Hypersynchronization in mild cognitive impairment: the 'X' model.


Journal

Brain : a journal of neurology
ISSN: 1460-2156
Titre abrégé: Brain
Pays: England
ID NLM: 0372537

Informations de publication

Date de publication:
01 12 2019
Historique:
received: 08 02 2019
revised: 06 08 2019
accepted: 13 08 2019
pubmed: 22 10 2019
medline: 22 5 2020
entrez: 22 10 2019
Statut: ppublish

Résumé

Hypersynchronization has been proposed as a synaptic dysfunction biomarker in the Alzheimer's disease continuum, reflecting the alteration of the excitation/inhibition balance. While animal models have verified this idea extensively, there is still no clear evidence in humans. Here we test this hypothesis, evaluating the risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease in a longitudinal study. We compared the functional resting state eyes-closed magnetoencephalographic networks of 54 patients with MCI who were followed-up every 6 months. According to their clinical outcome, they were split into: (i) the 'progressive' MCI (n = 27) group; and (ii) the 'stable' MCI group (n = 27). They did not differ in gender or educational level. For all participants, two magnetoencephalographic recordings were acquired. Functional connectivity was evaluated using the phase locking value. To extract the functional connectivity network with significant changes between both magnetoencephalographic recordings, we evaluated the functional connectivity ratio, defined as functional connectivity post-/pre-condition, in a network-based statistical model with an ANCOVA test with age as covariate. Two significant networks were found in the theta and beta bands, involving fronto-temporal and fronto-occipital connections, and showing a diminished functional connectivity ratio in the progressive MCI group. These topologies were then evaluated at each condition showing that at baseline, patients with progressive MCI showed higher synchronization than patients with stable MCI, while in the post-condition this pattern was reversed. These results may be influenced by two main factors in the post-condition: the increased synchrony in the stable MCI patients and the network failure in the progressive MCI patients. These findings may be explained as an 'X' form model where the hypersynchrony predicts conversion, leading subsequently to a network breakdown in progressive MCI. Patients with stable MCI showed an opposite phenomenon, which could indicate that they were a step beyond in the Alzheimer's disease continuum. This model would be able to predict the risk for the conversion to dementia in MCI patients.

Identifiants

pubmed: 31633176
pii: 5601465
doi: 10.1093/brain/awz320
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3936-3950

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Sandra Pusil (S)

Laboratory of Neuropsychology, University of the Balearic Islands, Spain.
Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.

María Eugenia López (ME)

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain.
Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.

Pablo Cuesta (P)

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain.

Ricardo Bruña (R)

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain.
Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.

Ernesto Pereda (E)

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain.

Fernando Maestú (F)

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain.
Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.

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