Limits...
Electrophysiological evidences demonstrating differences in brain functions between nonmusicians and musicians.

Zhang L, Peng W, Chen J, Hu L - Sci Rep (2015)

Bottom Line: However, evidence demonstrating that long-term music training modulates higher brain functions is surprisingly rare.We observed that compared to nonmusicians, musicians have (1) larger high-frequency steady-state responses, which reflect the auditory information processing within the sensory system, and (2) smaller low-frequency vertex potentials, which reflect higher cognitive information processing within the novelty/saliency detection system.Therefore, we speculate that long-term music training facilitates "bottom-up" auditory information processing in the sensory system and enhances "top-down" cognitive inhibition of the novelty/saliency detection system.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China.

ABSTRACT
Long-term music training can improve sensorimotor skills, as playing a musical instrument requires the functional integration of information related to multimodal sensory perception and motor execution. This functional integration often leads to functional reorganization of cerebral cortices, including auditory, visual, and motor areas. Moreover, music appreciation can modulate emotions (e.g., stress relief), and long-term music training can enhance a musician's self-control and self-evaluation ability. Therefore, the neural processing of music can also be related to certain higher brain cognitive functions. However, evidence demonstrating that long-term music training modulates higher brain functions is surprisingly rare. Here, we aimed to comprehensively explore the neural changes induced by long-term music training by assessing the differences of transient and quasi-steady-state auditory-evoked potentials between nonmusicians and musicians. We observed that compared to nonmusicians, musicians have (1) larger high-frequency steady-state responses, which reflect the auditory information processing within the sensory system, and (2) smaller low-frequency vertex potentials, which reflect higher cognitive information processing within the novelty/saliency detection system. Therefore, we speculate that long-term music training facilitates "bottom-up" auditory information processing in the sensory system and enhances "top-down" cognitive inhibition of the novelty/saliency detection system.

No MeSH data available.


The comparison of neural responses elicited by descending trains of quasi-steady-state auditory stimuli between nonmusicians and musicians.ERPs and TFDs of auditory-induced responses (single trial), auditory-evoked responses (average), and PLVs (group-level average; FCz-A1A2) are displayed from top to bottom for nonmusicians (left) and musicians (right) respectively. The region-of-interests (ROIs), outlined in purple curves, had (1) significantly different TFD values than those within the pre-stimulus interval and (2) significantly different TFD values between nonmusicians and musicians.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4559803&req=5

f4: The comparison of neural responses elicited by descending trains of quasi-steady-state auditory stimuli between nonmusicians and musicians.ERPs and TFDs of auditory-induced responses (single trial), auditory-evoked responses (average), and PLVs (group-level average; FCz-A1A2) are displayed from top to bottom for nonmusicians (left) and musicians (right) respectively. The region-of-interests (ROIs), outlined in purple curves, had (1) significantly different TFD values than those within the pre-stimulus interval and (2) significantly different TFD values between nonmusicians and musicians.

Mentions: Figure 4 shows the group-level average AEP waveforms (elicited by descending trains of quasi-steady-state auditory stimuli), baseline-corrected TFDs obtained from single-trial AEPs (auditory-induced responses) and single-subject average AEPs (auditory-evoked responses), as well as group-level average PLVs (FCz-A1A2; from top to bottom) for nonmusicians and musicians. All TFDs comprised clear responses located at low frequencies (i.e., 1–20 Hz) and high frequencies (i.e., 30–100 Hz). The low-frequency responses were phase-locked to each pulse of the quasi-steady-state auditory stimuli (showed in PLVs; Fig. 4, fourth row) and corresponded to late-latency AEPs in the time domain (Fig. 4, first row). Even the high-frequency responses were also phase-locked to each pulse of the quasi-steady-state auditory stimuli; these high frequency responses were composed of not only the transient responses (early/middle-latency AEPs in the time domain; Fig. 4, first row), but also the quasi-steady-state responses, which strictly followed the frequency profile of the stimuli (Fig. 1, blue curve). ROI-based statistical analysis revealed that the time-frequency region showing a significant difference of evoked TFDs (ROI1) between post-stimulus responses and pre-stimulus responses as well as between nonmusicians and musicians was observed at 4356–4478 ms and 1–11 Hz (Fig. 4, third row; marked in purple). Within ROI1 (Fig. 5, top panel), permutation testing indicated that the measured magnitudes were significantly larger for nonmusicians than musicians (0.12 ± 0.10 μV2 vs. 0.05 ± 0.03 μV2; P = 0.014). ROI-based statistical analysis also revealed that the time-frequency regions showing a significant difference of PLVs between post-stimulus responses and pre-stimulus responses as well as between nonmusicians and musicians was observed at 632–1016 ms and 42–62 Hz (ROI2); 4318–4492 ms and 1–12 Hz (ROI3); 28–152 ms and 36–50 Hz, 2432–2584 ms and 30–50 Hz, 3810–3918 ms and 1–13 Hz, 4136–4334 ms and 40–50 Hz, 5294–5412 ms and 1–22 Hz (other ROIs; Fig. 4, fourth row; marked in purple). Since strikingly similar results were observed among low frequency ROIs, and among high frequency ROIs, only the ROI with the largest number of significant time-frequency pixels in the low-frequency region (<30 Hz; i.e., ROI3) and the ROI with the largest number of significant time-frequency pixels in the high-frequency region (≥30 Hz; i.e., ROI2) were illustrated. Within ROI2 (Fig. 5, middle panel), permutation testing indicated that the measured PLVs were significantly smaller for nonmusicians than musicians (0.31 ± 0.12 vs. 0.44 ± 0.11; P = 0.009). Within ROI3 (Fig. 5, bottom panel), permutation testing indicated that the measured PLVs were significantly larger for nonmusicians than musicians 0.22 ± 0.07 vs. 0.14 ± 0.05; P = 0.001).


Electrophysiological evidences demonstrating differences in brain functions between nonmusicians and musicians.

Zhang L, Peng W, Chen J, Hu L - Sci Rep (2015)

The comparison of neural responses elicited by descending trains of quasi-steady-state auditory stimuli between nonmusicians and musicians.ERPs and TFDs of auditory-induced responses (single trial), auditory-evoked responses (average), and PLVs (group-level average; FCz-A1A2) are displayed from top to bottom for nonmusicians (left) and musicians (right) respectively. The region-of-interests (ROIs), outlined in purple curves, had (1) significantly different TFD values than those within the pre-stimulus interval and (2) significantly different TFD values between nonmusicians and musicians.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4559803&req=5

f4: The comparison of neural responses elicited by descending trains of quasi-steady-state auditory stimuli between nonmusicians and musicians.ERPs and TFDs of auditory-induced responses (single trial), auditory-evoked responses (average), and PLVs (group-level average; FCz-A1A2) are displayed from top to bottom for nonmusicians (left) and musicians (right) respectively. The region-of-interests (ROIs), outlined in purple curves, had (1) significantly different TFD values than those within the pre-stimulus interval and (2) significantly different TFD values between nonmusicians and musicians.
Mentions: Figure 4 shows the group-level average AEP waveforms (elicited by descending trains of quasi-steady-state auditory stimuli), baseline-corrected TFDs obtained from single-trial AEPs (auditory-induced responses) and single-subject average AEPs (auditory-evoked responses), as well as group-level average PLVs (FCz-A1A2; from top to bottom) for nonmusicians and musicians. All TFDs comprised clear responses located at low frequencies (i.e., 1–20 Hz) and high frequencies (i.e., 30–100 Hz). The low-frequency responses were phase-locked to each pulse of the quasi-steady-state auditory stimuli (showed in PLVs; Fig. 4, fourth row) and corresponded to late-latency AEPs in the time domain (Fig. 4, first row). Even the high-frequency responses were also phase-locked to each pulse of the quasi-steady-state auditory stimuli; these high frequency responses were composed of not only the transient responses (early/middle-latency AEPs in the time domain; Fig. 4, first row), but also the quasi-steady-state responses, which strictly followed the frequency profile of the stimuli (Fig. 1, blue curve). ROI-based statistical analysis revealed that the time-frequency region showing a significant difference of evoked TFDs (ROI1) between post-stimulus responses and pre-stimulus responses as well as between nonmusicians and musicians was observed at 4356–4478 ms and 1–11 Hz (Fig. 4, third row; marked in purple). Within ROI1 (Fig. 5, top panel), permutation testing indicated that the measured magnitudes were significantly larger for nonmusicians than musicians (0.12 ± 0.10 μV2 vs. 0.05 ± 0.03 μV2; P = 0.014). ROI-based statistical analysis also revealed that the time-frequency regions showing a significant difference of PLVs between post-stimulus responses and pre-stimulus responses as well as between nonmusicians and musicians was observed at 632–1016 ms and 42–62 Hz (ROI2); 4318–4492 ms and 1–12 Hz (ROI3); 28–152 ms and 36–50 Hz, 2432–2584 ms and 30–50 Hz, 3810–3918 ms and 1–13 Hz, 4136–4334 ms and 40–50 Hz, 5294–5412 ms and 1–22 Hz (other ROIs; Fig. 4, fourth row; marked in purple). Since strikingly similar results were observed among low frequency ROIs, and among high frequency ROIs, only the ROI with the largest number of significant time-frequency pixels in the low-frequency region (<30 Hz; i.e., ROI3) and the ROI with the largest number of significant time-frequency pixels in the high-frequency region (≥30 Hz; i.e., ROI2) were illustrated. Within ROI2 (Fig. 5, middle panel), permutation testing indicated that the measured PLVs were significantly smaller for nonmusicians than musicians (0.31 ± 0.12 vs. 0.44 ± 0.11; P = 0.009). Within ROI3 (Fig. 5, bottom panel), permutation testing indicated that the measured PLVs were significantly larger for nonmusicians than musicians 0.22 ± 0.07 vs. 0.14 ± 0.05; P = 0.001).

Bottom Line: However, evidence demonstrating that long-term music training modulates higher brain functions is surprisingly rare.We observed that compared to nonmusicians, musicians have (1) larger high-frequency steady-state responses, which reflect the auditory information processing within the sensory system, and (2) smaller low-frequency vertex potentials, which reflect higher cognitive information processing within the novelty/saliency detection system.Therefore, we speculate that long-term music training facilitates "bottom-up" auditory information processing in the sensory system and enhances "top-down" cognitive inhibition of the novelty/saliency detection system.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China.

ABSTRACT
Long-term music training can improve sensorimotor skills, as playing a musical instrument requires the functional integration of information related to multimodal sensory perception and motor execution. This functional integration often leads to functional reorganization of cerebral cortices, including auditory, visual, and motor areas. Moreover, music appreciation can modulate emotions (e.g., stress relief), and long-term music training can enhance a musician's self-control and self-evaluation ability. Therefore, the neural processing of music can also be related to certain higher brain cognitive functions. However, evidence demonstrating that long-term music training modulates higher brain functions is surprisingly rare. Here, we aimed to comprehensively explore the neural changes induced by long-term music training by assessing the differences of transient and quasi-steady-state auditory-evoked potentials between nonmusicians and musicians. We observed that compared to nonmusicians, musicians have (1) larger high-frequency steady-state responses, which reflect the auditory information processing within the sensory system, and (2) smaller low-frequency vertex potentials, which reflect higher cognitive information processing within the novelty/saliency detection system. Therefore, we speculate that long-term music training facilitates "bottom-up" auditory information processing in the sensory system and enhances "top-down" cognitive inhibition of the novelty/saliency detection system.

No MeSH data available.