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Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

Pedersen M, Omidvarnia AH, Walz JM, Jackson GD - Neuroimage Clin (2015)

Bottom Line: Graph theory represents a powerful quantitative framework for investigation of brain networks.We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures.It remains possible that this may be part of the epileptogenic process or an effect of medications.

View Article: PubMed Central - PubMed

Affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia ; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

ABSTRACT
Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

No MeSH data available.


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A schematic overview of the functional connectivity steps.
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f0005: A schematic overview of the functional connectivity steps.

Mentions: An in-house network analysis pipeline was used implementing Matlab codes from the Brain Connectivity Toolbox (BCT: Rubinov and Sporns (2010); https://sites.google.com/site/bctnet/Home). The pre-processedtask-free fMRI data (Fig.1A) of each subject was divided into sub-regions using a brain mask consisting of 278 nodes. This mask was derived from a previous study that used functional connectivity data from 78 healthy individuals to form functionally homogenous brain regions (Shen etal., 2013). The parcellation mask was used because of its biological plausibility for task-free fMRI analysis and accurate spatial grey matter boundaries (see Fig.1B). Subsequently, these 278 segregated brain regions were represented as nodes in our network framework. After averaging the time-series within each node (Fig.1C), a Pearson correlation score was calculated between all nodes to determine their pair-wise functional connectivity strength. This step resulted in a symmetric connectivity matrix of size 278 × 278 for each individual where each element was associated with a correlation score between the mean time series of two regions in the functional connectivity mask (Fig.1D). The resulting individual connectivity matrices (Fig.1E) were Fisher's R to Z transformed (Mudholkar, 2004).


Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

Pedersen M, Omidvarnia AH, Walz JM, Jackson GD - Neuroimage Clin (2015)

A schematic overview of the functional connectivity steps.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4477107&req=5

f0005: A schematic overview of the functional connectivity steps.
Mentions: An in-house network analysis pipeline was used implementing Matlab codes from the Brain Connectivity Toolbox (BCT: Rubinov and Sporns (2010); https://sites.google.com/site/bctnet/Home). The pre-processedtask-free fMRI data (Fig.1A) of each subject was divided into sub-regions using a brain mask consisting of 278 nodes. This mask was derived from a previous study that used functional connectivity data from 78 healthy individuals to form functionally homogenous brain regions (Shen etal., 2013). The parcellation mask was used because of its biological plausibility for task-free fMRI analysis and accurate spatial grey matter boundaries (see Fig.1B). Subsequently, these 278 segregated brain regions were represented as nodes in our network framework. After averaging the time-series within each node (Fig.1C), a Pearson correlation score was calculated between all nodes to determine their pair-wise functional connectivity strength. This step resulted in a symmetric connectivity matrix of size 278 × 278 for each individual where each element was associated with a correlation score between the mean time series of two regions in the functional connectivity mask (Fig.1D). The resulting individual connectivity matrices (Fig.1E) were Fisher's R to Z transformed (Mudholkar, 2004).

Bottom Line: Graph theory represents a powerful quantitative framework for investigation of brain networks.We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures.It remains possible that this may be part of the epileptogenic process or an effect of medications.

View Article: PubMed Central - PubMed

Affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia ; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

ABSTRACT
Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

No MeSH data available.


Related in: MedlinePlus