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Adolescence is a unique period in which higher cognition develops to adult-level, while plasticity of neuron and behavior is at one of its peak. Notably, cognitive training studies for adolescents has been sparse and neural correlates of the training effects yet to be established. This study investigated the effects of multi-component training of cognitive control (MTCC) in order to examine whether the training enhanced adolescents' cognitive control ability and if the effects were generalizable to other cognitive domains. Cognitive control refers to the ability to adjust a series of thoughts and behaviors in correspondence to an internal goal, and involves inhibition, working memory, shifting, and dual tasking as subcomponents. The participants were middle school students (aged 11-14) and randomly assigned to either a training group or an active control group. The training group performed 30 min of MTCC per day for 6 weeks. To identify the training effects, we examined the cognitive performance, regional gray matter, and their relationship. The training group showed modest improvement in a visuospatial fluid intelligence test (Block Design) after MTCC, which was not significant after correcting for multiple comparisons. In addition, the training effect on the gray matter volume (time × group interaction) was observed in the right inferior cortex (rIFC). While the control group showed a typical reduction in the rIFC volume, the training group showed a relative increase in the homologous region. The relative change in rIFC volume was associated with the change in Stroop performance. These results imply that MTCC may affect brain structure relevant to inhibitory control process.
Pubmed ID: 31379541
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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