White matter microstructural changes vary along the pain cycle in episodic menstrual migraine without aura

doi:10.21203/rs.3.rs-3727774/v1

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White matter microstructural changes vary along the pain cycle in episodic menstrual migraine without aura

https://doi.org/10.21203/rs.3.rs-3727774/v1

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Background: Migraine is a neurological disorder exhibiting a cyclic pattern of clinical manifestations. Although both structural and functional brain changes have been reported in comparison with healthy controls, it is not clear to what extent these vary across the migraine cycle. Here, we present a longitudinal study of menstrual migraine using diffusion MRI (dMRI) to investigate changes in white matter (WM) microstructure through the different phases of the pain cycle.

Methods: Multi-shell dMRI data was acquired in 14 patients with low-frequency episodic menstrual migraine without aura, in the preictal, ictal, postictal and interictal phases, and 15 healthy controls in matching phases of the menstrual cycle. Following data pre-processing, maps of the diffusion parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) were obtained and compared across groups in each phase of the cycle. Subsequently, we conducted a region-of-interest analysis to determine whether changes in the diffusion parameters of specific WM tracts in patients, compared to controls, varied across different phases of the migraine cycle.

Results: Patients showed a pronounced reduction in AD relative to controls across various brain regions, with weaker reductions also found in FA and MD, and increases found in MK and AK, mostly in the interictal but also in the ictal phases. Interestingly, AD and FA changes relative to controls varied across the migraine cycle in specific WM tracts. Specifically, the AD reduction observed interictally returned to baseline levels (comparable to those of the control group) during peri-ictal phases in the uncinate fasciculus, anterior corona radiata, posterior thalamic radiations, superior longitudinal fasciculus and cingulum tracts. In contrast, the body of the corpus callosum and posterior limb of the internal capsule tracts exhibited normal FA in the interictal phase, but this parameter decreased in the ictal phase.

Conclusions: This is the first study documenting transient and fast variations in diffusion parameters of specific WM tracts across the migraine cycle. Microstructural changes in the brain of female migraine patients in different phases of the migraine cycle occur which cannot be explained by their hormonal status.

episodic migraine

menstrual migraine

microstructure

white matter tracts

diffusion MRI (dMRI)

diffusion-tensor imaging (DTI)

diffusional-kurtosis tensor (DKI).

Migraine is a neurological condition with cyclical clinical expression. Its attacks manifest as episodes of mild to moderate headache accompanied by several other neurological symptoms, that can start before (preictal), during (ictal) and persist after (postictal) the headache itself. The ictal phase of the migraine cycle is a disabling episode that hinders or impedes the completion of daily tasks. During the interictal phases, which follow the episodes, patients are mostly asymptomatic. In episodic migraine, patients spend more time in the interictal than in the ictal phase [1–3]. The global prevalence of this condition is estimated to be approximately 15%, with a greater incidence among females, who are three times more susceptible than males [4–6].

Research on functional and structural neuroimaging has contributed to a more comprehensive understanding of the complex modifications occurring in the brain of individuals suffering from migraine [7–12]. Diffusion Magnetic Resonance Imaging (dMRI) studies have provided valuable insights into the presence of widespread microstructural abnormalities in the migraine brain [7, 13]. Most studies[14] have employed diffusion tensor imaging (DTI) [7, 15] to obtain maps of parameters describing water diffusion that reflect the underlying microstructure [16]. In the context of brain tissue, the direction and distance travelled by water molecules in their random diffusion motion is influenced by the microstructural barriers present within each imaging voxel [17–19]. Fractional anisotropy (FA) is a parameter utilized to characterize the extent of anisotropy (i.e., whether diffusion motion is preferentially oriented along a main direction, or it is equally likely to occur along all directions); higher values of FA indicate a greater level of organization and coherence in the microstructure, such as in fiber bundles [20]. Mean diffusivity (MD) is used to quantify the average magnitude of water diffusion motion across all diffusion directions, with higher values suggesting increased tissue damage or disruption [20]. Two additional parameters related to MD can also be obtained to characterize microstructure, axial diffusivity (AD) and radial diffusivity (RD) [20]. AD measures the diffusion of water along the primary tensor axis, assumed to coincide with the direction of white matter (WM) fiber bundles, providing information about axonal integrity, while RD measures the average water diffusivity perpendicular to the primary axis, and can indicate myelin damage or loss.

dMRI studies of migraine have primarily focused on the transversal evaluation of patients, mostly in the interictal phase, in comparison with controls. The most consistent finding has been a decrease in AD in multiple WM tracts [7, 21]. However, changes in other diffusion parameters such as MD and FA have also been reported albeit without a consistent pattern across studies, either concerning the affected diffusion parameters or the brain regions involved [7]. Nevertheless, abnormalities have mostly been reported in the corpus callosum [21–26], thalamus [21, 22, 27, 28], thalamic radiations [21, 22, 24–26, 28–30], superior and inferior longitudinal fasciculus [21, 22, 24, 29], and cingulate gyrus [24, 26, 30, 31]. Only two studies[27, 32] have applied a longitudinal design to examine microstructural changes throughout different phases of the migraine cycle, yet focusing on specific gray matter (GM) structures (i.e., brainstem nuclei and thalamus).

Marciszewski and colleagues[32] found oscillations across three phases of the migraine cycle (i.e., interictal, preictal and postictal) in the diffusivity of GM nuclei located in the brainstem, namely the spinal trigeminal nucleus, dorsolateral and dorsomedial pons, periaqueductal GM, and cuneiform nucleus. They observed increased MD/AD during the interictal phase, which reverted to normal levels during the preictal phase and increased again after the attack. These results supported the hypothesis of astrocyte activation with changes of adjacent neural activity, which could potentially impact the susceptibility of brainstem nuclei to initiate or alter migraine episodes. On the other hand, FA values were increased across all migraine phases in the medial lemniscus/ventral trigeminal thalamic tract, which suggests a higher microstructural coherence on the ascending trigeminal pathway.

In the other study, Coppola et al. [27] assessed patients during two phases of the cycle, the interictal and ictal phases. They observed higher FA and slightly lower MD values in bilateral thalami compared to controls in the interictal phase, which returned to normal in the ictal phase. These findings suggest that plastic peri-ictal modifications may occur in branching fibers and fiber crossing of the thalamic structures; yet, the mechanism causing these changes remains unclear.

Both studies report fluctuations in local GM microstructure throughout the migraine cycle, probably reflecting different aspects of its pathophysiology. However, there has been no investigation into whether similar rapid fluctuations occur in WM pathways across the brain. Examining WM tract microstructure in migraine not only offers insights into axonal connectivity and plasticity but also lends support to the involvement of glial cells [33] in migraine pathophysiology. Of particular interest are oligodendrocytes [34], which modulate axon structural proprieties and induce changes in its physiological proprieties, and WM microglia, a significant player in neuroinflammation, which has been shown to increase during migraine attacks [35].

The aim of our study was to investigate variations in the microstructure of WM fiber bundles throughout the four stages of the migraine cycle relative to matched controls. For this purpose, we chose to study a group of patients with low-frequency episodic menstrual-related migraine without aura, in a longitudinal design, compared with a group of healthy controls matched for the menstrual phase, using advanced dMRI techniques.

Study population

A group of 14 female patients with low-frequency episodic menstrual-related migraine without aura (according to the International Classification of Headache Disorders-3

(“Headache classification committee of the international headachesociety (IHS) the international classification of headachedisorders, 3rd edition,” 2018)

) were recruited by a neurologist during the medical appointment at the Headache Outpatient Clinic at Hospital da Luz. Besides the diagnostic criteria, other inclusion criteria consisted in: a) 18–55 years of age; and b) at least 9 years of education and native Portuguese. Exclusion criteria were: a) presence of psychiatric disorder; b) record of neurologic disease other than migraine; c) ongoing treatment with psychoactive drugs, including antiepileptics, antidepressants, benzodiazepines, stimulants or migraine prophylactics; d) breastfeeding, pregnancy or after menopause; and e) contraindications for MRI. The following clinical variables were recorded for all patients: disease duration (in years), attack frequency (days per month), and pain intensity (visual analogue scale; VAS scale). For the control group, we recruited 15 age-matched healthy native Portuguese female volunteers through advertisement in the general population. They met the same criteria for inclusion and exclusion, with the only difference being that they did not experience regular headaches, whichever the diagnosis and, in particular, could not have any type of migraine.

The study was approved by the Ethics Committee of Hospital da Luz, and informed written consent was obtained from all participants according to the Declaration of Helsinki 7th revision.

Study design

Figure 1 illustrates the study design. All migraine patients (M) were evaluated longitudinally in four sessions corresponding to the four phases of the migraine cycle, namely: 1) interictal (M-inter); 2) preictal, up to 72 hours before the start of a spontaneous migraine attack (M-pre); 3) ictal (M-ict); and 4) postictal, up to 48 hours after the end of a spontaneous migraine attack (M-post) [37]. The experimental sessions order was counterbalanced to ensure that an equal number of the first sessions occurred during the peri-ictal (preictal, ictal, and postictal) and the interictal phases. The M-pre and M-post sessions were pre-scheduled in accordance with the patients’ menstrual cycle, while the M-ict sessions were planned during a patients’ self-reported migraine attack.

The healthy controls (HC) were evaluated in two sessions corresponding to the phases of the menstrual cycle matching the interictal and the peri-ictal phases of the patients, respectively [38, 39]: 1) post-ovulation phase - up to 5 days after ovulation (HC-postov); and 2) perimenstrual phase - from 2 days before to 3 after menstruation (HC-pm).

Data acquisition

In each of the study sessions, participants underwent scanning on a 3T Siemens Vida system, utilizing a 64-channel RF-receive head coil. The MRI protocol included various types of images; here, we report only the dMRI study, including the required anatomical T₁-weighted scan. We obtained multi-shell dMRI data using 2D-echo-planar imaging (EPI) and the following imaging parameters: 66 slices with an in-plane Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) factor of 2, Simultaneous Multi-slice (SMS) factor of 3, and 2mm isotropic resolution. The TR/TE parameters were 6800/89 ms. The diffusion-weighted images were acquired with b-values of 400, 1000, and 2000 s/mm² along 32, 32, and 60 gradient directions, respectively. Additionally, 8 non-diffusion-weighted (b0) images were also collected as well as 3 b0 images with opposite phase encoding (Posterior-Anterior). We also acquired a T₁-weighted structural image using a 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence with the following parameters: TR = 2300 ms, TE = 2.98 ms, TI = 900 ms, Field-of-view (FOV) = 256 × 240 × 128 mm³, with 1mm isotropic resolution.

Data analysis

All dMRI data were pre-processed following the DESIGNER [40] pipeline. Firstly, denoising [41], Gibbs ringing correction [42]; and Rician bias correction [43] were carried out using MRTrix tools [44]. Subsequently, geometric distortions due to B₀ inhomogeneities and eddy currents, as well as head motion, were corrected using FSL tools [45–47]. Finally, bias field correction was performed using -ants option with MRTrix.

After pre-processing, we used two different diffusion signal representations to estimate maps of multiple diffusion parameters: the commonly used DTI [15] and an extension of this representation also including the Diffusional Kurtosis Imaging (DKI) [48] tensor. For conventional DTI, we extracted the b1000 shell to estimate FA, MD, AD and RD with dtifit from FSL. For DKI, we used both b1000 and b2000 shells, and besides the DTI parameters, Mean kurtosis (MK), Axial kurtosis (AK) and Radial kurtosis (RK) were also estimated, using DESIGNER [49]. All maps were non-linearly aligned to the standard FA template (1mm isotropic resolution FMRIB58_FA) using FNIRT [50] from FSL. Each diffusion parameter map was then skeletonized through restriction to the main WM fibers and thresholded at 0.3, using the tract-based spatial statistics (TBSS) [51] tool in FSL.

We investigated changes across the migraine cycle by performing a region-of-interest (ROI) analysis across 28 ROIs defined by combining each pair of corresponding right and left WM tracts from the ICBM-DTI-81 White-Matter Atlas [19, 52]. First, we extracted the median of each diffusion parameter within each ROI, for each participant and session. Then, for each diffusion parameter in each ROI, we obtained the change for each patient and session relative to controls, by calculating the difference between each patient/session and the average across controls in the respective session, i.e.: 1) [M-inter] \(-\)[HC-postov]; 2) [M-pre] – [HC-pm]; 3) [M-ict] – [HC-pm]; and 4) [M-post] – [HC-pm]. Only the DKI parameters were retained for the ROI analysis because they demonstrated greater sensitivity than DTI to identify changes between groups (see Results section).

Statistical analysis

Comparison between patients and controls

For the comparison between patients and controls across each of the matching sessions, we performed pairwise voxelwise permutation tests on the skeletonized diffusion parameter maps, using the FSL TBSS tool [51]: 1) [M-inter] vs. [HC-postov]; 2) [M-pre] vs. [HC-pm]; 3) [M-ict] vs. [HC-pm]; and 4) [M-post] vs. [HC-pm]. We utilized 5000 permutations along with threshold-free cluster enhancement correction to account for multiple comparisons [53] and the significance level was set at P < 0.05.

Variations across the migraine cycle

For the ROI analysis across the four phases of the migraine cycle in the patient group, we employed linear mixed effects modelling using the R software (www.r-project.org/). We used the cycle as a fixed effect with four levels (M-inter, M-pre, M-ictal, and M-post) and the patients as a random effect. Post-hoc analysis was performed when significant main effects were found, using False Discovery Rate (FDR) correction for multiple comparisons across ROIs (P < 0.05).

Correlation with clinical variables

We assessed whether diffusion parameters change in patients (in the interictal phase) compared with controls (in the matching phase) were correlated with the clinical variables’ disease duration, attack frequency and pain intensity, by computing the Spearman correlation using the JASP software (version 0.17.3.0).

Study population and MRI data

Both age and literacy levels were equivalent between the 14 female patients (age: 36 ± 9 years, literacy: 17 ± 2 years, p = 0.11) and the 15 female controls (age: 30 ± 7 years, literacy: 17 ± 1 years, p = 0.62). None of the participants had any daily medication other then contraception. Six migraine patients and 5 controls were taking combined oral contraceptives, three migraine patients had the copper intrauterine devices while all the remaining participants used barrier methods. Migraine’s average disease duration was 19 ± 10 years, having an average frequency of two attacks per month, with an average intensity of 7 ± 1 on the visual analogue scale (0–10 VAS).

A total of 30 datasets were collected from 15 HC, while 41 datasets were collected from 14 patients. All the recruited controls successfully completed both sessions, whereas only 5 patients were able to complete all four MRI sessions. The total number of concluded sessions among the patient cohort was as follows: 9 preictal, 9 ictal, 10 postictal, and 14 interictal. One ictal session was discarded because the number of volumes was incomplete.

Comparison between patients and controls

The results of the voxelwise pairwise comparisons between patients and controls in each of the phases of the migraine cycle are summarized in Table 1, for both DTI and DKI methods. Figures 2 and 3 show the spatial distribution of significant differences on each of the skeletonized diffusion parameter maps, for both DTI and DKI methods. Only two comparisons showed significant effects in some parameters: M-inter vs HC-postov and M-ict vs HC-pm. DKI was more sensitive than DTI, yielding more significant differences: decreased FA, MD and AD and increased MK and AK, for M-inter vs HC-postov, and decreased FA and AD and increased AK, for M-ictal vs HC-pm. Using DTI, only the decreased AD for M-inter vs HC-postov was detected. For each of the comparisons yielding significant differences. Figure 4 presents the percentage of voxels displaying significant differences within each WM tract of the ICBM-DTI-81 White-Matter atlas, using DTI or DKI representations. Decreased AD estimated in M-inter was the most robust finding, obtained both using DTI and DKI. Because of the clearly greater sensitivity of DKI compared with DTI to detected changes in patients relative to controls, only the DKI representation was used for the subsequent ROI analysis across the migraine cycle.

Table 1

Summary of the percentage of significant voxels identified in comparisons between groups (M and HC) and sessions (inter, postov, pre, pm, ict, post) for each diffusion parameter (FA, MD, AD, RD, MK, AK and RK) and both diffusion models (DTI and DKI).
	M-inter vs HC-postov		M-pre vs HC-pm		M-ict vs HC-pm		M-post vs HC-pm
Comparisons	DTI	DKI	DTI	DTI	DKI	DKI	DTI	DTI
FA	-	-	-	-	-	↓ (4%)	-	-
MD	-	-	-	-	-	-	-	-
AD	↓ (20%)	↓ (13%)	-	-	-	↓ (0.1%)	-	-
RD	↓ (31%)	↓ (44%)	-	-	-	-	-	-
MK	-	-	-	-	-	-	-	-
AK	-	-	-	-	-	↑ (3%)	-	-
RK	-	-	-	-	-	-	-	-
^a Groups and sessions: M-inter (Migraine interictal), M-pre (Migraine preictal), M-ictal (Migraine ictal), M-post (Migraine postictal), HC-postov (Healthy control - postovulation), HC-pm (Healthy control - perimenstrual). ^b Diffusion models: DTI (Diffusion Tensor Imaging), DKI (Diffusional Kurtosis Imaging). ^c Metrics: FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), RD (Radial Diffusivity), MK (Mean Kurtosis), AK (Axial Kurtosis), RK (Radial Kurtosis). ^d Symbols: ↓ (Decrease), ↑ (Increase), - (No Significant Change)

Variations across the migraine cycle

The ROI analysis of the diffusion parameter, where changes in patients were identified relative to controls (estimated using DKI), revealed significant main effects of the migraine cycle phase in 5 ROIs for AD and 3 ROIs for FA, as presented in Fig. 5. In the uncinate fasciculus (UNC), both FA and AD showed decreased values during the headache-free period (M-inter), which tended to return to normal values during the peri-ictal phases when they became comparable to those observed for controls. A similar pattern of variation along the cycle was also found for AD in the anterior corona radiata (Ant_Cor), posterior thalamic radiations (Post_thal), superior longitudinal fasciculus (SLF), and cingulate. For FA, both the posterior limb of the internal capsule (PLIC) and the body of the corpus callosum (CC_body) showed normal values in the interictal phase, but these were significantly reduced specifically in the ictal phase.

Correlation with clinical variables

The clinical parameters were correlated with the interictal values of FA and AD that were extracted from each WM fiber tract yielding significant effects. No significant correlations were observed among the variables, except for a negative correlation between the frequency of attacks and AD in Ant_Cor (Figure S1 in Supplementary Material). However, this correlation did not survive correction for multiple comparisons.

We report for the first-time variations in WM microstructure across the different phases of the migraine cycle, in a group of patients with low-frequency episodic menstrual-related migraine without aura relative to healthy controls in matched phases of their menstrual cycle. We identified microstructural changes across the brain of patients when compared with controls, both in the interictal and ictal phases, with reductions in AD, MD and FA consistent with previous literature [7, 54]. We then found that such changes varied along the different phases of the migraine cycle; with significant fluctuations in AD and FA in specific WM fibers.

Microstructural changes in patients vs. controls

Our voxelwise whole-brain analysis showed significant microstructural alterations in patients when compared to controls in the respective menstrual phases. Specifically, in both the ictal and interictal phases we found a reduction in FA, MD, and AD without a reduction of RD. This is consistent with a previous report of migraine without aura patients studied interictally using DTI and TBSS analysis in a similar approach to ours [21]. When using DKI, we also observed that the diffusion parameters MK and AK were increased when compared to controls in the interictal phase, with no differences in RK. These findings are also consistent with a prior report that used DKI and TBSS analysis to study multiple subgroups of migraine patients interictally [55].

Overall, changes in diffusion parameters FA, MD and AD suggest alterations in axonal integrity (such as short-term membrane permeability changes) and could be indicative of shifting axonal geometries [56–59]. Regarding the diffusion parameters MK and AK, these may reflect cellular changes, such as inflammation or axonal beading [60, 61], affecting the complexity of tissue microstructure. The lack of significant differences in the radial parameters, RD and RK, suggests that the observed changes may not be primarily linked to permanent myelin degeneration nor to more plastic changes, such as alterations in axonal diameter, density, or fiber organization [24, 57–59, 62].

The microstructural changes described in migraine patients interictally, compared to controls, were observed in various brain regions. In our study, for the interictal phase, we found changes in over 50% of the voxels in projection fibers (such as the posterior thalamic tract, the anterior corona radiata, the internal capsule and the cerebral peduncle), some association fibers (superior longitudinal, fronto-occipital and uncinate fasciculus) and the corpus callosum. In the ictal phase, other commissural tracts as the genu of the corpus callosum (CC_genu) and tapetum also showed alterations in over 50% of the voxels. Interestingly, the corpus callosum consistently displayed impairment during both the interictal and ictal phases. This finding may be associated with its role in pain control regulation, and transmission and integration of information, being in line with previous research showing abnormalities in the corpus callosum of migraine patients [23, 63, 64]. We also detected alterations in MK and AK within the brainstem, a structure relevant in the pathophysiology of migraine [32, 65]. These alterations in axonal integrity could lead to compromised signal transmission along these specific tracts, resulting in altered communication between different regions of the brain, potentially contributing to the initiation and propagation of migraine attacks. However, it is worth noting that these findings have not been consistently observed in previous studies, and the extent of changes varied across these different tracts [7].

Microstructural variations across the migraine cycle

The key observation of our study is the occurrence of transient and rapid microstructural variations in specific WM fiber tracts in patients throughout the migraine cycle, which are not explained by natural hormonal variations potentially taking place throughout the menstrual cycle, in controls. In particular, the uncinate fasciculus showed AD and FA reductions during the interictal phase that normalized in the peri-ictal phases, becoming comparable to those observed for controls in the respective menstrual phase. A similar pattern of variation of AD along the cycle was also found in thalamic projection tracts (the posterior thalamic association tract and thalamic-cortical projections on the anterior corona radiata) and association fibers (such as the superior longitudinal fasciculus and the cingulate). Besides their pathophysiological significance, these results also highlight that the time of imaging is a crucial factor for the interpretation of diffusion parameters in migraine. Indeed, the arbitrary inclusion of patients in different phases of the migraine cycle in previous transversal studies may explain the inconsistent findings previously reported.

The variation in diffusion characteristics over the migraine cycle indicates that time-limited changes in cellular activity patterns may occur, impacting water mobility and the microenvironment around WM tracts. Individuals with migraine exhibit altered microstructure between migraine episodes, which might be due to pre-existing or induced adaptive neuronal plastic modifications. These changes may represent a baseline condition of predisposition to migraine episodes, in which the brain may be easily triggered into hyperexcitability [66–68]. As a result of the initial changes associated with the beginning of a migraine episode [68], there may be a brief normalization or repair of the microstructure. A similar cyclical pattern has also been observed in the two previous longitudinal studies using functional MRI (fMRI) in migraine. These included GM nuclei of the brainstem [32], where regional microstructural changes have been linked to the activation of the ascending trigeminal nociceptive pathways [69], as well as thalamic structures. Additionally, previous research has also shown similar patterns of change over the cycle, with normalization in the peri-ictal phases, in different aspects of brain activity and behavior. For example, auditory evoked potentials that are altered interictally, reflecting a lack of habituation in patients, are found to normalize before the attack [70–72]. Similar cycling behavior have also been observed in the brain activity of the spinal trigeminal nuclei in response to nociceptive stimulation [73].

The involvement of these fiber bundles across different phases of the migraine cycle suggests that these pathways could be associated with migraine-related cognitive and behavioral symptoms. The migraine attacks themselves have sometimes been linked to specific trigger-related avoidance behaviors. In particular, the UNC is a monosynaptic association fiber connecting the orbitofrontal cortex to the anterior temporal lobes, involved in cognitive functions, including language, memory, and social-emotional processing [74]. In this context, it appears pertinent to examine the intersection of episodic memory and social emotional processes within the framework of emotionally or behaviorally driven decision making [74, 75]. The SLF and cingulate gyrus also exhibit a similar pattern of AD fluctuations throughout the migraine cycle, both sharing connections with the anterior cingulate cortex (ACC). The ACC, in turn, is part of the salience network receiving orbitofrontal input for action–outcome learning and its associated emotional responses. It is thought to play a role in processing the importance or relevance of stimuli when making decisions, especially in the context of pain [76]. Furthermore, differences in functional connectivity within the ACC between episodic and chronic migraine patients were observed using magnetoencephalography and resting-state functional MRI, imply the ACC's involvement in migraine chronification [77]. Two longitudinal functional MRI studies have brought attention to the presence of cyclical functional connectivity changes in the limbic and salience networks [78, 79]. Interestingly, the authors observed that the connectivity pattern increased over time during the interictal period, and then normalized to baseline values with the attack onset. This finding suggests the presence of a mechanism akin to a “brain reset” [79]. Consistent with these studies, our findings suggest that WM fiber pathways implicated in the circuitry of some networks also display a similar cyclic pattern of alteration, perhaps contributing to the impaired functionality of these networks.

Overall, our findings support the hypothesis that a predisposition to migraine episodes is associated with changes in pain processing regions of the ACC and trigeminal pathways [80]. Understanding the neurological underpinnings of these symptoms may facilitate the implementation of therapies that are more specifically targeted to migraine pathophysiology. For example, therapies focused on emotion regulation, sensory modulation, and cognitive training may be particularly beneficial for managing attacks and associated symptoms.

Methodological considerations

Several methodological considerations were made to enhance the reliability of our study. By focusing exclusively on otherwise healthy young women with low-frequency episodic menstrual-related migraine, our study offers a distinct advantage over previous studies that included various migraine subtypes and did not control for relevant facts that influence white matter microstructure, such as biological sex and hormonal state, age, comorbidities, education level nor current preventive medication or acute medication overuse [7, 25, 26, 28, 81–87]. The controls in our study were matched not only for sex, age and literacy but also for menstrual cycle phase, which allowed us to minimized potential confounding factors, in particular hormonal related changes in brain structure and function [39].

The choice of studying low-frequency episodic patients with menstrual-related migraine allowed not only a good distinction of the ictal and interictal periods, but also a reasonable prediction of attacks, which was critical for capturing spontaneously occurring attacks, one of the challenges of studying the migraine cycle [88]. Cross-sectional studies frequently neglect the longitudinal aspect of migraine, despite their valuable insights. Our study design involved scanning migraine patients throughout the four phases of the pain-cycle, as we believe this approach would offer a more comprehensive understanding of microstructural changes occurring over time, in migraine brains.

Regarding the choice of imaging technique, although most dMRI studies to date still employ the more conventional DTI methods, several studies have now shown that DKI [48] is a sensitive dMRI signal representation for detecting microstructural changes in episodic migraine [55, 89, 90]. To enhance reliability, we utilized DKI and compared it with DTI, employing suitable statistical analysis methods (specifically, TBSS) for robust voxelwise analysis of the whole-brain maps acquired for each diffusion parameter. While the reduction in AD was consistently detected by both DTI and DKI, the other differences were more effectively captured by DKI. Our results thus provide additional evidence for the increased sensitivity of DKI relative to DTI in identifying migraine-related microstructural alterations and highlight its potential to provide valuable imaging biomarkers for migraine. Indeed, this technique proves particularly useful in more complex brain tissue environments (e.g., crossing fibers) that are not adequately represented by DTI [56].

Limitations

It is important to acknowledge that our study has various limitations that require further attention in future research. Our findings should be confirmed by future studies due to the small sample size, which is difficult to overcome given the stringent demands of the longitudinal study design capturing spontaneously occurring attacks. Nevertheless, our results are in line with previous research results to the extent that they are comparable, namely in terms of the transversal changes between patients and controls. We believe that our experimental setup and methodology can inspire future studies with larger sample sizes.

Additionally, the fact that not all patients completed the four sessions of the study limited the power of the longitudinal study design to assess fluctuations in diffusion parameters across the pain-cycle. It is important to mention that data collection took place during the COVID pandemic, which also affected collaboration and access to the scanner that is not exclusively used for research. Nevertheless, the fact that some patients managed to finish the study shows that conducting this type of long-term research in a clinical environment is doable. This can serve as a useful starting point for future studies that might involve larger sample sizes.

Another limitation was that hormonal fluctuations were not quantified, which can be a confounding factor in the case of menstrual-related migraine. We suggest that future studies could incorporate hormonal tracking of both patients and controls to clarify the potential interplay between hormones and migraine regarding the associated brain changes. We aimed to minimize this bias by monitoring the menstrual cycle of control subjects, therefore improving the hormonal matching between patients and controls in each phase of the migraine cycle. However, relatively wide ranges of days of the menstrual cycle were found to correspond to the different phases of the migraine cycle, and finer hormonal effects would therefore be cancelled out or at least considerably smeared out.

We have shown for the first-time longitudinal variations in the diffusion parameters of WM fibers along the migraine cycle. Although further investigation is needed to better understand the biological mechanisms underlying these cyclic alterations, our findings have the potential to impact the development of personalized migraine treatments that might target specific phases of the cycle.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Acknowledgements

The authors express gratitude to Patrícia Lemos of "Círculo Perfeito" for her contribution in promoting the study to the general population. Furthermore, we would like to express our gratitude to João Pereira for his valuable time and expert guidance for the statistical analysis. We are grateful to all the participants of this study and collaborators from Hospital da Luz Lisboa.

Authors’ contributions

ARF, NAS, PV, RGN, RGG, and PF study conception and design. ARF, ART, IE and GC performed the experiment and data collection. ARF analyzed the data. IG contributed to the interpretation of the data. ARF, PF, RGG, and RGN were the major contributors in writing the manuscript and critical revision. All authors read and approved the final manuscript.

Funding

This study was funded by the Portuguese Science Foundation (FCT) through grants SFRH/BD/139561/2018, COVID/BD/153268/2023, PTDC/EMD-EMD/29675/2017 and UIDB/50009/2020 and Programa Operacional Regional de Lisboa 2020 LISBOA-01-0145-FEDER-029675. IG was supported by the Margarita Salas grants from Universidad Autónoma de Madrid, Spain, ref CA1/RSUE/2021-0080.

Competing interests

The authors report no competing interests.

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White matter microstructural changes vary along the pain cycle in episodic menstrual migraine without aura

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Abstract

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Introduction

Materials and methods

Study population

Study design

Data acquisition

Data analysis

Statistical analysis

Comparison between patients and controls

Variations across the migraine cycle

Correlation with clinical variables

Results

Study population and MRI data

Comparison between patients and controls

Variations across the migraine cycle

Correlation with clinical variables

Discussion

Microstructural changes in patients vs. controls

Microstructural variations across the migraine cycle

Methodological considerations

Limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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