Literature DB >> 28323202

Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

Francisco J Román1, Yasser Iturria-Medina2, Kenia Martínez3, Sherif Karama2, Miguel Burgaleta4, Alan C Evans2, Susanne M Jaeggi5, Roberto Colom6.   

Abstract

The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013).
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain plasticity; Connectome; Graph-theory indices; Network-based statistics (NBS)

Mesh:

Year:  2017        PMID: 28323202     DOI: 10.1016/j.nlm.2017.03.010

Source DB:  PubMed          Journal:  Neurobiol Learn Mem        ISSN: 1074-7427            Impact factor:   2.877


  8 in total

1.  Navigating the link between processing speed and network communication in the human brain.

Authors:  Govinda Poudel; Karen Caeyenberghs; Phoebe Imms; Juan F Domínguez D; Alex Burmester; Caio Seguin; Adam Clemente; Thijs Dhollander; Peter H Wilson
Journal:  Brain Struct Funct       Date:  2021-03-11       Impact factor: 3.270

2.  Cognitive Training in Parkinson's Disease Induces Local, Not Global, Changes in White Matter Microstructure.

Authors:  Chris Vriend; Tim D van Balkom; Henk W Berendse; Ysbrand D van der Werf; Odile A van den Heuvel
Journal:  Neurotherapeutics       Date:  2021-08-18       Impact factor: 7.620

3.  Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Authors:  Vasilis M Karlaftis; Joseph Giorgio; Petra E Vértes; Rui Wang; Yuan Shen; Peter Tino; Andrew E Welchman; Zoe Kourtzi
Journal:  Nat Hum Behav       Date:  2019-03-01

4.  Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training.

Authors:  Alexandru D Iordan; Katherine A Cooke; Kyle D Moored; Benjamin Katz; Martin Buschkuehl; Susanne M Jaeggi; John Jonides; Scott J Peltier; Thad A Polk; Patricia A Reuter-Lorenz
Journal:  Front Aging Neurosci       Date:  2018-01-04       Impact factor: 5.750

5.  Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.

Authors:  Erin D Anderson; J Sebastian Giudice; Taotao Wu; Matthew B Panzer; David F Meaney
Journal:  Front Bioeng Biotechnol       Date:  2020-04-15

6.  Eight-week multi-domain cognitive training does not impact large-scale resting-state brain networks in Parkinson's disease.

Authors:  Tim D van Balkom; Odile A van den Heuvel; Henk W Berendse; Ysbrand D van der Werf; Chris Vriend
Journal:  Neuroimage Clin       Date:  2022-01-30       Impact factor: 4.881

7.  White-Matter Pathways for Statistical Learning of Temporal Structures.

Authors:  Vasilis M Karlaftis; Rui Wang; Yuan Shen; Peter Tino; Guy Williams; Andrew E Welchman; Zoe Kourtzi
Journal:  eNeuro       Date:  2018-07-17

8.  MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions.

Authors:  Stephan Vogel; Martin Kaltenhäuser; Cora Kim; Nadia Müller-Voggel; Karl Rössler; Arnd Dörfler; Stefan Schwab; Hajo Hamer; Michael Buchfelder; Stefan Rampp
Journal:  Brain Sci       Date:  2021-11-30
  8 in total

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