Topological changes of the effective connectivity during the working memory training

Working memory (WM) refers to the retention of information over a short period of time. Accumulated evidence showed that training WM would lead to beneficial effects in untrained tasks, which could be attributed to the strengthening of the functional connections between brain regions through repeated training task. In this proof of concept investigation, we applied a graph theoretical approach to analyze the early changes of functional connectivity from two subjects undergoing a spatial n-back WM training task for three continuous days. A significant decreased clustering coefficient and normalized shortest path length was revealed, suggesting a reduced local efficiency with an increased global efficiency after WM training. Our findings thereby provide insightful implications for understanding the mechanisms of brain dynamics in cognitive training.

[1]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[2]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[3]  A. Baddeley.  Working memory: looking back and looking forward , 2003, Nature Reviews Neuroscience.

[4]  Shanbao Tong,et al.  Early Cortical Connective Network Relating to Audiovisual Stimulation by Partial Directed Coherence Analysis , 2009, IEEE Transactions on Biomedical Engineering.

[5]  Yvonne Brehmer,et al.  Working-memory training in younger and older adults: training gains, transfer, and maintenance , 2012, Front. Hum. Neurosci..

[6]  Yasuyuki Taki,et al.  Effects of working memory training on functional connectivity and cerebral blood flow during rest , 2013, Cortex.

[7]  Erika Borella,et al.  Working memory training in older adults: evidence of transfer and maintenance effects. , 2010, Psychology and aging.

[8]  Andrew R. A. Conway,et al.  Working memory capacity and its relation to general intelligence , 2003, Trends in Cognitive Sciences.

[9]  Tzyy-Ping Jung,et al.  Extended ICA Removes Artifacts from Electroencephalographic Recordings , 1997, NIPS.

[10]  T. Klingberg,et al.  Increased prefrontal and parietal activity after training of working memory , 2004, Nature Neuroscience.

[11]  M. Kaminski,et al.  Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method , 2003, Journal of Neuroscience Methods.

[12]  M. Buchsbaum,et al.  Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography , 1988 .

[13]  Yasuyuki Taki,et al.  Training of Working Memory Impacts Structural Connectivity , 2010, The Journal of Neuroscience.

[14]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[15]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[16]  E. McAuley,et al.  Frontiers in Aging Neuroscience Aging Neuroscience , 2022 .

[17]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[18]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[19]  Susanne M. Jaeggi,et al.  Short- and long-term benefits of cognitive training , 2011, Proceedings of the National Academy of Sciences.

[20]  Olaf Sporns,et al.  THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.

[21]  F. Tong,et al.  Training Improves Multitasking Performance by Increasing the Speed of Information Processing in Human Prefrontal Cortex , 2009, Neuron.

[22]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[23]  Susanne M. Jaeggi,et al.  Improving fluid intelligence with training on working memory: a meta-analysis , 2008, Psychonomic Bulletin & Review.

[24]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[25]  A. Bezerianos,et al.  Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks , 2014, Brain and Cognition.