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. 2025 May 6;52(11):4334–4344. doi: 10.1007/s00259-025-07282-3

Evidence for brain glial activity in chronic migraine patients: a [11C] PBR28 PET/MR study

Yan Chang 1,2,#, Xiwan Zhang 3,#, Shaobo Xiao 4, Jiajin Liu 5, Yuan Wang 5, Jingbin Song 5, Huaping Fu 5, Yungang Li 5, Hui Su 2, Huijie Yi 1,2, Wenjie Su 1,2, Nan Gao 1,2, JinJing Zhao 1,6, Ruimin Wang 1,5,✉, Ruozhuo Liu 1,2,✉
PMCID: PMC12397201  PMID: 40327090

Abstract

Purpose

Although neuroinflammation may play a key role in the pathology of migraine and its progression to chronic migraine (CM), its specific involvement-particularly the role of microglia- remains unclear. We investigated whether neuroinflammation is involved in the pathophysiology of CM and whether pro-inflammatory signals are associated with its clinical features.

Methods

Nineteen individuals with CM and 10 healthy controls (HCs) underwent integrated brain positron emission tomography (PET)/magnetic resonance (MR) using the translocator protein (TSPO) radioligand ([11C] PBR28, a marker of glial activation, together with the quantification of blood plasma inflammatory cytokine/chemokine. Volumes in regions of interest (ROI) were calculated based on MRI data and the standardized uptake value ratio (SUVR) for [11C] PBR28 was extracted for each ROI. The Spearman’s rank correlation coefficient between [11C] PBR28 SUVR and changes in plasma factors was calculated.

Results

CM patients had a significantly higher Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA) scores than that in HCs (p < 0.05). Participants with CM also exhibited reduced volume in the thalamus (p = 0.012), compared with HCs. Moreover [11C] PBR28 binding was increased in the midbrain, occipital lobe and vermis, along with increased interictal plasma interleukin-8 (IL-8) and CX3CL1 levels, in individuals with CM compared with HCs. Notably, the midbrain levels of TSPO were negatively correlated with the headache frequency (r=-0.462, p = 0.046).

Conclusions

These findings demonstrate increased central inflammation in CM participants compared to HCs, providing imaging evidence for the potential involvement of neuroinflammation in CM pathophysiology. Additionally, the observed reduction in thalamic volume may contribute to the chronification of migraine.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-025-07282-3.

Keywords: Chronic migraine, [11C] PBR28, TSPO PET, Inflammation, Chemokine, Neuroimaging

Background

Chronic migraine (CM) is a rare but debilitating neurological disorder that affects more than 1% of the global population, exerting a significant burden on both individuals and society [1, 2]. Given its clinical and economic impacts, there is an urgent need to identify novel pathogenic mechanisms and therapeutic targets. Despite its profound effects, the underlying pathophysiological mechanisms of CM remain poorly understood. Frequent activation of the trigeminal system leads to central sensitization, which is thought to be the underlying mechanism of CM [3]. However, emerging evidence from animal models, humans, and genetic studies suggests that neuroinflammation and microglial activation also play important roles in the pathophysiology of CM [4, 5]. Recent animal studies have increasingly recognized the importance of microglia and their interactions with neurons in CM.

Activated microglia can produce inflammatory cytokines, such as tumor necrosis factor alpha (TNF-α), interleukin 1β (IL-1β) and IL-6, which are thought to play important roles in the pathogenesis of CM [6]. Additionally, the chemokine CX3CL1 (also known as fractalkine) and its receptor CX3CR1 have been implicated in neuroinflammatory processes and pain modulation [7]. Primarily expressed by neurons, CX3CL1 modulates neuron-microglia interactions, thereby influencing neuroinflammatory responses [7]. Disruptions in the CX3CL1/CX3CR1 axis have been associated with altered pain processing and may contribute to the development and maintenance of chronic pain conditions, including CM. Elevated levels of CX3CL1 have been reported in the plasma and cerebrospinal fluid (CSF) of migraine patients, suggesting its potential involvement in disease pathogenesis [8]. Furthermore, genetic studies investigating polymorphisms in the CX3CR1 gene have indicated a possible link to migraine development [9].

Neuroimaging analysis using structural data has begun to provide insights into the pathophysiology of CM [11C] PBR28 is a positron emission tomography (PET) radioligand that binds to an 18 kDa translocator protein (TSPO) [10], which enables in vivo localization and relative quantification of neuroinflammatory activity [11, 12]. In the healthy central nervous system (CNS), TSPO is expressed at low levels by multiple cell types, including glial cells and neurons [13]. Although the precise quantitative interpretation of the TSPO PET signal remains under debate, it is generally considered a marker of microglial activation based on both rodent and human TSPO PET studies. Recent work suggests that TSPO PET signals in humans reflect the density of inflammatory cells rather than activation state [14]. In this paper, we will refer to elevated TSPO PET signals as reflecting microglial responses. Previous studies have suggested that microglial activation plays an important role in the development of pain disorders [15]. Positron emission tomography/ magnetic resonance imaging (PET/MRI) studies with [11C] PBR28 have demonstrated evidence of glial activation in the nociceptive processing areas and visual cortex in migraineurs with aura compared with healthy controls (HCs) [16]. Although these findings suggest that inflammation may be involved early in migraine, to our knowledge no study has systematically assessed neuroinflammation in CM using TSPO PET, nor explored its association with clinical progression (e.g., headache frequency) and peripheral biomarkers (e.g., CX3CL1). This knowledge gap hinders the development of targeted anti-inflammatory therapies for CM.

To address this gap, we hypothesize that CM patients exhibit distinct patterns of TSPO expression in pain-processing brain regions, which correlate with both clinical severity and peripheral inflammatory markers. Using integrated [11C] PBR28 PET/MR imaging, we aim to (1) quantify neuroinflammatory activity in CM-specific brain regions, (2) assess associations between central TSPO signals and peripheral cytokine/chemokine profiles, and (3) explore the clinical relevance of neuroinflammation in CM chronicity.

Methods

Ethics

The study was approved by the Human Ethics Committee of Chinese PLA General Hospital and conducted in accordance with Declaration of Helsinki. All participants provided written informed consent.

Participants

Nineteen participants and 10 HCs [only genetically high binders (Ala/Ala, HAB) were included, low-affinity binders (Thr/Thr, LAB) and mixed-affinity binders (Ala/Thr, MAB) were excluded] were recruited from the Department of Neurology at the Chinese PLA General Hospital between April 2023 and January 2024. CM was diagnosed by three experienced headache specialists (LRZ, ZJJ & SH) strictly based on the criteria from the International Classification of Headache Disorders, 3rd edition (ICHD-3) [17]. Inclusion criteria were (1) age 18–65 years; (2) a confirmed diagnosis of CM; (3) a history of migraine greater than 1 year; and (4) the ability to understand the purpose of the research and cooperate with the study procedures and provide written informed consent. Participants were excluded if they had a major psychiatric disorder, chronic inflammatory disease and other autoimmune conditions, known cancer, MRI/PET safety contraindications (e.g., pregnancy, claustrophobia, or ferromagnetic implants), or were noncompliant during the PET/MR examinations. Participants were required to avoid exposure to nonsteroidal anti-inflammatory drugs (NSAIDs), calcitonin gene-related peptide (CGRP) monoclonal antibodies, CGRP receptor antibodies, and triptans for 2 weeks prior to scanning. Additionally, preventive medications for migraine, including beta-blockers, anticonvulsants, antidepressants, and Flunarizine Hydrochloride, were discontinued 4 weeks before the PET/MR imaging session. Scans were performed during the interictal phase and were required to have had at least one migraine attack with aura at last 15 days prior to the imaging session.

Ten HCs were recruited from the general population. They were evaluated by a neurologist to exclude any neurological or psychiatric disorder, known cancer, or personal or family history of moderate or severe headache, and their brain MRI studies did not show any structural abnormalities.

Clinical assessment

All clinical assessments were performed at the Chinese PLA General Hospital over two consecutive days. During the first visit, participants underwent a comprehensive evaluation that included demographic and clinical data (age, sex, disease duration), as well as assessments of migraine attack frequency (attacks per month), attack duration (hours), and peak headache pain intensity [measured using a visual analogue scale (VAS)]. Additionally, participants completed the 6-item Headache Impact Test (HIT-6), Migraine Disability Assessment (MIDAS), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Hamilton Depression Rating Scale (HAMD), and Hamilton Anxiety Rating Scale (HAMA).

Blood plasma chemokine and cytokine measurement

On Day 2, eligible participants underwent imaging tests, and venous blood samples were collected on the same day as the scan. All samples from CM participants and HCs were stored at -80 °C. The concentrations of chemokine CX3CL1 and various cytokines, including baisc FGF, CTACK, eotaxin, G-CSF, GM-CSF, GRO-α, HGF, IFN-γ, IFN-α2, IL-1ra, IL-1α, IL-1β, IL-2, IL-2Rα, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-16, IL-17A, IL-18, IP-10, LIF, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, MIP-1α, MIP-1β, β-NGF, PDGF-BB, RANTES, SCF, SCGF-β, SCF-1α, SDF-1α, TNF-α, TNF-β were quantified by Wayen Biotechnology (Shanghai, China) using the human 48-Plex Luminex assay according to the manufacturer’s instructions.

PET/MR data acquisition

[11C] PBR28, synthesized in the Department of Nuclear Medicine, Chinese PLA General Hospital, was used for the TSPO-PET scans. The specific activity was 136 ± 32 GBq/µmol (3.69 ± 0.87 Ci/µmol) and the tracer activity concentration was 15 mCi/ml. The radiochemical purity was > 95%. All imaging data were obtained via an integrated time-of-flight 3T PET/MR scanner (Signa, GE Healthcare, Milwaukee, WI, USA). PET/MR acquisition was performed from 60 to 90 min in list mode after [11C] PBR28 was intravenously injected at a dose of 3.7–4.44 MBq/kg of body weight. Simultaneously, with the PET scan, a sagittal three-dimensional (3D) T1 bravo sequence was included [echo time (TE) = 3.2 ms, repetition time (TR) = 8.5 ms, matrix = 256 × 256 mm2 [], phase field of view (FOV) = 1, voxel size = 1.0 × 1.0 × 1.0 mm3 [], slice thickness = 1.0 mm]for the purpose of anatomical localization, exclusion of incidental pathology, and generation of attenuation correction maps.

Morphological features

Morphological characteristics mainly refer to the volume of regions of interest (ROI). T1 data were processed using the Computational Anatomy Toolbox (CAT12) (http://www.neuro.uni-jena.de/cat/), incorporated within SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12) on the MATLAB R2020a platform (MathWorks, Natick, MA). Detailed procedures for the calculations using CAT12 are available in the CAT12 Manual (http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf). Briefly, after converting the 3D T1 images from DICOM to NIFTI format via SPM12, the default processing pipelines in CAT12 were applied [18]. These steps included spatial normalization, brain extraction, segmentation of gray matter (GM), white matter (WM), and CSF, and registration of the brain images from native space to the Montreal Neurological Institute (MNI-152) standard space. The GM image was smoothed by SPM 12 with 8 mm full width at half maximum (FWHM) isotropic Gaussian check. The volume of the ROIs are extracted using the smooth GM image. The ROIs are defined by Neuromorphometrics and Anatomical Automatic Labeling (AAL) atlas (Amygdala, Cerebellum, Cerebellum_crus, Frontal lobe, Hippocampus, Insula, Midbrain, Occipital lobe, Parahippocampus, Parietal lobe, Pons, Postceretral, Preceretral, Temporal lobe, Thalamus, Vermis). Additionally, the thalamus was subdivided into six regions based on the AAL3 atlas: anterior thalamus, lateral thalamus, ventral thalamus, intralaminar thalamus, medial thalamus, and posterior thalamus.

PET image processing

All [11C] PBR28 images were spatially normalized into MNI space using SPM12 on MATLAB R2020a. This process began with the co-registration of PET and MRI scans, followed by normalization of the MRI scans to an template, and the subsequent application of the derived deformation parameters to the PET scans [19]. The standardized [11C] PBR28 images were then spatially smoothed via an isotropic Gaussian kernel of 8 mm FWHM to improve the signal-to-noise ratio and generate images consistent with the Gaussian random field (GRF). Voxel-wise standardized uptake value ratio (SUVR) images were generated using the whole brain as the reference region [15, 20, 21]. SUVRs for the ROIs described above were subsequently extracted from the smoothed [11C] PBR28 images for comparison between HCs and CM patients.

Statistical analysis

Statistical analyses of demographic and clinical characteristics were performed using SPSS 20.0 software (IBM Corp, Armonk, NY, USA). The normality of data distribution was evaluated with the Shapiro–Wilk test. Comparisons between HCs and CM groups were made using independent-sample t-tests for normally distributed continuous variables, the Wilcoxon signed rank test for non-normally distributed data, and the chi-square test for categorical variables (e.g., sex). Multiple comparisons were corrected using the Benjamini-Hochberg procedure for controlling the false discovery rate (FDR). Spearman’s rank correlation coefficient was used for all correlation analyses. A p-value < 0.05 was considered statistically significant.

Results

Participants

Among all 40 participants, six were excluded from refuse to stop taking anti-inflammatory drugs, four were excluded because they experienced intolerable migraine attacks during the scan session and required painkiller treatment, and one was excluded due to poor image quality. Consequently, 10 HCs and 19 CM patients were included in the final data analysis. None of the HCs reported any headaches before or after the TSPO PET scan. The demographic and clinical information of the study participants are displayed in Table 1. There were no significant differences in age (p = 0.096) or sex (p = 0.499) between the HCs and CM groups. CM had a headache history of 18.21 ± 11.55 years, with the headache frequency 24.11 ± 6.57 of days/month, and headache duration of 0.95 ± 1.04 days. As expected, the HAMD and HAMA scores in the CM group were significantly higher than those in the HCs group (P< 0.05).

Table 1.

Demographic and clinical characteristics of the chronic migraine and healthy controls

Feature Chronic Migraine Healthy Controls P value
N 19 10 -
Age, (yr) 45.53Inline graphic11.00 38.10Inline graphic11.06 0.096
Gender, (male/female) 11/8 6/4 0.499
Disease history 18.21Inline graphic11.55 NA
Headache frequency (days/mo) 24.11Inline graphic6.57 NA
Headache duration (days) 0.95Inline graphic1.04 NA
Headache intensity (VAS) 6.48Inline graphic2.28 NA
Photophobia 10 NA
Phonophobia 6 NA
Vomiting 14 NA
Nausea 16 NA
MIDAS 49.26Inline graphic88.85 NA
HIT-6 63.71Inline graphic9.29 NA
GAD-7 5.14Inline graphic5.09 NA
PHQ-9 8.14Inline graphic6.97 NA
HAMD 7.79Inline graphic5.92 0.50Inline graphic0.70 < 0.001
HAMA 5.00Inline graphic5.85 0.10Inline graphic0.31 0.002
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Notes: Data is presented as means (SD). Bold represents a p value less than 0.05

Abbreviations: MIDAS, Migraine Disability Assessment; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; HIT-6, The 6-item Headache Impact Test; GAD-7, Generalized Anxiety Disorder-7; HCs, healthy controls; NA, not applicable; PHQ-9, Patient Health Questionnaire-9; VAS, visual analogue scale; N,number; y,year; mo, month

Altered volume in participants with CM (vs. HCs)

Table 2 presents the findings on brain volume between the CM participants and HCs. In the CM group, volume was decreased only in the thalamus (P = 0.012, Table 2). However, upon applying FDR correction, all P-values became no significance (Supplemental, Table S1). Further analysis revealed that five subregions of thalamus (anterior, lateral, ventral, medial and posterior) exhibited volume reductions in the CM group (all P < 0.05, Supplemental, Table S2). No significant differences in volume were observed in other brain regions outside the thalamus between the CM and HCs groups.

Table 2.

Statistical significance of differences in brain area volume between groups

ROI Chronic Migraine Healthy Controls Z P value
Amygdala (cm3) 1.99Inline graphic0.21 2.07Inline graphic0.19 -1.285 0.199
Cerebellum (cm3) 43.57Inline graphic3.38 43.43Inline graphic2.60 -0.138 0.891
Cerebelum_crus (cm3) 29.33Inline graphic2.23 29.53Inline graphic2.53 -0.413 0.680
Frontal lobe (cm3) 156.38Inline graphic11.27 159.44Inline graphic15.32 -0.780 0.435
Hippocampus (cm3) 7.00Inline graphic0.61 7.17Inline graphic0.49 -1.147 0.251
Insula (cm3) 12.58Inline graphic1.10 13.14Inline graphic1.42 -1.147 0.251
Midbrain (cm3) 2.17Inline graphic0.24 2.17Inline graphic0.10 -0.688 0.491
Occipital lobe (cm3) 58.72Inline graphic4.26 59.17Inline graphic6.04 -0.101 0.286
Parahippocampus (cm3) 7.75Inline graphic0.78 8.04Inline graphic0.71 -1.147 0.251
Parietal lobe (cm3) 70.27Inline graphic5.45 71.98Inline graphic6.60 -1.147 0.251
Pons (cm3) 0.65Inline graphic0.08 0.62Inline graphic0.04 -0.505 0.614
Postceretral (cm3) 18.46Inline graphic1.36 19.40Inline graphic1.88 -1.239 0.215
Preceretral (cm3) 16.57Inline graphic1.30 17.03Inline graphic2.05 -1.147 0.251
Temporal lobe (cm3) 94.90Inline graphic7.64 97.96Inline graphic10.49 -1.376 0.169
Thalamus (cm3) 7.91Inline graphic0.83 8.57Inline graphic0.54 -2.524 0.012
Vermis (cm3) 6.38Inline graphic0.60 6.28Inline graphic0.31 -0.229 0.819
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Notes: Data is presented as means (SD)

Abbreviations: ROI, regions of interest

Increased TSPO expression in patients with CM

Patients with CM presented increased [11C] PBR28 SUVR relative to HCs in the midbrain, occipital lobe and vermis. However, after applying FDR correction, these differences were no longer statistically significant (Supplemental, Table S3). The specific locations of the SUVR comparisons are illustrated in Fig. 1 and detailed in Table 3. Additionally, an analysis of the thalamic subregions revealed no significant differences in PET SUVR between the CM and HCs group (Supplemental, Table S4).

Fig. 1.

Fig. 1

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TSPO levels were significantly elevated in the midbrain, vermis and occipital lobe

Table 3.

Statistical significance of group differences in the ([11C] PBR28 SUVR across brain regions

ROI Chronic Migraine Healthy Controls Z P value
Amygdala 1.05Inline graphic0.04 1.04Inline graphic0.05 -0.184 0.875
Cerebelum 1.11Inline graphic0.04 1.10Inline graphic0.02 -0.275 0.804
Cerebelum_crus 1.00Inline graphic0.04 1.02Inline graphic0.02 -1.009 0.330
Frontal lobe 0.98Inline graphic0.02 0.97Inline graphic0.02 -0.551 0.604
Hippocampus 1.04Inline graphic0.03 1.04Inline graphic0.05 -0.275 0.804
Insula 1.08Inline graphic0.05 1.07Inline graphic0.03 -0.596 0.573
Midbrain 1.14Inline graphic0.05 1.10Inline graphic0.04 -2.111 0.035
Occipital lobe 1.02Inline graphic0.04 1.06Inline graphic0.05 -2.248 0.024
Parahippocampus 1.00Inline graphic0.07 0.98Inline graphic0.03 -0.321 0.769
Parietal lobe 1.00Inline graphic0.03 1.00Inline graphic0.01 -0.826 0.429
Pons 1.18Inline graphic0.23 1.10Inline graphic0.04 -1.606 0.115
Postceretral 0.99Inline graphic0.04 0.98Inline graphic0.03 -0.780 0.456
Preceretral 1.03Inline graphic0.04 1.03Inline graphic0.03 -0.046 0.982
Temporal lobe 0.99Inline graphic0.02 1.00Inline graphic0.01 -1.560 0.126
Thalamus 1.21Inline graphic0.07 1.22Inline graphic0.04 -0.092 0.946
Vermis 1.11Inline graphic0.06 1.06Inline graphic0.03 -2.524 0.011
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Notes: Data is presented as means (SD). Bold represents a p value less than 0.05

Abbreviations: ROI, regions of interest; SUVR, standardized uptake value ratio

Between-group differences in inflammatory plasma cytokine and chemokine levels

Plasma levels of cytokines and chemokines were measured in all participants. Nine molecules basic FGF, GM-CSF, IFN-α2, IL-10, IL-12 (p70), IL-15, LIF, β-NGF, and MCP-1 -were below the detection threshold and were excluded from further analysis. Compared with HCs, plasma concentrations of IL-8 were significantly elevated in the CM group (P = 0.014, Fig. 2B). Although the levels of IL-12 were higher in the CM group than in HCs, this difference did not reach statistical significance (P = 0.056, Fig. 2A). No significant differences were observed between the two groups for the remaining cytokines (all P > 0.05). Furthermore, CM individuals exhibited significantly higher plasma CX3CL1 concentrations compared to HCs (P = 0.0004, Fig. 2C).

Fig. 2.

Fig. 2

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Concentrations of plasma cytokines in chronic migraine patients and healthy controls

Associations between imaging and clinical variables and plasma factors

Given the observed differences in [11C] PBR28 imaging, correlation analyses were performed to determine whether clinical measurements, as well as plasma cytokines and chemokines levels, were associated with changes in [11C] PBR28 uptake among CM participants. In CM patients, the midbrain levels of TSPO were negatively correlated with the headache frequency (r=-0.462, p = 0.046, Fig. 3). No significant association was observed between ([11C] PBR28 SUVR in the midbrain, occipital lobe, or vermis and plasma CX3CL1 levels in CM patients (p > 0.05 for all regions, Supplemental Figure S1). Additionally, there were no other significant correlations between SUVR values and clinical variables.

Fig. 3.

Fig. 3

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Correlations between midbrain PET SUVR and headache frequency. SUVR = standardized uptake value ratio

Discussion

We conducted a pilot study on glial activation using an integrated [11C] PBR28 PET/MR approach to examine the potential involvement of neuroinflammation in migraine chronicity. We provide in vivo evidence of neuroinflammation in the brains of patients with CM, as indicated by a pattern of elevated [11C] PBR28 signals in the midbrain, occipital lobe and vermis, suggestive of glial activation.

Structural imaging provides valuable insights into the cortical morphometry associated with CM, yet findings from studies examining structural brain changes must be interpreted with caution [22]. Voxel-based morphometry is a widely used technique for quantifying the volume, density, or concentration of GM in the brain, thereby enabling the exploration of structural changes across various brain regions. The small number of studies that analysed differences between CM and HCs, but results have sometimes been conflicting. Previous studies [23–25] have shown that morphological abnormalities in several different cortical regions, including the brainstem, cerebellum, and occipital areas, other studies using similar methods have shown no significant difference [26, 27]. Our study, employing a distinct methodology, ROIs (e.g., midbrain, vermis) were selected based on PET signal relevance to pain processing. We found that both the whole thalamus and five of its subregions (anterior, lateral, ventral, medial, and posterior) exhibited reduced volume in CM patients compared to HCs. In the pathogenesis of migraine, the thalamus is regarded as the central hub for integrating and processing nociceptive information [28]. Specifically, the ventral posteromedial, posterior, and lateral thalamic nuclei receive nociceptive inputs from the dura mater and subsequently relay this information to various regions of the cerebral cortex [29, 30]. Our study further supports the notion that thalamus may play an important role in migraine chronification. Due to differences in study purpose, methodological design, MRI analysis methods, statistical power, and number of study participants, our results cannot be directly compared with those of earlier studies.

The present study is the first to report microglial recruitment and/or density, as measured by TSPO expression, in patients with CM compared to HCs. TSPO binding was elevated in the midbrain, occipital lobe, and vermis, regions that are intricately involved in pain modulation, in CM patients relative to HCs. Moreover, in addition to the observed intergroup differences in [11C] PBR28 PET signaling, we discovered a negative correlation between midbrain [11C] PBR28 uptake and migraine frequency. This finding emphasized the importance of midbrain activity in the pathology of migraine and may reflect compensatory neuroinflammatory mechanisms. The midbrain, particularly the periaqueductal gray, serves as a crucial component in the endogenous pain inhibition system, exerts a dual control (including inhibition and facilitation) on nociceptive transmission. Elevated TSPO signal in this region could indicate microglial activation aimed at dampening nociceptive signaling, thereby reducing headache frequency in some patients. Alternatively, repeated migraine attacks might exhaust glial responses over time, leading to lower TSPO expression in individuals with higher headache frequency. In a previous [11C] PBR28 PET study, Loggia et al. [15] found that the thalamic levels of TSPO were negatively correlated with clinical pain, suggesting that microglial activation can have both promoting and anti-nociceptive roles depending on context. The small sample size may also contribute to this counterintuitive observation, emphasizing the need for replication in larger cohorts. The increased TSPO expression in these areas further substantiates the presence of a neuroinflammatory response in the brains of CM patients, indicating that this inflammatory response encompasses multiple brain regions associated with pain processing. CM predominantly evolves from migraine without aura, and abnormal cortical excitability, particularly heightened activity in the occipital cortex, is regarded as a key mechanism underlying the chronic progression of migraine. This evidence suggests that the occipital lobe may serve as a crucial region in the pathogenesis of migraines.

Previous studies have examined TSPO binding in migraine patients with aura in the interictal state. Albrecht et al. [16] reported brain elevations in [11C] PBR28 signals in the thalamus and primary/secondary somatosensory and insular cortices, suggesting glial activation and neuroinflammation in these patients. Another study reported increased tracer uptake in the meninges and occipital parameningeal tissues in migraine patients with visual aura compared with HCs and patients with low back pain [31]. These findings suggest that neuroimaging studies support the presence of neuroinflammation in migraine patients. Moreover, patients with widespread pain conditions such as fibromyalgia also exhibit elevated TSPO signals in large portions of the primary somatosensory cortex [32]. Increased TSPO expression, particularly in the vermis, may serve as an inflammatory biomarker for the chronicity of migraine. Many studies have suggested that the cerebellum is associated with pain and migraine [33–35]. The thalamus, a key structure transmitting nociceptive information to various cortical regions, has shown consistent TSPO signal elevations in migraine and chronic low back pain patients compared with HCs [16, 36]. However, we observed no significant difference in thalamic [11C] PBR28 binding between CM patients and HCs. One possible reason for this discrepancy is that our patients were all in the interictal phase of CM, and inflammation in pain-processing areas might have been too subtle to detect reliably. The observed reduction in thalamic volume may reflect long-term structural remodeling rather than acute inflammatory activity, suggesting that neuroinflammation and structural changes contribute to the pathophysiology of CM through distinct mechanisms. Nevertheless, a broader explanation of the role of the thalamus in nociceptive pain in CM warrants further investigation and validation.

A limitation of TSPO PET is the uncertainty regarding the specific cell type responsible for the TSPO binding signal. Identifying which cell types express TSPO is critical for the clinical interpretation of TSPO PET scans. TSPO is expressed in a variety of cell types, including microglia, astrocytes and endothelial cells [37, 38]. A recent ex vivo study has suggested that TSPO signal may be related more to glial cell binding density than microglial activation [14]. Although the interpretation of TSPO imaging data is complex, in vivo imaging of TSPO offers valuable insights into potential brain regions of interest and provides opportunities to elucidate condition-related mechanisms in future studies. In addition, imaging TSPO with PET remains methodologically challenging due to ligand-specific limitations. First-generation ligands, such as [11C] PK11195, although groundbreaking, exhibit poor signal-to-noise ratios and high nonspecific binding, which limit their utility in quantifying neuroinflammation [39]. As a second-generation ligand [11C] PBR28 demonstrates improved affinity and specificity for TSPO, enabling better visualization of glial activation [40]. However, its binding is influenced by the rs6971 genetic polymorphism in the TSPO gene, categorizing individuals into HAB, MAB, or LAB [40, 41]. To minimize this variability, our study exclusively enrolled HAB [42, 43]. Newer third-generation ligands, such as [18F] GE180 and [11C] ER176, show promise in reducing genetic dependency and improving binding characteristics. For example [18F] GE180 exhibits higher binding potential and lower sensitivity to the rs6971 polymorphism [44], while [11C] ER176 exhibits more consistent binding across genotypes [39, 45, 46]. These advancements could simplify study protocols by eliminating the need for genotyping and expand applicability to broader populations. These advancements could simplify study protocols by eliminating the need for genotyping and expand applicability to broader populations. Despite these innovations [11C] PBR28 remains widely used in neuroinflammatory research due to its established pharmacokinetic profile and validated utility in HAB cohorts. Our findings, derived from a genetically homogeneous sample, provide robust evidence of central inflammation in CM. Future studies employing third-generation ligands may further refine these observations and extend them to diverse clinical populations.

In our study, we detected CX3CL1 in serum but not in CSF in the interictal phase of the disease, and plasma CX3CL1 levels were higher in individuals with CM than in HCs. Interestingly, Marie et al. [8] reported that CX3CL1 was significantly elevated in the CSF but not in the serum of migraine patients, regardless of headache status. We considered that one possible reason may be that CSF CX3CL1 reflects direct CNS signaling, while plasma CX3CL1 levels might indicate systemic inflammation or blood-brain barrier (BBB) leakage in CM. Another possible reason may be related to sample timing, Marie et al. included both ictal and interictal phases, while our cohort was strictly limited to interictal patients. chronic neuroinflammation in CM may sustain peripheral chemokine elevation even during the interictal period. On the basis of the current evidence and that CX3CL1 acts as a regulator of microglial activation within the CNS [7], it can be speculated that increased microglial activity may represent a pro-neuroinflammatory feature, implying a concentration of neuronal transmission in microglia-dense areas such as the thalamus [47]. Although few studies have been conducted on the CX3CL1/CX3CR1 axis in migraine, published studies suggest that CX3CR1 may be involved in the occurrence of migraine [9]. The absence of a significant relationship between brain TSPO uptake and peripheral CX3CL1 supports the hypothesis that neuroinflammation in CM capture central neuroinflammation, with plasma CX3CL1 peripheral inflammatory processes rather than direct microglial interactions. This aligns with Marie et al. [8], who reported CSF (but not serum) CX3CL1 elevations in migraineurs, underscoring the BBB role in segregating central and peripheral inflammatory markers.

We also observed increased IL-8 concentrations in CM patients during the interictal period, which is consistent with the findings of other studies in migraine patients. A recently published meta-analysis of cytokine and chemokine levels in migraine patients revealed that TNF-α, IL-6, and IL-8 levels were higher in migraine patients than in controls [48]. These results suggest that proinflammatory chemokine-mediated neuroinflammation may be involved in the pathophysiology of migraine.

Little evidence is available to suggest that in vivo glial activation in humans is involved in the initiation of CM, and the role of inflammation in the sensitization process leads to enhanced responsiveness of target tissues. Neurogenic neuroinflammation caused by the continuous release of neurotransmitters could be central to understanding migraine chronification. There are several potential implications of our observations. First, the identification of neuroinflammation as a pivotal factor in the pathophysiology of CM may facilitate the development of targeted therapies aimed at mitigating neuroinflammatory activity, thereby potentially enhancing treatment outcomes for CM patients. Notably, some clinical trials have yielded negative results; for example, one study suggested that ibudilast treatment did not improve migraine in CM patients [49]. The methodology of these studies, however, limits the importance of these negative results. In this study, ibudilast was administered for 8 weeks, which is a significantly shorter duration than the 12 weeks typically employed in clinical trials. Second, employing [11C] PBR28 PET/MR imaging as a noninvasive modality for assessing glial activation could transform the diagnosis and monitoring of CM, offering a more precise and timely evaluation of disease progression. Finally, the correlation between imaging findings and plasma biomarkers paves the way for developing blood-based tests that could enable early detection and personalized treatment strategies for CM.

The present study has several limitations that should be noted. One notable limitation is the imaging methodology. We did not perform kinetic modeling, which allows quantitative measurements of functional brain imaging, including TSPO binding. Kinetic models of [11C] PBR28 have been established in other disease cohorts [50, 51], while, metabolic profile of migraine remain unreported. Previous studies [16, 31] have detected intergroup differences in [11C] PBR28 signaling among migraine patients using a semi-quantitative ratio measure. Based on these findings, we have chosen to conduct an exploratory study using TSPO PET in patients with CM, with the goal of offering a new perspective on the possible pathogenesis of the condition by visualizing neuroinflammatory responses, using semiquantitative ratio metric, SUVR60 − 90, as the primary outcome measure. Moreover, the SUVR method is more commonly used, less invasive, and reduces both patient burden and scanning time. Consequently, our conclusions regarding neuroinflammatory activity should be interpreted with caution until the static imaging protocol is validated against dynamic acquisition methods in CM cohorts. To further elucidate the dynamic evolution of neuroinflammation in CM patients, our further research will recruit CM patients for total-body PET/CT dynamic data acquisition. This approach will allow us to better define the specific kinetic parameters of [11C] PBR28 in migraine cohort, thereby addressing the current knowledge gap, enhancing the robustness of our findings, and ultimately providing a more accurate understanding of the role of [11C] PBR28 and neuroinflammation in CM. Another limitation is the relatively small sample size, and the differences observed in volume and PET SUVR comparisons did not remain significant after adjustment for multiple comparisons. We acknowledge the necessity of applying appropriate corrections in multiple comparisons, and while the corrected P-value results are provided in the supplementary materials, none of the differences reached significance after correction. This outcome may be attributed to the subtle changes in brain volume and TSPO expression observed in CM patients, combined with the small patient sample and the large number of ROI, which necessitates more stringent corrections and consequently reduces the statistical significance of the findings. Nevertheless, we present the uncorrected results to highlight potential trends in brain volume and TSPO expression in CM patients. Although these uncorrected results are not statistically rigorous, they suggest a trend toward more pronounced pathological changes in migraine patients. In addition, we discuss potential alterations in these brain regions and their associations with clinical manifestations and plasma biomarkers.

In conclusion, we found that [11C] PBR28 PET signal was elevated in CM patients, and correlated with headache frequency. These findings support the conclusion that central inflammation may play a role in the pathophysiology of CM and is associated with clinical migraine features. Additionally, the observed reduction in thalamic volume suggests that structural remodeling may occur independently of inflammatory responses and may jointly contribute to migraine chronification.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (34.5KB, docx)

Acknowledgements

The authors thank Dr. Chenghui Pi and Dr. Xiting Nie for their assistance in recruiting participants.

Author contributions

All authors contributed to the study conception and design. Yan Chang, Xiwan Zhang, Ruimin Wang and Ruozhuo Liu were responsible for the study concept and design. Yan Chang, Xiwan Zhang was responsible for drafting the original report which was reviewed and revised by all coauthors (Yan Chang, Xiwan Zhang, Shaobo Xiao, Jiajin Liu, Yuan Wang, Jingbin Song, Huaping Fu, Yungang Li, Hui Su, Huijie Yi, Wenjie Su, Nan Gao, JinJing Zhao, Ruimin Wang and Ruozhuo Liu). Jiajin Liu and Jingbin Song were responsible for the acquisition of the PET/MR scan. Huaping Fu and Yungang Li were responsible for providing the PET tracer. Yuan Wang was responsible for biofluid processing, storage and data analyses. Yan Chang and Xiwan Zhang were responsible for all neuroimaging analyses and statistical analyses. Shaobo Xiao, Hui Su Huijie Yi, Wenjie Su, Nan Gao, and JinJing Zhao were responsible for the acquisition of the clinical data. Yan Chang, Xiwan Zhang, Ruimin Wang and Ruozhuo Liu were responsible for the interpretation of data for the work.

Funding

This work was supported by the National Key Research and Development Program of China (2023YFC2508702 [R.Z.L]).

Data availability

The datasets generated during and analysed during this study are covered by data protection regulation and cannot be distributed.

Declarations

Ethical approval

The study was approved by the Human Ethics Committee of Chinese PLA General Hospital and conducted in accordance with Declaration of Helsinki.

Consent to participate

Written informed consent was obtained from all participants.

Conflict of interest

The authors report no disclosures relevant to the manuscript.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yan Chang and Xiwan Zhang contributed equally to this work.

Contributor Information

Ruimin Wang, Email: wrm@yeah.net.

Ruozhuo Liu, Email: liuruozhuo301@163.com.

References

  • 1.GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and National incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017 16;390 (10100):1211–59. 10.1016/S0140-6736(17) 32154–2. [DOI] [PMC free article] [PubMed]
  • 2.Edvinsson L, Haanes KA, Warfvinge K. Does inflammation have a role in migraine? Nat Rev Neurol. 2019;15(8):483–90. 10.1038/s41582-019-0216-y. [DOI] [PubMed] [Google Scholar]
  • 3.Dodick D, Silberstein S. Central sensitization theory of migraine: clinical implications. Headache. 2006;46:S182–91. 10.1111/j.1526-4610.2006.00602.x. [DOI] [PubMed] [Google Scholar]
  • 4.Peres MFP, Mercante JPP, Tobo PR, Kamei H, Bigal ME. Anxiety and depression symptoms and migraine: a symptom-based approach research. J Headache Pain. 2017;18 18(1):37. 10.1186/s10194-017-0742-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Zhang S, Azubuine J, Schmeer C. A systematic literature review on the role of glial cells in the pathomechanisms of migraine. Front Mol Neurosci. 2023;16:1219574. 10.3389/fnmol.2023.1219574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cho S, Chu MK. Serological biomarkers of chronic migraine. Curr Pain Headache Rep. 2023;27(10):531–42. 10.1007/s11916-023-01154-x. [DOI] [PubMed] [Google Scholar]
  • 7.Pawelec P, Ziemka-Nalecz M, Sypecka J, Zalewska T. The impact of the CX3CL1/CX3CR1 axis in neurological disorders. Cells. 2020;9(10):2277. 10.3390/cells9102277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Süße M, Kloetzer C, Strauß S, et al. Increased CX3CL1 in cerebrospinal fluid and ictal serum t-tau elevations in migraine: results from a cross-sectional exploratory case-control study. J Headache Pain. 2024;25(1):46. 10.1186/s10194-024-01757-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Combadière C, Godin O, Vidal C, Cangialosi A, Proust C, Tzourio C. Common CX3CR1 alleles are associated with a reduced risk of headaches. Headache. 2008;48(7):1061–6. 10.1111/j.1526-4610.2007.01011.x. [DOI] [PubMed] [Google Scholar]
  • 10.Rupprecht R, Papadopoulos V, Rammes G, et al. Translocator protein (18 kDa) (TSPO) as a therapeutic target for neurological and psychiatric disorders. Nat Rev Drug Discov. 2010;9:971–88. 10.1038/nrd3295. [DOI] [PubMed] [Google Scholar]
  • 11.Albrecht DS, Granziera C, Hooker JM, Loggia ML. In vivo imaging of human neuroinflammation. ACS Chem Nerosci. 2018;9(6):1515. 10.1021/acschemneuro.8b00188. [DOI] [PubMed] [Google Scholar]
  • 12.Cumming P, Burgher B, Patkar O, et al. Sifting through the surfeit of neuroinflammation tracers. J Cereb Blood Flow Metab. 2018;38(2):204–24. 10.1177/0271678X17748786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Cosenza-Nashat M, Zhao ML, Suh HS, et al. Expression of the translocator protein of 18 kda by microglia, macrophages and astrocytes based on immunohistochemical localization in abnormal human brain. Neuropathol Appl Neurobiol. 2009;35:306–28. 10.1111/j.1365-2990.2008.01006.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Nutma E, Fancy N, Weinert M, et al. Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases. Nat Commun. 2023;14(1):5247. 10.1038/s41467-023-40937-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Loggia ML, Chonde DB, Akeju O, et al. Evidence for brain glial activation in chronic pain patients. Brain. 2015;138(Pt 3):604–15. 10.1093/brain/awu377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Albrecht DS, Mainero C, Ichijo E, et al. Imaging of neuroinflammation in migraine with aura: A [11 C] PBR28 PET/MRI study. Neurology. 2019;92(17):e2038–50. 10.1212/WNL.0000000000007371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Headache Classification Committee of the International Headache Society (IHS). The international classification of headache disorders, 3rd edition. Cephalalgia. 2018;38(1):1–211. 10.1177/0333102417738202. [DOI] [PubMed]
  • 18.Seiger R, Ganger S, Kranz GS, et al. Cortical thickness estimations of freesurfer and the CAT12 toolbox in patients with Alzheimer’s disease and healthy controls. J Neuroimaging. 2018;28:515–23. 10.1111/jon.12521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Meyer J, Gunnz R, Myers R, et al. Assessment of Spatial normalization of PET ligand images using ligand-specific templates. NeuroImage. 1999. 10.1006/nimg.1999.0431. [DOI] [PubMed] [Google Scholar]
  • 20.Zurcher NR, Loggia ML, Lawson R, et al. Increased in vivo glial activation in patients with amyotrophic lateral sclerosis: assessed with [(11)C]-PBR28. Neuroimage Clin. 2015;7:409–14. 10.1016/j.nicl.2015.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bloomfield PS, Selvaraj S, Veronese M, et al. Microglial activity in people at ultra high risk of psychosis and in schizophrenia: an [(11)C] PBR28 PET brain imaging study. Am J Psychiatry. 2016;173:44–52. 10.1176/appi.ajp.2015.14101358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.May A. Morphing voxels: the hype around structural imaging of headache patients. Brain. 2009;132(Pt 6):1419–25. 10.1093/brain/awp116. [DOI] [PubMed] [Google Scholar]
  • 23.Lai TH, Chou KH, Fuh JL, et al. Gray matter changes related to medication overuse in patients with chronic migraine. Cephalalgia. 2016;36(14):1324–33. 10.1177/0333102416630593. [DOI] [PubMed] [Google Scholar]
  • 24.Bilgiç B, Kocaman G, Arslan AB, et al. Volumetric differences suggest involvement of cerebellum and brainstem in chronic migraine. Cephalalgia. 2016;36(4):301–8. 10.1177/0333102415588328. [DOI] [PubMed] [Google Scholar]
  • 25.Chen XY, Chen ZY, Dong Z, Liu MQ, Yu SY. Regional volume changes of the brain in migraine chronification. Neural Regen Res. 2020;15(9):1701–8. 10.4103/1673-5374.276360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Coppola G, Petolicchio B, Di Renzo A, Tinelli E, Di Lorenzo C, Parisi V, et al. Cerebral Gray matter volume in patients with chronic migraine: correlations with clinical features. J Headache Pain. 2017;18(1):115. 10.1186/s10194-017-0825-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Lai KL, Niddam DM, Fuh JL, Chen WT, Wu JC, Wang SJ. Cortical morphological changes in chronic migraine in a Taiwanese cohort: surface- and voxel-based analyses. Cephalalgia. 2020;40(6):575–85. 10.1177/0333102420920005. [DOI] [PubMed] [Google Scholar]
  • 28.Tracey I. Imaging pain. Br J Anaesth. 2008;101(1):32–9. 10.1093/bja/aen102. [DOI] [PubMed] [Google Scholar]
  • 29.Noseda R, Kainz V, Jakubowski M, et al. A neural mechanism for exacerbation of headache by light. Nat Neurosci. 2010;132:239–45. 10.1038/nn.2475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Burstein R, Jakubowski M, Garcia-Nicas E, et al. Thalamic sensitization transforms localized pain into widespread allodynia. Ann Neurol. 2010;681:81–91. 10.1002/ana.21994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Hadjikhani N, Albrecht DS, Mainero C, et al. Extra-Axial inflammatory signal in parameninges in migraine with visual aura. Ann Neurol. 2020;87(6):939–49. 10.1002/ana.25731. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Albrecht DS, Forsberg A, Sandstro¨m A, et al. Brain glial activation in fibromyalgia-A multisite positron emission tomography investigation. Brain Behav Immun. 2019;75:72–83. 10.1016/j.bbi.2018.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Coombes SA, Misra G. Pain and motor processing in the human cerebellum. Pain. 2016;157(1):117–27. 10.1097/j.pain.0000000000000337. [DOI] [PubMed] [Google Scholar]
  • 34.Vincent M, Hadjikhani N. The cerebellum and migraine. Headache. 2007;47(6):820–33. 10.1111/j.1526-4610.2006.00715.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Mehnert J, May A. Functional and structural alterations in the migraine cerebellum. J Cereb Blood Flow Metab. 2019;39(4):730–9. 10.1177/0271678X17722109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Torrado-Carvajal A, Toschi N, Albrecht DS, et al. Thalamic neuroinflammation as a reproducible and discriminating signature for chronic low back pain. Pain. 2021;162(4):1241–9. 10.1097/j.pain.0000000000002108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Gui Y, Marks JD, Das S, Hyman BT, Serrano-Pozo A. Characterization of the 18 kda translocator protein (TSPO) expression in post-mortem normal and Alzheimer’s disease brains. Brain Pathol. 2020;30(1):151–64. 10.1111/bpa.12763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Notter T, Coughlin JM, Gschwind T, et al. Translational evaluation of translocator protein as a marker of neuroinflammation in schizophrenia. Mol Psychiatry. 2018;23(2):323–34. 10.1038/mp.2016.248. [DOI] [PubMed] [Google Scholar]
  • 39.Fujita M, Kobayashi M, Ikawa M, et al. Comparison of four 11 C-labeled PET ligands to quantify translocator protein 18 kda (TSPO) in human brain: (R)-PK11195, PBR28, DPA-713, and ER176-based on recent publications that measured specific-to-non-displaceable ratios. EJNMMI Res. 2017;7(1):84. 10.1186/s13550-017-0334-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Kreisl WC, Fujita M, Fujimura Y, et al. Comparison of [11 C]®PK 11195 and [11 C]PBR28, two radioligands for translocator protein (18 kDa) in human and monkey: implications for positron emission tomographic imaging of this inflammation biomarker. NeuroImage. 2010;49:2924–32. 10.1016/j.neuroimage.2009.11.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Owen DR, Guo Q, Kalk NJ, et al. Determination of [(11)C] PBR28 binding potential in vivo: A first human TSPO blocking study. J Cereb Blood Flow Metab. 2014;34:989–94. 10.1038/jcbfm.2014.46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Pascoal TA, Benedet AL, Ashton NJ, et al. Microglial activation and Tau propagate jointly across Braak stages. Nat Med. 2021;27:1592–9. 10.1038/s41591-021-01456-w. [DOI] [PubMed] [Google Scholar]
  • 43.Ferrari-Souza JP, Lussier FZ, Leffa DT, et al. APOEε4 associates with microglial activation independently of Aβ plaques and Tau tangles. Sci Adv. 2023;9:eade1474. 10.1126/sciadv.ade1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Fan Z, Calsolaro V, Atkinson RA, et al. Flutriciclamide (18F-GE180) PET: First-in-Human PET study of novel Third-Generation in vivo marker of human translocator protein. J Nucl Med. 2016;57(11):1753–9. 10.2967/jnumed.115.169078. [DOI] [PubMed] [Google Scholar]
  • 45.Zanotti-Fregonara P, Zhang Y, Jenko KJ, et al. Synthesis and evaluation of translocator 18 kda protein (TSPO) positron emission tomography (PET) radioligands with low binding sensitivity to human single nucleotide polymorphism rs6971. ACS Chem Neurosci. 2014;5(10):963–71. 10.1021/cn500138n. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Ikawa M, Lohith TG, Shrestha S, et al. 11 C-ER176, a radioligand for 18-kDa translocator protein, has adequate sensitivity to robustly image all three affinity genotypes in human brain. J Nucl Med. 2017;58(2):320–5. 10.2967/jnumed.116.178996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Zhou Y, Zhang L, Hao Y, Yang L, Fan S, Xiao Z. FKN/CX3CR1 axis facilitates migraine-Like behavior by activating thalamic-cortical network microglia in status epilepticus model rats. J Headache Pain. 2022;23(1):42. 10.1186/s10194-022-01416-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Musubire AK, Cheema S, Ray JC, Hutton EJ, Matharu M. Cytokines in primary headache disorders: a systematic review and meta-analysis. J Headache Pain. 2023;24(1):36. 10.1186/s10194-023-01572-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Kwok YH, Swift JE, Gazerani P, Rolan P. A double-blind, randomized, placebo-controlled pilot trial to determine the efficacy and safety of Ibudilast, a potential glial attenuator, in chronic migraine. J Pain Res. 2016;9:899–907. 10.2147/JPR.S116968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Albrecht DS, Normandin MD, Shcherbinin S, et al. Pseudoreference regions for glial imaging with 11 C-PBR28: investigation in 2 clinical cohorts. J Nucl Med. 2018;59(1):107–14. 10.2967/jnumed. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Park E, Gallezot JD, Delgadillo A, et al. (11)C-PBR28 imaging in multiple sclerosis patients and healthy controls: test-retest reproducibility and focal visualization of active white matter areas. Eur J Nucl Med Mol Imaging. 2015;42(7):1081–92. 10.1007/s00259-015-3043-4. [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Supplementary Material 1 (34.5KB, docx)

Data Availability Statement

The datasets generated during and analysed during this study are covered by data protection regulation and cannot be distributed.

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