Abstract
The brain undergoes adaptive changes during learning. Spontaneous neural activity has been proposed to play an important role in acquiring new information and/or improve the interaction of task related brain regions. A promising approach is the investigation of resting state functional connectivity (rs-fc) and resting state networks, which rely on the detection of interregional correlations of ...
Abstract
The brain undergoes adaptive changes during learning. Spontaneous neural activity has been proposed to play an important role in acquiring new information and/or improve the interaction of task related brain regions. A promising approach is the investigation of resting state functional connectivity (rs-fc) and resting state networks, which rely on the detection of interregional correlations of spontaneous BOLD fluctuations. Using Morse Code (MC) as a model to investigate neural correlates of lexico-semantic learning we sought to identify patterns in rs-fc that predict learning success and/or undergo dynamic changes during a 10-day training period. Thirty-five participants were trained to decode twelve letters of MC. Rs-fMRI data were collected before and after the training period and rs-fc analyses were performed using a group independent component analysis. Baseline connectivity between the language-network (LANG) and the anterior-salience-network (ASN) predicted learning success and learning was associated with an increase in LANG ASN connectivity. Furthermore, a disconnection between the default mode network (DMN) and the ASN as well as the left fusiform gyrus, which is critically involved in MC deciphering, was observed. Our findings demonstrate that rs-fc can undergo behaviorally relevant changes within 10 training days, reflecting a learning dependent modulation of interference between task specific networks. (C) 2016 The Authors. Published by Elsevier Inc.
Altmetric