Dataset Information

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

ABSTRACT:

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 controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01).

Conclusion

With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.

SUBMITTER: de Haan W

PROVIDER: S-EPMC2736175 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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Publications

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

de Haan Willem W Pijnenburg Yolande A L YA Strijers Rob L M RL van der Made Yolande Y van der Flier Wiesje M WM Scheltens Philip P Stam Cornelis J CJ

BMC neuroscience 20090821

<h4>Background</h4>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 frontotemp ...[more]

PMID: 19698093

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Dataset Information

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

Background

Results

Conclusion

Publications

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

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