Graph analysis of resting state EEG functional networks

Graph analysis of resting state EEG functional networks

Smid, Annemijn (TU Delft Applied Sciences; TU Delft Mechanical, Maritime and Materials Engineering)

Schouten, Alfred (mentor)
Perenboom, Thijs (mentor)
Wehrmann, Caroline (graduation committee)
van de Ruit, Mark (graduation committee)

Delft University of Technology

2018-04-19

Migraine is associated with brain dysfunction, possibly due to disturbances in the interactions between distributed cortical regions. Detection of these disturbances in the topological organization of the brain’s functional network would contribute to further understanding of migraine pathophysiology. Altered cortical responses to external stimulation of different modalities are observed in migraine patients, also between attacks (in the interictal state). However, it is yet unclear if abnormalities are detectable in the functional network at rest, i.e. without external stimulation. Here, we assessed abnormalities in migraine functional networks on a global and a local level, based on resting state electroencephalography (EEG) data and graph analysis. Scalp-wide (128-channel)
eyes closed EEG was recorded in 18 episodic migraine patients with and without
aura and 15 healthy controls. We calculated functional connectivity based on
coherence and phase-lag index, and performed graph analysis to characterize
network topology. The minimum spanning tree, a subgraph with maximum
functional connectivity, was used for comparison. No significant differences were
found in network topology, nor in functional connectivity strength between groups. These results demonstrate that this type of graph analyses are not sensitive to any possible abnormalities in the interictal migraine functional network in resting state. Brain dysfunction in migraine might occur only on a local level, making EEG-based graph analysis a less suitable technique to uncover such abnormalities.

Graph analysis
Migraine
Functional networks
EEG analysis
Resting state

http://resolver.tudelft.nl/uuid:f8635ce3-5eeb-4c3c-ba11-9086444c50c8

Student theses

master thesis

© 2018 Annemijn Smid