Cognitive control training enhances the integration of intrinsic functional networks in adolescents

Raihyung Lee; Seyul Kwak; Dasom Lee; Jeanyung Chey

doi:10.3389/fnhum.2022.859358

Cognitive control training enhances the integration of intrinsic functional networks in adolescents

Front Hum Neurosci. 2022 Nov 24:16:859358. doi: 10.3389/fnhum.2022.859358. eCollection 2022.

Authors

Raihyung Lee^{1

2}, Seyul Kwak^{1

3}, Dasom Lee¹, Jeanyung Chey¹

Affiliations

¹ Department of Psychology, Seoul National University, Seoul, South Korea.
² Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
³ Department of Psychology, Pusan National University, Busan, South Korea.

Abstract

Introduction: We have demonstrated that intensive cognitive training can produce sustained improvements in cognitive performance in adolescents. Few studies, however, have investigated the neural basis of these training effects, leaving the underlying mechanism of cognitive plasticity during this period unexplained.

Methods: In this study, we trained 51 typically developing adolescents on cognitive control tasks and examined how their intrinsic brain networks changed by applying graph theoretical analysis. We hypothesized that the training would accelerate the process of network integration, which is a key feature of network development throughout adolescence.

Results: We found that the cognitive control training enhanced the integration of functional networks, particularly the cross-network integration of the cingulo-opercular network. Moreover, the analysis of additional data from older adolescents revealed that the cingulo-opercular network was more integrated with other networks in older adolescents than in young adolescents.

Discussion: These findings are consistent with the hypothesis that cognitive control training may speed up network development, such that brain networks exhibit more mature patterns after training.

Keywords: adolescence; cognitive control; cognitive training; development; graph theory; resting-state fMRI.