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Clin Neurophysiol. 2015 Mar;126(3):524-31. doi: 10.1016/j.clinph.2014.06.032. Epub 2014 Jul 6.

Neurophysiological basis of creativity in healthy elderly people: a multiscale entropy approach.

Author information

1
Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Japan.
2
Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Japan. Electronic address: takahash@u-fukui.ac.jp.
3
Department of Psychology, Faculty of Human Studies, Jin-ai University, Fukui, Japan.

Abstract

OBJECTIVES:

Creativity, which presumably involves various connections within and across different neural networks, reportedly underpins the mental well-being of older adults. Multiscale entropy (MSE) can characterize the complexity inherent in EEG dynamics with multiple temporal scales. It can therefore provide useful insight into neural networks. Given that background, we sought to clarify the neurophysiological bases of creativity in healthy elderly subjects by assessing EEG complexity with MSE, with emphasis on assessment of neural networks.

METHODS:

We recorded resting state EEG of 20 healthy elderly subjects. MSE was calculated for each subject for continuous 20-s epochs. Their relevance to individual creativity was examined concurrently with intellectual function.

RESULTS:

Higher individual creativity was linked closely to increased EEG complexity across higher temporal scales, but no significant relation was found with intellectual function (IQ score).

CONCLUSIONS:

Considering the general "loss of complexity" theory of aging, our finding of increased EEG complexity in elderly people with heightened creativity supports the idea that creativity is associated with activated neural networks.

SIGNIFICANCE:

Results reported here underscore the potential usefulness of MSE analysis for characterizing the neurophysiological bases of elderly people with heightened creativity.

KEYWORDS:

Complexity; Creativity; Electroencephalography (EEG); Healthy elderly people; Multiple temporal scales; Multiscale entropy (MSE); Neural network

PMID:
25066939
DOI:
10.1016/j.clinph.2014.06.032
[Indexed for MEDLINE]
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