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

By W. de Haan, Yolande A. L. Pijnenburg, R. Strijers, Y. van der Made, W. M. van der Flier, Philip Scheltens, Cornelis J. Stam. BMC Neuroscience, 2009

404 citations recorded by the source corpus.

BackgroundAlthough 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.ResultsIn 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).ConclusionWith 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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2d832d0d1baaaba224a21ca926a10c0529da3628

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, result): “…very close to the values of random networks (Supekar et al., 2008; Sanz-Arigita et al., 2010; Dai et al., 2015; delEtoile and Adeli, 2017), similar to the results of researchers who worked on other imaging modalities (de Haan et al., 2009, 2012; Stam et al., 2009; Kim et al., 2015; Jalili, 2017).”
Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. (2010, background): “Intriguingly, a recent study demonstrated divergent frontotem- poral dementia versus Alzheimer’s disease connectivity results using graph theoretical analysis of resting-state EEG data (de Haan et al., 2009).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “…seizures (Ponten et al. 2007), attention deficit hyperactivity disorder (Wang et al. 2010), stroke (de Vico Fallani et al. 2009, Wang et al. 2009), spinal cord injury (de Vico Fallani et al. 2008b), fronto-temporal lobar degeneration (de Haan et al. 2009), and early blindness (Shu et al. 2009).”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “synchronization likelihood [107], phase-lag index (PLI), phase-locking value (PLV) [100], Granger causality [108], etc.”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, methodology): “What are the defining features of the brainwide architecture? Can it be described by the small-world network or other types of complex networks (3, 56, 436, 491, 896, 993)? Can we use graph theoretical analysis to assess circuit properties [such as the path length (a measure of effective communicati”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “Abnormalities in these resting state networks appear to relate to neurological and psychiatric diseases: graph measures differ between patients and healthy controls in Alzheimer’s disease [22–25], schizophrenia [26,27], ADHD in children [28], and brain tumors [29,30].”
Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “Global CC indexes the average tendency of neighbouring regions to promote local functional synchronisation, whereas CPL indexes global (dis)integration (Watts and Strogatz 1998; De Haan et al. 2009; Rubinov and Sporns 2010; Sporns 2016 ).”