Sage Journals: Discover world-class research

Abstract

Objectives

The objective of this longitudinal study was to determine whether brain iron accumulation, measured using magnetic resonance imaging magnetic transverse relaxation rates (T2*), is associated with response to erenumab for the treatment of migraine.

Methods

Participants (n = 28) with migraine, diagnosed using international classification of headache disorders 3rd edition criteria, were eligible if they had six to 25 migraine days during a four-week headache diary run-in phase. Participants received two treatments with 140 mg erenumab, one immediately following the pre-treatment run-in phase and a second treatment four weeks later. T2* data were collected immediately following the pre-treatment phase, and at two weeks and eight weeks following the first erenumab treatment. Patients were classified as erenumab responders if their migraine-day frequency at five-to-eight weeks post-initial treatment was reduced by at least 50% compared to the pre-treatment run-in phase. A longitudinal Sandwich estimator approach was used to compare longitudinal group differences (responders vs non-responders) in T2* values, associated with iron accumulation. Group visit effects were calculated with a significance threshold of p = 0.005 and cluster forming threshold of 250 voxels. T2* values of 19 healthy controls were used for a reference. The average of each significant region was compared between groups and visits with Bonferroni corrections for multiple comparisons with significance defined as p < 0.05.

Results

Pre- and post-treatment longitudinal imaging data were available from 28 participants with migraine for a total of 79 quantitative T2* images. Average subject age was 42 ± 13 years (25 female, three male). Of the 28 subjects studied, 53.6% were erenumab responders. Comparing longitudinal T2* between erenumab responders vs non-responders yielded two comparisons which survived the significance threshold of p < 0.05 after correction for multiple comparisons: the difference at eight weeks between the erenumab-responders and non-responders in the periaqueductal gray (mean ± standard error; responders 43 ± 1 ms vs non-responders 32.5 ± 1 ms, p = 0.002) and the anterior cingulate cortex (mean ± standard error; responders 50 ± 1 ms vs non-responders 40 ± 1 ms, p = 0.01).

Conclusions

Erenumab response is associated with higher T2* in the periaqueductal gray and anterior cingulate cortex, regions that participate in pain processing and modulation. T2* differences between erenumab responders vs non-responders, a measure of brain iron accumulation, are seen at eight weeks post-treatment. Less iron accumulation in the periaqueductal gray and anterior cingulate cortex might play a role in the therapeutic mechanisms of migraine reduction associated with erenumab.

Keywords

Migraine iron magnetic resonance imaging T2*erenumab calcitonin gene-related peptide

Introduction

Iron accumulation in deep brain structures has been reported in people with migraine (1 –4). Furthermore, associations have been found between increased iron accumulation in multiple deep brain nuclei involved in pain processing (2,3) and frequency of migraine attacks. Elevated iron concentrations in periaqueductal gray and red nucleus, identified via magnetic resonance imaging (MRI), have been shown to correlate with duration of migraine and frequency of attacks (1 –3).

T2*-weighted MRI sequences are used for detection of paramagnetic or diamagnetic contrast agents in tissue (5,6). T2* decreases are observed in response to susceptibility artifacts arising from local air/tissue boundaries and shown to correlate with iron accumulation (7). Brain iron accumulation is associated with the presence and progression of neurological conditions such as cognitive decline, Alzheimer’s Disease, and Parkinson’s Disease (8 –13).

Erenumab, a monoclonal antibody (mAb) that targets the calcitonin gene-related peptide (CGRP) receptor, is effective for the prevention of episodic and chronic migraine (14 –16). Due to their large size and limited ability to cross the blood-brain barrier, the anti-CGRP pathway mAbs are thought to exert most, if not all, of their direct effects in the periphery, with secondary or indirect effects on the brain. Since the brain plays an essential role in migraine attack physiology and abnormalities in structure and function have been demonstrated amongst those with migraine (17,18), the potential impact of migraine therapeutics on brain function and structure is of interest. This study measured iron deposition, using quantitative T2* maps, in participants with migraine prior to and following treatment with erenumab. Associations between iron deposition and response to erenumab were interrogated.

Methods

This study was registered with clinicaltrials.gov prior to its initiation (NCT03773562). The study was approved by the Mayo Clinic Institutional Review Board. All subjects were enrolled from the Mayo Clinic in Arizona. Prior to participation, all patients reviewed and signed written informed consent forms. Data were collected over a two-year period between 2020 and 2022. The study enrolled episodic and chronic migraine participants aged 18–65 years who reported having between six and 25 migraine days per month during the prior three months. Migraine diagnoses were made according to the International Classification of Headache Disorders 3rd edition criteria (19). Exclusion criteria were contraindications to MRI, incidental MRI findings, over 50 years of age at migraine onset, migraine onset within the prior 12 months, cluster headache or hemiplegic migraine, continuous headache (i.e. no headache free periods during the one month prior to screening), current use of more than two migraine preventive medications, botulinum toxin treatment within the prior four months, nerve blocks used for treatment of headache within two months, opioids or butalbital use on six or more days per month during the two months prior to enrollment, current pregnancy, lack of therapeutic response to adequate trials of four or more migraine preventive medication categories, history of myocardial infarction, stroke, transient ischemic attack, unstable angina, coronary artery bypass surgery, or other revascularization procedures within the prior 12 months, anti-CGRP pathway mAb treatment within four months prior to study start day, chronic pain conditions other than migraine, and acute pain conditions other than migraine. Participants taking concomitant migraine preventive medications had to have stable doses for at least two months before the start of the run-in phase and the dose had to remain stable during the study. After completing the four-week run-in diary phase, those who had between six and 25 migraine days and were at least 80% compliant with providing headache diary data were eligible to continue in the study.

Exclusion criteria for healthy controls included history of migraine, chronic pain conditions, and acute pain conditions. Individuals with tension-type headaches on three or fewer days per month were included as healthy controls.

Participants who qualified for continued study participation after the run-in phase had a total of six research visits during a 16-week period. They completed questionnaires and structured interviews during these research visits, including collection of data used for these analyses, such as participant demographics, migraine history and characteristics, current and prior migraine medications, symptoms of anxiety (State-Trait Anxiety Inventory, STAI) and depression (Beck Depression Inventory, BDI), and disability (Migraine Disability Assessment, MIDAS) (20 –22). The total MIDAS score is the sum of answers to five questions that assess the number of days during the last 90 days on which there was disability related to work or school, household work, and family, social, or leisure activities. Total scores range from 0 to 270, with scores of at least 21 indicating severe disability, 11–20 indicating moderate disability, 6–10 indicating mild disability, and 0–5 indicating little or no disability. A headache diary was maintained for the entire 16 weeks. Brain MRIs were performed three times for each participant: immediately after the run-in phase (pre-treatment), two weeks after the first erenumab treatment, and eight weeks after the first erenumab treatment (four weeks after the second erenumab treatment).

Participants kept an e-diary, developed within REDCap (Research Electronic Data Capture), which is a secure, web-based electronic data capture tool (23,24). Each day, participants were asked to record the presence of headache and associated symptoms. The headache diary was used to determine the frequency of headache and migraine days. The definition of a migraine day was a calendar day during which a person experienced a qualified migraine headache. Consistent with prior studies of erenumab, a qualified migraine headache was defined as a migraine with or without aura, lasting for at least 30 minutes, and meeting at least one of the following criteria: 1) a minimum of two of the following pain features: unilateral, throbbing, moderate to severe, exacerbated with exercise/physical activity; 2) a minimum of one symptom of nausea and/or vomiting, photophobia and phonophobia (16,25,26). If the participant took a migraine-specific as-needed medication on a calendar day, it was counted as a migraine day regardless of the duration and pain features/associated symptoms (27).

All participants underwent imaging at Mayo Clinic Arizona on a Siemens 3T scanner (Siemens Magnetom Skyra, Erlangen, Germany) using a 20-channel head and neck coil. Sequences included a high-resolution anatomical 3D T1-weighted MPRAGE sequence with TE 3.03 ms, TR 2400 ms, voxel size 1 × 1 × 1.25 mm, field of view 256 × 256 × 160 mm, flip angle 8 degrees. T2* maps were created using 12 T2-weighted gradient echo (GRE) magnitude images with variable echo times (TE) of 2.81, 5.71, 8.06, 10.46, 12.93, 15.4, 17.86, 29.78, 42.34, 54.9, 67.46 and 80 ms. Each GRE was a stacked 2D axial image with repetition time (TR) of 3200 ms, flip angle (FA) of 60 degrees, in-plane resolution of 0.94 × 0.94 mm, slice thickness of 4 mm and an FOV of 240 × 240 × 132 mm.

The T2* maps of 19 healthy controls were included from a separate study to be used as reference for the baseline values in the post-hoc analysis.

Erenumab treatment

All participants received open-label treatment with 140 mg of erenumab, administered during in-office research appointments as two 70 mg subcutaneous injections into the upper arm per treatment. The first treatment was administered immediately following the pre-treatment imaging, and the second treatment was administered four weeks later. Treatment response was defined as at least a 50% reduction in the frequency of migraine days during weeks 5–8 compared to the four-week pre-treatment run-in phase.

T2* data postprocessing

The T2* maps were re-sliced to match the T1-weighted anatomical images which were then used to normalize to Montreal Neurological Institute (MNI) and smoothed using a 6 mm kernel by SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/).

Statistical analysis

Statistical analysis was performed using the Sandwich estimator for neuroimaging longitudinal data toolbox version 1.2.8 SWE (SWE, Guillaume et al., 2014) and interfaced with MATLAB (software version 2019b). All 79 smoothed T2* maps were added to the SWE longitudinal model that assume groups can share a covariance matrix. Default settings for small sample adjustments (C2) and estimated degrees of freedom (approx. III [28]) were used. Seven covariates were used in model: one for each visit for each group (for a total of six) and age. The group-visit effects were explored using F-statistic with a cluster forming threshold of 250 voxels and an uncorrected p-value of p < 0.005. The mean and standard error of the unsmoothed T2* values from each cluster were plotted. Demographic differences at baseline were examined between responders and non-responders. Sex differences were compared using chi-squared test. T2* values from the identified regions of the full brain analysis were compared between erenumab-responders vs non-responders at each visit, changes within groups between visits, and differences between those with migraine vs healthy controls with significance of p < 0.05 corrected for multiple comparisons with Bonferroni correction. Group comparisons were first tested for normality using a Shapiro-Wilks test (p < 0.05). Normally distributed data were compared using two-tailed t-test, whereas non-normally distributed data were compared using the Wilcoxon Rank Sum test (Mann Whitney U-test). Localization of significant clusters was assisted by the automated anatomical labeling atlas 3.1 (10.1016/j.neuroimage.2019.116189) as an extension of SPM12.

Results

Twenty-eight participants with migraine had pre- and post-erenumab imaging data and were included in the analysis (Table 1). The average age (±standard deviation) of the 28 migraine subjects was 42 ± 13 ranging from 23 to 66 years (25 female, three male). All 28 subjects were white (four were Hispanic and 24 were non-Hispanic). Twenty-three subjects had T2* data for all three time-points, and five subjects did not complete the final visit. The migraine patient demographics throughout the longitudinal study are provided in Table 2. At baseline, 5/28 (17.9%) MRIs were collected during a migraine and 14/28 (50%) MRIs were collected when headache was present. This included migraine being present in 4/15 (26.7%) MRIs for those who eventually became erenumab responders vs 1/13 (7.7%) for those who did not go on to become erenumab responders (p = 0.33), and headache being present in 9/15 (60%) MRIs for those who eventually became erenumab responders vs 5/13 (38.5%) MRI for those who did not go on to become erenumab responders (p = 0.45). For follow-up MRIs, 0/51 MRIs were collected during a migraine and 11/51 MRIs were collected when headache was present (4/27 MRIs for erenumab responders vs 7/24 MRIs for erenumab non-responders, p = 0.31).

Table 1.

Pre-treatment participant characteristics as one group and by erenumab-responder status. P-values are a comparison of responders to non-responders using nonparametric Wilcoxon rank-sum test, two sample t-test, or chi-square test as appropriate. Values are reported as mean and (standard deviation) where appropriate.

	All Participants(n = 28)	Responders(n = 15)	Non-Responders(n = 13)	Uncorrected p-value
Age	42(13)	44(13)	38(13)	0.21
Female, percentage	89%	87%	92%	0.99
Headache days/28 days	15(5)	15(5)	15(4)	0.73
Migraine days/28 days	13(5)	14(5)	13(5)	0.65
Number of prior migraine preventive medications	0.9(0.9)	1(1)	0.6(0.8)	0.92
Migraine with aura	32%	33%	31%	0.90
Migraine without aura	36%	40%	31%	0.93
Chronic migraine	57%	53%	62%	0.99
MIDAS score	37(28)	39(34)	35(20)	0.98(rank-sum)
Beck depression inventory score	6(5)	6(4)	6(6)	0.55(rank-sum)
Trait anxiety inventory score	30(8)	31(8)	30(8)	0.94(rank-sum)
State anxiety inventory score	29(9)	29(9)	30(9)	0.94(rank-sum)

Table 2.

Longitudinal responder and non-responder patient demographics and group size.

Visit	Baseline		Week 2		Week 8
Group	Responder	Non-responder	Responder	Non-responder	Responder	Non-responder
Number	15	13	15	13	12	11
Age	42(13)	44(13)	42(13)	44(13)	38(13)	38(13)
Female, percentage	89%	87%	89%	87%	92%	92%

Nineteen healthy controls from a previous study were included for reference. The controls were scanned on the same 3T scanner using the same protocols. The average age of the healthy controls was 39 ± 14 ranging from 26 to 66 years (13 female, six male).

Changes in headache day frequency, migraine day frequency, and Migraine Disability Assessment (MIDAS) scores are shown in Table 3.

Table 3.

Changes in headache and migraine day frequency and MIDAS scores.

	All Participants(n = 28)	Responders(n = 15)	Non-responders(n = 13)
Change in Headache Day Frequency/28 days	−6.5(2.0)	−9.2(2.2)	−3.4(3.0)
Change in Migraine Day Frequency/28 days	−6.1(2.0)	−9.5(2.0)	−2.3(3.0)
Change in MIDAS Score	−6.2(13)	−16.5(16)	5.8(13)

Negative numbers represent improvements while positive numbers represent worsening. Changes are calculated by comparing weeks 5-8 post-first erenumab treatment to the 4-week pre-treatment period. Results are reported as mean (standard error).

Only two regions from the SWE analysis had significant group-visit interactions, as shown in Figure 1.

Figure 1.

The two regions with significant group-visit effects (p < 0.005 with cluster forming threshold of 250 voxels) are in the periaqueductal grey matter (PAG) and the anterior cingulate cortex (ACC). The longitudinal T2* values are plotted below each region with standard error bars.

The first region was outside the automated anatomical labeling (AAL) atlas but was in the midbrain and contained periaqueductal gray (PAG). The second region contained the anterior cingulate cortex (ACC) with 19% of the cluster in the ACC Sup Left, 46% in the ACC Sup Right, and 35% outside of the AAL atlas. Both regions demonstrated bilateral symmetry. Pre-treatment, PAG T2* (mean +/− standard error) for responders (36 ± 2 ms) and non-responders (35 ± 2 ms) were similar to healthy controls (34 ± 2 ms). Pre-treatment ACC T2* for responders (45 ± 2 ms) and non-responders (45 ± 1.1 ms) were similar to that of healthy controls (46 ± 2 ms). Only two comparisons survived the significance threshold of p < 0.05 after correction for multiple comparisons: the difference at eight weeks between the erenumab-responders and non-responders in the PAG (p = 0.002) and the ACC (p = 0.01).

Discussion

The main finding of this study is that erenumab-responders have less iron deposition in the PAG and ACC compared to erenumab non-responders when iron-deposition is measured at eight weeks after initiating erenumab treatment. The PAG and ACC play key roles in pain modulation and processing. Reductions in iron deposition in these two regions might be part of the mechanism by which improvements in migraine occur following erenumab treatment.

Iron acts as a cofactor for several enzymatic and cellular processes in the brain, playing a key role in neuronal development, synaptic plasticity, myelination, neurotransmitter synthesis, and mitochondrial function (29 –32). However, excessive iron deposition in the brain is associated with neurodegenerative diseases such as Parkinson’s Disease, Multiple Sclerosis, and Alzheimer’s Disease, traumatic brain injury, and increased iron deposition is associated with normal aging (31,33 –38). Excessive iron deposition can lead to oxidative stress and brain tissue degeneration (39). MRI T2* imaging allows for quantification of iron concentrations in the brain. For deep gray matter structures, T2* signal is mainly determined by the amount of iron in the tissue (40), whereas other tissues have more contribution from calcium, lipid, and myelin content in addition to iron (41).

In our study, lower PAG iron levels at eight-weeks were detected amongst those with migraine who responded to erenumab compared to those who did not. The PAG is a key region of the brainstem pain modulating pathway. In normal states, the brainstem pain modulating system is mostly pain-inhibiting. However, a shift towards pain facilitation might be one factor that leads to development and maintenance of chronic pain (42 –45). Prior studies have demonstrated atypical PAG functional connectivity in those with migraine and associations between PAG functional connectivity strength with severity of migraine symptoms (46 –51). A previous study also demonstrated that non-responders to onabotulinumtoxinA for treatment of chronic migraine have lower pre-treatment T2* in the PAG (i.e. more iron) compared to onabotulinumtoxinA treatment responders (52). In our study, pre-treatment brain iron concentration did not differ between erenumab responders and non-responders. However, our study further supports a relationship between PAG iron deposition and treatment response.

Our study also demonstrated less iron deposition in the ACC in erenumab-responders compared to non-responders. The ACC is a key region of the pain matrix, with roles in emotional, somatosensory, cognitive, and autonomic aspects of pain processing (53). Abnormalities of ACC function and structure have been identified in several prior migraine studies (54 –60), with some demonstrating relationships between these ACC imaging measures and migraine disease burden. As discussed further below, changes in ACC function have been demonstrated following migraine treatment.

A few studies have investigated the impact of migraine treatment on brain function (48,61 –69), including studies of erenumab and galcanezumab. Other studies have investigated the impact of transcutaneous auricular vagus nerve stimulation, trigeminal neurostimulation, sphenopalatine ganglion blocks, citalopram and topiramate on brain function (48,55,61,63 –66). To our knowledge, there is a paucity of studies that have directly investigated the effects of migraine preventive treatment on brain structure, as was done in our study. One study investigated changes in brain structure following a series of sphenopalatine ganglion blocks for the treatment of chronic migraine with medication overuse headache (70). Twelve patients had brain MRI prior to and following a series of 12 sphenopalatine ganglion blocks performed during a six-week period. Along with improvements in migraine, there were volume decreases in the right hippocampus and pallidum, and a volume increase in the left nucleus accumbens. Cortical thickness decreases were seen in the left temporal pole and lateral occipitotemporal gyrus. Our study further demonstrates changes in brain structure related to migraine treatment, and shows that these changes can occur relatively rapidly, and demonstrates that the structural changes are related to treatment response. The relatively rapid change in T2* signal is consistent with the known fast effects of erenumab treatment on migraine patterns and with brain fMRI studies showing rapid changes in brain function and functional connectivity following migraine treatment with anti-CGRP pathway mAbs (67,68).

Limitations of this study include: 1) The relatively small sample sizes after dividing the participants into those who are treatment-responders vs non-responders. Because of the small sample size it is reasonable to consider the results of this study as hypothesis-generating and not conclusive. Future studies should attempt to validate our findings using a larger number of participants; 2) The aim of this study was to determine erenumab response and assess brain imaging findings early after starting erenumab. Future studies might consider classifying participants as erenumab responders or non-responders at three months after starting treatment, a time period that is commonly used for assessing treatment responses in clinical trials and in clinical practice; 3) The healthy controls were enrolled as part of a different study. However, the T2* sequences and MRI machine were the same as the one used for studying the migraine participants, limiting any concerns with including these healthy controls; 4) It is unknown if the changes in T2* are specific to treatment with erenumab or if similar changes would be seen with other migraine therapies. It is also possible that T2* changes like those identified in our study could be seen with naturally occurring decreases in migraine frequency that sometimes occur even without treatment. These possibilities should be determined in future studies; 5) The PAG is not a part of segmentation atlases. The anatomical localization of the midbrain cluster was based on the authors’ knowledge; 6) Whether or not these structural changes in the ACC and PAG are associated with changes in function or functional connectivity of these regions will be determined in future analyses; 7) The changes in T2* may be attributed to changes in water concentration in tissue; 8) Sample size did not permit subdivision of migraine participants according to their migraine characteristics such as presence of aura or baseline headache frequency; 9) Since this was a study of brain structure rather than brain function, whether T2* measurements were collected during or between migraine attacks or headaches was unlikely to impact study results. Nonetheless, because functional imaging was included in our study (although not included in this manuscript), we collected information that allows us to determine if research participants were experiencing a migraine attack or headache not meeting criteria for a migraine attack at the time of imaging. Since no follow-up MRIs were collected during migraine attacks and there were not significant differences in the proportion of follow-up MRIs collected during a headache, we do not think that the ictal vs interictal state impacted our results. Furthermore, unfortunately the number of MRIs collected during migraine was too small to allow for meaningful subgroup analyses. Future studies with longer periods of follow-up and brain MRI at later timepoints would help determine the long-term impact of erenumab treatment, if any, on brain structure, both during treatment with erenumab and after it is discontinued.

Conclusions

Response to erenumab for the treatment of migraine is associated with less iron deposition in the PAG and ACC when measured eight weeks after starting erenumab treatment. Less iron deposition in these two regions, that play key roles in pain processing, might be a direct or indirect effect of erenumab treatment, or a marker of reduced migraine frequency that occurs in response to effective erenumab treatment.

Key findings

Erenumab responders had higher T2* (i.e. lower iron deposition) in the periaqueductal grey (PAG) matter and anterior cingulate cortex (ACC) compared to non-responders at 8 weeks following the first erenumab treatment.

Less iron deposition in the periaqueductal gray (PAG) and anterior cingulate cortex (ACC), two regions that play key roles in pain processing, might be a direct or indirect effect of erenumab treatment, or a marker of reduced migraine frequency that occurs in response to effective erenumab treatment.

Footnotes

Acknowledgements

The authors would like to thank all participants and imaging coordinators for their time and dedication to this project.

Declaration of conflicting interests

Within the prior 12 months Todd Schwedt has served as a consultant for Abbvie, Allergan, Amgen (compensation to institution), Axsome, Collegium, Eli Lilly, Linpharma, Lundbeck, Satsuma, and Theranica. He has stock options in Aural Analytics and Nocira. He has received royalties from UpToDate. He has received research funding from: Amgen, Henry Jackson Foundation, Mayo Clinic, National Institutes of Health, Patient Centered Outcomes Research Institute, SPARK Neuro, and U.S. Department of Defense. He serves on the Board of Directors for the American Headache Society and the American Migraine Foundation.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Amgen, Inc., ISS 20187183. Healthy control data were used from a study funded by the United States Department of Defense, Award Number W81XWH1910534.

ORCID iDs

Catherine Daniela Chong

Gina M. Dumkrieger

References

Tepper

Lowe

Beall

, et al. Iron deposition in pain-regulatory nuclei in episodic migraine and chronic daily headache by MRI. Headache 2012; 52: 236–243.

Kruit

Launer

Overbosch

, et al. Iron accumulation in deep brain nuclei in migraine: a population-based magnetic resonance imaging study. Cephalalgia 2009; 29: 351–359.

Palm-Meinders

Koppen

Terwindt

, et al. Iron in deep brain nuclei in migraine? CAMERA follow-up MRI findings. Cephalalgia 2017; 37: 795–800.

Granziera

Daducci

Romascano

, et al. Structural abnormalities in the thalamus of migraineurs with aura: a multiparametric study at 3 T. Hum Brain Mapp 2014; 35: 1461–1468.

Gossuin

Muller

Gillis

Relaxation induced by ferritin: a better understanding for an improved MRI iron quantification. NMR Biomed 2004; 17: 427–432.

Schenck

JF.

Imaging of brain iron by magnetic resonance: T2 relaxation at different field strengths. J Neurol Sci. 1995; 134: 10–18.

Yao

Gelderen

, et al. Susceptibility contrast in high field MRI of human brain as a function of tissue iron content. Neuroimage 2009; 44: 1259–1266.

Daugherty

Haacke

Raz

Striatal iron content predicts its shrinkage and changes in verbal working memory after two years in healthy adults. J Neurosci 2015; 35: 6731–6743.

Daugherty

Raz

Appraising the role of iron in brain aging and cognition: promises and limitations of MRI methods. Neuropsychol Rev 2015; 25: 272–287.

10.

Ulla

Bonny

Ouchchane

, et al. Is R2* a new MRI biomarker for the progression of Parkinson's disease? A longitudinal follow-up. PLoS One 2013; 8: e57904.

11.

Spence

McNeil

Waiter

GD.

The impact of brain iron accumulation on cognition: A systematic review. PLoS One 2020; 15: e0240697.

12.

Wang

Zhuang

Zhu

, et al. Meta-analysis of brain iron levels of Parkinson's disease patients determined by postmortem and MRI measurements. Sci Rep 2016; 6: 36669.

13.

You

Wang

, et al. Characterization of brain iron deposition pattern and its association with genetic risk factor in Alzheimer's disease using susceptibility-weighted imaging. Front Hum Neurosci 2021; 15: 654381.

14.

Tepper

Ashina

Reuter

, et al. Safety and efficacy of erenumab for preventive treatment of chronic migraine: a randomised, double-blind, placebo-controlled phase 2 trial. Lancet Neurol 2017; 16: 425–434.

15.

Sun

Dodick

Silberstein

, et al. Safety and efficacy of AMG 334 for prevention of episodic migraine: a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Neurol 2016; 15: 382–390.

16.

Goadsby

Reuter

Hallstrom

, et al. A controlled trial of erenumab for episodic migraine. N Engl J Med 2017; 377: 2123–2132.

17.

Chong

Plasencia

Frakes

, et al. Structural alterations of the brainstem in migraine. Neuroimage Clin 2017; 13: 223–227.

18.

Chong

Schwedt

Dodick

DW.

Migraine: What imaging reveals. Curr Neurol Neurosci Rep 2016; 16: 64.

19.

Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 2018; 38: 1–211.

20.

Beck

Steer

Ball

, et al. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67: 588–597.

21.

Stewart

Lipton

Dowson

, et al. Development and testing of the Migraine Disability Assessment (MIDAS) Questionnaire to assess headache-related disability. Neurology 2001; 56: S20–28.

22.

Spielberger

Gorsuch

Lushene

, et al. Manual for the State-Trait Anxiety Inventory (Form Y1 – Y2). Palo Alto Ca: Consulting Psychologists Press; 1983.

23.

Harris

Taylor

Minor

, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019; 95: 103208.

24.

Harris

Taylor

Thielke

, et al. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42: 377–381.

25.

Dodick

Ashina

Brandes

, et al. ARISE: A Phase 3 randomized trial of erenumab for episodic migraine. Cephalalgia 2018; 38: 1026–1037.

26.

Wang

Roxas

Jr. Saravia

, et al. Randomised, controlled trial of erenumab for the prevention of episodic migraine in patients from Asia, the Middle East, and Latin America: The EMPOwER study. Cephalalgia 2021; 41: 1285–1297.

27.

Diener

Tassorelli

Dodick

, et al. Guidelines of the International Headache Society for controlled trials of preventive treatment of migraine attacks in episodic migraine in adults. Cephalalgia 2020; 40: 1026–1044.

28.

Guillaume

Hua

Thompson

, et al. Fast and accurate modelling of longitudinal and repeated measures neuroimaging data. Neuroimage 2014; 94: 287–302.

29.

Qian

ZM.

Brain iron metabolism: neurobiology and neurochemistry. Prog Neurobiol 2007; 83: 149–173.

30.

Ward

Zucca

Duyn

, et al. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 2014; 13: 1045–1060.

31.

Daglas

Adlard

PA.

The involvement of iron in traumatic brain injury and neurodegenerative disease. Front Neurosci 2018; 12: 981.

32.

Urrutia

Mena

Nunez

MT.

The interplay between iron accumulation, mitochondrial dysfunction, and inflammation during the execution step of neurodegenerative disorders. Front Pharmacol 2014; 5: 38.

33.

Ghassaban

Liu

Jiang

, et al. Quantifying iron content in magnetic resonance imaging. Neuroimage 2019; 187: 77–92.

34.

Nikolova

Schwedt

, et al. T2* reduction in patients with acute post-traumatic headache. Cephalalgia 2021; 42: 357–365.

35.

Smith

Harris

Sayre

, et al. Iron accumulation in Alzheimer disease is a source of redox-generated free radicals. Proc Natl Acad Sci U S A. 1997; 94: 9866–9868.

36.

Bilgic

Pfefferbaum

Rohlfing

, et al. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 2012; 59: 2625–2635.

37.

Haacke

Makki

, et al. Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging 2009; 29: 537–544.

38.

Bartzokis

Cummings

Markham

, et al. MRI evaluation of brain iron in earlier- and later-onset Parkinson's disease and normal subjects. Magn Reson Imaging 1999; 17: 213–222.

39.

Meng

Hou

Sun

TS.

Effect of oxidative stress induced by intracranial iron overload on central pain after spinal cord injury. J Orthop Surg Res 2017; 12: 24.

40.

Haacke

Cheng

House

, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005; 23: 1–25.

41.

Haacke

Liu

Buch

, et al. Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 2015; 33: 1–25.

42.

Porreca

Ossipov

Gebhart

GF.

Chronic pain and medullary descending facilitation. Trends Neurosci 2002; 25: 319–325.

43.

Wang

King

De Felice

, et al. Descending facilitation maintains long-term spontaneous neuropathic pain. J Pain 2013; 14: 845–853.

44.

Ossipov

Morimura

Porreca

Descending pain modulation and chronification of pain. Curr Opin Support Palliat Care 2014; 8: 143–151.

45.

Mills

Keay

Henderson

LA.

Brainstem pain-modulation circuitry and its plasticity in neuropathic pain: insights from human brain imaging investigations. Front Pain Res (Lausanne) 2021; 2: 705345.

46.

Schwedt

Larson-Prior

Coalson

, et al. Allodynia and descending pain modulation in migraine: a resting state functional connectivity analysis. Pain Med 2014; 15: 154–165.

47.

Schwedt

Schlaggar

Mar

, et al. Atypical resting-state functional connectivity of affective pain regions in chronic migraine. Headache 2013; 53: 737–751.

48.

Liu

Lan

, et al. Altered periaqueductal gray resting state functional connectivity in migraine and the modulation effect of treatment. Sci Rep 2016; 6: 20298.

49.

Mainero

Boshyan

Hadjikhani

Altered functional magnetic resonance imaging resting-state connectivity in periaqueductal gray networks in migraine. Ann Neurol 2011; 70: 838–845.

50.

Gecse

Baksa

Dobos

, et al. Sex differences of periaqueductal grey matter functional connectivity in migraine. Front Pain Res (Lausanne) 2021; 2: 767162.

51.

Solstrand Dahlberg

Linnman

Lee

, et al. Responsivity of periaqueductal gray connectivity is related to headache frequency in episodic migraine. Front Neurol 2018; 9: 61.

52.

Dominguez Vivero

Leira

Saavedra Pineiro

, et al. Iron deposits in periaqueductal gray matter are associated with poor response to onabotulinumtoxina in chronic migraine. Toxins (Basel) 2020; 12: 1–12.

53.

De Ridder

Adhia

Vanneste

The anatomy of pain and suffering in the brain and its clinical implications. Neurosci Biobehav Rev 2021; 130: 125–146.

54.

Chen

Liu

, et al. Disrupted functional connectivity of periaqueductal gray subregions in episodic migraine. J Headache Pain 2017; 18: 36.

55.

Russo

Esposito

Conte

, et al. Functional interictal changes of pain processing in migraine with ictal cutaneous allodynia. Cephalalgia 2017; 37: 305–314.

56.

Schwedt

Chong

, et al. Accurate classification of chronic migraine via brain magnetic resonance imaging. Headache 2015; 55: 762–777.

57.

Zhang

, et al. Discriminative analysis of migraine without aura: using functional and structural MRI with a multi-feature classification approach. PLoS One 2016; 11: e0163875.

58.

Vereb

Szabo

Tuka

, et al. Temporal instability of salience network activity in migraine with aura. Pain 2020; 161: 856–864.

59.

Magon

May

Stankewitz

, et al. Cortical abnormalities in episodic migraine: A multi-center 3T MRI study. Cephalalgia 2019; 39: 665–673.

60.

Qin

Liang

, et al. Disrupted white matter functional connectivity with the cerebral cortex in migraine patients. Front Neurosci 2021; 15: 799854.

61.

Cao

Zhang

, et al. Different modulation effects of 1 Hz and 20 Hz transcutaneous auricular vagus nerve stimulation on the functional connectivity of the periaqueductal gray in patients with migraine. J Transl Med 2021; 19: 354.

62.

Russo

Tessitore

Esposito

, et al. Functional changes of the perigenual part of the anterior cingulate cortex after external trigeminal neurostimulation in migraine patients. Front Neurol 2017; 8: 282.

63.

Edes

McKie

Szabo

, et al. Increased activation of the pregenual anterior cingulate cortex to citalopram challenge in migraine: an fMRI study. BMC Neurol 2019; 19: 237.

64.

Zhang

Huang

, et al. Transcutaneous auricular vagus nerve stimulation (taVNS) for migraine: an fMRI study. Reg Anesth Pain Med 2021; 46: 145–150.

65.

Hebestreit

May

Topiramate modulates trigeminal pain processing in thalamo-cortical networks in humans after single dose administration. PLoS One 2017; 12: e0184406.

66.

Krebs

Rorden

Androulakis

XM.

Resting state functional connectivity after sphenopalatine ganglion blocks in chronic migraine with medication overuse headache: a Pilot longitudinal fMRI study. Headache 2018; 58: 732–743.

67.

Basedau

Sturm

Mehnert

, et al. Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study. Elife 2022; 11: 1–15.

68.

Ziegeler

Mehnert

Asmussen

, et al. Central effects of erenumab in migraine patients: An event-related functional imaging study. Neurology 2020; 95: e2794–e802.

69.

Peek

Leaver

Foster

, et al. Increase in ACC GABA+ levels correlate with decrease in migraine frequency, intensity and disability over time. J Headache Pain 2021; 22: 150.

70.

Newman-Norlund

Rorden

Maleki

, et al. Cortical and subcortical changes following sphenopalatine ganglion blocks in chronic migraine with medication overuse headache: a preliminary longitudinal study. Womens Midlife Health 2020; 6: 7.

Longitudinal differences in iron deposition in periaqueductal gray matter and anterior cingulate cortex are associated with response to erenumab in migraine

Abstract

Objectives

Methods

Results

Conclusions

Keywords

Introduction

Methods

Erenumab treatment

T2* data postprocessing

Statistical analysis

Results

Discussion

Conclusions

Key findings

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iDs

References