Event Abstract

Musical training is related to altered functional connectivity during statistical learning: an MEG study

  • 1 School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • 2 Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
  • 3 School of Biology, Faculty of Science, Aristotle University of Thessaloniki, Greece
  • 4 Leibniz Research Center for Working Enviroment and Human Factors, Germany
  • 5 German Center for Neurodegenerative Diseases (DZNE), Germany

Our innate ability to automatically segment a stream of sensory information on the basis of the transitional probabilities that are inherent in the stimulus material is referred to as statistical learning. Recent neuroimaging studies show that long-term musical training may improve implicit learning of auditory material and enhance the ability to segment a stream of tones or syllables according to the underlying distributional properties of the material included in the stream. Although several neuroimaging studies have depicted the cortical regions that contribute to this process, there are still no studies analyzing changes in the extent of information sharing within the cortical networks related to statistical learning. The aim of the current study is to investigate functional connectivity alterations of the brain network that supports statistical learning using magnetoencephalographic (MEG) measurements, and to evaluate the reorganization of this network due to long-term musical training. To this aim we used MEG measurements comparing the cortical responses related to statistical learning in 15 musicians and 15 non-musicians and followed an approach in the analysis of the data that allowed us to quantify changes in the corresponding brain network via graph theory. Specifically, 2 sets of tone patterns were constructed from combinations of 11 pure tones having correspondingly 0.59 and 0.13 within sequence transitional probabilities. These tone pattern sets were randomly interleaved forming an oddball paradigm and presented in 3 runs. Current Density Reconstructions (CDRs) of the cortical activity of each subject, separately for each run’s correct and incorrect tone-patterns, were calculated using LORETA to solve the inverse problem. Its individual’s CDRs were calculated per sample point for the complete response time-window (i.e. 0 – 300 ms) of each condition and each run. In the following, CDR voxel time-series were used to calculate connectivity matrices while a node of the network was appointed to each voxel. Significant connections in each graph were identified using a general linear model approach comparing connectivity values of each node, condition and subject. Results showed that musicians had increased sharing of information between distributed brain regions underlying statistical learning and hence, increased cortical connectivity in comparison to non-musicians’ network. These findings indicate that long-term musical training enhances the cortical ability to use transitional probabilities in order to segment auditory streams, but at the same time it additionally reinforces the connectivity of brain networks that contribute to statistical learning. This result may have important implications with regard to potential therapeutic interventions based on musical training.

Keywords: functional connectivity, statistical learning, neuroscience of music, Cortical Plasticity, MEG

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral presentation in the Symposium in Neurosciences and Music

Topic: Symposium in Neurosciences and Music

Citation: Paraskevopoulos E, Chalas N, Kuchenbuch A, Herholz SC, Bamidis PD and Pantev C (2016). Musical training is related to altered functional connectivity during statistical learning: an MEG study. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00120

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Received: 01 Aug 2016; Published Online: 01 Aug 2016.

* Correspondence: Dr. Evangelos Paraskevopoulos, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece, parasvag@gmail.com

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