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Published in: Brain Topography 4/2017

01-07-2017 | Original Paper

EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training

Authors: Emiliano Santarnecchi, Arjun R. Khanna, Christian S. Musaeus, Christopher S. Y. Benwell, Paula Davila, Faranak Farzan, Santosh Matham, Alvaro Pascual-Leone, Mouhsin M. Shafi, on behalf of Honeywell SHARP Team authors

Published in: Brain Topography | Issue 4/2017

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Abstract

The neurobiological correlates of human fluid intelligence (Gf) remain elusive. Here, we demonstrate that spatiotemporal dynamics of EEG activity correlate with baseline measures of Gf and with its modulation by cognitive training. EEG dynamics were assessed in 74 healthy participants by examination of fast-changing, recurring, topographically-defined electric patterns termed “microstates”, which characterize the electrophysiological activity of distributed cortical networks. We find that the frequency of appearance of specific brain topographies, spatially associated with visual (microstate B) and executive control (microstate C) networks, respectively, is inversely related to Gf scores. Moreover, changes in Gf scores with cognitive training are inversely correlated with changes in microstate properties, indicating that the changes in brain network dynamics are behaviorally relevant. Finally, we find that cognitive training that increases Gf scores results in a posterior shift in the topography of microstate C. These results highlight the role of fast-changing brain electrical states in individual variability in Gf and in the response to cognitive training.
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Metadata
Title
EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training
Authors
Emiliano Santarnecchi
Arjun R. Khanna
Christian S. Musaeus
Christopher S. Y. Benwell
Paula Davila
Faranak Farzan
Santosh Matham
Alvaro Pascual-Leone
Mouhsin M. Shafi
on behalf of Honeywell SHARP Team authors
Publication date
01-07-2017
Publisher
Springer US
Published in
Brain Topography / Issue 4/2017
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI
https://doi.org/10.1007/s10548-017-0565-z

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