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Multi-frequency analysis of brain connectivity networks in migraineurs: a magnetoencephalography study.

Wu D, Zhou Y, Xiang J, Tang L, Liu H, Huang S, Wu T, Chen Q, Wang X - J Headache Pain (2016)

Bottom Line: The topographic patterns of neural network showed that the migraineurs had significantly increased functional connectivity in the slow wave (0.1-1 Hz) band in the frontal area as compared with controls.Graph theory analysis revealed that the migraineurs had significantly increased connection strength in the slow wave (0.1-1 Hz) band, increased path length in the theta (4-8 Hz) and ripple (80-250 Hz) bands, and increased clustering coefficient in the slow wave (0.1-1 Hz) and theta (4-8 Hz) bands.The clinical characteristics had no significant correlation with interictal MEG parameters.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Nanjing Brain Hospital, Nanjing Medical University, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, China.

ABSTRACT

Background: Although alterations in resting-state neural network have been previously reported in migraine using functional MRI, whether this atypical neural network is frequency dependent remains unknown. The aim of this study was to investigate the alterations of the functional connectivity of neural network and their frequency specificity in migraineurs as compared with healthy controls by using magnetoencephalography (MEG) and concepts from graph theory.

Methods: Twenty-three episodic migraine patients with and without aura, during the interictal period, and 23 age- and gender-matched healthy controls at resting state with eye-closed were studied with MEG. Functional connectivity of neural network from low (0.1-1 Hz) to high (80-250 Hz) frequency ranges was analyzed with topographic patterns and quantified with graph theory.

Results: The topographic patterns of neural network showed that the migraineurs had significantly increased functional connectivity in the slow wave (0.1-1 Hz) band in the frontal area as compared with controls. Compared with the migraineurs without aura (MwoA), the migraineurs with aura (MwA) had significantly increased functional connectivity in the theta (4-8 Hz) band in the occipital area. Graph theory analysis revealed that the migraineurs had significantly increased connection strength in the slow wave (0.1-1 Hz) band, increased path length in the theta (4-8 Hz) and ripple (80-250 Hz) bands, and increased clustering coefficient in the slow wave (0.1-1 Hz) and theta (4-8 Hz) bands. The clinical characteristics had no significant correlation with interictal MEG parameters.

Conclusions: Results indicate that functional connectivity of neural network in migraine is significantly impaired in both low- and high-frequency ranges. The alteration of neural network may imply that migraine is associated with functional brain reorganization.

No MeSH data available.


Related in: MedlinePlus

Diffrences in network parameters (strength, path length, clustering coefficient) between the migraineurs and controls. a Comparison of the strength of each frequency band of the migraineurs and controls. b Comparison of the path length of each frequency band of the migraineurs and controls. c Comparison of the clustering coefficient of each frequency band of the migraineurs and controls (*p < 0.016, **p < 0.005)
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Fig5: Diffrences in network parameters (strength, path length, clustering coefficient) between the migraineurs and controls. a Comparison of the strength of each frequency band of the migraineurs and controls. b Comparison of the path length of each frequency band of the migraineurs and controls. c Comparison of the clustering coefficient of each frequency band of the migraineurs and controls (*p < 0.016, **p < 0.005)

Mentions: Group comparison revealed that the average connection strength of the functional connectivity network of the slow wave (0.1–1 Hz) band in migraineurs significantly increased compared with that of the controls (p = 0.008). No significant difference was observed between the migraineurs and controls in the other frequency bands. The average connection strength for migraineurs and controls in seven frequency bands are shown in Fig. 5.Fig. 5


Multi-frequency analysis of brain connectivity networks in migraineurs: a magnetoencephalography study.

Wu D, Zhou Y, Xiang J, Tang L, Liu H, Huang S, Wu T, Chen Q, Wang X - J Headache Pain (2016)

Diffrences in network parameters (strength, path length, clustering coefficient) between the migraineurs and controls. a Comparison of the strength of each frequency band of the migraineurs and controls. b Comparison of the path length of each frequency band of the migraineurs and controls. c Comparison of the clustering coefficient of each frequency band of the migraineurs and controls (*p < 0.016, **p < 0.005)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4835413&req=5

Fig5: Diffrences in network parameters (strength, path length, clustering coefficient) between the migraineurs and controls. a Comparison of the strength of each frequency band of the migraineurs and controls. b Comparison of the path length of each frequency band of the migraineurs and controls. c Comparison of the clustering coefficient of each frequency band of the migraineurs and controls (*p < 0.016, **p < 0.005)
Mentions: Group comparison revealed that the average connection strength of the functional connectivity network of the slow wave (0.1–1 Hz) band in migraineurs significantly increased compared with that of the controls (p = 0.008). No significant difference was observed between the migraineurs and controls in the other frequency bands. The average connection strength for migraineurs and controls in seven frequency bands are shown in Fig. 5.Fig. 5

Bottom Line: The topographic patterns of neural network showed that the migraineurs had significantly increased functional connectivity in the slow wave (0.1-1 Hz) band in the frontal area as compared with controls.Graph theory analysis revealed that the migraineurs had significantly increased connection strength in the slow wave (0.1-1 Hz) band, increased path length in the theta (4-8 Hz) and ripple (80-250 Hz) bands, and increased clustering coefficient in the slow wave (0.1-1 Hz) and theta (4-8 Hz) bands.The clinical characteristics had no significant correlation with interictal MEG parameters.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Nanjing Brain Hospital, Nanjing Medical University, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, China.

ABSTRACT

Background: Although alterations in resting-state neural network have been previously reported in migraine using functional MRI, whether this atypical neural network is frequency dependent remains unknown. The aim of this study was to investigate the alterations of the functional connectivity of neural network and their frequency specificity in migraineurs as compared with healthy controls by using magnetoencephalography (MEG) and concepts from graph theory.

Methods: Twenty-three episodic migraine patients with and without aura, during the interictal period, and 23 age- and gender-matched healthy controls at resting state with eye-closed were studied with MEG. Functional connectivity of neural network from low (0.1-1 Hz) to high (80-250 Hz) frequency ranges was analyzed with topographic patterns and quantified with graph theory.

Results: The topographic patterns of neural network showed that the migraineurs had significantly increased functional connectivity in the slow wave (0.1-1 Hz) band in the frontal area as compared with controls. Compared with the migraineurs without aura (MwoA), the migraineurs with aura (MwA) had significantly increased functional connectivity in the theta (4-8 Hz) band in the occipital area. Graph theory analysis revealed that the migraineurs had significantly increased connection strength in the slow wave (0.1-1 Hz) band, increased path length in the theta (4-8 Hz) and ripple (80-250 Hz) bands, and increased clustering coefficient in the slow wave (0.1-1 Hz) and theta (4-8 Hz) bands. The clinical characteristics had no significant correlation with interictal MEG parameters.

Conclusions: Results indicate that functional connectivity of neural network in migraine is significantly impaired in both low- and high-frequency ranges. The alteration of neural network may imply that migraine is associated with functional brain reorganization.

No MeSH data available.


Related in: MedlinePlus