Literature DB >> 19964951

Default network and intelligence difference.

Ming Song1, Yong Liu, Yuan Zhou, Kun Wang, Chunshui Yu, Tianzi Jiang.   

Abstract

In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles and prepare us for the future. Here, we modeled default network as undirected weighted graph and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.

Entities:  

Mesh:

Year:  2009        PMID: 19964951     DOI: 10.1109/IEMBS.2009.5334874

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  18 in total

1.  An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks.

Authors:  Sean L Simpson; Malaak N Moussa; Paul J Laurienti
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2.  Unique Mapping of Structural and Functional Connectivity on Cognition.

Authors:  Joelle Zimmermann; John D Griffiths; Anthony R McIntosh
Journal:  J Neurosci       Date:  2018-09-24       Impact factor: 6.167

3.  Changes in resting-state functionally connected parietofrontal networks after videogame practice.

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Journal:  Hum Brain Mapp       Date:  2012-07-17       Impact factor: 5.038

4.  Stochastic geometric network models for groups of functional and structural connectomes.

Authors:  Eric J Friedman; Adam S Landsberg; Julia P Owen; Yi-Ou Li; Pratik Mukherjee
Journal:  Neuroimage       Date:  2014-07-25       Impact factor: 6.556

5.  Neuropsychological impacts of indirect revascularization for pediatric moyamoya disease.

Authors:  WooHyun Kim; Eun-Young Lee; Seong-Eun Park; Eun-Kyung Park; Ju-Seong Kim; Dong-Seok Kim; Kyu-Won Shim
Journal:  Childs Nerv Syst       Date:  2018-04-20       Impact factor: 1.475

Review 6.  Cognitive network neuroscience.

Authors:  John D Medaglia; Mary-Ellen Lynall; Danielle S Bassett
Journal:  J Cogn Neurosci       Date:  2015-03-24       Impact factor: 3.225

7.  Intrinsic Default Mode Network Connectivity Predicts Spontaneous Verbal Descriptions of Autobiographical Memories during Social Processing.

Authors:  Xiao-Fei Yang; Julia Bossmann; Birte Schiffhauer; Matthew Jordan; Mary Helen Immordino-Yang
Journal:  Front Psychol       Date:  2013-01-07

8.  Global connectivity of prefrontal cortex predicts cognitive control and intelligence.

Authors:  Michael W Cole; Tal Yarkoni; Grega Repovs; Alan Anticevic; Todd S Braver
Journal:  J Neurosci       Date:  2012-06-27       Impact factor: 6.167

9.  Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*†

Authors:  Sean L Simpson; F DuBois Bowman; Paul J Laurienti
Journal:  Stat Surv       Date:  2013

10.  Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

Authors:  Luke J Hearne; Luca Cocchi; Andrew Zalesky; Jason B Mattingley
Journal:  J Neurosci       Date:  2017-07-31       Impact factor: 6.167

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