Studies showed that the top-down control of the prefrontal cortex (PFC) on sensory/motor cortices changes during cognitive aging. Although music training has demonstrated efficacy on cognitive aging, its brain mechanism is still far from clear. Current music intervention studies have paid insufficient attention to the relationship between PFC and sensory regions. Functional gradient provides a new perspective that allows researchers to understand network spatial relationships, which helps study the mechanism of music training that affects cognitive aging. In this work, we estimated the functional gradients in four groups, young musicians, young control, older musicians, and older control. We found that cognitive aging leads to gradient compression. Compared with young subjects, older subjects presented lower and higher principal gradient scores in the right dorsal and medial prefrontal and the bilateral somatomotor regions, respectively. Meanwhile, by comparing older control and musicians, we found a mitigating effect of music training on gradient compression. Furthermore, we revealed that the connectivity transitions between prefrontal and somatomotor regions at short functional distances are a potential mechanism for music to intervene in cognitive aging. This work contributes to understanding the neuroplasticity of music training on cognitive aging.From the conclusion paragraph:
In a nutshell, we demonstrate the top-down control of prefrontal regions to the somatomotor network, which is associated with inhibitory function and represents a potential marker of cognitive aging, and reveal that music training may work by affecting the connectivity between the two regions. Although this work has investigated the neuroplasticity of music on cognitive aging by recruiting subjects of different age spans, the present study did not include the study of longitudinal changes of the same group. Further studies should include longitudinal follow-up of the same groups over time to more accurately evaluate the effect of music intervention on the process of cognitive aging.
| Film | |
| Originally produced by The Great Courses in 2015. | |
| Summary | Probe the ongoing research into the effects of musical training on the microstructure of the brain, which points to cognitive benefits in areas such as speech processing. Focus on how learning to play a musical instrument influences language acquisition and reading ability in children.. |
| Film | |
| Originally produced by The Great Courses in 2015. | |
| Summary | Probe the ongoing research into the effects of musical training on the microstructure of the brain, which points to cognitive benefits in areas such as speech processing. Focus on how learning to play a musical instrument influences language acquisition and reading ability in children.. |
| Technical Details | Mode of access: World Wide Web. |
| Language | In English |
| Description | 1 online resource (streaming video file) (30 minutes): digital, .flv file, sound |
| Time | 003000 |
| Publisher # | 1168201 Kanopy |
Alicja M. Olszewska1*, 
Maciej Gaca1, 
Aleksandra M. Herman1, 
Katarzyna Jednoróg2 and 
Artur Marchewka1*Learning to play a musical instrument is a complex task that integrates multiple sensory modalities and higher-order cognitive functions. Therefore, musical training is considered a useful framework for the research on training-induced neuroplasticity. However, the classical nature-or-nurture question remains, whether the differences observed between musicians and non-musicians are due to predispositions or result from the training itself. Here we present a review of recent publications with strong focus on experimental designs to better understand both brain reorganization and the neuronal markers of predispositions when learning to play a musical instrument. Cross-sectional studies identified structural and functional differences between the brains of musicians and non-musicians, especially in regions related to motor control and auditory processing. A few longitudinal studies showed functional changes related to training while listening to and producing music, in the motor network and its connectivity with the auditory system, in line with the outcomes of cross-sectional studies. Parallel changes within the motor system and between the motor and auditory systems were revealed for structural connectivity. In addition, potential predictors of musical learning success were found including increased brain activation in the auditory and motor systems during listening, the microstructure of the arcuate fasciculus, and the functional connectivity between the auditory and the motor systems. We show that “the musical brain” is a product of both the natural human neurodiversity and the training practice.
The constantly changing environment, the drive for new knowledge and skills, all require behavioral flexibility. The brain, as the source of behavior, adapts its architecture and functions to perform new tasks through processes broadly defined as neuroplasticity. These processes include, among others, dynamic reconfiguration of neural connections, cell shape, size, myelination, synaptic strength and neurogenesis, the last one limited to the olfactory bulb and the hippocampus in adults (Tardif et al., 2016). In human neuroimaging studies, it is possible to indirectly measure macroscopic effects of the neuroplastic biological dynamics via functional and structural modalities (for the overview of the relationship between macroscopic measures and the underlying biology, see Tardif et al., 2016). Although usually measured separately, functional and structural neuroplasticity reflect various aspects of the same neuroplastic processes and are thus inherently intertwined in a complex manner.
We currently understand that the human brain is not shaped exclusively during critical periods of development. Neuroplastic changes occur in response to internal and external stimuli throughout the entire lifetime (Draganski and May, 2008). From a social perspective, neuroplasticity processes underlie such phenomena as education, neurological rehabilitation, or healthy aging.
Generally, in studies on neuroplasticity, two questions arise: what are the structural and functional changes related to a particular behavioral need, and how do they occur over time. To effectively answer these questions, we first need to elicit a novel behavior. There is a wide spectrum of learning protocols which were employed so far to understand neuroplasticity. Simple ones engage only a single sensory modality, like auditory (Zatorre et al., 2012) or tactile (Hodzic, 2004). More complex ones utilize sensorimotor associations and higher-order cognitive functions tasks, like the acquisition of foreign languages or tactile reading (Li et al., 2014; Siuda-Krzywicka et al., 2016). The complexity of music performance requires a unique and multi-system involvement from the human brain (Münte et al., 2002; Herholz and Zatorre, 2012; Schlaug, 2015). Playing a musical instrument requires sensorimotor adaptations, as with the use of any tool, and more: a mapping of specific movements to the auditorily perceived outcomes, which follow a set of more or less intuitively understood rules of musical harmony, esthetics and pleasure. It comprises both feed-forward and feedback interactions between the integrated multisensory input (tactile, proprioceptive, auditory, and visual) with motor output, as well as higher-order cognitive functions such as memory, attention, emotion, and the processing of musical syntax (Zatorre et al., 2007; Brown et al., 2015). Additionally, as rewarding stimuli are learned better than non-rewarding ones (Schultz, 2000), it is likely that the highly rewarding nature of musical performance promotes learning and drives brain plasticity (Penhune, 2019). Therefore, learning to play a musical instrument provides a useful framework to study multimodal brain plasticity.
Secondly, the changes in brain structure and function have to be sampled frequently enough to capture the dynamics of the neuroplastic processes. Brain volume changes do not relate to practice in a monotonically increasing way (Lövdén et al., 2013; Wenger et al., 2017). Yet, we observe continuous behavioral improvement and the extent of behavioral and plastic changes correlate with training duration. The proposed model of neuroplasticity includes a period of initial growth, after which comes a renormalization phase, when the efficiency of brain circuits increases while cortical volume does not (Wenger et al., 2017). From a functional perspective, plastic changes can be reflected in increased functional activation of a brain area related to a function, its expansion on neighboring areas, or an involvement of novel, often distant, areas. Interestingly, cortical map plasticity may also follow a comparable pattern of expansion followed by retraction to pre-training levels during learning as seen in structural changes (for review see Wenger et al., 2017). Therefore, the functional (and structural) expansion temporarily increases the available pool of circuits to be used “exploratively” until the most efficient circuit to perform the task is determined. As learning continues, the selected circuitry is further stabilized through practice, the performance increasingly relies on that circuit and thus the cortical map renormalizes (Wenger et al., 2017).
Two experimental approaches are typically employed in cognitive neuroscience to understand brain reorganization following training, namely the cross-sectional and the longitudinal design. Comparing musically naive and proficient individuals in cross-sectional studies can provide important insights into the neuroplasticity of the human brain (Münte et al., 2002). Musicians practice musical performance regularly for most of their lives, often starting in early childhood and practising for many years. Juxtaposing musicians and non-musicians can show changes associated with very long training. However, while it might be tempting, the causal relationship between musical training and the observed differences cannot be inferred from correlational studies (Schellenberg, 2019). The cross-sectional study design does not reveal the time course of the plastic changes nor does it correct for any possible predispositions. To infer causality, a theoretical model needs to be constructed and validated against properly designed longitudinal studies. Longitudinal studies can account for the interindividual variability pre-training, but are costly, with costs increasing with the duration of the experiments.
Finally, advances in non-invasive neuroimaging methods gave scientists specific tools to non-invasively study brain plasticity in living humans. Structural and functional neuroimaging techniques were used to compare brain anatomy and function between groups of musicians and non-musicians, and, more recently, to study the plastic changes related to musical training in longitudinal studies.
This review aims to present the newest evidence for experience-related neuroplasticity in the context of musical training in adults, concentrating on neuroimaging and with an emphasis on longitudinal studies. Since the scope of this review is limited, and the focus is on musical training as a model for studying brain plasticity in neurotypical adults, studies of complex developmental and aging-related changes are not discussed. We particularly focus on experimental designs in order to better understand both brain reorganization and the neuronal markers of predispositions when learning to play a musical instrument. Since we include studies which use a multitude of functional as well as structural neuroimaging techniques, we also provided a brief overview of such methods highlighting the advantages and disadvantages of each method for neuroplasticity research (Table 1).
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