Dynamic functional connectivity in migraine during the interictal phase: a resting-state fMRI study

• Autor(a) principal: Esteves, I.
• Data de Publicação: 2022
• Outros Autores: Fonseca, C., Xavier, M., Fouto, A., Ruiz-Tagle, A., Caetano, G., Nunes, R., Gil-Gouveia, R., Cabral, J., Martins, I. Pavão, Rosa, A, Figueiredo, P.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/40068

Question: Migraine is a cyclic and complex disorder, characterized by attacks of headache, sensory and cognitive disturbances1. Thalamocortical connectivity in migraine has been found to be transiently abnormal2. Our aim was to assess if the dynamical properties of the migraine brain are affected during the interictal phase. Methods: Resting-state functional MRI data was collected from 14 menstrual migraine patients without aura (interictal phase) and 12 healthy controls (menstrual post-ovulation phase). fMRI data processing included3: motion and distortion correction, temporal highpass filter, regression of motion and physiological confounds, spatial smoothing, and parcellation with the Desikan atlas. Dynamic functional connectivity (dFC) between regions was computed using phase coherence, and recurrent dFC states were identified by kmeans clustering (k ranging between 3 and 15) of the leading eigenvectors of dFC in each time point4. Permutation tests were performed to evaluate statistically significant differences between patients and controls in the probability of occurrence and the mean lifetime of the dFC states. Results: Similar dFC states were found consistently across different numbers of clusters, k, which resembled the canonical resting-state networks as expected. Compared to healthy controls, migraine patients show a significantly lower mean lifetime in one dFC state, when grouping in 4, 5 and 6 clusters. No differences were found for the probability of occurrence. Conclusions: Migraine may be linked to a disruption of brain networks dynamics. This emphasizes the need to adopt time-resolved methods, in addition to static, to study functional connectivity, to better understand the mechanisms of migraine. Our next step will be to assess the dynamics of the migraine brain throughout the migraine cycle.