Neural network retuning and neural predictors of learning success associated with cello training

Abstract

The auditory and motor neural systems are closely intertwined, enabling people to carry out tasks such as playing a musical instrument whose mapping between action and sound is extremely sophisticated. While the dorsal auditory stream has been shown to mediate these audio-motor transformations, little is known about how such mapping emerges with training. Here, we use longitudinal training on a cello as a model for brain plasticity during the acquisition of specific complex skills, including continuous and many-to-one audio-motor mapping, and we investigate individual differences in learning. We trained participants with no musical background to play on a specially designed MRI-compatible cello and scanned them before and after 1 and 4 wk of training. Activation of the auditory-to-motor dorsal cortical stream emerged rapidly during the training and was similarly activated during passive listening and cello performance of trained melodies. This network activation was independent of performance accuracy and therefore appears to be a prerequisite of music playing. In contrast, greater recruitment of regions involved in auditory encoding and motor control over the training was related to better musical proficiency. Additionally, pre-supplementary motor area activity and its connectivity with the auditory cortex during passive listening before training was predictive of final training success, revealing the integrative function of this network in auditory-motor information processing. Together, these results clarify the critical role of the dorsal stream and its interaction with auditory areas in complex audio-motor learning.

Inherent auditory skills rather than formal music training shape the neural encoding of speech

“…Insomuch as lesser noise reflects a greater efficiency in auditory processing and perception (34,35), the higher-quality neural representations we find among high PROMS scorers may allow more veridical readout of signal identity and thus account for their superior behavioral abilities. Our data are also consistent with recent fMRI findings demonstrating that the strength of (passively measured) resting-state connectivity between auditory and motor brain regions before training is related to better musical proficiency in short-term instrumental learning (43). These findings, along with current EEG data, suggest that intrinsic differences in neural function may predict outcomes in a variety of auditory contexts, from understanding someone at the cocktail party, where pitch and timbre cues are vital for understanding a noise-degraded talker (10,39,48), to success in music-training programs (43).…”

Short‐term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging

“…The motor system has been the subject of many neural plasticity studies in recent years (Bezzola, Mérillat, Gaser, & Jäncke, 2011;Dayan & Cohen, 2011;Diedrichsen & Kornysheva, 2015;Doyon, Gabitov, Vahdat, Lungu, & Boutin, 2018;Draganski et al, 2004;Herholz, Coffey, Pantev, & Zatorre, 2016;Muellbacher, Ziemann, Boroojerdi, Cohen, & Hallett, 2001;Sale et al, 2017;Sanes & Donoghue, 2000;Scholz et al, 2009;Zatorre, Carpentier, Segado, Wollman, & Penhune, 2018). Using various imaging techniques, it is possible to follow on both structural (Bezzola et al, 2011;Draganski et al, 2004;Rudebeck et al, 2009) and functional (Floyer-Lea & Matthews, 2005;Poldrack, 2000;Reithler, van Mier, & Goebel, 2010;Ungerleider, Doyon, & Karni, 2002;Zatorre et al, 2018) brain changes, mainly as a result of learning and memory of motor-related procedures.…”

“…The motor system has been the subject of many neural plasticity studies in recent years (Bezzola, Mérillat, Gaser, & Jäncke, 2011;Dayan & Cohen, 2011;Diedrichsen & Kornysheva, 2015;Doyon, Gabitov, Vahdat, Lungu, & Boutin, 2018;Draganski et al, 2004;Herholz, Coffey, Pantev, & Zatorre, 2016;Muellbacher, Ziemann, Boroojerdi, Cohen, & Hallett, 2001;Sale et al, 2017;Sanes & Donoghue, 2000;Scholz et al, 2009;Zatorre, Carpentier, Segado, Wollman, & Penhune, 2018). Using various imaging techniques, it is possible to follow on both structural (Bezzola et al, 2011;Draganski et al, 2004;Rudebeck et al, 2009) and functional (Floyer-Lea & Matthews, 2005;Poldrack, 2000;Reithler, van Mier, & Goebel, 2010;Ungerleider, Doyon, & Karni, 2002;Zatorre et al, 2018) brain changes, mainly as a result of learning and memory of motor-related procedures. Different parts of the brain were found to be activated in early stages of learning as opposed to later stages (Dayan & Cohen, 2011;Diedrichsen & Kornysheva, 2015;Doyon et al, 2018;Hikosaka, Nakamura, Sakai, & Nakahara, 2002;Lehéricy et al, 2005): Initial experience with a new motor learning task involves associative cerebellar and striatal regions, primary motor (M1), prefrontal and premotor cortices (Doyon et al, 2009;Doyon et al, 2018;Verwey, Shea, & Wright, 2014), whereas continuous practice is associated with increased contribution of the sensorimotor regions of the striatum (e.g., the putamen; Coynel et al, 2010;Lehéricy et al, 2005) and motor cortical regions (Dayan & Cohen, 2011;L...…”

Dynamic reconfiguration of functional subgraphs after musical training in young adults

“…In network neuroscience, greatly interconnected nodes known as modules can reconfigure over time when healthy human participants engage in motor skill learning [3], executive cognition tasks [4] and musical training [5]. Musical performance is one of the most complex skills, like instrument performance involving musical notations sight-reading, hand movements, auditory feedback and higher-order cognition mediation, which needs continuous and dynamic information integration of multiple functional systems [6][7][8].…”

“…When NMF is used for spatially and temporally overlapping subgraph detection in neuroimage, the functional connectivity matrix, concatenated from all subjects' functional networks in sliding time windows, can be decomposed into a matrix of subgraphs and a matrix of time-dependent coefficients that quantify the level of expression in each time window for each subgraph [13]. Other coactiviation pattern 6 driven approaches, such as principal component analysis (PCA) [21] or independent component analysis (ICA) [22], may yield positive or negative subgraph interactions and time-dependent expression coefficients. NMF enforces nonnegativity giving rise to the nonnegative combination of basis subgraphs and time-dependent expression coefficients, which eases the neurophysiological interpretability of the expressed functional subgraphs over time.…”