Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.
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).
Universidad Autónoma de Madrid, Madrid, Spain; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, USA. Electronic address: fjr@illinois.edu.
Francisco J
FJ
Román
Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
Yasser
Y
Iturria-Medina
Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain; Ciber del área de Salud Mental (CIBERSAM), Madrid, Spain.
Kenia
K
Martínez
Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
Sherif
S
Karama
Universidad Pompeu Fabra, Barcelona, Spain.
Miguel
M
Burgaleta
Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
Alan C
AC
Evans
University of California at Irvine, Irvine, USA.
Susanne M
SM
Jaeggi
Universidad Autónoma de Madrid, Madrid, Spain. Electronic address: roberto.colom@uam.es.
Roberto
R
Colom
D000293:Adolescent; D001288:Attention / Q000502:physiology*; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D063132:Connectome; D005260:Female; D006801:Humans; D007266:Inhibition, Psychological; D008279:Magnetic Resonance Imaging; D008570:Memory, Short-Term / Q000502:physiology*; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology*; D009483:Neuropsychological Tests; D055815:Young Adult
Brain plasticity; Connectome; Graph-theory indices; Network-based statistics (NBS)