Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease

Abstract

In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimer's disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimer's disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimer's disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimer's disease patients. Network changes in the lower alpha band were better explained by a 'Targeted Attack' model than by a 'Random Failure' model. Thus, Alzheimer's disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimer's disease is more random. The modelling results suggest that highly connected neural network 'hubs' may be especially at risk in Alzheimer's disease.

Hyperconnectivity is a fundamental response to neurological disruption.

“…Therefore, the increased synchronization between regions can be interpreted as increased involvement of the network, but little can be said about the nature of that involvement. It should be noted that there is significant evidence in studies using electrophysiological methods (e.g., MEG, EEG) supporting the current findings in fMRI (see Castellanos et al, 2010;Stam et al, 2009;Stam et al, 2006;Stam et al, 2007;Tijms et al, 2013), so although neurovascular methods cannot clarify the nature of the neural activity, it is highly unlikely that the current findings can be explained as a vascular anomaly alone (e.g., decoupling between vascular and neuronal signaling).…”

Early dysfunction of functional connectivity in healthy elderly with subjective memory complaints

“…MCI subject showed a hyper-synchronization (compensatory effect), whereas SMC a hypo-synchronization (the brain is not still compensating for the fails of the memory networks, being a pathological or dementia effect). AD patients show an impairment of their functional connectivity (Stam et al 2009) zation in favour of the MCIs over anterior and central regions, these patterns are widely distributed when comparing MCIs and SMC, which accounts for their diminution in the synchronization values along the whole sensor area. This hypo-synchronization could underlie the memory difficulties in the day-to-day activities reported by SMC subjects.…”

“…This hypo-synchronization could underlie the memory difficulties in the day-to-day activities reported by SMC subjects. Thus, the difficulties in achieving a correct coupling between brain regions diminish the probability of an efficient transmission of information (Stam et al 2009) leading to memory failures. A compensation mechanism showed by MCI subjects, seems to be unnecessary at this point since memory difficulties are not yet severe enough.…”

“…Taking the healthy elderly subjects' profile of functional connectivity as a standard at the SMC stage, subjects show a hypo-synchronization of their memory-related networks and at the MCI stage, memory networks increase their coupling as a compensatory mechanism (Bajo et al 2010) and finally AD patients show an impairment of their functional connectivity (Stam et al 2009). Thus, at the stage of SMC, compensatory mechanisms are still not necessary because the memory deficit is not severe enough to be detected by memory tests and does not dramatically disturb daily living activities.…”

Spatiotemporal Analysis of Brain Functional Connectivity

“…These neuroimaging techniques can be used to identify brain networks involved in normal brain functions(behavioral response to stimulus, learning , memory) as well as in neurological disorders like epilepsy, Alzheimer, Schizophrenia [1].…”