Training changes the EEG complexity and functional connectivity of precise timing prediction*

Jiayuan Meng, Xiaoyu Li, Yingru Zhao, Hongzhan Zhou, Minpeng Xu, Dong Ming
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Abstract

Precise timing prediction is the ability to estimate time in millisecond timescale, it can speed up behavior, optimize perception, benefit adaptive behaviors. Behavioral studies have demonstrated training can improve the performance of precise timing prediction. However, neural evidence is still lacking in describing how training changes the neural characteristics of precise timing prediction. This study designed a cue-tapping (task1)/ timing-in-mind (task2) experiment, collected behavioral and electroencephalogram (EEG) data of 24 subjects in both before and after training period. Sample entropy (SampEn) and Lempel-Ziv complexity (LZC) were calculated to measure EEG complexity, functional connectivity based on phase locking value was also involved. Consequently, behavioral results showed that error time declined and accuracy rate increased as training progressed. SampEn was much smaller after training in almost all frequency-bands in task1, and reduced in task2 as well. LZC showed a decreased tendency after training, but no statistical significance was found. Moreover, after training, much stronger functional connectivity was found in low frequency-band in both tasks. The results can shed light on the modeling of training and precise predictive timing.
训练改变了精确时间预测的脑电复杂度和功能连通性*
精确的时间预测是在毫秒时间尺度上估计时间的能力,它可以加速行为,优化感知,有利于自适应行为。行为学研究表明,训练可以提高精确计时预测的表现。然而,在描述训练如何改变精确时间预测的神经特性方面,仍然缺乏神经证据。本研究设计了提示敲击(task1)/记忆计时(task2)实验,收集了24名被试在训练前后的行为和脑电图数据。计算样本熵(SampEn)和Lempel-Ziv复杂度(LZC)来衡量脑电复杂度,并考虑基于锁相值的功能连通性。结果表明,随着训练的进行,错误时间减少,准确率提高。在task1的几乎所有频带训练后,SampEn要小得多,在task2中也减小了。训练后LZC有下降趋势,但无统计学意义。此外,经过训练后,在两个任务中,低频段的功能连通性都更强。研究结果可以为训练建模和精确的预测时机提供启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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