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

Migraine Disorders Clinical Trial

— FreMRI  

Official title:

A Prospective Structural, Diffusion and Connectomics MRI Study on Migraine Patients Treated With Fremanezumab: The FreMRI Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06244823
• Other study ID #: CASVE 20-469
• Secondary ID: 2020-004509-30
• Status: Recruiting
• Phase: Phase 4
• Start date: March 1, 2023
• Est. completion date: January 1, 2025

Study information

• Verified date: January 2024
• Source: Hospital Clínico Universitario de Valladolid
• Contact: David Garcia Azorin, MD, PhD
• Phone: +34 665872228
• Email: davilink[@]hotmail.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 87
• Est. completion date: January 1, 2025
• Est. primary completion date: October 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Brain Diseases
• Migraine Disorders
• Migraine With Aura
• Migraine without Aura

Intervention

Drug:
• 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.

Locations

Country	Name	City	State
Spain	Hospital Clínico Universitario de Valladolid	Valladolid

Sponsors (4)

Lead Sponsor	Collaborator
Hospital Clínico Universitario de Valladolid	Complejo Asistencial Universitario de Palencia, Complejo Público Asistencial de Zamora, University of Valladolid

Country where clinical trial is conducted

Spain, 

References & Publications (67)

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. — View Citation

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.

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

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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.

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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.

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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

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. — View Citation

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. — View Citation

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.

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 67 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Relationship between morphometric MRI parameters and the change in monthly intense headache days 1.	Cortical curvature	Baseline, week 12-15
Other	Relationship between morphometric MRI parameters and the change in monthly intense headache days 2.	Cortical thickness	Baseline, week 12-15
Other	Relationship between morphometric MRI parameters and the change in monthly intense headache days 3.	Gray matter volume	Baseline, week 12-15
Other	Relationship between morphometric MRI parameters and the change in monthly intense headache days 4.	Surface area,	Baseline, week 12-15
Other	Difference between clinical response groups in the morphometric parameters, diffusion descriptors, structural connectivity and resting-state functional connectivity	Clinical response assessed as reduction by at least 30%, 50% and 100% reduction in monthly migraine days in the last month compared to baseline.	Baseline, week 12-15
Primary	Morphometric MRI parameters change from baseline during the 12-week period after the administration of Fremanezumab 1	Cortical curvature	Baseline, week 12-15
Primary	Morphometric MRI parameters change from baseline during the 12-week period after the administration of Fremanezumab 2.	Cortical thickness	Baseline, week 12-15
Primary	Morphometric MRI parameters change from baseline during the 12-week period after the administration of Fremanezumab 3.	Gray matter volume	Baseline, week 12-15
Primary	Morphometric MRI parameters change from baseline during the 12-week period after the administration of Fremanezumab 4.	Surface area,	Baseline, week 12-15
Primary	Diffusion MRI descriptors change from baseline during the 12-week period after the administration of Fremanezumab 1.	Fractional anisotropy	Baseline, week 12-15
Primary	Diffusion MRI descriptors change from baseline during the 12-week period after the administration of Fremanezumab 2.	Mean diffusivity	Baseline, week 12-15
Primary	Diffusion MRI descriptors change from baseline during the 12-week period after the administration of Fremanezumab 3.	Radial diffusivity	Baseline, week 12-15
Primary	Diffusion MRI descriptors change from baseline during the 12-week period after the administration of Fremanezumab 4.	Axial diffusivity	Baseline, week 12-15
Primary	Structural connectivity change from baseline during the 12-week period after the administration of Fremanezumab 1.	Number of streamlines in the connection between two regions	Baseline, week 12-15
Primary	Structural connectivity change from baseline during the 12-week period after the administration of Fremanezumab 2.	Mean fractional anisotropy in the connection between two regions	Baseline, week 12-15
Primary	Structural connectivity change from baseline during the 12-week period after the administration of Fremanezumab 3.	Mean axial diffusivity in the connection between two regions	Baseline, week 12-15
Primary	Resting-state functional connectivity change from baseline during the 12-week period after the administration of Fremanezumab.	Z-score	Baseline, week 12-15
Secondary	Relationship between morphometric MRI parameters and the change in monthly migraine days 1.	Cortical curvature	Baseline, week 12-15
Secondary	Relationship between morphometric MRI parameters and the change in monthly migraine days 2.	Cortical thickness	Baseline, week 12-15
Secondary	Relationship between morphometric MRI parameters and the change in monthly migraine days 3.	Gray matter volume	Baseline, week 12-15
Secondary	Relationship between morphometric MRI parameters and the change in monthly migraine days 4.	Surface area.	Baseline, week 12-15
Secondary	Relationship between diffusion descriptors and the change in monthly migraine days 1.	Fractional anisotropy	Baseline, week 12-15
Secondary	Relationship between diffusion descriptors and the change in monthly migraine days 2.	Mean diffusivity	Baseline, week 12-15
Secondary	Relationship between diffusion descriptors and the change in monthly migraine days 3.	Radial diffusivity	Baseline, week 12-15
Secondary	Relationship between diffusion descriptors and the change in monthly migraine days 4.	Axial diffusivity.	Baseline, week 12-15
Secondary	Relationship between structural connectivity and the change in monthly migraine days 1. [Time Frame: baseline, week 12]	Number of streamlines in the connection between two regions.	Baseline, week 12-15
Secondary	Relationship between structural connectivity and the change in monthly migraine days 2. [Time Frame: baseline, week 12]	Mean fractional anisotropy in the connection between two regions.	Baseline, week 12-15
Secondary	Relationship between structural connectivity and the change in monthly migraine days 3. [Time Frame: baseline, week 12]	Mean axial diffusivity in the connection between two regions.	Baseline, week 12-15
Secondary	Relationship between resting-state functional connectivity and the change in monthly migraine days. [Time Frame: baseline, week 12]	Z-score	Baseline, week 12-15
Secondary	Baseline morphometric MRI parameters will be assessed as potential predictors of change in monthly migraine days 1.	Cortical curvature	Baseline
Secondary	Baseline morphometric MRI parameters will be assessed as potential predictors of change in monthly migraine days 2.	Cortical thickness	Baseline
Secondary	Baseline morphometric MRI parameters will be assessed as potential predictors of change in monthly migraine days 3.	Gray matter volume	Baseline
Secondary	Baseline morphometric MRI parameters will be assessed as potential predictors of change in monthly migraine days 4.	Surface area.	Baseline
Secondary	Baseline diffusion MRI descriptors will be assessed as potential predictors of change in monthly migraine headache days 1.	Fractional anisotropy	Baseline
Secondary	Baseline diffusion MRI descriptors will be assessed as potential predictors of change in monthly migraine headache days 2.	Mean diffusivity	Baseline
Secondary	Baseline diffusion MRI descriptors will be assessed as potential predictors of change in monthly migraine headache days 3.	Radial diffusivity	Baseline
Secondary	Baseline diffusion MRI descriptors will be assessed as potential predictors of change in monthly migraine headache days 4.	Axial diffusivity.	Baseline
Secondary	Baseline structural connectivity will be assessed as potential predictors of change in monthly migraine days.	Number of streamlines in the connection between two regions.	Baseline
Secondary	Baseline resting-state functional connectivity will be assessed as potential predictors of change in monthly migraine days	Z-score	Baseline

External Links

•   Aja-Fernández S, de Luis-García R, Afzali M, Molendowska M, Pieciak T, Tristán-Vega A. Microstructure Diffusion Scalar Measures from Reduced MRI Acquisitions. bioRxiv. 2019. (accessed 2 November 2019)