MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment

• Main Authors: Maria E. López, Marjolein M. A. Engels, Elisabeth C. W. van Straaten, Ricardo Bajo, María L. Delgado, Philip Scheltens, Arjan Hillebrand, Cornelis J. Stam, Fernando Maestú
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-04-01
• Series: Frontiers in Aging Neuroscience
• Subjects: mild cognitive impairment; magnetoencephalography; phase lag index; brain networks; minimum spanning tree
• Online Access: http://journal.frontiersin.org/article/10.3389/fnagi.2017.00107/full

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.