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Clin Neurophysiol. 2014 Apr;125(4):694-702. doi: 10.1016/j.clinph.2013.08.033. Epub 2014 Jan 7.

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task.

Author information

1
Netherlands Institute for Neuroscience, Meibergdreef 47, Amsterdam, The Netherlands.
2
Department of Neurology, The Ohio State University, 395 W 12th Ave, 7th Floor Columbus, OH 43210, United States.
3
Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain.
4
Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, United States; Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, United States; Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, United States; Department of Neurological Surgery, The Ohio State University, Columbus, OH 43210, United States; Department of Neuroscience, The Ohio State University, Columbus, OH 43210, United States. Electronic address: adeli.1@osu.edu.

Abstract

OBJECTIVES:

The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task.

METHODS:

MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks' complexities are measured by Graph Index Complexity (C(r)) and Efficiency Complexity (C(e)).

RESULTS:

The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere.

CONCLUSIONS:

C(e) would be a better measurement for showing the global differences between normal and MCI brains compared with C(r).

SIGNIFICANCE:

The high accuracy of the classification shows C(e) at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MCI.

KEYWORDS:

Complexity of functional connectivity networks; Efficiency Complexity; Graph Index Complexity; Magneto encephalography; Mild cognitive impairment; Working memory

Comment in

PMID:
24405905
DOI:
10.1016/j.clinph.2013.08.033
[Indexed for MEDLINE]
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