The FreMRI Study: Advanced MRI on Migraine Patients Treated With Fremanezumab

NCT ID: NCT06244823

Last Updated: 2024-02-06

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: PHASE4
• Total Enrollment: 87 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-03-01
• Study Completion Date: 2025-01-01

Brief Summary

The goal of this open-label, single-blind, controlled-trial is to evaluate brain changes evaluated with diffusion Magnetic Resonance Imaging (MRI) and functional MRI in patients with high-frequency episodic migraine and chronic migraine that will be treated with Fremanezumab, 12 weeks after the treatment onset, compared with the baseline.

Type of study: Phase IV clinical trial Participant population: high-frequency episodic migraine and chronic migraine. Participants will be treated with Fremanezumab.

Detailed Description

Fremanezumab is a monoclonal antibody targeting calcitonin C-reactive peptide (CGRP) that has proven to be effective in the treatment of episodic and chronic migraine. Due to the molecular weight of the drug, it is not supposed to cross the blood-brain barrier, acting peripherally.

Patients with migraine have shown gray and white matter changes that can be evaluated by magnetic resonance imaging (MRI).

In the present study, we aim to evaluate the presence of MRI changes in patients treated with Fremanezumab, 12 weeks after the treatment onset, compared with the baseline.

Number of Subjects: 87 patients. Study Duration per Subject Study duration per patient will be 8 months.

Study procedures:

In the first visit, the screening visit, patients will receive a detailed explanation of the study and will sign informed consent form. Inclusion-exclusion criteria will be reviewed and evaluation of vital signs, general and neurological examination will be done. Patients will receive a headache diary and will be trained in its use.

First MRI scan will be acquired within 0-14 days, prior to Fremanezumab injection.

Patients will do four monthly additional hospital visits. During each visit, occurrence of any adverse event will be interrogated; clinical situation will be analyzed and Fremanezumab will be administered. Visits will be performed at weeks 0, 4 and 8. Last visit will be performed after 12 weeks, without Fremanezumab injection.

The second MRI will be acquired at 12 ± 1 weeks after the first Fremanezumab injection.

Intervention:

MRI acquisition Images will be acquired during interictal periods, defined as at least 24 hours from last migraine attack. High-resolution 3D T1-weighted, diffusion-weighted and resting-state functional MRI data will be acquired using a Philips Achieva 3T MRI unit (Philips Healthcare, Best, The Netherlands) with a 32-channel head coil in the MRI facility at the Universidad de Valladolid (Valladolid, Spain).

For the anatomical T1-weighted images, the following acquisition parameters will be used: Turbo Field Echo (TFE) sequence, repetition time (TR) = 8.1 ms, echo time (TE) = 3.7 ms, flip angle = 8º, 256 x 256 matrix size, 1 x 1 x 1 mm3 of spatial resolution and 170 sagittal slices covering the whole brain.

Diffusion-weighted images (DWI) will be obtained using the next parameters: TR = 9000 ms, TE = 86 ms, flip angle = 90º, 61 gradient directions, two baseline volumes with opposite phase encoding direction, b-value = 1000 s/mm2, 128 x 128 matrix size, 2 x 2 x 2 mm3 of spatial resolution and 66 axial slices covering the whole brain.

Resting-state functional MRI (rs-fMRI) will be acquired with the following parameters: TR = 3000 ms, TE = 30 ms, flip angle = 80º, 80 x 80 matrix size, 3 x 3 x 4 mm3 of spatial resolution, 35 axial slices covering the whole brain and 197 volumes. During this acquisition, the patient will close the eyes but remain awake.

All the scans will be acquired during the same session, starting with the T1-weighted scan, followed by the diffusion-weighted scan and ending with the rs-fMRI scan. Total acquisition time for a single subject is approximately 28 minutes, divided in the following periods of time: six minutes for the T1-weighted scan, 12 minutes for the diffusion-weighted scan and 10 minutes for the rs-fMRI scan. If we consider patient preparation, obtainment of documents and informed consent form, the whole process will take about 50-70 minutes.

Image processing T1-weighted morphometric parameters MRI images will be processed in order to obtain cortical curvature, cortical thickness, gray matter volume and surface area of the different gray matter regions.

Grey matter volume will be obtained for all the 84 grey matter regions from the Desikan-Killiany atlas (Reuter et al., 2012). Also, cortical curvature, cortical thickness and area will be calculated for the 68 regions from the atlas that are cortical regions.

DWI processing DWI will be processed to carry out two types of analysis: Tract-Based Spatial Statistics (TBSS) and structural connectomics.

Prior to the beginning of both processing pipelines, diverse preprocessing procedures will be implemented on the DWI data. Diffusion-weighted images will be denoised, using "dwidenoise" tool from MRtrix (www.mrtrix.org), eddy currents, motion and B0 field inhomogeneity corrected, using "dwipreproc" tool from MRtrix, and B1 field inhomogeneity corrected, using "dwibiascorrect" tool with the "-fast" option from MRtrix.

Four diffusion descriptors from Diffusion Tensor Imaging will be obtained: Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD). We will also be using measures non based on the diffusion tensor, like the return-to-origin probability (RTOP), which reflects cellularity and restrictions better than MD.

Once the DWI data are preprocessed, a whole brain mask for each image will be generated using "dwi2mask" tool from MRtrix and, next, diffusion tensors at each voxel will be estimated using the "dtifit" tool from FSL, also obtaining FA, MD and AD maps. RD will be manually calculated by obtaining the mean of the second and the third eigenvalues, which will also be previously computed with "dtifit". RTOP will be estimated using the method called "Apparent Measures Using Reduced Acquisitions" (AMURA).

Structural connectomics The analysis of structural connectivity will use the segmentation results from T1-weighted processing pipeline.

Anatomically-Constrained Tractography (ACT) will be implemented. Previously, five-tissue-type (5TT) segmented images for each subject will be obtained from the T1-weighted images using the "5ttgen" tool from MRtrix to have suitable images for ACT. 5TT image and the automated parcellation from FreeSurfer will be inearly registered to the FA image using the FLIRT tool from FSL.

Finally, structural connectivity matrices will be computed from the filtered tractography output and the registered cortical segmentation volumes. 84 × 84 connectivity matrices, corresponding to the 84 cortical and subcortical regions from the Desikan-Killiany atlas, will be obtained using mean FA and the number of streamlines in each connection as connectome metrics. Connectivity matrices constructed in this manner are symmetric, so only a half of each matrix will be employed for further analysis.

Due to the tractography method employed, it is possible that streamlines start and end in different points belonging to the same gray matter region from the Desikan-Killiany atlas. For this reason, these connections ("self-connections") will also be included in the analysis.

fMRI processing The fMRI processing pipeline will be fully implemented in the software CONN. Firstly, some preprocessing steps will be implemented. fMRI volumes will be realigned and unwarpped to estimate and correct subject motion. To identify outliers, ARtifact detection Tools (ART) based outlier detection algorithm will be implemented. Then, fMRI volumes will be directly coregistered to the corresponding structural volumes (T1-weighted images) using a rigid body transformation. T1-weighted images will be segmented to detect different types of tissue, i.e., gray matter, white matter and cerebrospinal fluid (CSF).

Finally, the functional connectivity matrix for each case will be computed considering whole-brain region-to-region connections, using the 84 cortical and subcortical regions from the Desikan-Killiany atlas in each subject as regions of interest. The matrices will contain the Z-values from the Fisher's r-to-z transformation.

Safety Variables Safety and side effects will be evaluated according to reported adverse events, both spontaneously by patients and systematically analyzed during study visits. Vital signs (blood pressure, pulse, temperature and respiratory rate), physical examination and 12-lead electrocardiography will be done prior to the enrollment. Presence of any adverse event or local injection-site reaction will be systematically addressed. Columbia-Suicide Severity Rating Scale will assess suicidal ideation and behavior at baseline.

Statistical Analysis Parameter evolution in time and relationship with change in monthly migraine days for each of the parameters, i.e., morphometric (gray matter) parameters, diffusion descriptors (white matter), structural connectivity and resting-state functional connectivity, longitudinal evolution will be assessed.

n order to analyze the structural connectivity matrices, firstly, the mean number of streamlines in each connection (cell from the connectivity matrix) will be computed for each group. Next, connections with less than 500 streamlines (group mean) will be discarded in order to exclude weak connections, for which results could be unreliable, from further analysis.

In the case of functional resting-state connectivity matrices, a similar rejection of non-important connections will be selected, connections with absolute Z-value lower than 0.1 are excluded, i.e., a Pearson correlation value of r = 0.1 following the Fisher's r-to-z transformation.

In the previous two cases, if the thresholds are too high or too low, i.e., almost all the connections are rejected, or almost no connections are rejected, the thresholds will be changed.

A model will be implemented for each of the four types of analysis described in the document. In each of the models, the significant effect of the covariates and the predictive capability of the model using the lowest number of covariates will be considered. To compare the different models, Akaike's Information Criterion (AIC) will be used (67). In the case of very similar AIC values, the model with a lower number of regressors will be chosen.

For each of the four types of analysis, the predictive model will be computed for a single regressor. Regressors with p-value equal or higher than 0.05 in the singular predictive model will not be further considered to be included in the final model; while the remaining regressors will be considered for the final model of the corresponding type of analysis.

Relationship between MRI parameters evolution and clinical response A linear mixed-effect model will be prepared using the same strategy that was employed for the analysis of change in monthly migraine days and MRI parameters. To evaluate the clinical response to treatment, a dichotomic variable showing positive or negative response to treatment will be used, instead of change in monthly migraine days.

Positive response to treatment is considered as 50% reduction in number of monthly migraine days.

Exploratory variables analysis The final model used in the analysis of the relationship between the diverse MRI parameters and change in monthly migraine days will be used. The variable representing the change in monthly migraine days will be replaced by the change in monthly intense headache days, in monthly days of acute treatment medication.

In the case of response to treatment, the same final model from the analysis of clinical response considered as 50% reduction in number of monthly migraine days will be used. Response to treatment, in this case, will be represented by reduction of 75%, 100% and 30% of monthly migraine days.

Analysis of adverse events Categorical adverse events will be compared using Fisher's exact test. A p-value < 0.05 will be considered as statistically significant.

Conditions

Migraine Disorders; Migraine With Aura; Migraine Without Aura; Brain Diseases

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Fremanezumab

Patients will be treated with 225 mg of subcutaneous injections of Fremanezumab per month.

Patients will do four monthly additional hospital visits. During each visit, occurrence of any adverse event will be interrogated; clinical situation will be analyzed and Fremanezumab will be administered. Visits will be performed at weeks 0, 4 and 8. Last visit will be performed after 12 weeks, without Fremanezumab injection.

Group Type: EXPERIMENTAL

Fremanezumab Prefilled Syringe

Intervention Type: DRUG

MRI will be scanned prior to the first administration of Fremanezumab, within 0-14 days, prior to Fremanezumab injection. The second MRI will be acquired at 12 ± 1 weeks after the first Fremanezumab injection.Images will be acquired during interictal periods, defined as at least 24 hours from last migraine attack. All the scans will be acquired during the same session, starting with the T1-weighted scan, followed by the diffusion-weighted scan and ending with the rs-fMRI scan. Total acquisition time for a single subject is approximately 28 minutes, divided in the following periods of time: six minutes for the T1-weighted scan, 12 minutes for the diffusion-weighted scan and 10 minutes for the rs-fMRI scan. If we consider patient preparation, obtainment of documents and informed consent form, the whole process will take about 50-70 minutes.

Interventions

Fremanezumab Prefilled Syringe

MRI will be scanned prior to the first administration of Fremanezumab, within 0-14 days, prior to Fremanezumab injection. The second MRI will be acquired at 12 ± 1 weeks after the first Fremanezumab injection.Images will be acquired during interictal periods, defined as at least 24 hours from last migraine attack. All the scans will be acquired during the same session, starting with the T1-weighted scan, followed by the diffusion-weighted scan and ending with the rs-fMRI scan. Total acquisition time for a single subject is approximately 28 minutes, divided in the following periods of time: six minutes for the T1-weighted scan, 12 minutes for the diffusion-weighted scan and 10 minutes for the rs-fMRI scan. If we consider patient preparation, obtainment of documents and informed consent form, the whole process will take about 50-70 minutes.

Intervention Type: DRUG

Eligibility Criteria

Inclusion Criteria

1. Definite diagnosis of Migraine With Aura or Migraine Without Aura according to the International Classification of Headache Disorders, 3rd version (IHCD-3) (1).

2. Age between 18 and 65 years old.

3. Providing signed informed consent form.

4. Diagnosis of migraine before 50 years old.

5. History of migraine during at least 12 months prior to the study.

6. With eight or more migraine days per month within the last three months

Exclusion Criteria

1.Presence of other primary headache disorders other than infrequent tension-type headache or medication overuse headache (MOH).

1. Participation of MOH patients will be restricted to a maximum of 50% of the total sample.

2. Prior use of Fremanezumab or another monoclonal antibody targeting CGRP or CGRP receptor.

3. Prior use of less than two or more than four preventive drugs according to the local national guidelines (34), with inadequate response after sufficient doses and enough time or lack of tolerability.

4. Any medical condition that might prevent study completion or interfere with interpretation of results.

5. History of any neurological or neurosurgical condition affecting the brain.

6. History of moderate-severe head trauma.

7. History of other chronic pain syndrome with a frequency of five or more days of pain per month.

8. Presence of daily headache

9. Pregnant or breastfeeding women.

10. Current or recent use of any other prophylactic treatment in the preceding five half-lives prior to the start.

11. Exposure to onabotulinumtoxinA in the preceding four months.

12. Any expected surgery during the study.

13. Use of opioids or barbiturates.

14. Any condition contraindicating an MRI acquisition.

15. Completing headache diary at least 80% of the time during the screening period

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Valladolid

OTHER

Sponsor Role: collaborator

Complejo Asistencial Universitario de Palencia

UNKNOWN

Sponsor Role: collaborator

Complejo Público Asistencial de Zamora

UNKNOWN

Sponsor Role: collaborator

Hospital Clínico Universitario de Valladolid

OTHER

Sponsor Role: lead

Responsible Party

David García Azorín

Headache Unit, Department of Neurology, Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Angel L Guerrero Peral, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Sanidad de Castilla y León

Locations

Hospital Clínico Universitario de Valladolid

Valladolid, , Spain

Site Status: RECRUITING

Countries

Spain

Central Contacts

David Garcia Azorin, MD, PhD

Role: CONTACT

davilink@hotmail.com

+34 665872228

Yesica Gonzalez Osorio, MBA

Role: CONTACT

ygoinvestigacion@outlook.com

+34 634330426

Facility Contacts

David Garcia Azorin, MD, PhD

Role: primary

References

Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia. 2018 Jan;38(1):1-211. doi: 10.1177/0333102417738202. No abstract available.

Reference Type: BACKGROUND

PMID: 29368949 (View on PubMed)

Karsan N, Bose P, Goadsby PJ. The Migraine Premonitory Phase. Continuum (Minneap Minn). 2018 Aug;24(4, Headache):996-1008. doi: 10.1212/CON.0000000000000624.

Reference Type: BACKGROUND

PMID: 30074545 (View on PubMed)

Quintela E, Castillo J, Munoz P, Pascual J. Premonitory and resolution symptoms in migraine: a prospective study in 100 unselected patients. Cephalalgia. 2006 Sep;26(9):1051-60. doi: 10.1111/j.1468-2982.2006.01157.x.

Reference Type: BACKGROUND

PMID: 16919055 (View on PubMed)

Giffin NJ, Ruggiero L, Lipton RB, Silberstein SD, Tvedskov JF, Olesen J, Altman J, Goadsby PJ, Macrae A. Premonitory symptoms in migraine: an electronic diary study. Neurology. 2003 Mar 25;60(6):935-40. doi: 10.1212/01.wnl.0000052998.58526.a9.

Reference Type: BACKGROUND

PMID: 12654956 (View on PubMed)

Afridi KS, Kaube H, Goadsby JP. Glyceryl trinitrate triggers premonitory symptoms in migraineurs. Pain. 2004 Aug;110(3):675-680. doi: 10.1016/j.pain.2004.05.007.

Reference Type: BACKGROUND

PMID: 15288408 (View on PubMed)

Guo S, Vollesen ALH, Olesen J, Ashina M. Premonitory and nonheadache symptoms induced by CGRP and PACAP38 in patients with migraine. Pain. 2016 Dec;157(12):2773-2781. doi: 10.1097/j.pain.0000000000000702.

Reference Type: BACKGROUND

PMID: 27842045 (View on PubMed)

Burstein R, Jakubowski M. Unitary hypothesis for multiple triggers of the pain and strain of migraine. J Comp Neurol. 2005 Dec 5;493(1):9-14. doi: 10.1002/cne.20688.

Reference Type: BACKGROUND

PMID: 16258903 (View on PubMed)

Noseda R, Kainz V, Borsook D, Burstein R. Neurochemical pathways that converge on thalamic trigeminovascular neurons: potential substrate for modulation of migraine by sleep, food intake, stress and anxiety. PLoS One. 2014 Aug 4;9(8):e103929. doi: 10.1371/journal.pone.0103929. eCollection 2014.

Reference Type: BACKGROUND

PMID: 25090640 (View on PubMed)

Denuelle M, Fabre N, Payoux P, Chollet F, Geraud G. Hypothalamic activation in spontaneous migraine attacks. Headache. 2007 Nov-Dec;47(10):1418-26. doi: 10.1111/j.1526-4610.2007.00776.x.

Reference Type: BACKGROUND

PMID: 18052951 (View on PubMed)

Maniyar FH, Sprenger T, Monteith T, Schankin C, Goadsby PJ. Brain activations in the premonitory phase of nitroglycerin-triggered migraine attacks. Brain. 2014 Jan;137(Pt 1):232-41. doi: 10.1093/brain/awt320. Epub 2013 Nov 25.

Reference Type: BACKGROUND

PMID: 24277718 (View on PubMed)

Stankewitz A, Aderjan D, Eippert F, May A. Trigeminal nociceptive transmission in migraineurs predicts migraine attacks. J Neurosci. 2011 Feb 9;31(6):1937-43. doi: 10.1523/JNEUROSCI.4496-10.2011.

Reference Type: BACKGROUND

PMID: 21307231 (View on PubMed)

Schulte LH, May A. The migraine generator revisited: continuous scanning of the migraine cycle over 30 days and three spontaneous attacks. Brain. 2016 Jul;139(Pt 7):1987-93. doi: 10.1093/brain/aww097. Epub 2016 May 5.

Reference Type: BACKGROUND

PMID: 27190019 (View on PubMed)

Beissner F, Meissner K, Bar KJ, Napadow V. The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function. J Neurosci. 2013 Jun 19;33(25):10503-11. doi: 10.1523/JNEUROSCI.1103-13.2013.

Reference Type: BACKGROUND

PMID: 23785162 (View on PubMed)

Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012 Aug 15;62(2):782-90. doi: 10.1016/j.neuroimage.2011.09.015. Epub 2011 Sep 16.

Reference Type: BACKGROUND

PMID: 21979382 (View on PubMed)

Jensen K, Tuxen C, Olesen J. Pericranial muscle tenderness and pressure-pain threshold in the temporal region during common migraine. Pain. 1988 Oct;35(1):65-70. doi: 10.1016/0304-3959(88)90277-1.

Reference Type: BACKGROUND

PMID: 3200599 (View on PubMed)

Burstein R, Yarnitsky D, Goor-Aryeh I, Ransil BJ, Bajwa ZH. An association between migraine and cutaneous allodynia. Ann Neurol. 2000 May;47(5):614-24.

Reference Type: BACKGROUND

PMID: 10805332 (View on PubMed)

Rodriguez C, Herrero-Velazquez S, Ruiz M, Baron J, Carreres A, Rodriguez-Valencia E, Guerrero AL, Madeleine P, Cuadrado ML, Fernandez-de-Las-Penas C. Pressure pain sensitivity map of multifocal nummular headache: a case report. J Headache Pain. 2015;16:523. doi: 10.1186/s10194-015-0523-7. Epub 2015 Apr 30.

Reference Type: BACKGROUND

PMID: 25929432 (View on PubMed)

Fernandez-de-las-Penas C, Madeleine P, Cuadrado ML, Ge HY, Arendt-Nielsen L, Pareja JA. Pressure pain sensitivity mapping of the temporalis muscle revealed bilateral pressure hyperalgesia in patients with strictly unilateral migraine. Cephalalgia. 2009 Jun;29(6):670-6. doi: 10.1111/j.1468-2982.2008.01831.x.

Reference Type: BACKGROUND

PMID: 19891059 (View on PubMed)

Dodick D, Silberstein S. Central sensitization theory of migraine: clinical implications. Headache. 2006 Nov;46 Suppl 4:S182-91. doi: 10.1111/j.1526-4610.2006.00602.x.

Reference Type: BACKGROUND

PMID: 17078850 (View on PubMed)

Bigal ME, Ashina S, Burstein R, Reed ML, Buse D, Serrano D, Lipton RB; AMPP Group. Prevalence and characteristics of allodynia in headache sufferers: a population study. Neurology. 2008 Apr 22;70(17):1525-33. doi: 10.1212/01.wnl.0000310645.31020.b1.

Reference Type: BACKGROUND

PMID: 18427069 (View on PubMed)

Castillo J, Munoz P, Guitera V, Pascual J. Kaplan Award 1998. Epidemiology of chronic daily headache in the general population. Headache. 1999 Mar;39(3):190-6. doi: 10.1046/j.1526-4610.1999.3903190.x.

Reference Type: BACKGROUND

PMID: 15613213 (View on PubMed)

Stovner LJ, Zwart JA, Hagen K, Terwindt GM, Pascual J. Epidemiology of headache in Europe. Eur J Neurol. 2006 Apr;13(4):333-45. doi: 10.1111/j.1468-1331.2006.01184.x.

Reference Type: BACKGROUND

PMID: 16643310 (View on PubMed)

Bigal ME, Serrano D, Buse D, Scher A, Stewart WF, Lipton RB. Acute migraine medications and evolution from episodic to chronic migraine: a longitudinal population-based study. Headache. 2008 Sep;48(8):1157-68. doi: 10.1111/j.1526-4610.2008.01217.x.

Reference Type: BACKGROUND

PMID: 18808500 (View on PubMed)

Hubbard CS, Becerra L, Smith JH, DeLange JM, Smith RM, Black DF, Welker KM, Burstein R, Cutrer FM, Borsook D. Brain Changes in Responders vs. Non-Responders in Chronic Migraine: Markers of Disease Reversal. Front Hum Neurosci. 2016 Oct 6;10:497. doi: 10.3389/fnhum.2016.00497. eCollection 2016.

Reference Type: BACKGROUND

PMID: 27766076 (View on PubMed)

Bigal ME, Edvinsson L, Rapoport AM, Lipton RB, Spierings EL, Diener HC, Burstein R, Loupe PS, Ma Y, Yang R, Silberstein SD. Safety, tolerability, and efficacy of TEV-48125 for preventive treatment of chronic migraine: a multicentre, randomised, double-blind, placebo-controlled, phase 2b study. Lancet Neurol. 2015 Nov;14(11):1091-100. doi: 10.1016/S1474-4422(15)00245-8. Epub 2015 Sep 30.

Reference Type: BACKGROUND

PMID: 26432181 (View on PubMed)

Bigal ME, Dodick DW, Rapoport AM, Silberstein SD, Ma Y, Yang R, Loupe PS, Burstein R, Newman LC, Lipton RB. Safety, tolerability, and efficacy of TEV-48125 for preventive treatment of high-frequency episodic migraine: a multicentre, randomised, double-blind, placebo-controlled, phase 2b study. Lancet Neurol. 2015 Nov;14(11):1081-90. doi: 10.1016/S1474-4422(15)00249-5. Epub 2015 Sep 30.

Reference Type: BACKGROUND

PMID: 26432182 (View on PubMed)

Dodick DW, Silberstein SD, Bigal ME, Yeung PP, Goadsby PJ, Blankenbiller T, Grozinski-Wolff M, Yang R, Ma Y, Aycardi E. Effect of Fremanezumab Compared With Placebo for Prevention of Episodic Migraine: A Randomized Clinical Trial. JAMA. 2018 May 15;319(19):1999-2008. doi: 10.1001/jama.2018.4853.

Reference Type: BACKGROUND

PMID: 29800211 (View on PubMed)

Silberstein SD, Dodick DW, Bigal ME, Yeung PP, Goadsby PJ, Blankenbiller T, Grozinski-Wolff M, Yang R, Ma Y, Aycardi E. Fremanezumab for the Preventive Treatment of Chronic Migraine. N Engl J Med. 2017 Nov 30;377(22):2113-2122. doi: 10.1056/NEJMoa1709038.

Reference Type: BACKGROUND

PMID: 29171818 (View on PubMed)

Silberstein SD, McAllister P, Ning X, Faulhaber N, Lang N, Yeung P, Schiemann J, Aycardi E, Cohen JM, Janka L, Yang R. Safety and Tolerability of Fremanezumab for the Prevention of Migraine: A Pooled Analysis of Phases 2b and 3 Clinical Trials. Headache. 2019 Jun;59(6):880-890. doi: 10.1111/head.13534. Epub 2019 Apr 12.

Reference Type: BACKGROUND

PMID: 30977520 (View on PubMed)

Aoki KR. Review of a proposed mechanism for the antinociceptive action of botulinum toxin type A. Neurotoxicology. 2005 Oct;26(5):785-93. doi: 10.1016/j.neuro.2005.01.017. Epub 2005 Jul 5.

Reference Type: BACKGROUND

PMID: 16002144 (View on PubMed)

Planchuelo-Gomez A, Garcia-Azorin D, Guerrero AL, Rodriguez M, Aja-Fernandez S, de Luis-Garcia R. Gray Matter Structural Alterations in Chronic and Episodic Migraine: A Morphometric Magnetic Resonance Imaging Study. Pain Med. 2020 Nov 1;21(11):2997-3011. doi: 10.1093/pm/pnaa271.

Reference Type: BACKGROUND

PMID: 33040149 (View on PubMed)

Planchuelo-Gomez A, Garcia-Azorin D, Guerrero AL, Aja-Fernandez S, Rodriguez M, de Luis-Garcia R. White matter changes in chronic and episodic migraine: a diffusion tensor imaging study. J Headache Pain. 2020 Jan 2;21(1):1. doi: 10.1186/s10194-019-1071-3.

Reference Type: BACKGROUND

PMID: 31898478 (View on PubMed)

Planchuelo-Gomez A, Garcia-Azorin D, Guerrero AL, Aja-Fernandez S, Rodriguez M, de Luis-Garcia R. Structural connectivity alterations in chronic and episodic migraine: A diffusion magnetic resonance imaging connectomics study. Cephalalgia. 2020 Apr;40(4):367-383. doi: 10.1177/0333102419885392. Epub 2019 Nov 1.

Reference Type: BACKGROUND

PMID: 31674222 (View on PubMed)

Planchuelo-Gómez A, García-Azorín D, Guerrero AL, Aja-Fernández S, Antón-Juarrós S, de Luis García R. Development of a response prediction model for the chronic migraine treatment response by grey matter morphometry in magnetic resonance. LXXI Spanish Society of Neurology congress, Sevilla Nov 21st 2019, Spain.

Reference Type: BACKGROUND

García-Azorín D, Porta-Etessam J, Guerrero-Peral AL. Official guidelines of the Neuropharmacology study group, Spanish Society of Neurology, Start & Stop Guidelines, Ed Luzan 5, 2019

Reference Type: BACKGROUND

Krebs K, Rorden C, Androulakis XM. Resting State Functional Connectivity After Sphenopalatine Ganglion Blocks in Chronic Migraine With Medication Overuse Headache: A Pilot Longitudinal fMRI Study. Headache. 2018 May;58(5):732-743. doi: 10.1111/head.13318.

Reference Type: BACKGROUND

PMID: 29878344 (View on PubMed)

Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. 2013 Sep;37(8):1680-91. doi: 10.1016/j.neubiorev.2013.06.001. Epub 2013 Jun 14.

Reference Type: BACKGROUND

PMID: 23769814 (View on PubMed)

Yrondi A, Peran P, Sauvaget A, Schmitt L, Arbus C. Structural-functional brain changes in depressed patients during and after electroconvulsive therapy. Acta Neuropsychiatr. 2018 Feb;30(1):17-28. doi: 10.1017/neu.2016.62. Epub 2016 Nov 23.

Reference Type: BACKGROUND

PMID: 27876102 (View on PubMed)

Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002 Nov;17(3):143-55. doi: 10.1002/hbm.10062.

Reference Type: BACKGROUND

PMID: 12391568 (View on PubMed)

Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999 Feb;9(2):179-94. doi: 10.1006/nimg.1998.0395.

Reference Type: BACKGROUND

PMID: 9931268 (View on PubMed)

Fischl B, Liu A, Dale AM. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging. 2001 Jan;20(1):70-80. doi: 10.1109/42.906426.

Reference Type: BACKGROUND

PMID: 11293693 (View on PubMed)

Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002 Jan 31;33(3):341-55. doi: 10.1016/s0896-6273(02)00569-x.

Reference Type: BACKGROUND

PMID: 11832223 (View on PubMed)

Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B. A hybrid approach to the skull stripping problem in MRI. Neuroimage. 2004 Jul;22(3):1060-75. doi: 10.1016/j.neuroimage.2004.03.032.

Reference Type: BACKGROUND

PMID: 15219578 (View on PubMed)

Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012 Jul 16;61(4):1402-18. doi: 10.1016/j.neuroimage.2012.02.084. Epub 2012 Mar 10.

Reference Type: BACKGROUND

PMID: 22430496 (View on PubMed)

Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006 Jul 1;31(3):968-80. doi: 10.1016/j.neuroimage.2006.01.021. Epub 2006 Mar 10.

Reference Type: BACKGROUND

PMID: 16530430 (View on PubMed)

Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006 Jul 15;31(4):1487-505. doi: 10.1016/j.neuroimage.2006.02.024. Epub 2006 Apr 19.

Reference Type: BACKGROUND

PMID: 16624579 (View on PubMed)

Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics. 2006 Oct;26 Suppl 1:S205-23. doi: 10.1148/rg.26si065510.

Reference Type: BACKGROUND

PMID: 17050517 (View on PubMed)

Fornito A, Bullmore ET. Connectomics: a new paradigm for understanding brain disease. Eur Neuropsychopharmacol. 2015 May;25(5):733-48. doi: 10.1016/j.euroneuro.2014.02.011. Epub 2014 Mar 5.

Reference Type: BACKGROUND

PMID: 24726580 (View on PubMed)

Sinke MRT, Otte WM, Christiaens D, Schmitt O, Leemans A, van der Toorn A, Sarabdjitsingh RA, Joels M, Dijkhuizen RM. Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct Funct. 2018 Jun;223(5):2269-2285. doi: 10.1007/s00429-018-1628-y. Epub 2018 Feb 20.

Reference Type: BACKGROUND

PMID: 29464318 (View on PubMed)

Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016 Nov 15;142:394-406. doi: 10.1016/j.neuroimage.2016.08.016. Epub 2016 Aug 11.

Reference Type: BACKGROUND

PMID: 27523449 (View on PubMed)

Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016 Jan 15;125:1063-1078. doi: 10.1016/j.neuroimage.2015.10.019. Epub 2015 Oct 20.

Reference Type: BACKGROUND

PMID: 26481672 (View on PubMed)

Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208-19. doi: 10.1016/j.neuroimage.2004.07.051.

Reference Type: BACKGROUND

PMID: 15501092 (View on PubMed)

Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001 Jan;20(1):45-57. doi: 10.1109/42.906424.

Reference Type: BACKGROUND

PMID: 11293691 (View on PubMed)

Aja-Fernández S, Tristán-Vega A, Molendowska M, Pieciak T, de Luis-García R. Return-to-the-origin probability calculation in single shell acquisitions. International Society of Magnetic Resonance in Medicine 26th Annual Meeting and Exhibition. Paris, France; 2018:1414.

Reference Type: BACKGROUND

Dhollander T, Raffelt D, Connelly A. Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. ISMRM Work Break Barriers Diffus MRI 2016; 5.

Reference Type: BACKGROUND

Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999 Aug;18(8):712-21. doi: 10.1109/42.796284.

Reference Type: BACKGROUND

PMID: 10534053 (View on PubMed)

Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S. Fiber tract-based atlas of human white matter anatomy. Radiology. 2004 Jan;230(1):77-87. doi: 10.1148/radiol.2301021640. Epub 2003 Nov 26.

Reference Type: BACKGROUND

PMID: 14645885 (View on PubMed)

Oishi K, Zilles K, Amunts K, Faria A, Jiang H, Li X, Akhter K, Hua K, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Zhang J, Huang H, Miller MI, van Zijl PC, Mazziotta J, Mori S. Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage. 2008 Nov 15;43(3):447-57. doi: 10.1016/j.neuroimage.2008.07.009. Epub 2008 Jul 18.

Reference Type: BACKGROUND

PMID: 18692144 (View on PubMed)

Smith RE, Tournier JD, Calamante F, Connelly A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012 Sep;62(3):1924-38. doi: 10.1016/j.neuroimage.2012.06.005. Epub 2012 Jun 13.

Reference Type: BACKGROUND

PMID: 22705374 (View on PubMed)

Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002 Oct;17(2):825-41. doi: 10.1016/s1053-8119(02)91132-8.

Reference Type: BACKGROUND

PMID: 12377157 (View on PubMed)

Tournier JD, Calamante F, Connelly A. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 2013 Dec;26(12):1775-86. doi: 10.1002/nbm.3017. Epub 2013 Aug 29.

Reference Type: BACKGROUND

PMID: 24038308 (View on PubMed)

Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004 Nov;23(3):1176-85. doi: 10.1016/j.neuroimage.2004.07.037.

Reference Type: BACKGROUND

PMID: 15528117 (View on PubMed)

Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage. 2007 May 1;35(4):1459-72. doi: 10.1016/j.neuroimage.2007.02.016. Epub 2007 Feb 21.

Reference Type: BACKGROUND

PMID: 17379540 (View on PubMed)

Tournier J-D, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc Int Soc Magn Reson Med. 2010; 1670.

Reference Type: BACKGROUND

Smith RE, Tournier JD, Calamante F, Connelly A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage. 2015 Oct 1;119:338-51. doi: 10.1016/j.neuroimage.2015.06.092. Epub 2015 Jul 8.

Reference Type: BACKGROUND

PMID: 26163802 (View on PubMed)

Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125-41. doi: 10.1089/brain.2012.0073. Epub 2012 Jul 19.

Reference Type: BACKGROUND

PMID: 22642651 (View on PubMed)

Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr 1974; 19: 716-23.

Reference Type: BACKGROUND

Other Identifiers

2020-004509-30

Identifier Type: EUDRACT_NUMBER

Identifier Source: secondary_id

CASVE 20-469

Identifier Type: -

Identifier Source: org_study_id