Decoding Pain Sensitivity in Migraine With Multimodal Brainstem-based Neurosignature

Study Overview
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Brief Summary
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Migraine is a highly prevalent and disabling neurological disease, which has a tremendous impact on sufferers, healthcare systems, and the economy. According to the 2016 WHO report, migraine is the second leading cause of years lived with disability, greater than all other neurological diseases combined. Yet, the treatment in migraine is far from optimum; the sufferers often abuse painkillers and complicated with medication overuse headache. Migraine is characterized by the hypersensitivity of the sensory system, potentially attributed to dysfunctional pain modulatory networks located in the deep brain structures, particularly the brainstem. However, the current understanding of these deeply seated, dysregulated pain modulatory circuits in migraine is limited due to technological constraints. Besides, studies with an in-depth analysis of the clinical manifestations (i.e., deep phenotyping) are lacking, and there is no corresponding animal model readily available for translational research. In this project, the investigators propose a multimodal approach to address these issues by applying the technologies and platforms developed by our team to explore the correlation between pain sensitivity and dysregulated connectivities from brainstem to other brain regions. In this four-year project, the investigators will recruit 400 migraine patients and 200 healthy subjects. The investigators aim at decomposing the key brainstem mechanisms underlying dysmodulated pain sensitivity in migraine from 5 comprehensive perspectives: (1) clinical deep phenotyping, (2) high-resolution brainstem structural MRI and functional connectivity analysis, (3) innovative brainstem EEG signal detecting technique, (4) multimodal data fusion platform with neural network analysis, and (5) ultrahigh-resolution brainstem-based connectomes, intravital manipulations and recording, and connectome-sequencing in animal models. Moreover, the investigators will collaborate with Taiwan Semiconductor Research Institute to develop a wearable high-density EEG equipment, integrated with a System-on-Chip capable of edge-computing the signal using algorithms derived from our brainstem decoding platform. The ultimate goal is to build a real-time brainstem decoding system for clinical application.

Detailed Description
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Migraine causes a tremendous disease burden around the world. Migraine is one of the most prevalent neurological disorders and is reported by the WHO as the second leading cause of disease-related disabilities globally (No. 1 in the population under the 50s). There has been no much change in the ranking of disability for migraine for the past two decades, reflecting an unmet need for better treatment options. Even with the recently available calcitonin-gene related peptide (CGRP)-based treatment, the treatment response versus placebo is still disappointing (6.4-17.6% in acute treatment, 10.2-23.7% in preventive treatment). There is an urgent need to push further the current understanding of the pathophysiology of migraine, based on which novel treatment strategies can be developed. The lack of appropriate research tools hinders the acceleration of migraine research. As a neurological disorder, many neuroimaging studies have been focused on brain alterations; however, the majority focused on the cerebrum. Limited by the currently available neuroimaging and electrophysiological technologies, the deep brain structures especially the brainstem involved in the sensory and nociceptive neurotransmission in migraine, such as the trigeminal nucleus, could only be investigated to a limited extent. Obviously, there is an unmet need for novel technologies that can be used to delineate structural or functional alterations in the brainstem. Elucidation of the role of these deep brain structures may fill the gap in the current understanding of migraine pathophysiology, and pave the way to precise and efficient treatment. Studies restricted to single methodologies are insufficient for the complexity of migraine. Migraine is a complex and dynamic disorder. However, most prior studies were limited to single methodologies and provided limited insights into such a multifaceted disorder. Studies with an integrated approach are lacking. An exhaustive examination of the discrete components of a phenotype, i.e., 'deep phenotyping', can help understand different aspects of its clinical manifestations, and facilitate patient classification. Coupled with neuroimaging and electrophysiological research methodologies, a multi-modal decoding approach would help identify a constellation of migraine-specific biosignatures, rather than just one. This can not only provide clues to decipher migraine pathophysiology in various dimensions but also serve as the basis of the development of a prediction algorithm that can be applied in clinical practice. To pursue the overall goal, the present project schemes as a composition of the following 5 aims: Aim 1: Deep phenotyping for sensory processing in patients with migraine Aim 2: Brainstem-based functional and structural connectomics in migraine Aim 3: Capturing brainstem electro-neurosignature in migraine Aim 4: Constructing a data fusion platform and developing an EEG cap with a built-in analytic chip Aim 5: Exploring brainstem-based connectome sequencing in migraine animal model

Official Title
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Decoding Pain Sensitivity in Migraine With Multimodal Brainstem-based Neurosignature

Conditions
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Migraine

Intervention / Treatment
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* Drug: Flunarizine
* Other: healthy control


Participation Criteria
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Eligibility Criteria
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Migraine: Inclusion criteria: fulfill the diagnostic criteria of migraine in ICHD-3, 20-65 yrs, understand the study design and willing to join the study at least four headache days per month, the onset of headache is prior to 50 yrs., normal neurological examination findings. Exclusion criteria: history or family history of epilepsy, taking migraine prophylactics, women who are breastfeeding or pregnant, severe psychological disorders, including major depression, PTSD, personality disorders, bipolar disorder, schizophrenia, medical, neurological or psychiatric disease discovered by the researcher that would hinder the research, contraindications for MR scan (pacemaker, claustrophobia, stent, metal implants…). Healthy: Inclusion criteria: 20-65 yrs, normal neurological examination findings, understand the study design and willing to join the study. Exclusion criteria: history or family history of epilepsy, women who are breastfeeding or pregnant, severe psychological disorders, including major depression, PTSD, personality disorders, bipolar disorder, schizophrenia, medical, neurological or psychiatric disease discovered by the researcher that would hinder the research, contraindications for MR scan (pacemaker, claustrophobia, stent, metal implants…), history of headache will be included (the tension-type headache occurs < 1 time per month is allowed)

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: patients with migraine<br>patient with migraine will be prescribed with flunarizine or routine clinical care per clinician's decision based on the condition of each individual patient | Drug: Flunarizine<br>* The flunarizine will be given per clinical routine<br>|
| Other: healthy control<br>healthy control | Other: healthy control<br>* no intervention for healthy control<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Clinical change after treatment (1) headache frequency | clinical change (headache frequency) after treatment unit: attacks per month analysis: comparing the mean headache frequency in each month after treatment (M1/M2/M3/M4/M5/M6) to that before treatment (M0) | 6 months | 
| Clinical change after treatment (2) headache intensity | clinical change (headache intensity) after treatment unit: NRS (numeric rating scale, 0-10) analysis: comparing the mean headache intensity in each month after treatment (M1/M2/M3/M4/M5/M6) to that before treatment (M0) | 6 months | 
| Clinical change after treatment (3) headache duration | clinical change (headache duration) after treatment unit: hours/day analysis: comparing the mean headache duration (hours/day) in each month after treatment (M1/M2/M3/M4/M5/M6) to that before treatment (M0) | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| EEG change after treatment (1) Linear analysis of EEG before and after treatment | power spectral density change of EEG before and after treatment • Four EEG sessions will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| EEG change after treatment (2) Nonlinear analysis of EEG before and after treatment | functional connectivity change of EEG before and after treatment • Four EEG sessions will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| EEG change after treatment (3) Nonlinear analysis of EEG before and after treatment | evoked potential amplitude change of EEG before and after treatment • Four EEG sessions will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| Sensory threshold change after treatment | Using quantitative sensory testing (QST) to evaluate the sensory threshold before and after treatment • Four standard QST sessions will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| fMRI change after treatment (1) | functional connectivity change of fMRI before and after treatment • Three fMRI sessions will be arranged. The first one is done before treatment, and the 2nd/3rd one will be done after a 6-month/12-month treatment course, respectively. | 12 months | 
| fMRI change after treatment (2) | activation change of fMRI before and after treatment • Three fMRI sessions will be arranged. The first one is done before treatment, and the 2nd/3rd one will be done after a 6-month/12-month treatment course, respectively. | 12 months | 
| MRI change after treatment (1) | VBM changes of MRI before and after treatment • Three MRI sessions will be arranged. The first one is done before treatment, and the 2nd/3rd one will be done after a 6-month/12-month treatment course, respectively. | 12 months | 
| MRI change after treatment (2) | SBM changes of MRI before and after treatment • Three MRI sessions will be arranged. The first one is done before treatment, and the 2nd/3rd one will be done after a 6-month/12-month treatment course, respectively. | 12 months | 
| Humoral change after treatment (1) | Test the cytokine level using ELISA kit to evaluate the status before and after treatment • Four blood test sessions and saliva collection will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| Humoral change after treatment (2) | Test the hormone level using ELISA kit to evaluate the status before and after treatment • Four blood test sessions and saliva collection will be arranged. The first one is done before treatment, and the 2nd/3rd/4th one will be done after a 3-month/6-month/12-month treatment course, respectively. | 12 months | 
| Genetic variance | Genetic variants associated with baseline demographics and treatment response as assessed with genome-wide association study using the genotyping data derived from the Axiom Genome-wide array • Blood draw before the treatment to extract DNA for further sequencing | 5 minutes |