Reorganization of Functional Networks in Mild Cognitive Impairment

Abstract

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

“…This increase in functional connectivity is usually interpreted as a compensatory mechanism and is associated with the risk for the progression to AD (Rossini et al 2006). Indeed, it seems to indicate a loss of brain efficiency (Buldú et al 2011). This discrepancy between cognitive task and resting state studies could be due to the differences in the nature of brain state during resting and cognitive tasks.…”

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

“…The common linear methods [85,86] mainly include Pearson correlation, partial correlation, and partial coherence. The common nonlinear methods mainly include synchronization likelihood [21,34], mutual information [87], and wavelet correlation [88,89]. For example, the Pearson correlation between brain regional activity time series is calculated as the edges of the brain network of interest, which are weighted and undirected.…”

“…The signals recorded by EEG and MEG directly reflect current flows generated by neurons within a brain. EEG/MEG also have been utilized for the studies of brain disorders [33][34][35][36]. Network-based analysis has been widely used in various fields, such as medical image analysis [37][38][39][40] and bioinformatics [41][42][43][44].…”

Functional Brain Networks: beyond the small-world paradigm

“…Interestingly, the resting state functional network of AD patients shows a decrease of the clustering coefficient that is associated to an evolution towards a random topology as a consequence of the deterioration of the local synchrony [Supekar et al (2008)]. The increase of randomness, together with a loss of the network modularity, is also reported in mild cognitive impairment [Buldu et al (2011)], a disease with a high rate of conversion into AD. In the case of schizophrenia, similar studies have detected an abnormal configuration of the anatomical network, consisting on a reduction of the hierarchical structure of the network, an enhance of the mean shortest path and a loss of frontal hubs [Basset et al (2008)].…”