, 554–571. doi: 10.1375/136905202762342026
Reybrouck, M., and Eerola, T. (2017). Music and its inductive power: a psychobiological and evolutionary approach to musical emotions. Front. Psychol. 8, 494. doi: 10.3389/fpsyg.2017.00494
Sala, G., and Gobet, F. (2020). Cognitive and academic benefits of music training with children: a multilevel meta-analysis. Mem. Cogn. 48, 1429–1441. doi: 10.3758/s13421-020-01060-2
Sanfilippo, K. R. M., Stewart, L., and Glover, V. (2021). How music may support perinatal mental health: an overview. Arch. Womens Ment. Health 24, 831–839. doi: 10.1007/s00737-021-01178-5
Savage, P. E. (2019). Cultural evolution of music. Palgrave Commun. 5, 1–12. doi: 10.1057/s41599-019-0221-1
Savage, P. E., Loui, P., Tarr, B., Schachner, A., Glowacki, L., Mithen, S., et al. (2020). Music as a coevolved system for social bonding. Behav. Brain Sci. 44, 1469–1825. doi: 10.31234/osf.io/qp3st
Schellenberg, E. G. (2004). Music lessons enhance IQ. Psychol. Sci. 15, 511–514. doi: 10.1111/j.0956-7976.2004.00711.x
Schellenberg, E. G. (2020). “Music training, individual differences, and plasticity,” in Educational Neuroscience, 1st ed, (Routledge), 415–441.
Slater, J., Skoe, E., Strait, D. L., O'Connell, S., Thompson, E., and Kraus, N. (2015). Music training improves speech-in-noise perception: longitudinal evidence from a community-based music program. Behav. Brain Res. 291, 244–252. doi: 10.1016/j.bbr.2015.05.026
Sluming, V., Barrick, T., Howard, M., Cezayirli, E., Mayes, A., and Roberts, N. (2002). Voxel-based morphometry reveals increased gray matter density in Broca's area in male symphony orchestra musicians. Neuroimage 17, 1613–1622. doi: 10.1006/nimg.2002.1288
Swaminathan, S., and Schellenberg, E. G. (2018). “Music training and cognitive abilities: associations, causes, and consequences,” in The Oxford Handbook of Music and Neuroscience, eds Michael, H. T., and Donald, A. H., (Oxford University Press), 645–661. doi: 10.1093/oxfordhb/9780198804123.013.26
Swaminathan, S., Schellenberg, E. G., and Khalil, S. (2017). Revisiting the association between music lessons and intelligence: training effects or music aptitude? Intelligence 62, 119–124. doi: 10.1016/j.intell.2017.03.005
Talwar, N., Crawford, M. J., Maratos, A., Nur, U., McDermott, O., and Procter, S. (2006). Music therapy for in-patients with schizophrenia: exploratory randomised controlled trial. Br. J. Psychiatry 189, 405–409. doi: 10.1192/bjp.bp.105.015073
Teorell, T., Lennartsson, A. K., Mosing, M. A., and Ullen, F. (2014). Musical activity and emotional competence - a twin study. Front. Psychol. 5, 774. doi: 10.3389/fpsyg.2014.00774
Tierney, A. T., Krizman, J., and Kraus, N. (2015). Music training alters the course of adolescent auditory development. PNAS 112, 10062–10067. doi: 10.1073/pnas.1505114112
Van de Winckel, A., Feys, H., De Weerdt, W., and Dom, R. (2004). Cognitive and behavioural effects of music-based exercises in patients with dementia. Clin. Rehabil. 18, 253–260. doi: 10.1191/0269215504cr750oa
Keywords: mental health, cognition, music, neuroplasticity, research quality, development, music neuroimaging
Citation: Agapaki M, Pinkerton EA and Papatzikis E (2022) Music and neuroscience research for mental health, cognition, and development: Ways forward. Front. Psychol. 13:976883. doi: 10.3389/fpsyg.2022.976883
Received: 23 June 2022; Accepted: 02 August 2022;
Published: 25 August 2022.
Edited by:
Barbara Colombo, Champlain College Neuroscience Lab, United StatesReviewed by:
Phakkharawat Sittiprapaporn, Mae Fah Luang University, ThailandCopyright © 2022 Agapaki, Pinkerton and Papatzikis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Efthymios Papatzikis, efthymio@oslomet.no