Searching for Novel Biological Phenotypes in Migraine Patients Resistant to Treatments

High Density-electroencephalogram (HD-EEG) and functional-magnetic resonance imaging (fMRI) are widely available as powerful means to non-invasively study brain connectivity features in migraine patients. They both enables to record brain activity with high spatial resolution. HD-EEG also contributes to high temporal resolution, while fMRI contributes to precise evaluation of deep cortical and subcortical areas, relevant in migraine pathophysiology. Aim of the study is to evaluate changes in resting-state functional connectivity in patients undergoing preventive mAbs treatment using HD- EEG and fMRI. These findings will allow the identification of a multibiomarkers panel signature of migraine patients resisting to specifically targeted preventive treatments and potentially unveiling other molecular targets for therapeutic approaches. STUDY DESIGN: This study is part of the SPHERA project receiving funding from the Italian Ministry of Health (GR-2021-12372429). Patients will be enrolled from those attending the outpatient clinic of IRCCS Mondino Institute (Pavia) and Neurology Department of the University of L'Aquila (Avezzano). The investigators will collect clinical data and perform neurofunctional profiling of migraine patients at baseline (T0) and after three months of mAbs treatment (T3). METHODS: All patients will undergo a resting-state functional evaluation through high density-EEG and subset of 40 patients will also be studied in parallel with fMRI at baseline. \- HD-EEG: the investigators will randomly acquire 4 recordings (6 minutes each) in resting-state condition, 2 with opened eyes, and 2 with closed eyes. The investigators will consider the following frequency bands: delta (1-4 Hz), theta (4-8 Hz), alfa (8-13 Hz), beta (13-30 Hz), gamma (30-80 Hz). Acquisition parameters will be: High-Pass: 0.5 Hz; Low-Pass: 100 Hz; Notch: 50 Hz. For analysis of HD-EEG data, the investigators will use a tailored analysis pipe-line that was previously developed and validated to reconstruct neural sources from cortical/subcortical gray matter (Semprini, 2021). EEG signals will be band-pass filtered (1-80 Hz) and down-sampled at 250 Hz. Biological artifacts will be rejected using Independent Component Analysis (ICA). EEG signals will be referenced with a customized version of the Reference Electrode Standardization Technique (REST) (Mantini, 2007). A matrix will estimate the relationship between the measured scalp potentials and the dipoles corresponding to brain sources. Sources reconstruction will be performed with the exact low-resolution brain electromagnetic tomography (eLORETA) algorithm. \- Resting state fMRI: ICA will be applied to resting state-fMRI to characterize the RSNs. For each subject and for each identified RSN, the mean functional connectivity will be calculated. A seed based component analysis will be applied in order to obtain a whole brain map to describe the strength of the connectivity of each voxel of the brain image with the region of interest (seed). The region of interests selection will be based on available literature and previous experience.