Brain networking analysis in migraine with and without aura

Marina de Tommaso, Gabriele Trotta, Eleonora Vecchio, Katia Ricci, R Siugzdaite, Sebastiano Stramaglia, Marina de Tommaso, Gabriele Trotta, Eleonora Vecchio, Katia Ricci, R Siugzdaite, Sebastiano Stramaglia

Abstract

Background: To apply effective connectivity by means of nonlinear Granger Causality (GC) and brain networking analysis to basal EEG and under visual stimulation by checkerboard gratings with 0.5 and 2.0 cpd as spatial frequency in migraine with aura (MA) and without aura (MO), and to compare these findings with Blood Oxygen Level Dependent (BOLD) signal changes.

Methods: Nineteen asymptomatic MA and MO patients and 11 age and sex matched controls (C) were recorded by 65 EEG channels. The same visual stimulation was employed to evaluate BOLD signal changes in a subgroup of MA and MO. The GC and brain networking were applied to EEG signals.

Results: A different pattern of reduced vs increased GC respectively in MO and MA patients, emerged in resting state. During visual stimulation, both MA and MO showed increased information transfer toward the fronto-central regions, while MA patients showed a segregated cluster of connections in the posterior regions, and an increased bold signal in the visual cortex, more evident at 2 cpd spatial frequency.

Conclusions: The wealth of information exchange in the parietal-occipital regions indicates a peculiar excitability of the visual cortex, a pivotal condition for the manifestation of typical aura symptoms.

Keywords: EEG; Granger causality; Migraine with Aura.

Conflict of interest statement

Competing interests

The authors declare that they have no competing interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Statistical differences in information transfer and their relative directions among nodes in resting state EEG bands are depicted in the Migraine with aura (MA), Migraine without aura (MO) and Controls (C) groups . Hot colors refer to a statistical prevalence of information transfer in the first element of the comparison, and cold colors for the vice-versa. The color intensity is graduated on the relative percent difference
Fig. 2
Fig. 2
Statistical differences in information transfer and their relative directions among nodes are depicted for the comparison of EEG bands under 0.5 cpd spatial frequency visual stimulation vs resting state condition in the Migraine with aura (MA), Migraine without aura (MO) and Controls (C) groups . Hot colors refer to a statistical prevalence of information transfer in the 0.5 cpd spatial frequency visual stimulation condition, cold colors for a prevalence in resting state. The color intensity is graduated on the relative percent difference. (further data on statistical analysis are available in the Additional file 1: section, chapter 5.6.1)
Fig. 3
Fig. 3
Statistical differences in information transfer and their relative directions among nodes during 0.5 cpd spetial frequency are depicted in the Migraine with aura (MA), Migraine without aura (MO) and Controls (C) groups . Hot colors refer to a statistical prevalence of information transfer in the first element of the comparison, and cold colors for the vice-versa. The color intensity is graduated on the relative percent difference. (further data on statistical analysis are available in the Additional file 1: section, chapter 5.6.1)
Fig. 4
Fig. 4
Statistical differences in information transfer and their relative directions among nodes are depicted for the comparison of EEG bands under 2 cpd spatial frequency visual stimulation vs resting state condition in the Migraine with aura (MA), Migraine without aura (MO) and Controls (C) groups . Hot colors refer to a statistical prevalence of information transfer in the 0.5 cpd spatial frequency visual stimulation condition, cold colors for a prevalence in resting state. The color intensity is graduated on the relative percent difference. (further data on statistical analysis are available in the Additional file 1: section, chapter 5.6.1)
Fig. 5
Fig. 5
Statistical differences in information transfer and their relative directions among nodes during 2 cpd spetial frequency are depicted in the Migraine with aura (MA), Migraine without aura (MO) and Controls (C) groups . Hot colors refer to a statistical prevalence of information transfer in the first element of the comparison, and cold colors for the vice-versa. The color intensity is graduated on the relative percent difference. (further data on statistical analysis are available in the Additional file 1: section, chapter 5.6.1)
Fig. 6
Fig. 6
ANOVA tests on Characteristic Path Length features of Migraine with Aura versus Migraine Without Aura. The first column shows the Student t-test on the “lambda” and “diameter” factors (which stem for the medium path length and its distribution) in basal condition and during 0.5 cpd and 2 cpd spatial frequency visual stimulation. The lambda is clearly smaller for MA, as one can argue observing the “eccentricity” (distribution of the minima, right column) at all the stimulations and for all cortical bands (alpha band is here represented). The red horizontal line stems for the t-test confidence level (Bonferroni corrected) under which the probability of having distinct population is relevant
Fig. 7
Fig. 7
Comparison of global efficiency in Migraine with aura (MA) and Migraine Without Aura populations: the matrix elements in red (+1) account for larger values in MWA, while those in blue (−1) account for larger values od MWoA. Green cells mean no distinction. (further data on analysis are available in the Additional file 1: section, chapter 5.6.2) The EEG electrodes were merged into 4 main scalp zones: fl, frontal, cl, central, tl, temporal, pl, parietal, ol, occipital
Fig. 8
Fig. 8
Network diagrams for Migraine with aura (MA) and without aura (MO) for alpha band at 2 cpd stimulation. The by-pass role of the occipital area (O) in the MA in the anterior / posterior connection emerges. (further data on analysis are available in the Supplementary section, chapter 5.6.2) F: Frontal; C: Central; P: Parietal; RT: Right Temporal; LT: Left Temporal
Fig. 9
Fig. 9
Statistical probability maps reporting the comparison of bold signal changes in migraine with aura vs migraine without aura sub groups during 2 cps spatial frequency visual stimulation

References

    1. Ayata C. Cortical spreading depression triggers migraine attack. Pro Headache. 2010;4:725–730. doi: 10.1111/j.1526-4610.2010.01647.x.
    1. Hadjikhani N, Sanchez Del Rio M, Wu O, Schwartz D, Bakker D, Fischl B, et al. Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc Natl Acad Sci U S A. 2001;98:4687–4692. doi: 10.1073/pnas.071582498.
    1. Schoenen J. Neurophysiological features of the migrainous brain. Neurol Sci. 2006;27:S77–S81. doi: 10.1007/s10072-006-0575-1.
    1. Genco D, de Tommaso N, Prudenzano AM, Savarese M, Puca FM. EEG features in juvenile migraine: topographic analysis of spontaneous and visual evoked brain electrical activity: a comparison with adult migraine. Cephalalgia. 1994;14:41–46. doi: 10.1046/j.1468-2982.1994.1401041.x.
    1. Simon RH, Zimmerman AW, Tasman A, Hale MS. Spectral analysis of photic stimulation in migraine. Electroencephalogr Clin Neurophysiol. 1982;53:270–276. doi: 10.1016/0013-4694(82)90084-0.
    1. Shibata K, Yamane K, Otuka K, Iwata M. Abnormal visual processing in migraine with aura: a study of steady-state visual evoked potentials. J Neurol Sci. 2008;271(1-2):119–126. doi: 10.1016/j.jns.2008.04.004.
    1. Shibata K, Yamane K, Nishimura Y, Kondo H, Otuka K. Spatial frequency differentially affects habituation in migraineurs: a steady-state visual-evoked potential study. Doc Ophthalmol. 2011;123(2):65–73. doi: 10.1007/s10633-011-9281-2.
    1. Rosenblum MG, Pikovsky AS, Kurths J. Phase synchronization of chaotic oscillators. Phys Rev Lett. 1996;76:1804. doi: 10.1103/PhysRevLett.76.1804.
    1. Angelini L, de Tommaso M, Guido M, Hu K, Ivanov P, Marinazzo D, et al. Steady-state visual evoked potentials and phase synchronization in migraine patients. Phys Rev Lett. 2004;93:038103. doi: 10.1103/PhysRevLett.93.038103.
    1. Friston K. Functional and effective connectivity: a review. Brain Connectivity. 2011;1:13–36. doi: 10.1089/brain.2011.0008.
    1. Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969;37:424–438. doi: 10.2307/1912791.
    1. Blinowska KJ, Kus R, Kaminski M. Granger causality and information flow in multivariate processes. Phys Rev E. 2004;70:050902. doi: 10.1103/PhysRevE.70.050902.
    1. Dhamala M, Rangarajan G, Ding M. Estimating granger causality from Fourier and wavelet transforms of time series data. PhysRevLett. 2008;100:18701.
    1. Friston KJ, Harrison L, Penny W. Dynamic causal modeling. NeuroImage. 2003;19:1273–1302. doi: 10.1016/S1053-8119(03)00202-7.
    1. Marinazzo D, Pellicoro M, Stramaglia S. Kernel method for nonlinear granger causality. Phys RevLett. 2008;100:144103.
    1. Sporns O, Tononi G, Kotter R. The human connectome: a structural description of the human brain. PLoS Comp Biol. 2005;1:245. doi: 10.1371/journal.pcbi.0010042.
    1. de Tommaso M, Stramaglia S, Marinazzo D, Trotta G, Pellicoro M. Functional and effective connectivity in EEG alpha and beta bands during intermittent flash stimulation in migraine with and without aura. Cephalalgia. 2013;33(11):938–947. doi: 10.1177/0333102413477741.
    1. Datta R, Aguirre GK, Hu S, Detre JA, Cucchiara B. Interictal cortical hyperresponsiveness in migraine is directly related to the presence of aura. Cephalalgia. 2013;33(6):365–374. doi: 10.1177/0333102412474503.
    1. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52:1059–1069. doi: 10.1016/j.neuroimage.2009.10.003.
    1. Headache Classification Committee of the International Headache Society The international classification of headache disorders, 3rd edition (beta version) Cephalalgia. 2013;33(9):629–808. doi: 10.1177/0333102413485658.
    1. de Tommaso M, Trotta G, Vecchio E, Ricci K, Van de Steen F, Montemurno A, et al.(2015) Functional connectivity of EEG signals under laser stimulation in migraine. Front Hum Neurosci. 24;9:640
    1. Geweke J. Measurement of linear dependence and feedback between multiple time series. J Am Stat Assoc. 1982;77:304–313. doi: 10.1080/01621459.1982.10477803.
    1. Kaminski M, Ding M, Truccolo WA, Bressler SL. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern. 2001;85:145–157. doi: 10.1007/s004220000235.
    1. Wu D, Zhou Y, Xiang J, Tang L, Liu H, Huang S, et al (2016) Multi-frequency analysis of brain connectivity networks in migraineurs: a magnetoencephalography study. J Headache Pain. 2016;17:38
    1. Maleki N, Gollub RL. What have we learned from brain functional connectivity studies in migraine headache? Headache. 2016;56(3):453–461. doi: 10.1111/head.12756.
    1. Chong CD, Gaw N, Fu Y, Li J, Wu T, Schwedt TJ (2017) Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia. 2017;37:828–44.
    1. Zhang J, Su J, Wang M, Zhao Y, Yao Q, Zhang Q, et al. Increased default mode network connectivity and increased regional homogeneity in migraineurs without aura. J Headache Pain. 2016;17(1):98. doi: 10.1186/s10194-016-0692-z.
    1. Chen Z, Chen X, Liu M, Liu S, Shu S, Ma L, Yu S. Altered functional connectivity of the marginal division in migraine: a resting-state fMRI study. J Headache Pain. 2016;17(1):89. doi: 10.1186/s10194-016-0682-1.
    1. Chen Z, Chen X, Liu M, Liu S, Ma L, Yu S. Disrupted functional connectivity of periaqueductal gray subregions in episodic migraine. J Headache Pain. 2017;18(1):36. doi: 10.1186/s10194-017-0747-9.
    1. Coppola G, Di Renzo A, Tinelli E, Lepre C, Di Lorenzo C, Di Lorenzo G, et al. Thalamo-cortical network activity between migraine attacks: insights from MRI-based microstructural and functional resting-state network correlation analysis. J Headache Pain. 2016;17(1):100. doi: 10.1186/s10194-016-0693-y.
    1. Tedeschi G, Russo A, Conte F, Corbo D, Caiazzo G, Giordano A, et al. Increased interictal visual network connectivity in patients with migraine with aura. Cephalalgia. 2016;36(2):139–147. doi: 10.1177/0333102415584360.
    1. Maggioni E, Zucca C, Reni G, Cerutti S, Triulzi FM, Bianchi AM, Arrigoni F. Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: an EEG-fMRI study. Hum Brain Mapp. 2016;37(6):2247–2262. doi: 10.1002/hbm.23170.
    1. Hougaard A, Amin FM, Hoffmann MB, Rostrup E, Larsson HB, Asghar MS, et al. Interhemispheric differences of fMRI responses to visual stimuli in patients with side-fixed migraine aura. Hum Brain Mapp. 2014;35(6):2714–2723. doi: 10.1002/hbm.22361.
    1. de Tommaso M, Ambrosini A, Brighina F, Coppola G, Perrotta A, Pierelli F, et al. Altered processing of sensory stimuli in patients with migraine. Nat Rev Neurol. 2014;10(3):144–155. doi: 10.1038/nrneurol.2014.14.
    1. de Tommaso M, Delussi M, Vecchio E, Sciruicchio V, Invitto S, Livrea P. (2014) Sleep features and central sensitization symptoms in primary headache patients. J Headache Pain. 26;15:64

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe