Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

作者：Willem de Haan, Yolande A.L. Pijnenburg, R.L.M. Strijers, Yolande van der Made, Wiesje M. van der Flier, Philip Scheltens, Cornelis J. Stam · 发表于：BMC Neuroscience · 年份：2009 · DOI：10.1186/1471-2202-10-101 · 被引用次数：409 · 研究领域：Functional Brain Connectivity Studies、Neural dynamics and brain function、EEG and Brain-Computer Interfaces

BACKGROUND: Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5-4 Hz, 4-8 Hz, 8-10 Hz, 10-13 Hz, 13-30 Hz and 30-45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks. RESULTS: In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to c...