| 1) Functional brain networks and dementia Introduction Over the past decades a substantial amount of evidence has been gathered showing that the human brain is a very complex, coordinated dynamic network. However, without a better understanding of how brain network structure relates to cognition, this finding generates more questions than answers. What is a "good" network in this regard? To answer this question, we apply mathematical graph theory to neuroscience data, in particular to neurophysiologic data. This enables us to examine the relation between the structure and function of the brain, and hopefully to gain more insight about brain function in health and disease. Methods Electro-encephalography (EEG) and magneto-encephalography (MEG) recordings are performed on awake, alert persons in an eyes-closed no-task condition. Both healthy persons as well as patients with Alzheimer's disease and Frontotemporal dementia are studied. Functional brain networks can be formed based on functional connectivity measures, indicating interaction between different brain areas. These networks can then be characterized by several quantitative measures that describe different network properties. Results Functional brain networks in healthy persons show a so called "small world" network architecture, presumed to be optimal for efficient information circulation. In demented persons, the network organization deviates towards a less efficient type, and the way in which this happens is different in Alzheimer's disease than in Frontotemporal dementia. Conclusions Network analysis is able to demonstrate functional brain network changes in different disease processes. This new approach can improve our understanding of global functional brain organization in health and disease, and might perhaps eventually also be used as a clinical tool for diagnostic, prognostic or monitoring purposes. 2)Functional neural network analysis in FTLD, AD, and SMC using resting-state EEG and graph theory 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), where it showed a strong negative correlation with MMSE (r = -0.80, p<0.01). Conclusions: With decreasing local and global connectivity parameters in several frequency bands, and a lower degree correlation, 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, reflecting a different underlying pathophysiological process. |
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