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Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine.

ABSTRACT: Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache accompanying functionally and structurally maladaptive changes in brain. As the headache days/month is often measured by patient self-report and tends to be overestimated than actually experienced, the possibility of using neuroimaging data to predict migraine attack frequency is of great interest. To identify neuroimaging features that could objectively evaluate patients' headache days, a total of 179 migraineurs were recruited from two data center with one dataset used as the training/test cohort and the other used as the validating cohort. The guidelines for controlled trials of prophylactic treatment of chronic migraine in adults were used to identify the frequency of attacks and migraineurs were divided into low (MOl) and high (MOh) subgroups. Whole-brain functional connectivity was used to build multivariate logistic regression models with model iteration optimization to identify MOl and MOh. The best model accurately discriminated MOh from MOl with AUC of 0.91 (95%CI [0.86, 0.95]) in the training/test cohort and 0.79 in the validating cohort. The discriminative features were mainly located within the limbic lobe, frontal lobe, and temporal lobe. Permutation tests analysis demonstrated that the classification performance of these features was significantly better than chance. Furthermore, the indicator of functional connectivity had a higher odds ratio than behavioral variables with implementing a holistic regression analysis. The current findings suggested that the migraine attack frequency could be distinguished by using machine-learning algorithms, and highlighted the role of brain functional connectivity in revealing underlying migraine-related neurobiology.

SUBMITTER: Mu J

PROVIDER: S-EPMC7267923 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionMigraine without aura (MwoA) is a primary type of migraine, a common disabling disorder, and a disabling neurological condition. The headache is a complex experience, a common form of pain, in which multiple sensory information dimensions are combined to provide a unified conscious event. Migraine ictal have unique neuroimage biomarkers, but the brain is also affected during the inter-ictal phase. According to the current studies, a hypothesis was constructed that the altered integration of pain spatial and intensity information impacts headache intensity in the inter-ictal period.MethodsIn this study, we applied theory-based region-to-region functional connectivity (FC) analyses to compare the differences in resting-state FC between MwoA participants and healthy controls with the pain integration hypothesis. After the correlation matrices between FC edges and clinical symptoms were constructed, the moderating effect and simple slope tests were investigated to explain whether and how the dysfunction of pain features discrimination affects the clinical symptoms.ResultsFunctional connectivity analyses showed significantly decreased FC edges between the left dorsolateral superior frontal gyrus (SFGdor) and left insula, and an increased FC edge between the left SFGdor and bilateral angular gyrus. The correlation matrix showed no significant correlation between significantly altered FC edge and headache duration, frequency, Zung self-rating anxiety scale, and Zung self-rating depression scale. Only one significantly altered edge in the MwoA condition was significantly correlated with headache intensity. Moderating Module 1 and 2 manifested the moderator variable (altered rs-FC edge) moderated the link between the normal edges and headache intensity.ConclusionThe pain features integration processes in migraineurs vary from HCs, related to the clinical symptoms during a migraine attack. Moreover, the clinical symptoms will be affected by one or more discrimination modules. And the spatial or intensity discrimination modules have a higher impact when combined with another module on clinical symptoms than the single module.

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Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine.

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