کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6862821 1439397 2018 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Brain electroencephalographic segregation as a biomarker of learning
ترجمه فارسی عنوان
جداسازی الکتروانسفالوگرافی مغز به عنوان نشانگر زیستی یادگیری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
The aim of the present study was to understand whether modeling brain function in terms of network structure makes it possible to find markers of prediction of motor learning performance in a sensory motor learning task. By applying graph theory indexes of brain segregation - such as modularity and transitivity - to functional connectivity data derived from electroencephalographic (EEG) rhythms, we further studied pre- (baseline) versus post-task brain network architecture to understand whether motor learning induces changes in functional brain connectivity. The results showed that, after the training session with measurable learning, transitivity increased in the alpha1 EEG frequency band and modularity increased in the theta band and decreased in the gamma band, suggesting that brain segregation is modulated by the cognitive task. Furthermore, it was observed that theta modularity at the baseline negatively correlated with the performance improvement; namely, the lower this connectivity index at the baseline pre-task period, the higher the improvement of performance with training. The present results show that brain segregation is modulated by the cognitive task and that it is possible to predict performance by the study of pre-task EEG rhythm connectivity parameters.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 106, October 2018, Pages 168-174
نویسندگان
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