The main aim of the present pilot study is to prove the possibility to use the Nitroglycerin (NTG) model to describe the pathophysiology of headache using task-free advanced Magnetic Resonance Imaging (MRI) techniques, in order to depict the static changes of the ictal and inter-ictal phase of migraine attacks vs the pain free state in healthy subjects and to compare that with the spontaneous headache attack experienced by chronic migraineurs.
Resting state functional magnetic resonance imaging (rs-fMRI) has depicted cyclical functional connectivity changes during the ictal and inter-ictal phase of the migraine attack. In this pilot study, Functional Connectivity (FC) changes during nitroglycerin (NTG) induced migraine attacks were assessed vs the pain-free condition in healthy subjects. To this end, subjects with episodic migraine (EM) without aura were enrolled. NTG-triggered a spontaneous-like migraine attack in the subjects. They underwent 4 rs-fMRI scan repetitions during different phases of the attack (baseline, prodromal, full blown, recovery phase) with a 3 Tesla MR scanner. According to the pain field literature, several regions of interests were studied, in particular the thalamic areas and the salience network (SN) were selected as primary areas of interest for the analyses. Subjects' rs-fMRI data were first processed with a seed-based correlation analysis (SCA) to assess the static changes in FC between the thalamus and the rest of the brain during the experiment. The wavelet coherence approach (WCA) were also applied to test the changes in time-in-phase coherence between the thalamus and the salience network (SN). Healthy subject were administered nitroglycerin as well and scanned at a pain free baseline and after 3 hours in order to compare the response. The rebound headache that followed acute drug withdrawal were used as a surrogate paradigm of spontaneous attack. Patients with chronic migraine and medication overuse were hospitalized for a supervised withdrawal program at the Mondino Foundation; during the program if they experienced a rebound headache attack, they were scanned with a rs-fMRI acquisition. The acquired imagines were analyzed with the same procedure regarding the evaluation of static and dynamic functional connectivity fluctuation.
Study Type
OBSERVATIONAL
Enrollment
15
Headache Science Center
Pavia, Italy
Functional Connectivity (FC) changes
Functional connectivity pattern of changes profiling the different condition of the migraine experience. To depict the static and dynamics changes of brain activity during a migraine attack; ii) To validate the use of the NTG-induced attacks paradigm as a reliable instrument combined with an fMRI approach to compare the induced vs the spontaneous attack; iii) To describe possible differences in brain activity between attacks in chronic and episodic migraineurs.
Time frame: Up to 6 hours
Magnetic Resonance Imaging (MRI)
To acquire sufficient MRI to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms. This can be achieved by combining clinical, psychological, biological, neurophysiological and MRI-derived features into a multimodal multi-parametric approach suitable for patient's classification. The ML and DL approaches could also be adopted to predict chronification, as well as the response to a withdrawal program for medication overuse headache.
Time frame: Up to 6 hours
Monthly migraine frequency (day/month)
To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Disease duration (years)
To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Nausea (number)
As a feature of the migraine attack.To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
Time frame: Up to 6 hours
Vomiting (number)
As a feature of the migraine attack.To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Photophobia (number)
As a feature of the migraine attack.To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Phonophobia (number)
To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Aggravation by movement (number)
As a feature of the migraine attack. To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Throbbing pain (number)
As a feature of the migraine attack. To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours
Abortive medication (number of intake/month)
To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms.
Time frame: Up to 6 hours