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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

Distribution map of MEG sensors. a 2D map of the distributionof all MEG sensors. b 3D front view of the distribution of all MEG sensors. c 3D bottom view of the distribution of all MEG sensors
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Fig1: Distribution map of MEG sensors. a 2D map of the distributionof all MEG sensors. b 3D front view of the distribution of all MEG sensors. c 3D bottom view of the distribution of all MEG sensors

Mentions: Similar to previous reports [30, 31], MEG waveforms were visually inspected for identifying magnetic noise and other artifacts. Any MEG data with noticeable magnetic noise or artifact (>6 pT) were excluded from the analyses. The MEG data were preprocessed by removing the direct current offset. The neural network at the sensor levels was analyzed by computing the coherence of MEG signals in multiple regions. A custom-designed program, MEG Processor, was used to compute and visualize functional networks at the sensor level [32, 33]. Neural activity (120 s) was filtered into seven frequency bands: slow wave (0.1–1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), and ripple (80–250 Hz). The distribution of the coherence for each possible pair of the 275 sensors was displayed in topographic patterns (network contourmaps, see Fig. 1). Red color indicated excitatory connection on contourmaps. An excitatory connection was a positive connection where the amplitude of signals in two connected sensors was positively correlated. Blue color indicated inhibitory connections on contourmaps. An inhibitory connection was a negative connection where the amplitude of signals in a sensor pair was negatively correlated. To compare migraineurs and controls, the same threshold value was used to exhibit contourmaps for all subjects.Fig. 1


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)

Distribution map of MEG sensors. a 2D map of the distributionof all MEG sensors. b 3D front view of the distribution of all MEG sensors. c 3D bottom view of the distribution of all MEG sensors
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835413&req=5

Fig1: Distribution map of MEG sensors. a 2D map of the distributionof all MEG sensors. b 3D front view of the distribution of all MEG sensors. c 3D bottom view of the distribution of all MEG sensors
Mentions: Similar to previous reports [30, 31], MEG waveforms were visually inspected for identifying magnetic noise and other artifacts. Any MEG data with noticeable magnetic noise or artifact (>6 pT) were excluded from the analyses. The MEG data were preprocessed by removing the direct current offset. The neural network at the sensor levels was analyzed by computing the coherence of MEG signals in multiple regions. A custom-designed program, MEG Processor, was used to compute and visualize functional networks at the sensor level [32, 33]. Neural activity (120 s) was filtered into seven frequency bands: slow wave (0.1–1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), and ripple (80–250 Hz). The distribution of the coherence for each possible pair of the 275 sensors was displayed in topographic patterns (network contourmaps, see Fig. 1). Red color indicated excitatory connection on contourmaps. An excitatory connection was a positive connection where the amplitude of signals in two connected sensors was positively correlated. Blue color indicated inhibitory connections on contourmaps. An inhibitory connection was a negative connection where the amplitude of signals in a sensor pair was negatively correlated. To compare migraineurs and controls, the same threshold value was used to exhibit contourmaps for all subjects.Fig. 1

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