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Keywords:

  • Migraine;
  • Functional Connectivity;
  • Functional Magnetic Resonance Imaging;
  • Allodynia;
  • Central Sensitization

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Objective

Most migraineurs develop cutaneous allodynia during migraines, and many have cutaneous sensitization between attacks. Atypical pain modulation via the descending pain system may contribute to this sensitization and allodynia. The objective of this study was to test the hypothesis that compared with non-allodynic migraineurs, allodynic migraineurs have atypical periaqueductal gray (PAG) and nucleus cuneiformis (NCF) resting-state functional connectivity (rs-fc) with other pain processing regions.

Design

Ten minutes resting-state blood-oxygen-level-dependent data were collected from 38 adult migraineurs and 20 controls. Seed-based analyses compared whole-brain rs-fc with PAG and with NCF in migraineurs with severe ictal allodynia (N = 8) to migraineurs with no ictal allodynia (N = 8). Correlations between the strength of functional connections that differed between severely allodynic and non-allodynic migraineurs with allodynia severity were determined for all migraineurs (N = 38). PAG and NCF rs-fc in all migraineurs was compared with rs-fc in controls.

Results

Migraineurs with severe allodynia had stronger PAG and NCF rs-fc to other brainstem, thalamic, insula and cerebellar regions that participate in discriminative pain processing, as well as to frontal and temporal regions implicated in higher order pain modulation. Evidence that these rs-fc differences were specific for allodynia included: 1) strong correlations between some rs-fc strengths and allodynia severity among all migraineurs; and 2) absence of overlap when comparing rs-fc differences in severely allodynic vs non-allodynic migraineurs with those in all migraineurs vs controls.

Conclusion

Atypical rs-fc of brainstem descending modulatory pain regions with other brainstem and higher order pain-modulating regions is associated with migraine-related allodynia.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Cutaneous allodynia, a symptomatic manifestation of central sensitization, is the abnormal perception of normally non-noxious stimulation of the skin as being painful [1]. The majority of migraineurs develop cutaneous allodynia during migraine attacks [1]. Due to this allodynia, migraineurs may experience pain from light touch of the face or head, wearing earrings, eyeglasses, headbands, shaving one's face, and combing one's hair. Many migraineurs have persistent sensitization between migraine attacks (interictal), although often asymptomatic (sometimes called “pre-allodynia”) [2]. Measurements of cutaneous pain thresholds confirm that pain thresholds are lower during migraine attacks compared with the interictal period and that pain and pain tolerance thresholds are lower in interictal migraineurs than in healthy non-migraine controls [2,3]. In addition to causing cutaneous allodynia, central sensitization may also reduce the effectiveness of migraine medication and may elevate the risk for development of more frequent migraine attacks [4].

Central sensitization may develop in migraine due to atypical modulation of pain by the descending pain modulatory system, a system that primarily inhibits nociceptive transmission [5,6]. The descending pain system consists of several regions including the periaqueductal gray (PAG), nucleus cuneiformis (NCF), and rostral ventral medulla [6–8]. Prior functional imaging studies of experimental pain (e.g., heat-capsaicin model) have implicated the PAG and NCF in central sensitization [7,9]. Furthermore, migraineurs exposed to painful stimulation have hypofunctional response of the descending pain system, suggestive of inadequate pain inhibition [5].

Due to the clinical importance of central sensitization and allodynia in migraine and the likely role of the descending pain modulatory system in the development and/or maintenance of sensitization and allodynia in migraine, the current study further investigated the role of the descending pain modulatory system in migraine-related allodynia. Resting-state functional connectivity (rs-fc) magnetic resonance imaging (MRI) was used to test the hypothesis that there is atypical interictal functional connectivity with two key regions of the descending pain modulatory system, the PAG and NCF, in migraineurs who have allodynia during migraine attacks.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Inclusion/Exclusion Criteria

Thirty-eight subjects (ages 18–64 years) with migraine diagnosed according to International Classification of Headache Disorders II (ICHD-II) criteria and 20 healthy controls (ages 20–53 years) without migraine were recruited from the Washington University Department of Neurology and from the surrounding community [10]. All procedures were approved by Washington University's Human Research Protection Office. Subjects were excluded if they met ICHD-II criteria for medication overuse headache. Although the majority of subjects were not taking migraine prophylactic medication, use of medications that could be considered migraine prophylactic therapies was allowed as long as there were no changes in medications or their dosages within the 8 weeks prior to study participation. Potential subjects were excluded if they had any contraindication to MRI, had a prior brain injury, had a neurological disorder other than migraine, had a psychiatric disorder other than anxiety or depression, or if they had any acute or chronic pain disorder other than migraine. Although control subjects had no history of migraine, they were not excluded if they had tension-type headache on 3 or fewer days per month.

Clinical Parameters

The following data were collected from all migraine subjects: 1) number of years with migraine; 2) headache frequency; 3) Migraine Disability Assessment Scale score; 4) Beck Depression Inventory (BDI) score; 5) State-Trait Anxiety Inventory (STAI) scores; and 6) Allodynia Symptom Checklist 12 (ASC-12) score [11–13]. The ASC-12 was used to determine the presence and severity of allodynia symptoms during the migraine attack. This validated, 12-item questionnaire quantifies subject perception of cutaneous allodynia symptoms yielding a score that is placed into one of the four categories: 0–2 = no allodynia; 3–5 = mild allodynia; 6–8 = moderate allodynia; and 9 or more = severe allodynia [1,14,15].

Imaging Protocol

Migraineurs were studied when they had been migraine free ≥48 hours and had not used migraine abortive medications for ≥48 hours. Migraineurs who experienced a migraine within 48 hours of their scheduled time were rescheduled for a later date. Controls were studied when they were in their usual state of good health. All structural and functional images were obtained on a Siemens MAGNETOM Trio 3T scanner (Erlangen, Germany) with total imaging matrix technology using a 12-channel head matrix coil. Structural anatomic scans included a high-resolution T1-weighted sagittal magnetization-prepared rapid gradient echo (MP-RAGE) series (TR 2400 milliseconds, TE 1.13 milliseconds, 176 slices, 1.0 mm3 voxels) and a coarse T2-weighted turbo spin echo series (TR 6150, TE 86.0, 36 axial slices, 1 × 1 × 4 mm3 voxels). Functional imaging was performed using a blood-oxygen-level-dependent (BOLD) contrast-sensitive sequence (T2* evolution time = 25 milliseconds, flip angle = 90°, resolution = 4 × 4 × 4 mm). Whole-brain echo planar imaging volumes (magnetic resonance [MR] frames) of 36 contiguous, 4 mm thick axial slices were obtained every 2.5 seconds. BOLD data were collected via two 5-minute runs. During the rs-fc MRI scans, participants were instructed to keep their eyes closed, remain still, and not fall asleep.

Data Processing and Analysis

Following acquisition, functional MRI (fMRI) BOLD data were preprocessed via standard methods used in our lab [16–18]. Briefly, all images from a single subject were combined into a four-dimensional (x, y, z, time) time series and adjusted for timing offsets using sinc interpolation. Images were adjusted for the slice intensity differences introduced by contiguous interleaved slice acquisition. Next, a six-parameter rigid body realignment process was used to minimize movement-induced noise across all frames in all runs for each subject. Images were resliced by three-dimensional cubic spline interpolation. Data were transformed into a common stereotactic space based on Talairach and Tournoux (1988 [19]) but using an in-house atlas composed of the average anatomy of 12 healthy young adults (ages 21–29 years) (see Lancaster et al. and Snyder, 1996 for methods [20,21]). As part of the atlas transformation, the data were resampled isotropically at 3 × 3 × 3 mm. Registration was accomplished via a 12-parameter affine warping of each individual's MP-RAGE to the atlas target, using difference image variance minimization as the objective function. The subject's T2-weighted image served as an intermediate target for transforming the BOLD images. The atlas-transformed images were checked against a reference average to ensure appropriate registration. Preprocessing for the rs-fc series was carried out in order to optimize the time-series data and to remove spurious variance [22]. These steps included removal of the linear trend and temporal band-pass filtering (0.009 Hz < f < 0.08 Hz), Gaussian blur of two voxels full width at half maximum, as well as regression of several “noise” parameters and their time-based derivatives including six motion parameters, a ventricular signal, a white matter signal, and a whole brain signal. The use of whole brain signal regression is an area of controversy because of the argument that doing so may produce spurious anticorrelations. However, evidence supporting its benefit for removal of the submillimeter movement-related functional connectivity artifact has been published [23–26]. Work from our own laboratory substantiating this critical effect is currently under review for publication. This study used a volume censoring technique to identify and remove the aforementioned motion-related artifact that is not adequately addressed by frame realignment routines and movement parameter regression [23]. Briefly, data volumes (i.e., frames) with a frame-by-frame movement greater than 0.5 mm or a whole brain signal change greater than 0.5% were identified and eliminated.

Functional connectivity analyses used a seed-based/region-of-interest (ROI) approach. Three-millimeter diameter spheres were created around coordinates for the right PAG (Talairach 4, −36, −7) and the right NCF (Talairach 9, −29, −14) (Figure 1). Coordinates were chosen based upon our lab's prior resting functional connectivity work showing that these PAG and NCF regions were functionally connected to other pain matrix regions in migraine patients and based upon coordinates used in the published literature [5,9,27]. For each seed, a resting-state time series was extracted separately for each subject by computing the mean of the BOLD intensity of all voxels enclosed by the seed region boundaries at each MR frame (time point). Correlations with this time series were calculated for each voxel in the brain, then Fisher z transformed to produce a functional connectivity map for each seed in each subject.

figure

Figure 1. Periaqueductal gray (PAG) and nucleus cuneiformis (NCF) seed regions of interest. Seed regions were 3 mm diameter spheres centered on Talairach coordinates 4, −36, −7 for right PAG and Talairach coordinates 9, −29, −14 for right NCF.

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One-sample t-tests (two-tailed) were performed on maps from all subjects (N = 58) to identify functional connections that significantly differed from zero (P ≤ 0.01). Two-sample t-tests were used to compare PAG and NCF rs-fc in migraineurs with severe ictal allodynia (ASC-12 score ≥9) to that of migraineurs without ictal allodynia (ASC-12 score ≤2). Regions were created from the results of these two-sample t-test images using an in-house peak-finding algorithm. This algorithm located peaks within contiguous voxels in each image and defined regions by first smoothing with a 1 mm kernel, then extracting peaks with a minimum distance of 10 mm from other peaks, a peak z-value of at least 2.0, and a minimum size of six voxels (3 × 3 × 3 mm). The functional connectivity strength between each of the resulting regions and the seed region (either PAG or NCF) was calculated, and two-sample t-tests (two-tailed) compared functional connectivity strengths among subject groups. If the absolute value of the functional connectivity strength between the seed region and another brain region was less than 0.1 in both subject groups (lack of significant functional connectivity in both subject groups), the functional connection was excluded from further analyses. Benjamini–Hochberg correction for multiple comparisons allowing for a false discovery rate of 2.5% was employed to identify functional connections that significantly differed between subject groups.

The strengths of functional connections that differed between migraineurs with severe allodynia and migraineurs with no allodynia were correlated with ictal allodynia scores in all 38 migraineurs using Pearson correlations. Because the 16 migraineurs (8 with severe allodynia and 8 with no allodynia) used to identify these functional connections were included in the sample of migraineurs used to determine correlations between functional connectivity strength and allodynia scores (N = 38), this analysis is to be considered exploratory, and P values are not reported. Linear regression, stepwise entry, was employed to determine the set of functional connections that best explained the variance in ictal allodynia scores. In order to consider the potential effect of confounding variables on the correlations between functional connection strength and ictal allodynia scores, Pearson correlations between the strength of functional connections and number of years with migraine, headache frequency, age, state anxiety, trait anxiety, and depression scores were calculated.

The validity of PAG and NCF rs-fc differences between migraineurs with severe allodynia and migraineurs without allodynia, and the specificity of findings for the presence of allodynia were further investigated by comparing PAG and NCF rs-fc strength between all migraineurs (N = 38) and a group of non-migraine controls (N = 20). t-tests (two-sample) were used to compare migraineurs with controls using the rs-fc strengths of those functional connections that differed in migraineurs with severe allodynia compared with migraineurs without allodynia. Benjamini–Hochberg correction for multiple comparisons allowing for a false discovery rate of 2.5% was employed to identify functional connections that significantly differed between subject groups. Voxels with rs-fc correlations to PAG or NCF that differed both between migraineurs vs controls and in migraineurs with severe allodynia vs migraineurs with no allodynia were identified.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Subject Characteristics (Table 1)

Table 1. Subject characteristics
Subject GroupAge (Years)SexHeadache Frequency (Days/Month)Years with MigraineASC-12Allodynia CategoryState AnxietyTrait AnxietyBDI
  1. Numbers are means followed by standard deviation in parenthesis (except for sex and allodynia category that are counts). ASC-12 scores are indicative of cutaneous allodynia symptoms during a migraine attack. State and trait anxiety scores are from the State-Trait Anxiety Inventory. Mean state and trait anxiety scores among migraineurs are within one standard deviation of the normal mean (mean scores in the general population are 36.4 ± 10.6) [39]. Depression scores are from the Beck Depression Inventory (BDI). Average BDI scores are consistent with no depression (1–10, normal; 11–16, mild mood disturbance; 17–20, borderline clinical depression; 21–30, moderate depression; 31–40, severe depression; over 40, extreme depression). Anxiety and depression scores are available from 18/20 control subjects.

  2. F = female; M = male; ASC-12 = Allodynia Symptom Checklist 12.

Migraine (N = 38)32 (±11)

F = 32

M = 6

15 (±8)14 (±8)6 (±5)

None = 8

Mild = 12

Moderate = 10

Severe = 8

35 (±12)37 (±11)6 (±8)
Migraine—severe ictal allodynia (N = 8)32 (±8)

F = 6

M = 2

20 (±11)13 (±8)13 (±5)Severe = 839 (±10)37 (±16)6 (±11)
Migraine—no ictal allodynia (N = 8)33 (±16)

F = 6

M = 2

14 (±8)12 (±10)1 (±1)None = 835 (±15)37 (±11)2 (±4)
Control (N = 20)34 (±10)

F = 15

M = 5

NANANANA26 (±7)32 (±11)4 (±6)

Thirty-eight migraine subjects and 20 controls were included in this study. Table 1 shows subject age and sex, headache frequency, number of years with migraine, ASC-12 scores, category of allodynia severity, STAI scores, and BDI scores. All but eight of the 38 migraineurs (79%) reported having at least mild symptoms of cutaneous allodynia during migraine attacks, including eight with severe, 10 with moderate, and 12 with mild allodynia. Eight of 38 (21%) migraine subjects were taking migraine prophylactic medications, including three of the eight with severe ictal allodynia and two of the eight with no ictal allodynia.

PAG and NCF Functional Connectivity Differs in Migraineurs with Severe Ictal Allodynia Compared with Migraineurs without Ictal Allodynia

Using the PAG as a seed region, comparison of rs-fc in migraineurs with severe ictal allodynia to migraineurs with no ictal allodynia yielded 19 functional connections that significantly differed between these migraine subgroups (Figure 2). Functional connections that were stronger in subjects with severe allodynia than those with no allodynia (stronger positive or stronger negative BOLD temporal correlations) included PAG with: pons, thalamus, cerebellum, precuneus, posterior insula, inferior temporal cortex, and inferior and superior frontal cortex. Functional connections with the PAG that were weaker in subjects with severe allodynia (weaker positive or weaker negative BOLD temporal correlations) included: middle and superior frontal regions.

figure

Figure 2. Functional connectivity to periaqueductal gray (PAG) differs in migraineurs with severe allodynia compared with migraineurs with no allodynia. The strength of 19 functional connections with the PAG differed in migraine subjects with severe ictal allodynia (red squares on scatterplot) compared with migraineurs with no ictal allodynia (blue triangles on scatterplot). Average functional connectivity strength is demonstrated with open red squares (migraineurs with severe ictal allodynia) and open blue triangles (migraineurs with no ictal allodynia). Voxel locations of each region included in the scatterplot and the location of the PAG seed are shown on the brain slices. Red coloration of voxels indicates that PAG functional connectivity was more positive in migraineurs with severe ictal allodynia compared with migraineurs with no ictal allodynia. Blue coloration of voxels indicates that PAG functional connectivity was more negative in migraineurs with severe ictal allodynia compared with migraineurs with no ictal allodynia. Axial slices are shown with the left hemisphere on the left side. BOLD = blood-oxygen-level-dependent; MD = medial dorsal.

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NCF rs-fc in migraineurs with severe ictal allodynia and in migraineurs with no ictal allodynia significantly differed for 30 functional connections (Figure 3). NCF functional connections that were stronger in migraineurs with severe ictal allodynia included: dorsal pons, midbrain, ventral medulla, cerebellum, thalamus, precuneus, inferior and middle frontal cortex, superior temporal cortex, occipital cortex, and inferior and superior parietal cortex. Functional connections with NCF that were weaker in migraineurs with severe ictal allodynia included regions in middle and superior temporal cortex and occipital cortex.

figure

Figure 3. Functional connectivity to nucleus cuneiformis (NCF) differs in migraineurs with severe allodynia compared with migraineurs with no allodynia. The strength of 30 functional connections with the NCF differed in migraine subjects with severe ictal allodynia (red squares on scatterplot) compared with migraineurs with no ictal allodynia (blue triangles on scatterplot). Average functional connectivity strength is demonstrated with open red squares (migraineurs with severe ictal allodynia) and open blue triangles (migraineurs with no ictal allodynia). Voxel locations of each region included in the scatterplot and the location of the NCF seed are shown on the brain slices. Red coloration of voxels indicates that NCF functional connectivity was more positive in migraineurs with severe ictal allodynia compared with migraineurs with no allodynia. Blue coloration of voxels indicates that NCF functional connectivity was more negative in migraineurs with severe ictal allodynia compared with migraineurs with no ictal allodynia. Axial slices are shown with the left hemisphere on the left side. BOLD = blood-oxygen-level-dependent; VPM = ventral posterior medial.

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Correlations between PAG rs-fc and NCF rs-fc with ictal allodynia (ASC-12) scores among all 38 migraine subjects are shown in Tables 2 and 3. Linear regression analysis yielded a model consisting of functional connections between PAG and medial dorsal thalamus, precuneus, and superior temporal cortex that best accounted for the variance in ictal allodynia scores (r2 = 0.512, P < 0.001). Functional connectivity strength of the NCF with occipital, cerebellum, precuneus, and temporal cortex best accounted for the variance in ictal allodynia scores (r2 = 0.667, P < 0.001). There were no significant correlations between the strength of rs-fc with PAG or NCF and subject age, number of years with migraine, migraine frequency, state anxiety, trait anxiety, or depression scores.

Table 2. Correlations between periaqueductal gray functional connectivity strength and allodynia severity scores
Region NameRegion #CoordinatesCorrelationSlope
  1. Correlations between the strengths of functional connections that differed between migraineurs with no allodynia and migraineurs with severe allodynia, and ASC-12 scores were calculated. P values are not reported because the 16 subjects used to determine rs-fc differences (migraineurs with severe allodynia vs migraineurs without allodynia) were also included in the sample of 38 migraineurs used to calculate these correlations. Talairach coordinates. Region number refers to the number assigned to that region in Figure 2.

  2. ASC-12 = Allodynia Symptom Checklist 12; MD = medial dorsal; rs-fc, resting-state functional connectivity; Slope = slope of the linear regression line (regression coefficient).

Left precuneus7−5, −75, 370.51716.45
MD thalamus51, −21, 70.50411.01
Right inferior temporal 11841, −65, −22−0.474−13.45
Right inferior cerebellum214, −54, −570.46212.84
Ventral medulla4−7, −33, −440.4188.59
Right superior temporal1256, −39, 20−0.398−12.12
Left ventral pons11−9, −26, −290.3948.55
Right superior frontal 21718, 48, 32−0.390−13.64
Dorsal pons15, −37, −260.38911.45
Left inferior temporal9−38, −14, −250.37510.87
Left MD thalamus3−9, −21, 170.3678.98
Right superior frontal 11410, 15, 57−0.341−10.76
Left superior frontal15−32, 42, 31−0.330−12.94
Right inferior temporal 21947, −71, 2−0.310−10.28
Cerebellum—posterior60, −61, −450.2938.93
Left middle frontal13−30, −9, 58−0.282−10.17
Right posterior insula825, −23, 140.2779.67
Right ventral pons108, −22, −310.2456.98
Left inferior frontal16−44, 19, 7−0.218−8.98
Table 3. Correlations between nucleus cuneiformis functional connectivity strength and allodynia severity scores
Region NameRegion #CoordinatesCorrelationSlope
  1. Correlations between the strengths of functional connections that differed between migraineurs with no allodynia and migraineurs with severe allodynia and ASC-12 scores were calculated. P values are not reported because the 16 subjects used to determine rs-fc differences (migraineurs with severe allodynia vs migraineurs without allodynia) were also included in the sample of 38 migraineurs used to calculate these correlations. Talairach coordinates. Region number refers to the number assigned to that region in Figure 3.

  2. ASC-12 = Allodynia Symptom Checklist 12; rs-fc, resting-state functional connectivity; Slope = slope of the linear regression line (regression coefficient); VPM = ventral posterior medial.

Left occipital27−31, −83, 33−0.599−17.50
Left middle frontal 226−39, 33, 35−0.516−16.98
Right inferior frontal 1643, 29, 40.45414.83
Left cerebellum—anterior lobe 211−15, −55, −210.44912.80
Left cerebellum—anterior lobe—medial3−10, −46, −380.44212.41
Right occipital2830, −81, 37−0.441−17.05
Right cerebellum—posterior lobe 42339, −63, −390.44012.66
Left posterior middle temporal18−50, −56, 130.43610.16
Right superior temporal 1437, −32, 50.41714.21
Left precuneus19−7, −60, 360.41310.46
Left superior parietal25−14, −63, 55−0.402−12.44
Right cerebellum—posterior lobe 32217, −68, −230.39611.82
Right cerebellum—anterior lobe—lateral547, −56, −440.3909.87
Right precuneus1217, −54, 330.38914.20
Left cerebellum—anterior lobe 11−6, −58, −120.3878.93
Right cerebellum—anterior lobe26, −59, −140.3858.28
Right pulvinar/VPM1013, −28, 60.38512.01
Right inferior parietal3052, −30, 31−0.381−11.50
Right inferior frontal 21648, 25, 130.36812.42
Right cerebellum—posterior lobe 11531, −64, −280.36611.07
Right cerebellum—anterior lobe—medial 1911, −48, −360.3648.94
Ventral medulla172, −40, −500.3427.63
Right superior temporal 2743, −48, 100.33612.29
Left anterior middle temporal29−31, 2, −41−0.311−8.36
Left middle frontal 120−26, 57, 130.30610.96
Right cerebellum—anterior lobe—medial 21411, −45, −200.2995.42
Left pulvinar/VPM8−2, −22, 50.2916.59
Right cerebellum—posterior lobe 22131, −54, −420.2898.44
Dorsal pons138, −35, −320.2655.98
Dorsal midbrain246, −35, −120.2605.40

Migraineurs and non-migraine controls were compared for rs-fc strength of the 19 PAG functional connections and 30 NCF functional connections that differed between migraineurs with severe allodynia and migraineurs with no allodynia. There were no significant differences in rs-fc between controls and migraineurs for these 49 functional connections. Compared with controls, whole-brain rs-fc to PAG and NCF in migraineurs significantly differed for 26 functional connections (14 to PAG, 12 to NCF) (Supplementary Figures S1 and S2). There was no substantial overlap between rs-fc differences found when comparing migraineurs with severe allodynia with migraineurs with no allodynia, with those differences found when comparing migraineurs with controls. The only overlap present was for PAG to a few voxels in inferior temporal cortex (Supplementary Figure S3).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

The major finding in this study is the demonstration of differences in rs-fc with PAG and NCF in interictal migraineurs who experience severe allodynia during migraine attacks compared with migraineurs without ictal allodynia during migraine attacks. Correlations between the strengths of several of these functional connections with the severity of ictal allodynia symptoms among our entire cohort of migraine subjects and an absence of similar differences between all migraineurs and non-migraine controls suggest that the observed differences in rs-fc are directly related to the presence of ictal allodynia. Overall, our findings further implicate the PAG and NCF, two key regions of the brainstem descending pain modulatory system, as participants in the development and/or maintenance of central sensitization in migraine.

Central Sensitization and Allodynia in Migraine

Central sensitization is a condition in which neurons have lower activation thresholds, increased responsiveness to afferent inputs, increased spontaneous activity, and enlarged receptive fields [7,8,28]. Trigeminal sensitization in migraine leads to cutaneous hypersensitivity, cutaneous allodynia, referral of pain beyond trigeminal innervated locations, and may predispose the migraineur to future migraine attacks [29]. In addition, central sensitization may lead to poorer response to abortive migraine medications and may increase the risk of transforming from less frequent to very frequent migraine attacks (transformation from episodic migraine to chronic migraine) [1,4,15,30]. Evidence for central sensitization is found in ≈75% of migraineurs during attacks, and sensitization persists between attacks in a proportion of migraineurs [2,3,14]. In a population study of >11,000 migraineurs, 2/3 of respondents completing the ASC-12 had scores consistent with the presence of ictal allodynia [1,15]. Severe allodynia was present in ≈1/5 of all migraineurs [1]. Thus, central sensitization and cutaneous allodynia are common in migraine patients, are responsible for several symptoms of migraine, may reduce the efficacy of migraine treatment, and may contribute to the worsening of migraine patterns [1–4]. Research leading to mechanistic descriptions of migraine-related sensitization and allodynia may eventually lead to treatments that more effectively reduce sensitization and its clinical consequences.

Descending Modulation of Pain and Central Sensitization

The descending modulatory pain system is responsible for modulating spinal cord and trigeminal nociceptive transmission. The PAG and NCF are key regions of the descending modulatory pain system, serving as the major sources of input to rostral ventral medulla, a region responsible for the final output from this pain-modulating system [5,9,31,32]. These brainstem regions play prominent roles in modulating perceived pain intensity and are anatomically connected to each other as well as to higher order pain processing regions in the frontal lobes, thalamus, cingulate cortex, amygdala, insula, and hypothalamus [6,33]. Regions functionally connected to PAG, according to prior fMRI studies, include midbrain tegmentum, substantia nigra, raphe nucleus, striatum, globus pallidus, hypothalamus, thalamus, cerebellum, anterior cingulate cortex, rostral ventromedial medulla, insula, frontal cortices, post-central gyrus, superior temporal gyrus, and inferior parietal lobule [34,35]. Although inhibition of nociceptive transmission is the predominant role of the brainstem descending pain modulatory system, the PAG, NCF, and rostral ventral medulla also facilitate nociception [6,9,36,37]. A balance between pain facilitation and inhibition via the descending pain system is hypothesized to be necessary for appropriate pain processing [6]. Low levels of pain inhibition, high levels of pain facilitation, or an imbalance of these two opposing processes could participate in the development or maintenance of sensitization and could contribute to the production of chronic pain.

Prior investigations have implicated the brainstem descending pain modulatory system in sensitization and allodynia. Functional imaging studies identified an inverse relationship between activity in the ventrolateral PAG and pain intensity [7,38]. Electrical stimulation of the ventrolateral PAG results in analgesia and reductions in allodynia [39,40]. fMRI of humans using punctate mechanical stimulation in an area of secondary hyperalgesia (heat/capsaicin sensitization model) showed significant increases in brainstem activation localized to two midbrain regions consistent with PAG and NCF [9]. Migraine studies have implicated PAG and NCF in migraine-related sensitization. An fMRI study of 12 interictal migraineurs measured brainstem activity in response to painful cutaneous heat stimulation [5]. Compared with controls, migraineurs had less pain-induced activation of NCF, suggesting less pain inhibition in migraineurs. A study comparing interictal PAG rs-fc in five migraineurs with ictal allodynia to five migraineurs with no ictal allodynia found the migraineurs with allodynia to have weaker PAG rs-fc to prefrontal regions, anterior cingulate, and anterior insula, findings interpreted by the investigators as further evidence that pain modulatory systems might be involved in the development of allodynia [41].

PAG and NCF Functional Connectivity Differs in Migraineurs with Severe Allodynia

Migraineurs with severe ictal allodynia have stronger PAG and NCF functional connectivity with other brainstem regions including a region consistent with the location of the rostral ventral medulla. There are several possible explanations for these stronger PAG and NCF functional connections in migraineurs with severe allodynia: 1) frequent co-activations of descending pain modulatory system regions leads to stronger rs-fc among these regions; 2) stronger rs-fc develops as a compensatory response to the increased pain of allodynia, allowing for greater pain inhibition by the descending modulatory system; 3) stronger rs-fc among regions of the descending modulatory system results in increased facilitation of pain and thus predisposes the individual to the development of allodynia. Longitudinal studies of rs-fc and allodynia within individual migraineurs may help to differentiate among these proposed explanations.

Migraineurs with severe ictal allodynia also had stronger rs-fc to other regions that primarily participate in sensory-discriminative pain (i.e., intensity and location of pain), including thalamus, cerebellum, and posterior insula (PAG only). rs-fc connections to “higher order” pain processing regions showed a mixed picture with generally stronger connections in migraineurs with severe allodynia, although weaker connections were found for a few functional connections. These higher order pain-modulating regions predominantly participate in affective (i.e., emotional response to pain, fear, motivation to stop the painful stimulus) and cognitive (e.g., pain memory, attention, expectation) pain processing. Because these regions may be responsible for mediating activity of brainstem pain-modulating regions (“top-down modulation”), atypical rs-fc between higher order pain-modulating regions and brainstem regions identified in this study may be indicative of aberrant higher order control over the brainstem descending pain modulatory system [6,42,43].

There was very little overlap between rs-fc that differed between migraineurs with severe allodynia and those with no allodynia and rs-fc that differed between migraineurs and controls. Absence of significant overlap suggests that the rs-fc differences found between migraineurs with severe allodynia and those with no allodynia are specifically related to allodynia and not attributable to migraine. Correlations between ASC-12 scores and several PAG and NCF functional connection strengths and an absence of correlations between rs-fc strengths and years with migraine further support this assertion. Our findings, and the fact that the majority of migraineurs develop allodynia during migraine attacks, suggest that it is important for future migraine studies investigating the descending pain modulatory system to account for potential effects of allodynia on study findings, differentiating effects of allodynia from potential effects of migraine. Future studies will also investigate similarities and differences in PAG and NCF rs-fc in migraine-related allodynia to allodynia associated with other diseases (e.g., fibromyalgia).

Study Limitations

The ASC-12 requires subjects to recall ictal allodynia symptoms and estimate their frequency during their most severe headaches. This data collection method is vulnerable to recall bias. Although prospective reporting of allodynia symptoms would avoid recall bias, it would require data collection over a prolonged time period in order to assess usual migraine symptoms and avoid temporal sampling bias. The ASC-12 is a standard and validated tool assessing cutaneous allodynia in migraine [1,15]. A few subjects were taking medications used for migraine prophylaxis (8/38 migraine subjects including 3/8 subjects with severe allodynia and 2/8 subjects with no allodynia). Although we are not aware of any studies that have specifically investigated the effects of migraine prophylactic medications on rs-fc in migraineurs, there are studies showing that rs-fc can change within individuals who initiate medications [44,45]. Although it is often difficult to determine whether these changes in rs-fc are directly attributable to medication effects or to improvements in the clinical condition for which the medication was prescribed, it is likely that medications have direct influences on rs-fc [46]. It is unlikely that medication use is driving our results because a minority of subjects was taking prophylactic medications and their use was relatively balanced among the severe allodynia and no allodynia cohorts. Furthermore, examination of scatterplots graphing rs-fc strengths by subject revealed that subjects taking prophylactic medications did not consistently have the strongest or weakest rs-fc among all subjects (i.e., always stronger or always weaker rs-fc strength compared with the group mean). The relatively low spatial resolution of fMRI makes it difficult to definitively determine that our PAG ROI was entirely within the ventrolateral subdivision of the PAG, a subdivision of the PAG that has been implicated to participate in transmission of trigeminovascular nociception [47–49]. However, we maximized the likelihood that the PAG ROI was within the ventrolateral PAG by choosing coordinates based upon prior studies demonstrating functionally connectivity to other pain matrix regions, activation of the PAG in response to painful stimuli, visualizing the ROIs on our atlas, and limiting the ROI to a 3-mm diameter sphere. Furthermore, although we imaged 38 migraine subjects, only the eight with severe allodynia and the eight with no allodynia were included in the primary analysis. Inclusion of relatively small numbers of subjects in the primary analysis is a substantial limitation of this study. Results need to be validated in similar studies with larger sample sizes. Given small sample sizes, we were not able to determine potential rs-fc differences between men and women with migraine. One published migraine study has suggested that such differences might exist [50]. Correlations between ASC-12 scores and rs-fc strength were calculated including the subjects with no allodynia and severe allodynia. Thus, the correlations are considered exploratory and should be validated or refuted in future studies using new cohorts of migraineurs with varying levels of allodynia.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Study findings further implicate PAG and NCF, key regions of the descending pain modulatory system, as regions involved in migraine-related allodynia. Compared with migraineurs without allodynia, migraineurs with severe allodynia have atypical PAG and NCF rs-fc to other regions that modulate sensory-discriminative pain processing and with higher order pain-modulating regions. Additional studies are necessary to determine if atypical PAG and NCF rs-fc predispose the migraineur to developing allodynia or if atypical rs-fc results from allodynia. Because allodynia is common in migraine, results in increased pain, may predispose to the development of more frequent migraine attacks, and might reduce the efficacy of migraine medications, methods of treating and preventing allodynia are warranted. Treatments that “normalize” atypical PAG and NCF rs-fc might be successful in this regard.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

This work was supported by the National Institutes of Health (K23NS070891) and the National Headache Foundation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
pme12267-sup-0001-si.jpg72KFigure S1 PAG resting functional connectivity in migraineurs vs controls. The strength of 14 functional connections with PAG differed in migraine subjects (red circles on scatterplot) compared with controls (blue diamonds on scatterplot). Average functional connectivity strength is demonstrated with open red circles (migraineurs) and open blue diamonds (controls). The locations of voxels in each region included in the scatterplot and the location of the PAG seed are shown on the brain slices. Red coloration of voxels indicates that PAG functional connectivity is more positive in migraineurs compared with controls. Blue coloration of voxels indicates that PAG functional connectivity is more negative in migraineurs compared with controls. Axial slices are shown with the left hemisphere on the left side.
pme12267-sup-0002-si.pdf140KFigure S2 NCF resting functional connectivity in migraineurs vs controls. The strength of 12 functional connections with NCF differed in migraine subjects (red circles on scatterplot) compared with controls (blue diamonds on scatterplot). Average functional connectivity strength is demonstrated with open red circles (migraineurs) and open blue diamonds (controls). The locations of voxels in each region included in the scatterplot and the location of the NCF seed are shown on the brain slices. Red coloration of voxels indicates that NCF functional connectivity is more positive in migraineurs compared with controls. Blue coloration of voxels indicates that NCF functional connectivity is more negative in migraineurs compared with controls. Axial slices are shown with the left hemisphere on the left side.
pme12267-sup-0003-si.jpg7KFigure S3 Overlap between PAG rs-fc that differs in severely allodynic vs non-allodynic migraineurs and in migraineurs vs controls. Voxels colored in yellow are those voxels that have rs-fc strength to PAG that differs in migraineurs with severe allodynia vs migraineurs with no allodynia and in migraineurs vs controls. Green- and purple-colored voxels are those that have rs-fc strength to PAG that differs in migraineurs with severe allodynia vs migraineurs with no allodynia or in migraineurs vs controls, but not in both contrasts.

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