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Sci Rep. 2018 Jul 12;8(1):10525. doi: 10.1038/s41598-018-28747-6.

Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics.

Author information

INSERM U-1127, Institut du Cerveau et de la Moelle Épinière (ICM), Hôpital Pitié Salpêtrière, Paris, France.
GISC & Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.
Department of Experimental Psychology, Cognitive Processes and Speec, Complutense University of Madrid, Madrid, Spain.
CNRS UMR-7225, Hôpital Pitié-Salpêtrière, Paris, France.
Centro Integral de Neurociencias AC (CINAC), HM Puerta del Sur, Madrid, Spain.
CEU San Pablo University, Madrid, Spain.
Department of Experimental Psychology, Cognitive Processes and Speec, Complutense University of Madrid, Madrid, Spain.
Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Madrid, Spain.
Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.
Geriatrics Department, San Carlos University Hospital, Madrid, Spain.
Complex Systems Group (GISC), Universidad Rey Juan Carlos, Madrid, Spain.


We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.

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