Migraine
Conditions
Keywords
Episodic migraine, Chronic migraine, Medication overuse, Nitroglycerin, fMRI
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
IRCCS National Neurological Institute C. Mondino Foundation
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 60 Years
Healthy volunteers
Yes
Inclusion criteria
Episodic migraineurs Inclusion Criteria: * age between 18-60 years; * diagnosis of episodic migraine without aura developed before the age of 50; * no current prophylactic treatment for migraine prevention; * chronic migraineurs with medication overuse according to the ICHDIII criteria
Exclusion criteria
* chronic or medication-overuse headache or cluster headache diagnosis; * any chronic pain condition or disorders other than migraine; * an alleged diagnosis of major psychiatric disorders such as depression, bipolar affective disorder and schizophrenia; * a diagnosis of tension type headache with a frequency of more than 5 days per month; * any cardiovascular diseases in which the NTG use could be contraindicated; * blood pressure hypotension, closed angle glaucoma, anaemia; * women in child bearing, breast feeding; continuous use of benzodiazepines; * any neuroradiological pathological findings at a previous MRI scan of the head. Chronic migraineurs Inclusion Criteria: * age between 18-60 years; * diagnosis of migraine without aura developed before the age of 50 according to the ICHD III criteria; * currently chronic migraineurs with medication overuse according to The International Classification of Headache Disorders 3rd edition (ICHDIII) criteria.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Functional Connectivity (FC) changes | Up to 6 hours | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Throbbing pain (number) | Up to 6 hours | 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. |
| Abortive medication (number of intake/month) | Up to 6 hours | To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms. |
| Magnetic Resonance Imaging (MRI) | Up to 6 hours | 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. |
| Monthly migraine frequency (day/month) | Up to 6 hours | To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms. |
| Aggravation by movement (number) | Up to 6 hours | 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. |
| Nausea (number) | Up to 6 hours | 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. |
| Vomiting (number) | Up to 6 hours | 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. |
| Photophobia (number) | Up to 6 hours | 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. |
| Phonophobia (number) | Up to 6 hours | To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms. |
| Disease duration (years) | Up to 6 hours | To acquire clinical data to identify feature patterns that can profile patient's condition using machine learning (ML) and deep learning (DL) algorithms. |
Countries
Italy
Outcome results
None listed