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Abstract

Background

Chronic migraine (CM) is a disabling neurologic condition that often evolves from episodic migraine. There has been mounting evidence on the volumetric changes detected by magnetic resonance imaging (MRI) technique in migraineurs. These studies mainly focused on episodic migraine patients and less is known about the differences in CM patients.

Method

A total of 24 CM patients and 24 healthy control individuals (all females) were included in this study. All participants underwent neurological examination and MRI. High-resolution anatomical MRI images were processed with an automated segmentation method (FreeSurfer). White-matter abnormalities of the brain were also evaluated with the Age-Related White-Matter-Changes Scale.

Results

The volumes of the cerebellum and brainstem were found to be smaller in CM patients compared to healthy controls. White-matter abnormalities were also found in CM patients, speciﬁcally in the bilateral parieto-occipital areas. There was no correlation between the clinical variables and volume decrease in these regions.

Conclusion

CM patients showed significant volume differences in infratentorial areas and white-matter abnormalities in the posterior part of the brain. It is currently unclear whether the structural brain changes seen in migraine patients are the cause or the result of headaches. Longitudinal volumetric neuroimaging studies with larger groups, especially on the chronification of migraine, are needed to shed light on this topic.

Keywords

Chronic migraine magnetic resonance imaging cerebellum brainstem

Introduction

Chronic migraine (CM) is an important form of migraine disorder that attracted attention because of the burden it imposes both on the patients and society. It is defined as “headache occurring on 15 or more days per month for more than 3 months” (1) with the condition that at least eight of such attacks per month should carry the features of migraine. The classical episodic form of the disease usually evolves into a chronic form while the reverse is also possible. Risk factors associated with progression to CM include age, low educational level, head injury, high baseline attack frequency, obesity, stressful life events, snoring, overuse of certain classes of medication, and caffeine (2).

The pathophysiology of CM has not been fully understood. However, both functional and structural abnormalities in pain-related regions, sensitization of the trigeminal system, cortical hyperexcitability and superfluous release of vasoactive peptides have been postulated as the pathological mechanisms responsible for CM (3 –6).

Certain structural and functional differences in various brain regions in migraine patients have been highlighted in recent years by using advanced neuroimaging techniques. Magnetic resonance imaging (MRI) of the brain is a more valuable method owing to its sensitivity to structural changes of the brain, its high spatial resolution, and its capacity to analyze functional changes (7 –9), compared to other neuroimaging modalities such as computerized tomography (CT) and positron emission tomography (PET).

There has been mounting evidence in the last decade on the volumetric changes in migraineurs and voxel-based morphometry has been the most common analysis method in these studies. Since the majority of these studies targeted a population of episodic migraine patients, there are only limited data on the structural brain changes in CM patients (3).

The goal of this study was to investigate whether there are any gray-matter and/or white-matter volume changes in the cerebrum, cerebellum and brainstem of CM patients, using a reliable automated MRI tissue segmentation technique (10).

Methods

Participants

Twenty-four consecutive patients were recruited from the tertiary headache outpatient clinics of two university-based neurology departments. The diagnosis of CM was based on the revised second edition of the International Classification of Headache Disorders Criteria (ICHD-2R). The CM patients included in the study had no history of chronic and/or active systemic diseases, major psychiatric diseases except depression or other neurological diseases except migraine. Patients with medication-overuse headache according to the criteria proposed by the ICHD-2R were included after a successful washout period of one month. The control group consisted of 24 randomly recruited healthy individuals matched for age and gender. Participants in the control group had no history of any systemic, psychiatric and neurological disease, including primary and secondary headaches or other facial pain syndromes. The Beck Depression Inventory (BDI) was applied to all participants to exclude the depression in the control group and to assess the depression severity in the patient group. A cut-off score of 10 was used to rule out presence of depression in the control group, and individuals with BDI scores higher than 10 were not included. None of the participants reported a history of stroke or transient ischemic attack. All participants gave written informed consent for participation in this study, which was approved by the institutional ethics committee (ID number 71306642-050.01.04-22/3).

All participants had a normal neurological examination. A standardized headache assessment form was completed for CM patients, including sociodemographic information such as age, gender, occupation, and clinical information about migraine to verify the CM diagnosis (presence of aura, headache duration, frequency, character, pain location, nausea/vomiting, photo-/phonophobia, aggravation with head motion). The Migraine Disability Assessment Scale (MIDAS) was applied to all patients to assess headache-related disability. Body mass index (BMI) was calculated for each participant before MRI acquisition. Data on medication and other comorbid conditions were obtained from the past medical records of the patients.

Image acquisition and processing

High-resolution T1-weighted images were acquired for each CM patient and control participant using a 1.5-T MR scanner with eight-channel head coil. The pulse sequence parameters were: repetition time (TR)/echo time (TE) = 8.6/4.0 seconds, ﬂip angle = 8, field of view (FOV) = 240 mm, acquired voxel size = 1.25/1.25/1.2 mm (reconstructed = 0.94/0.94/1.2 mm), 150 coronal slices without gap, scan duration = 7.23 minutes (per volume). The acquired images were analyzed with FreeSurfer 4.05 using the same workstation. This procedure, described previously (10), automatically segmented ≤40 unique structures based on their voxel densities and assigned a neuroanatomical label to each voxel. This assignment is performed automatically by the algorithm, using the probabilistic information gathered from a manually labeled training set of brain volumes. Each individual segmentation was then visually inspected for accuracy and manual editing was carried out when necessary. Our regions of interest (ROI) were the cerebral and cerebellar gray matter and white matter, as well as more speciﬁc structures including the basal ganglia, limbic areas (hippocampus, amygdala), thalamus and brainstem. A sample image of the automatic segmentation performed by FreeSurfer is given in Figure 1.

Figure 1.

Coronal view of the automatic segmentation performed with FreeSurfer in a chronic migraine patient. Note the brainstem in gray, cerebellar cortex in dark brown and cerebellar white matter in a light brown color.

Fluid-attenuated inversion recovery (FLAIR) images were also obtained in axial plane (TR/TE = 9000/117 s, FOV = 230 mm, voxel size = 1.2/0.5 mm, slice thickness = 5 mm, acquisition time (TA) = 2.08 min) for the assessment of white-matter hyperintensities (WMHs). The Age-Related White Matter Changes Scale (ARWMCS) (11) was used to quantify the white-matter lesions.

Statistical analysis

Descriptive statistics were applied to demographic and clinical variables. The analysis of covariance (ANCOVA) test was used to compare brain volumes, controlling for age at scan, total intracranial volume and BDI scores, followed by a post-hoc Tukey test. The Mann-Whitney test was applied to compare WMHs. Kruskal-Wallis tests were conducted to compare the ARWMCS scores of the patients with migraine with aura (MA), migraine without aura (MO) and controls. The Spearman correlation was used to examine the relationship between volumetric data and clinical measures. A value of p < 0.05 was considered statistically signiﬁcant for all tests.

Results

Twenty-four CM patients (all women; mean age ± standard deviation, 32.70 ± 8.40) and 24 age- and sex-matched control participants (all women; 32.20 ± 8.08) were included in this study. Eight of the CM patients had both MA and MO before CM onset while the rest of the patients had only MO. Seven patients had a history of medication-overuse headache. Five of these patients were on ergotamine and two of them were on metamizole. Demographic and clinical characteristics are shown in Table 1.

Table 1.

Demographic and clinical characteristics of chronic migraine patients and controls.

	Patients (n = 24)	Control participants (n = 24)	p value
Mean age, years (y)	32.70 ± 8.40 (19–57)	32.20 ± 8.08 (20–56)	0.894
Mean BMI score	25.2 ± 5.0 (19.9–42.1)	24.73 ± 2.84 (19.1–30.5)	0.791
Mean BDI score	20.10 ± 10.53 (4–44)	6.29 ± 1.65 (3–9)	0.0001
Mean disease duration, y	14.06 ± 7.70 (1.5–25)	NA
Mean chronic migraine duration, months	53.04 ± 59.59 (3–240)	NA
Mean MIDAS score^a	20.96 ± 7.79 (13–46)	NA
Migraine attack frequency	15.20 ± 6.62 (8–28)	NA
Headache days	21.79 ± 4.64 (15–28)	NA
Migraine days	16.92 ± 5.83 (9–28)	NA

Data are expressed as the mean ± SD (range).

BMI: body mass index; BDI: Beck Depression Inventory.

Migraine Disability Assessment Scale (MIDAS); scores of 0–5 indicate grade 1 (little or no disability); 6–10 grade 2 (mild disability); 11–20 grade 3 (moderate disability); ≥21 grade 4 (severe disability).

Only seven of 24 patients were on prophylactic treatment for CM at the time of MRI scan and all of those patients were using selective serotonin reuptake inhibitor (SSRI) or serotonin-norepinephrine reuptake inhibitor (SNRI) treatment for the prophylaxis of the migraine. Except for beta-blockers, there was no history of use of any other prophylactic medication in the patient group, including valproate.

Univariate analyses corrected for total intracranial volume (ICV) and age at the time of the scan revealed that volumes of the bilateral cerebellar white matter, cerebellar cortex and brainstem were smaller in CM patients while the other regions did not show significant changes (Table 2). Bilateral cerebellar cortex, cerebellar white-matter and brainstem volumes were compared in patients with BDI scores ≥ 10 (n = 18) and patients with BDI scores <10 (n = 6), and no significant differences were found in bilateral cerebellar and brainstem volumes (p values of the post-hoc test were as follows: left cerebellar cortex = 0.185, right cerebellar cortex = 0.134, left cerebellar white matter = 0.233, right cerebellar white matter = 0.632, brainstem = 0.934). Also, a subgroup analysis between the patients with MO and patients with MA yielded no significant difference (p values of the post-hoc test were as follows: left cerebellar cortex = 0.996, right cerebellar cortex = 0.793, left cerebellar white matter = 0.999, right cerebellar white matter = 0.639, brainstem = 0.965).

Table 2.

Mean volumes (and standard deviation) for various structures in chronic migraine (CM) patients and healthy controls, measured with an automated volumetric method (FreeSurfer)^a. Values are given as mm³.

	CM patients (n = 24)	Controls (n = 24)	p ^b
L cerebellar white matter	13,259 ± 1665 (10,440–16,156)	14,377 ± 2212 (10,692–16,531)	0.032^c
L cerebellar cortex	49,404 ± 3838 (41,149–55,428)	52,147 ± 4956 (40,304–58,841)	0.016^c
L thalamus	6637 ± 679 (5392–8112)	6919 ± 633 (6140–8469)	0.064
L caudate	3327 ± 470 (2738–4562)	3407 ± 422 (2357–3747)	0.496
L putamen	5310 ± 538 (4530–6769)	5398 ± 362 (4374–5875)	0.471
L pallidum	1626 ± 192 (1339–1995)	1651 ± 206 (1322–2072)	0.644
L hippocampus	3952 ± 359 (3353–4780)	4001 ± 291 (3527–4426)	0.546
L amygdala	1460 ± 185 (1167–1826)	1460 ± 137 (1225–1806)	0.999
L cerebral white matter	231035 ± 18595 (210332–286205)	230337 ± 18721 (204316–282504)	0.894
L cerebral cortex	224,014 ± 19,684 (191,366–360489)	220,027 ± 16,032 (186,931–248,883)	0.512
R cerebellar white matter	12,762 ± 2287 (9059–15,943)	14,305 ± 2151 (104,77–17,520)	0.017^c
R cerebellar cortex	49,501 ± 4394 (42,001–57,830)	53,530 ± 5581 (41,381–60,070)	0.002^c
R thalamus	6783 ± 639 (5574–7796)	6896 ± 499 (6252–9358)	0.356
R caudate	3406 ± 461 (2821–4587)	3500 ± 491 (2253–4033)	0.462
R putamen	5243 ± 432 (4425–6359)	5034 ± 423 (3885–5634)	0.082
R pallidum	1467 ± 153 (1219–1842)	1445 ± 207 (1121–1880)	0.654
R hippocampus	4073 ± 343 (3443–4719)	4106 ± 339 (3513–4700)	0.720
R amygdala	1482 ± 180 (1166–1916)	1498 ± 181 (1212–1795)	0.764
R cerebral white matter	234,233 ± 20,946 (205,240–285,992)	230,833 ± 19,260 (209,675–289,054)	0.542
R cerebral cortex	225,118 ± 19,791 (191,985–258,241)	220,027 ± 17,224 (182758–249,400)	0.243
Brainstem	19,816 ± 2404 (15,435–25,397)	21,268 ± 2335 (17,275–26,073)	0.027^c

Data are expressed as the mean ± SD (range).

For each neuroanatomic volume, ANCOVA statistical test is performed with intracranial volume (ICV), age and Beck Depression Inventory scores as covariates.

p value of the post-hoc Tukey test.

Significant difference after post-hoc analysis between groups performed with Tukey test.

L: left; R: right; ANCOVA: analysis of covariance.

In the entire sample including patients and controls, there were no significant correlations between BDI scores and the volumes of the left cerebellar cortex (r = –0.1278, p = 0.4274), right cerebellar cortex (r = –0.1118, p = 0.4807), left cerebellar white matter (r = –0.1813, p = 0.2504), right cerebellar white matter (r = –0.2092, p = 0.1835) and brainstem (r = –0.1765, p = 0.2634).

In the patient group, disease duration did not show any correlation with the volumes of the left cerebellar cortex (r = –0.1337, p = 0.5333), right cerebellar cortex (r = –0.1713, p = 0.4234), left cerebellar white matter (r = 0.2775, p = 0.1892), right cerebellar white matter (r = 0.0939, p = 0.6623) and brainstem (r = 0.37772, p = 0.0691). Similarly in the patient group, there was no significant correlation between MIDAS scores with the volumes of the left cerebellar cortex (r = 0.2714, p = 0.1925), right cerebellar cortex (r = 0.3491, p = 0.1062), left cerebellar white matter (r = 0.1681, p = 0.4549), right cerebellar white matter (r = 0.1399, p = 0.5351) and brainstem (r = 0.1347, p = 0.5322).

Total scores along with bilateral parieto-occipital subscores of the ARWMCS revealed significantly higher proportion of WMHs in the CM patients (Figure 2). No significant difference was observed between CM patients with MA and MO and controls (p = 0.177). There were no infarct and infarct-like lesions in any part of the brain in any patient.

Figure 2.

Mean scores of Age-Related White Matter Changes Scale (ARWMCS) in chronic migraine patients and controls.

Discussion

Following the recent advances in neuro-imaging acquisition and analysis techniques, researchers turned their attention to the structural changes in the brains of migraineurs (3,12). The main finding of the present study is that the cerebellum volume was lower in the CM group compared to healthy individuals. There are a number of volumetry studies on episodic migraine with or without aura and they have been reviewed in a recent meta-analysis (3). In these studies, the involved areas were reported as frontal, temporal, cingulate, insula, inferior and posterior parietal cortices, frontal, parietal and occipital white matter, and posterior fossa structures, including the cerebellum and brainstem (13 –18).

Despite the large number of studies on episodic migraine patients, we found only one study on CM. In this volumetric study using voxel-based morphometry on 11 patients, CM patients showed a significant focal gray matter reduction in the bilateral anterior cingulate cortex, left amygdala, left parietal operculum, left middle and inferior frontal gyrus, right inferior frontal gyrus, and bilateral insula compared to episodic migraine patients (15). Our study, using a different methodology, supported that the CM brain showed volume differences in comparison to normal participants, and attributed these changes to the cerebellum and brainstem.

The brainstem is highlighted as a critical brain structure for migraine pathogenesis because it involves areas that are associated with pain perception and modulation, such as trigeminal spinal, periaqueductal gray and cuneiform nuclei, and their afferent and efferent connections. In CM, the proposed mechanism for the persistence of clinical findings is the atypical pain modulation. In one study, evidence of increased cortical excitability was found along with hypermetabolism in the brainstem of CM patients (4). In other neuroimaging studies investigating the brainstem’s role in CM, the brainstem nuclei associated with pain modulation showed hypoactivation in functional MRI (fMRI) (19). In another recent study that used resting-state fMRI, connectivity problems were identified between periaqueductal gray and cuneiform nuclei, thalamus, cerebellum, insula, and some other cortical areas (20). There are also diffusion tensor imaging (DTI) studies showing involvement of brainstem pathways in migraine (17). Another condition that could be associated with brainstem atrophy is the nonspecific gliotic lesions seen more frequently in migraine patients (21); this, however, was not the case in our study. A voxel-based morphometry study conducted with episodic migraine patients without auras showed volume differences in the anterior cingulate cortex and also in the brainstem and cerebellum (18). There may be atrophy in the pain-related nuclei of the brainstem but the automatic segmentation methods used in this study instead of voxel-based morphometry will likely be insensitive to the millimeter-scale brainstem nuclei. Volume loss in the brainstem is more likely to indicate the atrophy of the afferent and efferent connections of these nuclei.

Another important finding of the present study is the smaller cerebellar volumes seen in CM. The cerebellum has cognitive, behavioral as well as motor functions, and it has been shown to be involved in pain pathogenesis (22 –24). There have been both clinical and neuroimaging studies on the relationship between the cerebellum and migraine. The smaller cerebellar volumes that we observed in CM patients were also previously seen in episodic migraine patients without auras (18). The possibility of cerebellar neuronal damage in migraine patients was also reported (25,26). Migraine presents a condition in which the cerebral cortex is hyperexcitable and the cerebellar inhibition on the cortex in migraine patients was found to be impaired (27). It is also known that balance problems (28,29), and vestibulocerebellar dysfunction (30), which are often considered as cerebellar findings, are commonly seen in migraine patients. Cerebellar findings accompany the clinical profile both in basilar migraine and familial hemiplegic migraine. Posterior circulation infarcts involving the cerebellum are more common in MA (31). Subclinical cerebellar involvement can also be seen in patients (32).

It is currently well known that WMHs of unknown cause are frequently found in migraine patients (33 –37). However, there is only one study specifically targeting CM patients showing WMH burden in a CM patient group (38). Using the ARWMCS, a tool for analyzing WMHs in five different brain regions, we also demonstrated the high rate of WMHs with a predilection for parieto-occipital white matter in CM patients. On the other hand, current research does not seem to show infarcts or infarct-like lesions in the cerebellum and brainstem of episodic migraine patients (31).

Another important point is the possible effects of prophylactic treatments used for CM on the brain volume. There are numerous anecdotal case reports with reversible brain atrophy related to valproate treatment in patients with epilepsy, bipolar disorder and dementia in various age groups (39 –45). The mechanism of this rarely observed side effect underlying these brain changes is poorly understood and it was not reported for migraineurs before. Since there was no use of antiepileptics in migraine prophylaxis in our CM group, this may not be the cause for the volume differences seen in this patient population, and there were no studies reporting changes in brain volume in patients using other treatments, including SSRI and SNRIs for migraine.

There is increasing interest in the field on the association between obesity and gray- or white-matter atrophy. Intriguingly, obesity has been highlighted as an independent risk factor for migraine transformation but not for chronic tension-type headache (2). A recent literature review suggested that higher adiposity had no clear association with global or regional white-matter volumes in any age group, but it may be associated with frontal gray matter atrophy across all ages, and parietal and temporal gray-matter atrophy in middle and old age (46,47). Since the mean BMI value of our patient group is in the overweight range, nine of the patients had BMI values above the obesity cut off (≥30), and higher amount of adipose tissue in CM patients may be related to brain volume changes in this patient group. Future studies on the brain volumes in migraineurs should include various measures of obesity to investigate this association.

Depression is one of the neuropsychiatric diseases that may lead to differences in the volumes of the various brain regions, including the cerebellum (48 –50). Two meta-analyses of the volumetric studies conducted in patients with depression showed that the areas with the most consistent volume differences are the prefrontal and limbic areas, anterior cingulate and basal ganglia (51,52). Migraine and depression are often co-morbid conditions. In fact, migraineurs have approximately a two-fold risk of depression (53). Moreover, CM patients are further at about a two-fold risk of having depression compared to episodic migraine patients (54). In a recent study, significant volume differences were seen in migraine patients with concomitant depression but migraine patients without depression did not show any volumetric differences (55). Because of this possible interaction, we included BDI scores as a covariate in our model to control for the effect of depression on the volumes of the brain regions. A limitation of our study is the small number of CM patients without depression in our cohort. Owing to the high rate of depression in CM patients, multicenter studies are needed to recruit more CM patients without depression. Another limitation of our study is the heterogeneous nature of the patient population, including both MA and MO patients. Future studies should target more balanced patient subgroups or include more MA and MO patients to allow a more reliable comparison between these subgroups that may have an effect on the brain volumes.

It is unclear whether the structural changes found in such migraine studies are the cause or the result of headaches. The fact that gray-matter changes reported in disorders like chronic back pain (56) or chronic phantom pain (57) may indicate that the differences seen in CM patients are due to the chronic nature of the pain rather than migraine per se. Central disorganization as well as degeneration may develop in chronic pain syndromes and consequently the pain areas and the associated white matter may undergo certain structural changes. All of these speculations call for longitudinal studies with higher samples. Especially in the case of familial migraine, a neuroimaging study starting from the presymptomatic stage and continuing longitudinally into later stages may prove to be informative.

Clinical implications

There is atrophy in the brainstem and cerebellum of chronic migraine patients.

Parieto-occipital white matter is significantly involved but it has no effect on volume changes in chronic migraine patients

Successful treatment in migraine patients before the chronification of symptoms might prevent brainstem and cerebellar atrophy and white-matter lesions in chronic migraine patients.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest

None declared.

Acknowledgment

The authors would like to thank Geetika Kalloo for helping in the preparation of this manuscript.

References

Headache Classification Committee, Olesen

Bousser

. Headache Classification Committee. New appendix criteria open for a broader concept of chronic migraine. Cephalalgia 2006; 26: 742–746.

Bigal

Lipton

. What predicts the change from episodic to chronic migraine? Curr Opin Neurol 2009; 22: 269–276.

Bashir

Lipton

Ashina

. Migraine and structural changes in the brain: A systematic review and meta-analysis. Neurology 2013; 81: 1260–1268.

Aurora

Barrodale

Tipton

. Brainstem dysfunction in chronic migraine as evidenced by neurophysiological and positron emission tomography studies. Headache 2007; 47: 996–1003.

Prescot

Becerra

Pendse

. Excitatory neurotransmitters in brain regions in interictal migraine patients. Mol Pain 2009; 5: 34–34.

Mathew

. Pathophysiology of chronic migraine and mode of action of preventive medications. Headache 2011; 51 (Suppl 2): 84–92.

Bhaskar

Saeidi

Borhani

. Recent progress in migraine pathophysiology: Role of cortical spreading depression and magnetic resonance imaging. Eur J Neurosci 2013; 38: 3540–3551.

Colombo

Dalla Costa

Dalla Libera

. From neuroimaging to clinical setting: What have we learned from migraine pain? Neurol Sci 2012; 33(Suppl 1): S95–S97.

Aydın-Özemir

Baykan

. The face of chronic migraine which has started to be clarified. Arch Neuropsychiatry 2013; 50(Suppl 1): 21–25.

10.

Fischl

Salat

Busa

. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33: 341–355.

11.

Wahlund

Barkhof

Fazekas . A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001; 32: 1318–1322.

12.

Ellerbrock

Engel

May

. Microstructural and network abnormalities in headache. Curr Opin Neurol 2013; 26: 353–359.

13.

Rocca

Ceccarelli

Falini

. Brain gray matter changes in migraine patients with T2-visible lesions: A 3-T MRI study. Stroke 2006; 37: 1765–1770.

14.

Schmidt-Wilcke

Gänssbauer

Neuner

. Subtle grey matter changes between migraine patients and healthy controls. Cephalalgia 2008; 28: 1–4.

15.

Valfrè

Rainero

Bergui

. Voxel-based morphometry reveals gray matter abnormalities in migraine. Headache 2008; 48: 109–117.

16.

Kim

Suh

Seol

. Regional grey matter changes in patients with migraine: A voxel-based morphometry study. Cephalalgia 2008; 28: 598–604.

17.

Schmitz

Admiraal-Behloul

Arkink

. Attack frequency and disease duration as indicators for brain damage in migraine. Headache 2008; 48: 1044–1055.

18.

Jin

Yuan

Zhao

. Structural and functional abnormalities in migraine patients without aura. NMR Biomed 2013; 26: 58–64.

19.

Moulton

Burstein

Tully

. Interictal dysfunction of a brainstem descending modulatory center in migraine patients. PloS One 2008; 3: e3799–e3799.

20.

Schwedt

Larson-Prior

Coalson

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

21.

Kruit

van Buchem

Launer

. Migraine is associated with an increased risk of deep white matter lesions, subclinical posterior circulation infarcts and brain iron accumulation: The population-based MRI CAMERA study. Cephalalgia 2010; 30: 129–136.

22.

Peyron

Laurent

García-Larrea

. Functional imaging of brain responses to pain. A review and meta-analysis. Neurophysiol Clin 2000; 30: 263–288.

23.

Borsook

Moulton

Tully

. Human cerebellar responses to brush and heat stimuli in healthy and neuropathic pain subjects. Cerebellum 2008; 7: 252–272.

24.

Moulton

Schmahmann

Becerra

. The cerebellum and pain: Passive integrator or active participator? Brain Res 2010; 65: 14–27.

25.

Dichgans

Herzog

Freilinger

. 1H-MRS alterations in the cerebellum of patients with familial hemiplegic migraine type 1. Neurology 2005; 64: 608–613.

26.

Zielman

Teeuwisse

Bakels

. Biochemical changes in the brain of hemiplegic migraine patients measured with 7 tesla 1H-MRS. Cephalalgia 2014; 34: 959–967.

27.

Brighina

Palermo

Panetta

. Reduced cerebellar inhibition in migraine with aura: A TMS study. Cerebellum 2009; 8: 260–266.

28.

Ishizaki

Mori

Takeshima

. Static stabilometry in patients with migraine and tension-type headache during a headache-free period. Psychiatry Clin Neurosci 2002; 56: 85–90.

29.

Cho

Clark

Rupert

. Visually triggered migraine headaches affect spatial orientation and balance in a helicopter pilot. Aviat Space Environ Med 1995; 66: 353–358.

30.

Sándor

Mascia

Seidel

. Subclinical cerebellar impairment in the common types of migraine: A three-dimensional analysis of reaching movements. Ann Neurol 2001; 49: 668–672.

31.

Kruit

Launer

Ferrari

. Infarcts in the posterior circulation territory in migraine. The population-based MRI CAMERA study. Brain 2005; 128: 2068–2077.

32.

Gerwig

Rauschen

Gaul

. Subclinical cerebellar dysfunction in patients with migraine: Evidence from eyeblink conditioning. Cephalalgia 2014; 34: 904–913.

33.

Fazekas

Koch

Schmidt

. The prevalence of cerebral damage varies with migraine type: A MRI study. Headache 1992; 32: 287–291.

34.

Rovaris

Bozzali

Rocca

. An MR study of tissue damage in the cervical cord of patients with migraine. J Neurol Sci 2001; 183: 43–46.

35.

Kruit

van Buchem

Hofman

PAM

. Migraine as a risk factor for subclinical brain lesions. JAMA 2004; 291: 427–434.

36.

Kurth

Mohamed

Maillard

. Headache, migraine, and structural brain lesions and function: Population based Epidemiology of Vascular Ageing-MRI study. BMJ 2011; 342: c7357–c7357.

37.

Palm-Meinders

Koppen

Terwindt

. Structural brain changes in migraine. JAMA 2012; 308: 1889–1897.

38.

Zheng

Xiao

Shi

. White matter lesions in chronic migraine with medication overuse headache: A cross-sectional MRI study. J Neurol 2014; 261: 784–790.

39.

Papazian

Cañizales

Alfonso

. Reversible dementia and apparent brain atrophy during valproate therapy. Ann Neurol 1995; 38: 687–691.

40.

Galimberti

Diegoli

Sartori

. Brain pseudoatrophy and mental regression on valproate and a mitochondrial DNA mutation. Neurology 2006; 67: 1715–1717.

41.

McLachlan

. Pseudoatrophy of the brain with valproic acid monotherapy. Can J Neurol Sci 1987; 14: 294–296.

42.

Yamanouchi

Ota

Imataka

. Reversible altered consciousness with brain atrophy caused by valproic acid. Pediatr Neurol 2003; 28: 382–384.

43.

Abreu

Issler

Lafer

. Valproate-induced reversible pseudoatrophy of the brain and hyperammonemic encephalopathy in a bipolar patient. Aust N Z J Psychiatry 2009; 43: 484–485.

44.

Evans

Shinar

Yaari

. Reversible dementia and gait disturbance after prolonged use of valproic acid. Seizure 2011; 20: 509–511.

45.

Fleisher

Truran

Mai

. Chronic divalproex sodium use and brain atrophy in Alzheimer disease. Neurology 2011; 77: 1263–1271.

46.

Willette

Kapogiannis

. Does the brain shrink as the waist expands? Ageing Res Rev 2015; 20: 86–97.

47.

Bobb

Schwartz

Davatzikos

. Cross-sectional and longitudinal association of body mass index and brain volume. Hum Brain Mapp 2014; 35: 75–88.

48.

Grieve

Korgaonkar

Koslow

. Widespread reductions in gray matter volume in depression. Neuroimage Clin 2013; 3: 332–339.

49.

Peng

Liu

Nie

. Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder: A voxel-based morphometry study. Eur J Radiol 2011; 80: 395–399.

50.

Zeng

Liu

. Antidepressant treatment normalizes white matter volume in patients with major depression. PloS One 2012; 7: e44248–e44248.

51.

Kempton

Salvador

Munafò

. Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Arch Gen Psychiatry 2011; 68: 675–690.

52.

Bora

Harrison

Davey

. Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal-thalamic circuits in major depressive disorder. Psychol Med 2012; 42: 671–681.

53.

Antonaci

Nappi

Galli

. Migraine and psychiatric comorbidity: A review of clinical findings. J Headache Pain 2011; 12: 115–125.

54.

Buse

Manack

Serrano

. Sociodemographic and comorbidity profiles of chronic migraine and episodic migraine sufferers. J Neurol Neurosurg Psychiatry 2010; 81: 428–432.

55.

Gudmundsson

Scher

Sigurdsson

. Migraine, depression, and brain volume: The AGES-Reykjavik Study. Neurology 2013; 80: 2138–2144.

56.

Schmidt-Wilcke

Leinisch

Gänssbauer

. Affective components and intensity of pain correlate with structural differences in gray matter in chronic back pain patients. Pain 2006; 125: 89–97.

57.

Draganski

Moser

Lummel

. Decrease of thalamic gray matter following limb amputation. Neuroimage 2006; 31: 951–957.

Volumetric differences suggest involvement of cerebellum and brainstem in chronic migraine

Abstract

Background

Method

Results

Conclusion

Keywords

Introduction

Methods

Participants

Image acquisition and processing

Statistical analysis

Results

Discussion

Clinical implications

Footnotes

Funding

Conflict of interest

Acknowledgment

References