Atogepant modulates brain connectivity in episodic migraine: a longitudinal cohort fMRI study

Atogepant modulates brain connectivity in episodic migraine: a longitudinal cohort fMRI study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Atogepant modulates brain connectivity in episodic migraine: a longitudinal cohort fMRI study Danilo Antonio Montisano, Davide Fedeli, Greta Demichelis, Giuseppe Ciullo, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8406852/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Episodic Migraine (EM) is a highly prevalent and disabling neurological disorder. Recent therapeutic advances have targeted the calcitonin gene-related peptide (CGRP), a neuropeptide implicated in the pathophysiology of migraine. Promising outcomes have been obtained with monoclonal antibodies and, more recently, with a new class of drugs consisting of small-molecule CGRP receptor antagonists, known as gepants. Atogepant, an oral agent of the gepant class, has demonstrated efficacy in migraine prevention, yet its effects on central brain activity and neurotransmitter circuits remain largely unclear. This study investigates changes in brain functional connectivity and changes related to key neurotransmitter systems, following 12 weeks of treatment with atogepant in a group of EM patients. Methods This is a prospective single-arm cohort study with a pre–post neuroimaging evaluation of the effects of atogepant. We enrolled patients diagnosed with EM according to ICHD-3 criteria, without prior exposure to anti-CGRP therapies. Participants underwent clinical assessments (monthly migraine days MMD, acute drugs intake MAM) and resting-state functional MRI (rs-fMRI) before and after 12 weeks treatment of daily atogepant 60mg administration. Longitudinal functional connectivity analyses were performed at the whole-brain level with a region-of-interest analysis. Additionally, longitudinal neurotransmitter-related functional connectivity was investigated within the serotoninergic and the dopaminergic systems. Results A total of 15 patients completed the evaluation. Following 12 weeks of atogepant treatment, all patients showed a sizable reduction in MMD (T=-7.09, p < .001) and MAM, (T=-6.35, p < .001), accompanied by a reduction of allodynia symptoms. Patients exhibited significant longitudinally increased functional connectivity, involving the right superior frontal gyrus, bilateral putamen, left pallidum, left anterior and bilateral posterior cingulate cortex. Greater the longitudinal increase in cingulate cortices connectivity, the larger the improvement in MDD, MAM, and allodynia symptoms after treatment. Additionally, longitudinal connectivity changes were observed within the orbitofrontal cortex in the mesocorticolimbic dopaminergic system. Conclusions Significative clinical improvement after atogepant treatment is associated with functional connectivity adaptations in EM patients. These findings may reflect either direct or indirect central modulation mediated by atogepant. A deeper understanding of the observed central changes may help to clarify the mechanisms underlying anti-CGRP therapies for migraine. Atogepant resting-state fMRI migraine functional connectivity neurostransmitter gepant Figures Figure 1 1. Introduction Migraine is a common disabling neurological condition, characterized by severe recurrent headaches, accompanied by several neurological and autonomic symptoms [ 1 ]. Its pathophysiology involves peripheral nociceptive signals and complex central alterations from the trigemino-vascular system, neurotransmitter systems, and central pain modulation regions [ 1 ]. Some neuroimaging studies have provided evidence of central dysfunction in migraine population, reporting pain-induced brain alterations and suggesting a dysregulation of pain facilitation and inhibition processes [ 2 ]. Resting-state fMRI (rs-fMRI) studies have further revealed widespread disrupted functional connectivity (FC) across multiple cortical and subcortical regions and networks implicated in sensory, cognitive, and affective aspects of pain [ 3 – 6 ]. Novel migraine preventive treatments acting on Calcitonin Gene Related Peptide (CGRP) pathway have shown remarkable efficacy in clinical practice [ 7 ]. CGRP, plays a major role in migraine pathophysiology by promoting meningeal vessels dilation, local neurogenic inflammation, thereby releasing pro-nociceptive mediators that enhance pain transmission and sensitivity. Two main classes of CGRP-targeting treatments are available: monoclonal antibodies (mAbs anti-CGRP; e.g., erenumab, galcanezumab) directed against CGRP or its receptor, and small-molecule CGRP receptor antagonists, gepants [ 7 ]. In this context, atogepant, a recently approved gepant theapeutic drug, has demonstrated high safety, tolerability, and efficacy in migraine prevention. Clinical evidence shows a rapid and robust preventive effect, with 60–80% of patients achieving a ≥ 50% reduction in monthly migraine episodes within three months, and up to ~ 50% reaching complete remission at one year [ 8 – 10 ]. Only few neuroimaging studies have investigated whether anti-CGRP mAbs could modify brain activity. For erenumab, Schwedt et al. showed changes in network FC efficiency and central processing of extracranial painful stimuli involving cingulate and frontal cortices, putamen, and periaqueductal gray in treatment responders [ 11 ]. For galcanezumab, reduction FC between the somatosensory, motor, and insular cortices in responders was observed [ 12 ], moreover, a decreases hypothalamic activation stronger in responders was observed [ 13 ]. By contrast, no neuroimaging evidence are available for gepants. Whereas mAbs act mainly peripherally and have limited blood-brain barrier (BBB) permeability [ 14 ], the degree of BBB penetration of gepant is still unclear, and this leaves open the question of its potential central neurofunctional involvement. Moreover, since migraine pathophysiology involves multiple neurotransmitter systems, including serotonin, which contributes to migraine generation, and dopamine, which has been associated with prodromal symptoms such as yawning and nausea, understanding how anti-CGRP treatments modulate neurotransmitter activity may provide new insights into their central mechanisms of action [ 15 , 16 ]. On these grounds, this longitudinal study aimed to investigate the effects of a 12-week atogepant treatment on resting state brain FC and on FC modulated by the spatial distribution of different neurotransmitter receptors in a cohort of episodic migraine (EM). 2. Methods 2.1 Subjects and clinical assessment We conducted a prospective, longitudinal, single-arm cohort study (pre-post design) including patients with EM who were naïve to anti-CGRP therapies. Participants underwent clinical and resting-state fMRI assessments at baseline and after 12 weeks of continuous treatment with atogepant 60 mg/day. Patients with a diagnosis of EM were recruited at our headache centre at Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy. Inclusion criteria were: age > 18 and diagnosis of EM according to the ICHD-3 [ 17 ]. Patients with prior exposure to anti-CGRP therapies, comorbidity with neurological, cardiovascular, psychiatric disease or diabetes, and pregnant women were escluded. Included patients underwent a 12-week treatment with daily atogepant 60mg, in line with usual recommendations [ 8 ]. Clinical assessment and MRI scans were acquired before (T0, baseline) and after completing the treatment period (T1, follow-up), from May 2024 to December 2024. The study was approved by the local Ethical Committee (approval CET 48/24 BST 61/23) and all participants provided their consent to the study. We also collected for each patient: age, sex, monthly migraine days (MMD), monthly acute medication (MAM), allodynia (ASC-12). 2.2 MRI acquisition Patients underwent MRI acquisition, including a high-resolution structural 3D T1-weighted (T1w) image (Repetition Time [TR] = 8.11 s; Echo Time [TE] = 3.71 ms; Field-of-View [FOV] = 240 × 240 mm; voxel size = 1 × 1 × 1 mm 3 ; flip angle = 8°; 185 sagittal slices) and a rs-fMRI scan (15 min, eyes-opened; T2*-weighted BOLD echo-planar imaging gradient-echo sequence; TR = 2000 ms; TE = 30 ms; FOV = 80 × 80 mm; voxel size = 3 × 3 × 3.2 mm 3 ; interslice gap = 0.4 mm; flip angle = 80°; 34 axial slices; 450 volumes; Phase Encoding direction = posterior/anterior), using a 3T Philips Achieva-dStream scanner, with a 32-head channel coil. Patients were headache-free for at least 24 hours prior to and during the MRI acquisitions. All images were inspected by an expert neuroradiologist (A.E.) to exclude brain abnormalities or artefacts affecting the exam quality. 2.3 rs-fMRI pre-processing Image quality control of rs-fMRI scan was performed using MRIQC software [ 18 ]. Rs-fMRI data were analysed with the CONN toolbox (v. 20.b; running on Matlab 2020b; [ 19 ]) using the default-MNI pipeline. For each patient and each session, functional volumes were realigned to the first volume, unwrapped, and slice-time corrected. The ART toolbox, implemented within CONN, was used for outlier volumes identification and scrubbing (framewise displacement > 1.1 mm; global BOLD signal changes > 5 SD). Both rs-fMRI and T1w images were normalized to the MNI space and functional images were smoothed with a 6 mm 3 full width at half-maximum (FWHM) gaussian kernel. Rs-fMRI data were denoised using the anatomical component-based noise correction, with white matter and cerebrospinal fluid component derived from T1w image segmentation. The effect of realignment parameters, outlier volumes scrubbing parameters, physiological noise components, and initial magnetization transient effects regressed out. Data was then bandpass-filtered with a 0.008-0.1 Hz filter. 2.4 Neurotransmitter-related functional connectivity pre-processing To investigate the FC in relation to the spatial distribution of underlying neurotransmitter systems, we employed the Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) approach. REACT-fMRI toolbox ( https://github.com/ottaviadipasquale/react-fmri ) [ 20 ] employs a two-step regression approach (also referred to as a “dual regression”) to estimate subject-specific FC maps guided by the distribution of neurotransmitters, using publicly available molecular templates generated from PET scans of healthy individuals [ 21 , 22 ]. Reference molecular maps from serotonin and dopamine were used to generate neurotransmitter-enriched FC maps. The serotonin atlas included maps of 5-HT 1a , 5-HT 1b , 5-HT 2a , 5-HT 4 receptors, and the serotonin transporter (5-HTT). The dopamine atlas included maps of D 1 and D 2 receptors, and the dopamine transporter (DAT). The cerebellum was removed from the serotonin atlas and the D 1 and D 2 templates, while the occipital cortex was removed from the DAT map as it was used as reference regions in radioligands kinetic models [ 21 , 22 ]. Intensity values were then normalized (i.e., 0 minimum to 1 maximum) for each image. FC maps informed by serotonin maps were masked with the standard MNI gray matter binary mask provided within REACT. Dopamine maps were further subdivided into two pathways: mesocorticolimbic (MCL) and nigrostriatal (NS) systems. Two binary gray matter masks were created to better characterize these pathways [ 23 ]. The MCL mask included the following cortical and subcortical regions: anterior cingulate cortex (ACC), amygdala, hippocampus, parahippocampus, inferior frontal orbital cortex, entorhinal cortex, subcallosal gyrus, nucleus accumbens, lateral frontal orbital cortex, middle frontal gyrus, medial frontal gyrus, superior frontal gyrus (SFG) from WFU PickAtlas toolbox ( https://www.nitrc.org/projects/wfu_pickatlas ), as well as the ventral tegmental [ 24 ]; the NS mask included caudate nucleus, substantia nigra, putamen and globus pallidus from WFU PickAtlas toolbox. 2.5 Statistical analyses 2.5.1 Demographic and clinical assessment Normality of clinical variables was assessed using the Kolmogorov-Smirnov test. Within-subject longitudinal differences in these variables were assessed using paired-sample t-tests (or the corresponding nonparametric equivalent in case normality was not satisfied). All analyses were performed in Python. 2.5.2 rs-fMRI analysis To test the effects of a 12-week atogepant treatment on FC in EM patients, we performed a longitudinal region of interest (ROI)-to-ROI analysis in CONN toolbox. For each participant and each session, a connectivity matrix including ROIs from the Hammersmith atlas [ 25 – 28 ] was computed. This atlas was selected due to its fine-grained parcellation of insular, frontal, temporal, and subcortical regions, which are key areas implicated in both migraine and pain processing, offering more precise anatomical details than the standard Harvard-Oxford atlas included in CONN. Pearson correlation coefficients were calculated between the time series of each ROI and those of all other ROIs. These correlation values were subsequently transformed into Z scores using the Fisher R-to-Z transformation, resulting in a whole-brain connectivity matrix for each participant and time point. These matrices were then analysed using a second-level general linear model to assess longitudinal changes in network connectivity pre- and post-treatment. To correct for multiple comparisons and control the false discovery rate, a cluster-level False Discovery Rate (FDR) threshold of p < .05 was applied. Only ROI-to-ROI connections that met this criterion were retained for further analysis. Additionally, a connection-level uncorrected threshold of p < .05 (as per CONN's default setting) was used to identify the strongest individual connections within each significant cluster. 2.5.3 Neurotransmitter-related functional connectivity analysis To test the effects of atogepant treatment on FC in relation to the spatial distribution of neurotransmitters, longitudinal neurotransmitter-related FC maps were generated for each subject and each neurotransmitter of interest by subtracting the T0 maps from the corresponding T1 map. These difference maps were then entered into a second-level general linear model using SPM12. Results were thresholded with a p < .001 uncorrected at the voxel level and Family-wise Error Rate (FWE) corrected for multiple comparisons at p < .05 at the cluster level. 2.5.4 Correlation between rs-fMRI connectivity and clinical scales Longitudinal differences in FC values from significant ROI-to-ROI connections (i.e., connectivity at T1 minus connectivity at T0) and neurotransmitter-related FC of significant clusters obtained with REACT were correlated with longitudinal differences in clinical variables, such as MMD, MAM and allodynia scores (i.e ΔMMD, ΔMAM, ΔASC-12). All analyses were performed in Python. 3. Results 3.1 Subjects and clinical data A total of 17 EM patients was enrolled. Two patients did not complete the program due to MRI intolerance, leaving a total of 15 patients (Table 1 ). All patients were without aura except for one, with sporadic migraine with visual aura and EM without aura. Patients showed a significant longitudinal decrease in MMD and MAM. Similarly, allodynia scores decreased at the follow-up, although the change did not reach statistical significance (see Table 1 ). Table 1 Demographic and clinical data. The results of within-subjects t-test are reported. Abbreviations F/M = Females/Males; T0 = baseline; T1 = follow-up after 12 weeks of atogepant treatment; MMD = monthly migraine days; MAM = acute drugs intake; ASC-12 = allodynia score. Sex (F/M) Age at T0 (years) 13/2 43 ± 15.3 T0 T1 T-value; p -value MMD 10 ± 2.3 2 ± 2.8 -7.09; <0.001 MAM 12 ± 3 2 ± 3.6 -6.35; <0.001 ASC-12 4 ± 4.1 0.5 ± 6.2 -0.34, 0.74 – insert Table 1 about here – 3.2 rs-fMRI analysis Longitudinal ROI-to-ROI analyses revealed that atogepant treatment was associated with significant connectivity changes in frontal, cingulate, and subcortical regions. Specifically, we observed increased FC of the rSFG with the bilateral putamen, the left pallidum, and the left ACC. Increased connectivity was also observed between the left PCC and the left putamen, and between the right PCC and the left ACC. (Table 2 , Fig. 1 A). Table 2 Longitudinal ROI-to-ROI analyses. Results of Region-of-Interest to Region-of-Interest analyses of functional connectivity changes after 12 weeks of atogepant treatment are reported. All results are thresholded at p < 0.05 FDR-corrected at the cluster level and p < 0.05 uncorrected ( p -unc) at the connection level. Abbreviations R = right hemisphere; L = left hemisphere. Cluster F-value P-FDR Whole network 19.5 0.046 Region 1 MNI xyz coordinates Region 2 MNI xyz coordinates T-value p -unc Superior frontal gyrus R 13,32,36 Putamen R 25,4,0 3.54 0.003 Superior frontal gyrus R 13,32,36 Putamen L -26,2,0 3.17 0.007 Superior frontal gyrus R 13,32,36 Pallidum L -20,-2,-1 3 0.009 Superior frontal gyrus R 13,32,36 Anterior cingulate gyrus L -6,27,21 2.76 0.012 Posterior cingulate gyrus L -5,-30,35 Putamen L -26,2,0 2.31 0.037 Posterior cingulate gyrus R 6,-27,35 Anterior cingulate gyrus L -6,27,21 2.15 0.049 – insert Table 2 and Fig. 1 about here – 3.3 REACT analysis Longitudinal neurotransmitter-related analyses revealed that atogepant treatment was associated with significant changes in dopaminergic-related FC. Following 12-week atogepant treatment, patients exhibited increased FC within the MCL dopaminergic system (D1 receptor and DAT transporter), in a cluster located in the medial orbitofrontal cortex (Table 3 , Fig. 1 B). Table 3 Longitudinal neurotransmitter-related functional connectivity analyses. Results of neurotransmitter-related functional connectivity changes after 12 weeks of atogepant treatment are reported. All results are thresholded at p < 0.05 FWE-corrected at the cluster level and p < 0.001 uncorrected (unc.) at the connection level. Neurotransmitter Receptor/ Transporter Contrast Region (Hammersmith) MNI x y z Cluster (FWE) K (mm 3 ) T value Z score Peak (unc.) Cohen's d (95% CI) Dopamine (MCL) D1 T1 > T0 R Superior frontal gyrus/ Medial orbital gyrus 6,58,-8 0.05 66 6.37 4.3 T0 R Superior frontal gyrus/ Medial orbital gyrus 6,62,-8 0.04 74 5.67 4.02 < .001 2.35 Abbreviations: MCL = mesocorticolimbic system; T0 = baseline; T1 = follow-up after 12 weeks of atogepant treatment; R = right hemisphere; K = cluster extent; CI = confidence Interval – insert Table 3 about here – 3.4 Correlation between rs-fMRI connectivity and clinical scales The correlation analysis of longitudinal changes in ROI-to-ROI FC with longitudinal differences in clinical variables showed a significant association. Greater longitudinal increases in FC between the right PCC and the left ACC were associated with larger reductions over the 12-week treatment period in MDD (ΔMDD; r= -0.52, p = 0.05), MAM (ΔMAM; r= -0.56, p = 0.03), and ASC-12 scores (ΔASC-12 r= -0.60, p = 0.02). No significant correlations were found between longitudinal changes in neurotransmitter-related FC clusters and clinical variables. 4. Discussion In this study, we observed that 12-week atogepant treatment in EM patients is associated with significant changes in the brain’s FC at rest and dopamine-related FC within the mesocorticolimbic system, accompanied by a sizable clinical improvement, in both MMD and MAM, consistently with atogepant efficacy data [ 10 ]. Furthermore, we also observed a concomitant decrease in allodynia, a marker of central sensitization. This is partly consistent with previous findings which showed marked decrease in ASC-12, [ 29 , 30 ], for example with erenumab [ 31 ], where this effect was attributed to prolonged suppression of peripheral nociceptive input. Our findings therefore may suggest that atogepant may similarly modulate central sensitization mechanisms. Longitudinal ROI-to-ROI analyses showed a pattern of increased connectivity at T1, involving the right SFG, bilateral putamen, left pallidum, left ACC, and bilateral PCC. Importantly, greater the longitudinal increase in ACC-PCC connectivity, the larger the improvement in MDD, MAM, and allodynia scores after treatment, linking FC modulation with clinical response. All these regions participate in different aspects of the neural processing of the pain experience, with either pain-facilitating or pain-inhibiting functions and show functional alterations in migraine patients [ 1 – 3 , 32 ]. The SFG emerged as the node showing the largest number of post-treatment connectivity changes with other ROIs, indicating a central role in the network reorganization. This finding should be considered alongside previous data [ 33 ] reporting reduced connectivity between the dorsolateral prefrontal cortex and several cortical and basal ganglia regions, including the putamen, in EM patients. Given the critical involvement of SFG in pain modulation, both findings point to a dysfunction of descending pain-control pathways both interictally and in relation to treatment-related reorganization. In this framework, the increased frontal-putaminal connectivity observed after atogepant may reflect a strengthening of interictal fronto-striatal coupling and a reinforcement of top-down pain modulation mechanisms. The SFG also showed longitudinally increased connectivity with the ACC, which in turn exhibited enhanced connectivity with the PCC, suggesting an atogepant-related strengthening of frontal-anterior cingulate-posterior cingulate interactions. Within this axis, the ACC plays a central role: it integrates the sensory, emotional, and motivational components of the pain experience, contributes to autonomic regulation, and acts as a key node of descending pain-control pathways [ 34 – 36 ]. The PCC, on the other hand, is a core hub of the default mode network, involved in the conscious experience of pain [ 36 ]. Together with the superior frontal cortex, these regions form a network implicated in higher-order modulation of nociceptive processing. Findings from migraine cohorts further highlight the relevance of these midline regions. Consistent structural and functional alterations were reported in the prefrontal cortex, ACC, and PCC [ 1 , 37 – 40 ]. These functional abnormalities include reduced interictal metabolism in both cingulate regions, particularly in the ACC, where lower metabolic activity correlates with longer disease duration and higher attack frequency [ 41 ] as well as altered FC. In particular, stronger coupling between the PCC, ACC, and medial prefrontal regions has been associated with more efficient conditioned pain modulation [ 42 ], whereas pain catastrophizing corresponds to reduced activation of these same nodes [ 43 ]. Resting-state ACC-PCC [ 44 ] and PCC [ 43 ] connectivity has also been negatively correlated with pain intensity, and patients with cutaneous allodynia exhibit heightened cingulate responses to painful stimulation [ 44 ], reinforcing the role of this pathway is critical for endogenous pain regulation and central sensitization in migraine. In this context, the increase in frontal-ACC-PCC connectivity observed in our cohort, and the ACC-PCC FC association with reductions in MMD, MAM, and allodynia, suggests that atogepant may help restore the functional balance of this midline regulatory system. Interestingly, the regions identified in our study (i.e. frontal areas, cingulate cortex, and putamen) overlap with those showing increased connectivity in response to painful stimulation in patients who responded to an 8-week erenumab treatment compared with non-responders [ 11 ]. Considering that erenumab and atogepant targets both the CGRP receptor, these converging findings raise the possibility of a similar, albeit likely indirect, central mechanism of action underlying therapeutic response [ 35 , 37 , 40 ]. With respect to neurotransmitter-related FC, we observed increased dopamine-related connectivity in the mesocorticolimbic dopaminergic system (D1 receptor and DAT transporter), in a cluster located in the medial orbitofrontal cortex, a key node region of the mesocorticolimbic circuit, that contributes to pain modulation by integrating the affective and motivational dimensions of painful stimuli and by exerting inhibitory coupling with regions involved in nociceptive information processing [ 45 ]. Alterations of this dopaminergic pathway have already been described in migraine, where mesocorticolimbic FC alterations associated with dopamine availability have been previously reported in EM patients, suggesting a dopaminergic contribution to pain processing and affective response to pain [ 46 ]. Importantly, similar patterns have been observed across chronic pain conditions. Reduced dopamine synthesis and release, together with gray matter volume loss of its major nodes, have been observed [ 47 ]. Likewise, disrupted mesocorticolimbic connectivity has been associated with central pain sensitization and chronification in conditions such as chronic low back pain [ 48 ]. Based on these observations, the dopamine-related FC increases observed here may reflect a normalization of altered mesocorticolimbic dynamics, suggesting that atogepant may exert part of its therapeutic effect by directly or indirectly normalizing disrupted dopaminergic connectivity patterns, restoring adaptive pain processing mechanisms. Although atogepant antagonism of the CGRP receptor is well established, the extent to which atogepant crosses the BBB remains uncertain. Both gepants and mAbs exhibit limited BBB permeability and are therefore believed to exert their therapeutic effects outside the central nervous system (CNS) [ 49 , 50 ]. For mAbs, the prevailing view is that their reduction of peripheral activation afferents, may secondarily influence central brain regions involved in pain perception, salience processing, and descending inhibitory control [ 11 – 14 , 51 , 52 ]. On the other hand, even small quantities reaching the CNS may still contribute to treatment response [ 14 ]. Although slightly higher in BBB penetration, a similar mechanism is plausible even for gepants. In this framework, the clinical improvements observed in our cohort together with the longitudinal increases in connectivity across regions involved in multiple dimensions of pain processing, including the mesocorticolimbic dopaminergic system, may reflect indirect central modulation mediated by atogepant resulting from reduced peripheral nociceptive drive. Further work is needed to clarify whether atogepant, and gepants broadly, exert central actions directly or predominantly through peripheral CGRP-receptor blockade and comparative studies with mAbs will be crucial to clarify exact mechanisms of anti-CGRP therapies. Limitations Our findings should be considered in light of some limitations. First , our sample size of EM patients was modest, although homogeneous, reflecting the explorative nature of the study. Second , the follow-up period was limited to 12 weeks. While this interval aligns with standard clinical practice, longer observation may help clarify the extent and durability of atogepant CNS effects. Third , this was a cohort study, and therefore we cannot completely exclude that the observed variations in brain connectivity are the solely effect of atogepant administration. Studies with a control condition could further explore the specific neuromodulation activity of atogepant. Conclusion In this study, we provide the first evidence of longitudinal FC changes in brain regions involved in pain processing and within the dopaminergic mesocorticolimbic system in EM patients after 12 weeks of atogepant treatment. These findings may reflect direct or indirect central neurofunctional adaptations associated with clinical improvement. A deeper understanding of such central changes could offer new insights into the mechanism underlying successful preventive therapies for migraine. Declarations Data availability Data may be made available upon reasonable request to corresponding author. Acknowledgements The Authors are grateful to the GARR consortium for the high-performance infrastructure used for the analyses at Neuroradiology Unit of Fondazione IRCCS Istituto Neurologico Carlo Besta (Milan, Italy). DAM has received personal fee as speaker and travel grant from: AbbVie, Lundbeck, Pfizer, TEVA Pharm Ind. LG has received consultancy and advisory fees from: Abbvie, EliLilly, Lundbeck, Organon, Pfizer, TEVA Pharm Ind. Competing interests The authors declare that they have no competing interests. AR is associate editor of The Journal of Headache and Pain. He was not involved in the journal’s peer review process of, or decisions related to, this manuscript. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Fondazione IRCCS Istituto Neurologico Carlo Besta (approval CET 48/24 BST 61/23). Funding This work was supported by the Fondazione Cariplo (Biomedical Research conducted by Young Researchers 2022) research grant MESH 2022 − 0610 (to AN) and RRC (to MG). GC has been funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 3.3 - Call for tender No. 117 of 02/03/2023 of Italian Ministry of University and Research funded by the European Union - NextGenerationEU. Contributions DAM, LG, AN, DF, and GD conceived and designed the study; DAM and LG performed patient recruitment and data curation; DAM, LG, GD, AP, AR, AM, and GR performed patient clinical evaluation; AN, DF, GC, JPMC, and GD contributed to neuroimaging data collection and curation; AE, DF and GC performed neuroimaging data clinical assessment; DF, GC, and JPMC performed statistical and neuroimaging data analyses; DAM, DF, AN, GD, and GC drafted the manuscript; JPMC, EC, AE, MG, AM, GR, AP, AR, and LG conducted a rigorous review of the manuscript and provided critical feedback; AN and MG contributed to funding acquisition; MG provided MRI facility resources; AR, AN, LG, MG supervised the project. All authors contributed toward the revision and writing of the final draft and approved the final version of the manuscript before submission. DF performed manuscript submission and author correspondence. References Raggi A, Leonardi M, Arruda M et al (2024) Hallmarks of primary headache: part 1 - migraine. J Headache Pain 25:189. https://doi.org/10.1186/s10194-024-01889-x Schwedt TJ, Chiang C-C, Chong CD, Dodick, David W (2015) Functional Magnetic Resonance Imaging of Migraine. Lancet Neurol 14:81–91. https://doi.org/10.1016/S1474-4422(14)70193-0 Schramm S, Börner C, Reichert M et al (2023) Functional magnetic resonance imaging in migraine. A systematic review Xue T, Yuan K, Cheng P et al (2013) Alterations of regional spontaneous neuronal activity and corresponding brain circuit changes during resting state in migraine without aura. NMR Biomed 26:1051–1058. https://doi.org/10.1002/nbm.2917 Cai M, Liu J, Wang X et al (2023) Spontaneous brain activity abnormalities in migraine: A meta-analysis of functional neuroimaging. Hum Brain Mapp 44. https://doi.org/10.1002/hbm.26085 Xue T, Yuan K, Zhao L et al (2012) Intrinsic Brain Network Abnormalities in Migraines without Aura Revealed in Resting-State fMRI. PLoS ONE 7. https://doi.org/10.1371/journal.pone.0052927 Haghdoost F, Puledda F, Garcia-Azorin D et al (2023) Evaluating the efficacy of CGRP mAbs and gepants for the preventive treatment of migraine: A systematic review and network meta-analysis of phase 3 randomised controlled trials. Cephalalgia 43. https://doi.org/10.1177/03331024231159366 Pozo-Rosich P, Ailani J, Ashina M et al (2023) Atogepant for the preventive treatment of chronic migraine (PROGRESS): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 402. https://doi.org/10.1016/S0140-6736(23)01049-8 Ashina M, Tepper SJ, Reuter U et al (2023) Once-daily oral atogepant for the long-term preventive treatment of migraine: Findings from a multicenter, randomized, open-label, phase 3 trial. Headache 63:79–88. https://doi.org/10.1111/head.14439 Lipton RB, Pozo-Rosich P, Blumenfeld AM et al (2022) Rates of Response to Atogepant for Migraine Prophylaxis among Adults: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 5:E2215499. https://doi.org/10.1001/jamanetworkopen.2022.15499 Schwedt TJ, Nikolova S, Dumkrieger G et al (2022) Longitudinal changes in functional connectivity and pain-induced brain activations in patients with migraine: a functional MRI study pre- and post- treatment with Erenumab. J Headache Pain 23:1–15. https://doi.org/10.1186/s10194-022-01526-5 Szabo E, Bolo NR, Borsook D et al (2025) Peripherally acting anti-CGRP monoclonal antibodies attenuate cortical resting-state connectivity in migraine patients. Cephalalgia 45. https://doi.org/10.1177/03331024241313377 Basedau H, Sturm LM, Mehnert J et al (2022) Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study. Elife 11:1–15. https://doi.org/10.7554/eLife.77146 Ziegeler C, Mehnert J, Asmussen K, May A (2020) Central effects of erenumab in migraine patients: An event-related functional imaging study. Neurology 95:E2794–E2802. https://doi.org/10.1212/WNL.0000000000010740 Recober A (2021) Pathophysiology of Migraine. Continuum (Minneap Minn) 27:. https://doi.org/10.1212/CON.0000000000000983 Villalón CM, VanDenBrink AM (2017) The Role of 5-Hydroxytryptamine in the Pathophysiology of Migraine and its Relevance to the Design of Novel Treatments. Mini-Reviews Med Chem 17. https://doi.org/10.2174/1389557516666160728121050 Headache Classification Comittee of the International Headache Society (IHS) (2018) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38:1–211. https://doi.org/10.1177/0333102417738202 Esteban O, Birman D, Schaer M et al (2017) MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12. https://doi.org/10.1371/journal.pone.0184661 Nieto-Castanon A, Whitfield-Gabrieli S (2020) CONN functional connectivity toolbox: RRID SCR_009550, release 20 Dipasquale O, Selvaggi P, Veronese M et al Receptor-Enriched Analysis of functional connectivity by targets (REACT): A novel, multimodal analytical approach informed by PET to study the pharmacodynamic response of the brain under MDMA. Neuroimage 195:. https://doi.org/10.1016/j.neuroimage.2019.04.007 Beliveau V, Ganz M, Feng L et al (2017) A high-resolution in vivo atlas of the human brain’s serotonin system. J Neurosci 37:120–128. https://doi.org/10.1523/JNEUROSCI.2830-16.2016 Hansen JY, Shafiei G, Markello RD et al (2022) Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci 25:1569–1581. https://doi.org/10.1038/s41593-022-01186-3 Manca R, De Marco M, Soininen H et al (2025) Changes in neurotransmitter-related functional connectivity along the Alzheimer’s disease continuum. Brain Commun 7. https://doi.org/10.1093/braincomms/fcaf008 Trutti AC, Fontanesi L, Mulder MJ et al (2021) A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data. Brain Struct Funct 226:1155–1167. https://doi.org/10.1007/s00429-021-02231-w Hammers A, Allom R, Koepp MJ et al (2003) Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp 19:224–247. https://doi.org/10.1002/hbm.10123 Gousias IS, Rueckert D, Heckemann RA et al (2008) Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. NeuroImage 40:672–684. https://doi.org/10.1016/j.neuroimage.2007.11.034 Faillenot I, Heckemann RA, Frot M, Hammers A (2017) Macroanatomy and 3D probabilistic atlas of the human insula. NeuroImage 150:88–98. https://doi.org/10.1016/j.neuroimage.2017.01.073 Wild HM, Heckemann RA, Studholme C, Hammers A (2017) Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases. PLoS ONE 12. https://doi.org/10.1371/journal.pone.0180866 Montisano DA, Giossi R, Canella M et al (2024) Reducing the Impact of Headache and Allodynia Score in Chronic Migraine: An Exploratory Analysis from the Real-World Effectiveness of Anti-CGRP Monoclonal Antibodies Compared to Onabotulinum Toxin A (RAMO) Study. Toxins (Basel) 16. https://doi.org/10.3390/toxins16040178 Mínguez-Olaondo A, Quintas S, Morollón Sánchez-Mateos N et al (2022) Cutaneous Allodynia in Migraine: A Narrative Review. Front Neurol 12:1–10. https://doi.org/10.3389/fneur.2021.831035 Lipton RB, Burstein R, Buse DC et al (2021) Efficacy of erenumab in chronic migraine patients with and without ictal allodynia. Cephalalgia 41:1152–1160. https://doi.org/10.1177/03331024211010305 Martucci KT, Mackey SC (2018) Neuroimaging of Pain: Human Evidence and Clinical Relevance of Central Nervous System Processes and Modulation. Anesthesiology 128:1241–1254. https://doi.org/10.1097/ALN.0000000000002137 Mungoven TJ, Marciszewski KK, Macefield VG et al (2022) Alterations in pain processing circuitries in episodic migraine. J Headache Pain 23. https://doi.org/10.1186/s10194-021-01381-w Beissner F, Meissner K, Bar K-J et al (2013) The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function. J Neurosci 33:10503–10511. https://doi.org/10.1523/JNEUROSCI.1103-13.2013 Bushnell MC, Čeko M, Low LA (2013) Cognitive and emotional control of pain and its disruption in chronic pain. Nat Rev Neurosci 14:502–511. https://doi.org/10.1038/nrn3516 Benarroch EE (2020) Physiology and Pathophysiology of the Autonomic Nervous System. Contin Lifelong Learn Neurol 26:12–24. https://doi.org/10.1212/CON.0000000000000817 Yu D, Yuan K, Luo L et al (2017) Abnormal functional integration across core brain networks in migraine without aura. Mol Pain 13:1–10. https://doi.org/10.1177/1744806917737461 Maleki N, Becerra L, Brawn J et al (2012) Concurrent functional and structural cortical alterations in migraine. Cephalalgia 32:607–620. https://doi.org/10.1177/0333102412445622 Jin C, Yuan K, Zhao L et al (2013) Structural and functional abnormalities in migraine patients without aura. NMR Biomed 26:58–64. https://doi.org/10.1002/nbm.2819 Cahill CM, Cook C, Pickens S et al (2014) Migraine and Reward System—Or Is It Aversive? Curr Pain Headache. https://doi.org/10.1007/s11916-014-0410-y . Reports 2014 185 18: Kim JH, Kim S, Suh SI et al (2010) Interictal metabolic changes in episodic migraine: A voxel-based FDG-PET study. Cephalalgia 30:53–61. https://doi.org/10.1111/j.1468-2982.2009.01890.x Argaman Y, Kisler LB, Granovsky Y et al (2020) The Endogenous Analgesia Signature in the Resting Brain of Healthy Adults and Migraineurs. J Pain 21. https://doi.org/10.1016/j.jpain.2019.12.006 Mathur VA, Moayedi M, Keaser ML et al (2016) Frontiers | High Frequency Migraine Is Associated with Lower Acute Pain Sensitivity and Abnormal Insula Activity Related to Migraine Pain Intensity, Attack Frequency, and Pain Catastrophizing. Front Hum Neurosci 10. https://doi.org/10.3389/fnhum.2016.00489 Russo A, Esposito F, Conte F et al (2017) Functional interictal changes of pain processing in migraine with ictal cutaneous allodynia. Cephalalgia 37:305–314. https://doi.org/10.1177/0333102416644969 Becker S, Gandhi W, Pomares F et al (2017) Orbitofrontal cortex mediates pain inhibition by monetary reward. Soc Cogn Affect Neurosci 12:651–661. https://doi.org/10.1093/scan/nsw173 Kim DJ, Jassar H, Lim M et al (2021) Dopaminergic regulation of reward system connectivity underpins pain and emotional suffering in migraine. J Pain Res 14:631–643. https://doi.org/10.2147/JPR.S296540 Yang S, Boudier-Revéret M, Choo YJ, Chang MC (2020) Association between chronic pain and alterations in the mesolimbic dopaminergic system. Brain Sci 10:1–14. https://doi.org/10.3390/brainsci10100701 Yu S, Li W, Shen W et al (2020) Impaired mesocorticolimbic connectivity underlies increased mechanical pain sensitivity in chronic low back pain. NeuroImage 218. https://doi.org/10.1016/j.neuroimage.2020.116969 Edvinsson L, Warfvinge K (2019) Recognizing the role of CGRP and CGRP receptors in migraine and its treatment. Cephalalgia 39:366–373. https://doi.org/10.1177/0333102417736900 Eftekhari S, Salvatore CA, Johansson S et al (2015) Localization of CGRP, CGRP receptor, PACAP and glutamate in trigeminal ganglion. Relation to the blood-brain barrier. Brain Res 1600:93–109. https://doi.org/10.1016/j.brainres.2014.11.031 Basedau H, Peng KP, Schellong M, May A (2024) Double-blind, randomized, placebo-controlled study to evaluate erenumab-specific central effects: an fMRI study. J Headache Pain 25:1–9. https://doi.org/10.1186/s10194-023-01709-8 De Tommaso M, Vecchio E, Quitadamo SG et al (2021) Pain-related brain connectivity changes in migraine: A narrative review and proof of concept about possible novel treatments interference. Brain Sci 11:1–21. https://doi.org/10.3390/brainsci11020234 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8406852","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565619379,"identity":"079ac0d8-ac64-407a-b676-3dd7ef47e078","order_by":0,"name":"Danilo Antonio Montisano","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Danilo","middleName":"Antonio","lastName":"Montisano","suffix":""},{"id":565619381,"identity":"812125d2-a1ae-42a6-baaf-cf2e25553a29","order_by":1,"name":"Davide Fedeli","email":"data:image/png;base64,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","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":true,"prefix":"","firstName":"Davide","middleName":"","lastName":"Fedeli","suffix":""},{"id":565619383,"identity":"13966a87-bb60-4486-b81e-edc10e3ced62","order_by":2,"name":"Greta Demichelis","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Greta","middleName":"","lastName":"Demichelis","suffix":""},{"id":565619384,"identity":"cb2e6520-8f40-47b0-89ea-c41689a03020","order_by":3,"name":"Giuseppe Ciullo","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Ciullo","suffix":""},{"id":565619385,"identity":"56fcb624-5f38-4a9a-a991-54e91c333ccf","order_by":4,"name":"Jean Paul Medina Carrion","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Paul Medina","lastName":"Carrion","suffix":""},{"id":565619386,"identity":"a90cf6b9-7b3b-4fea-be01-8817241029ce","order_by":5,"name":"Emilio Ciusani","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Emilio","middleName":"","lastName":"Ciusani","suffix":""},{"id":565619387,"identity":"931f7f03-e7f6-4af9-8bc3-12485b10994f","order_by":6,"name":"Alessandra Erbetta","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"","lastName":"Erbetta","suffix":""},{"id":565619388,"identity":"b7203193-1eff-4330-bb9b-ec387ad82459","order_by":7,"name":"Marina Grisoli","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Grisoli","suffix":""},{"id":565619389,"identity":"774411e3-89a4-4d47-8854-cb4d4e1802a8","order_by":8,"name":"Alessia Marcassoli","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Alessia","middleName":"","lastName":"Marcassoli","suffix":""},{"id":565619390,"identity":"64a9b892-9fce-42f2-b8a1-9037a9f1ccd4","order_by":9,"name":"Giulia Regonesi","email":"","orcid":"","institution":"ASST Bergamo Est","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Regonesi","suffix":""},{"id":565619391,"identity":"f5b1ae3c-82ee-48bb-a8b5-468e10662a0d","order_by":10,"name":"Alessandra Parisi","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"","lastName":"Parisi","suffix":""},{"id":565619392,"identity":"00ad386b-81c6-4e68-8ff8-ba95f5a76fd6","order_by":11,"name":"Alberto Raggi","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Raggi","suffix":""},{"id":565619394,"identity":"86ec5806-615d-440e-9875-c90a33ad18ce","order_by":12,"name":"Anna Nigri","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Nigri","suffix":""},{"id":565619396,"identity":"df225e21-1e84-4d99-90e4-4002f68a73d2","order_by":13,"name":"Licia Grazzi","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Neurologico Carlo Besta","correspondingAuthor":false,"prefix":"","firstName":"Licia","middleName":"","lastName":"Grazzi","suffix":""}],"badges":[],"createdAt":"2025-12-19 17:17:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8406852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8406852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99221685,"identity":"d0ff9754-5f77-4fd8-8154-480e223aa00c","added_by":"auto","created_at":"2025-12-30 09:51:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptJHP19122025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/5ab93879c3fb29f33bdb88e4.docx"},{"id":99318149,"identity":"1c85ee57-baa6-419b-ac8f-83940ef91c45","added_by":"auto","created_at":"2025-12-31 16:31:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8428,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/518a858e9a6f35a3ffc73ca8.docx"},{"id":99221686,"identity":"204ed6ee-2a64-4fd5-9eb3-ffb99e0e88c5","added_by":"auto","created_at":"2025-12-30 09:51:18","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15560,"visible":true,"origin":"","legend":"","description":"","filename":"e376cace86404a73af21e384f209b16b.json","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/512dfb126dd2b8c0cb1747ea.json"},{"id":99221681,"identity":"b3c1b417-4b86-435f-9d6d-0ca091bfe702","added_by":"auto","created_at":"2025-12-30 09:51:18","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129897,"visible":true,"origin":"","legend":"","description":"","filename":"e376cace86404a73af21e384f209b16b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/3e0a3173195fd14973b83fe3.xml"},{"id":99318801,"identity":"9a903502-ecf9-45ca-8c5b-dd25daf9c143","added_by":"auto","created_at":"2025-12-31 16:34:44","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4984818,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/4c6f9967d00dd45a9439df68.png"},{"id":99221678,"identity":"98c43b7e-f8f4-4f96-99c9-cefa19348a3c","added_by":"auto","created_at":"2025-12-30 09:51:18","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":735948,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/94efcc37bc72c1669f66bf53.png"},{"id":99318765,"identity":"ae882296-5d0e-4f94-bea2-b0382ab3f7b9","added_by":"auto","created_at":"2025-12-31 16:34:24","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127095,"visible":true,"origin":"","legend":"","description":"","filename":"e376cace86404a73af21e384f209b16b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/dd24dd7741a77b5cdea8963b.xml"},{"id":99221682,"identity":"c5b30c14-ee2b-403f-ac47-25a0cf9cca7e","added_by":"auto","created_at":"2025-12-30 09:51:18","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142254,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/149447946a7961909f427772.html"},{"id":99318574,"identity":"988eed07-d2e4-4e97-b16f-ec33d5208c4d","added_by":"auto","created_at":"2025-12-31 16:33:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4984818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePanel A \u003c/strong\u003eshows longitudinally increased Region-of-Interest to Region-of-Interest functional connectivity changes after 12 weeks of atogepant treatment. All results are reported at p\u0026lt;0.05 FDR-corrected at the cluster level and p\u0026lt;0.05 uncorrected at the connection level. Results are reported in sagittal view, axial view, and as a connectivity graph. Correlations between connectivity differences and clinical improvement are reported in the connectivity graph. \u003cstrong\u003ePanel B\u003c/strong\u003e shows longitudinally increased neurotransmitter-related functional connectivity changes after 12 weeks of atogepant treatment in the mesocorticolimbic dopaminergic system for D1 receptor and DAT transporter. All results are reported at p \u0026lt;.05 FWE-corrected at the cluster level and p\u0026lt;.001 uncorrected at the connection level. Axial slices are reported at MNI z = -8; sagittal slices are reported at MNI x = 6; \u003cem\u003eAbbreviations: SFG = Superior Frontal Gyrus; ACC = Anterior Cingulate Cortex; PCC = Posterior Cingulate Cortex; PUT = Putamen; PAL = Globus Pallidus; r/R = right hemisphere; l/L = left hemisphere; Δ = longitudinal difference (treatment vs. baseline); MMD = monthly migraine days; MAM = acute drugs intake; ASC12 = allodynia score.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/9f372262165405e9a5de858c.png"},{"id":99789833,"identity":"52973214-f06a-4d87-a48a-b0b5ff9bf18d","added_by":"auto","created_at":"2026-01-08 12:50:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8307156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8406852/v1/a7600f54-015d-4214-84b1-e747b563aaab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAtogepant modulates brain connectivity in episodic migraine: a longitudinal cohort fMRI study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMigraine is a common disabling neurological condition, characterized by severe recurrent headaches, accompanied by several neurological and autonomic symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its pathophysiology involves peripheral nociceptive signals and complex central alterations from the trigemino-vascular system, neurotransmitter systems, and central pain modulation regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome neuroimaging studies have provided evidence of central dysfunction in migraine population, reporting pain-induced brain alterations and suggesting a dysregulation of pain facilitation and inhibition processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Resting-state fMRI (rs-fMRI) studies have further revealed widespread disrupted functional connectivity (FC) across multiple cortical and subcortical regions and networks implicated in sensory, cognitive, and affective aspects of pain [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNovel migraine preventive treatments acting on Calcitonin Gene Related Peptide (CGRP) pathway have shown remarkable efficacy in clinical practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CGRP, plays a major role in migraine pathophysiology by promoting meningeal vessels dilation, local neurogenic inflammation, thereby releasing pro-nociceptive mediators that enhance pain transmission and sensitivity. Two main classes of CGRP-targeting treatments are available: monoclonal antibodies (mAbs anti-CGRP; e.g., erenumab, galcanezumab) directed against CGRP or its receptor, and small-molecule CGRP receptor antagonists, gepants [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, atogepant, a recently approved gepant theapeutic drug, has demonstrated high safety, tolerability, and efficacy in migraine prevention. Clinical evidence shows a rapid and robust preventive effect, with 60\u0026ndash;80% of patients achieving a\u0026thinsp;\u0026ge;\u0026thinsp;50% reduction in monthly migraine episodes within three months, and up to ~\u0026thinsp;50% reaching complete remission at one year [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnly few neuroimaging studies have investigated whether anti-CGRP mAbs could modify brain activity. For erenumab, Schwedt et al. showed changes in network FC efficiency and central processing of extracranial painful stimuli involving cingulate and frontal cortices, putamen, and periaqueductal gray in treatment responders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For galcanezumab, reduction FC between the somatosensory, motor, and insular cortices in responders was observed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], moreover, a decreases hypothalamic activation stronger in responders was observed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By contrast, no neuroimaging evidence are available for gepants. Whereas mAbs act mainly peripherally and have limited blood-brain barrier (BBB) permeability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the degree of BBB penetration of gepant is still unclear, and this leaves open the question of its potential central neurofunctional involvement. Moreover, since migraine pathophysiology involves multiple neurotransmitter systems, including serotonin, which contributes to migraine generation, and dopamine, which has been associated with prodromal symptoms such as yawning and nausea, understanding how anti-CGRP treatments modulate neurotransmitter activity may provide new insights into their central mechanisms of action [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn these grounds, this longitudinal study aimed to investigate the effects of a 12-week atogepant treatment on resting state brain FC and on FC modulated by the spatial distribution of different neurotransmitter receptors in a cohort of episodic migraine (EM).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects and clinical assessment\u003c/h2\u003e \u003cp\u003eWe conducted a prospective, longitudinal, single-arm cohort study (pre-post design) including patients with EM who were na\u0026iuml;ve to anti-CGRP therapies. Participants underwent clinical and resting-state fMRI assessments at baseline and after 12 weeks of continuous treatment with atogepant 60 mg/day.\u003c/p\u003e \u003cp\u003ePatients with a diagnosis of EM were recruited at our headache centre at Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy. Inclusion criteria were: age\u0026thinsp;\u0026gt;\u0026thinsp;18 and diagnosis of EM according to the ICHD-3 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Patients with prior exposure to anti-CGRP therapies, comorbidity with neurological, cardiovascular, psychiatric disease or diabetes, and pregnant women were escluded. Included patients underwent a 12-week treatment with daily atogepant 60mg, in line with usual recommendations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Clinical assessment and MRI scans were acquired before (T0, baseline) and after completing the treatment period (T1, follow-up), from May 2024 to December 2024.\u003c/p\u003e \u003cp\u003eThe study was approved by the local Ethical Committee (approval CET 48/24 BST 61/23) and all participants provided their consent to the study.\u003c/p\u003e \u003cp\u003eWe also collected for each patient: age, sex, monthly migraine days (MMD), monthly acute medication (MAM), allodynia (ASC-12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 MRI acquisition\u003c/h2\u003e \u003cp\u003ePatients underwent MRI acquisition, including a high-resolution structural 3D T1-weighted (T1w) image (Repetition Time [TR]\u0026thinsp;=\u0026thinsp;8.11 s; Echo Time [TE]\u0026thinsp;=\u0026thinsp;3.71 ms; Field-of-View [FOV]\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm; voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e; flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;; 185 sagittal slices) and a rs-fMRI scan (15 min, eyes-opened; T2*-weighted BOLD echo-planar imaging gradient-echo sequence; TR\u0026thinsp;=\u0026thinsp;2000 ms; TE\u0026thinsp;=\u0026thinsp;30 ms; FOV\u0026thinsp;=\u0026thinsp;80 \u0026times; 80 mm; voxel size\u0026thinsp;=\u0026thinsp;3 \u0026times; 3 \u0026times; 3.2 mm\u003csup\u003e3\u003c/sup\u003e; interslice gap\u0026thinsp;=\u0026thinsp;0.4 mm; flip angle\u0026thinsp;=\u0026thinsp;80\u0026deg;; 34 axial slices; 450 volumes; Phase Encoding direction\u0026thinsp;=\u0026thinsp;posterior/anterior), using a 3T Philips Achieva-dStream scanner, with a 32-head channel coil. Patients were headache-free for at least 24 hours prior to and during the MRI acquisitions. All images were inspected by an expert neuroradiologist (A.E.) to exclude brain abnormalities or artefacts affecting the exam quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 rs-fMRI pre-processing\u003c/h2\u003e \u003cp\u003eImage quality control of rs-fMRI scan was performed using MRIQC software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Rs-fMRI data were analysed with the CONN toolbox (v. 20.b; running on Matlab 2020b; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) using the default-MNI pipeline. For each patient and each session, functional volumes were realigned to the first volume, unwrapped, and slice-time corrected. The ART toolbox, implemented within CONN, was used for outlier volumes identification and scrubbing (framewise displacement\u0026thinsp;\u0026gt;\u0026thinsp;1.1 mm; global BOLD signal changes\u0026thinsp;\u0026gt;\u0026thinsp;5 SD). Both rs-fMRI and T1w images were normalized to the MNI space and functional images were smoothed with a 6 mm\u003csup\u003e3\u003c/sup\u003e full width at half-maximum (FWHM) gaussian kernel. Rs-fMRI data were denoised using the anatomical component-based noise correction, with white matter and cerebrospinal fluid component derived from T1w image segmentation. The effect of realignment parameters, outlier volumes scrubbing parameters, physiological noise components, and initial magnetization transient effects regressed out. Data was then bandpass-filtered with a 0.008-0.1 Hz filter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Neurotransmitter-related functional connectivity pre-processing\u003c/h2\u003e \u003cp\u003eTo investigate the FC in relation to the spatial distribution of underlying neurotransmitter systems, we employed the Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) approach. REACT-fMRI toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ottaviadipasquale/react-fmri\u003c/span\u003e\u003cspan address=\"https://github.com/ottaviadipasquale/react-fmri\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] employs a two-step regression approach (also referred to as a \u0026ldquo;dual regression\u0026rdquo;) to estimate subject-specific FC maps guided by the distribution of neurotransmitters, using publicly available molecular templates generated from PET scans of healthy individuals [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reference molecular maps from serotonin and dopamine were used to generate neurotransmitter-enriched FC maps. The serotonin atlas included maps of 5-HT\u003csub\u003e1a\u003c/sub\u003e, 5-HT\u003csub\u003e1b\u003c/sub\u003e, 5-HT\u003csub\u003e2a\u003c/sub\u003e, 5-HT\u003csub\u003e4\u003c/sub\u003e receptors, and the serotonin transporter (5-HTT). The dopamine atlas included maps of D\u003csub\u003e1\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e receptors, and the dopamine transporter (DAT). The cerebellum was removed from the serotonin atlas and the D\u003csub\u003e1\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e templates, while the occipital cortex was removed from the DAT map as it was used as reference regions in radioligands kinetic models [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Intensity values were then normalized (i.e., 0 minimum to 1 maximum) for each image.\u003c/p\u003e \u003cp\u003eFC maps informed by serotonin maps were masked with the standard MNI gray matter binary mask provided within REACT. Dopamine maps were further subdivided into two pathways: mesocorticolimbic (MCL) and nigrostriatal (NS) systems. Two binary gray matter masks were created to better characterize these pathways [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The MCL mask included the following cortical and subcortical regions: anterior cingulate cortex (ACC), amygdala, hippocampus, parahippocampus, inferior frontal orbital cortex, entorhinal cortex, subcallosal gyrus, nucleus accumbens, lateral frontal orbital cortex, middle frontal gyrus, medial frontal gyrus, superior frontal gyrus (SFG) from WFU PickAtlas toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/wfu_pickatlas\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/wfu_pickatlas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as the ventral tegmental [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; the NS mask included caudate nucleus, substantia nigra, putamen and globus pallidus from WFU PickAtlas toolbox.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Demographic and clinical assessment\u003c/h2\u003e \u003cp\u003eNormality of clinical variables was assessed using the Kolmogorov-Smirnov test. Within-subject longitudinal differences in these variables were assessed using paired-sample t-tests (or the corresponding nonparametric equivalent in case normality was not satisfied). All analyses were performed in Python.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.5.2 rs-fMRI analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo test the effects of a 12-week atogepant treatment on FC in EM patients, we performed a longitudinal region of interest (ROI)-to-ROI analysis in CONN toolbox. For each participant and each session, a connectivity matrix including ROIs from the Hammersmith atlas [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was computed. This atlas was selected due to its fine-grained parcellation of insular, frontal, temporal, and subcortical regions, which are key areas implicated in both migraine and pain processing, offering more precise anatomical details than the standard Harvard-Oxford atlas included in CONN. Pearson correlation coefficients were calculated between the time series of each ROI and those of all other ROIs. These correlation values were subsequently transformed into Z scores using the Fisher R-to-Z transformation, resulting in a whole-brain connectivity matrix for each participant and time point. These matrices were then analysed using a second-level general linear model to assess longitudinal changes in network connectivity pre- and post-treatment. To correct for multiple comparisons and control the false discovery rate, a cluster-level False Discovery Rate (FDR) threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;.05 was applied. Only ROI-to-ROI connections that met this criterion were retained for further analysis. Additionally, a connection-level uncorrected threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;.05 (as per CONN's default setting) was used to identify the strongest individual connections within each significant cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.5.3 Neurotransmitter-related functional connectivity analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo test the effects of atogepant treatment on FC in relation to the spatial distribution of neurotransmitters, longitudinal neurotransmitter-related FC maps were generated for each subject and each neurotransmitter of interest by subtracting the T0 maps from the corresponding T1 map. These difference maps were then entered into a second-level general linear model using SPM12. Results were thresholded with a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 uncorrected at the voxel level and Family-wise Error Rate (FWE) corrected for multiple comparisons at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 at the cluster level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Correlation between rs-fMRI connectivity and clinical scales\u003c/h2\u003e \u003cp\u003eLongitudinal differences in FC values from significant ROI-to-ROI connections (i.e., connectivity at T1 minus connectivity at T0) and neurotransmitter-related FC of significant clusters obtained with REACT were correlated with longitudinal differences in clinical variables, such as MMD, MAM and allodynia scores (i.e ΔMMD, ΔMAM, ΔASC-12). All analyses were performed in Python.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Subjects and clinical data\u003c/h2\u003e \u003cp\u003eA total of 17 EM patients was enrolled. Two patients did not complete the program due to MRI intolerance, leaving a total of 15 patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All patients were without aura except for one, with sporadic migraine with visual aura and EM without aura. Patients showed a significant longitudinal decrease in MMD and MAM. Similarly, allodynia scores decreased at the follow-up, although the change did not reach statistical significance (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical data. The results of within-subjects t-test are reported. \u003cem\u003eAbbreviations F/M\u0026thinsp;=\u0026thinsp;Females/Males; T0\u0026thinsp;=\u0026thinsp;baseline; T1\u0026thinsp;=\u0026thinsp;follow-up after 12 weeks of atogepant treatment; MMD\u0026thinsp;=\u0026thinsp;monthly migraine days; MAM\u0026thinsp;=\u0026thinsp;acute drugs intake; ASC-12\u0026thinsp;=\u0026thinsp;allodynia score.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at T0 (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT-value;\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.09; \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.35; \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASC-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.34, 0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026ndash; insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here \u0026ndash;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 rs-fMRI analysis\u003c/h2\u003e \u003cp\u003eLongitudinal ROI-to-ROI analyses revealed that atogepant treatment was associated with significant connectivity changes in frontal, cingulate, and subcortical regions. Specifically, we observed increased FC of the rSFG with the bilateral putamen, the left pallidum, and the left ACC. Increased connectivity was also observed between the left PCC and the left putamen, and between the right PCC and the left ACC. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal ROI-to-ROI analyses. Results of Region-of-Interest to Region-of-Interest analyses of functional connectivity changes after 12 weeks of atogepant treatment are reported. All results are thresholded at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 FDR-corrected at the cluster level and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 uncorrected (\u003cem\u003ep\u003c/em\u003e-unc) at the connection level. \u003cem\u003eAbbreviations R\u0026thinsp;=\u0026thinsp;right hemisphere; L\u0026thinsp;=\u0026thinsp;left hemisphere.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhole network\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMNI xyz coordinates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRegion 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMNI xyz coordinates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-unc\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuperior frontal gyrus R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,32,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutamen R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,4,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuperior frontal gyrus R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,32,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutamen L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-26,2,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuperior frontal gyrus R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,32,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePallidum L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20,-2,-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuperior frontal gyrus R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,32,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnterior cingulate gyrus L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6,27,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosterior cingulate gyrus L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5,-30,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutamen L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-26,2,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosterior cingulate gyrus R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,-27,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnterior cingulate gyrus L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6,27,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026ndash; insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here \u0026ndash;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 REACT analysis\u003c/h2\u003e \u003cp\u003eLongitudinal neurotransmitter-related analyses revealed that atogepant treatment was associated with significant changes in dopaminergic-related FC. Following 12-week atogepant treatment, patients exhibited increased FC within the MCL dopaminergic system (D1 receptor and DAT transporter), in a cluster located in the medial orbitofrontal cortex (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal neurotransmitter-related functional connectivity analyses. Results of neurotransmitter-related functional connectivity changes after 12 weeks of atogepant treatment are reported. All results are thresholded at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 FWE-corrected at the cluster level and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 uncorrected (unc.) at the connection level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurotransmitter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceptor/\u003c/p\u003e \u003cp\u003eTransporter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContrast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003cp\u003e(Hammersmith)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMNI \u003c/p\u003e \u003cp\u003ex y z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003cp\u003e(FWE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003cp\u003e(mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZ score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePeak\u003c/p\u003e \u003cp\u003e(unc.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCohen's d\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDopamine (MCL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1\u0026thinsp;\u0026gt;\u0026thinsp;T0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR Superior frontal gyrus/\u003c/p\u003e \u003cp\u003eMedial orbital gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e6,58,-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDopamine (MCL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1\u0026thinsp;\u0026gt;\u0026thinsp;T0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR Superior frontal gyrus/\u003c/p\u003e \u003cp\u003eMedial orbital gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e6,62,-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eAbbreviations: MCL\u0026thinsp;=\u0026thinsp;mesocorticolimbic system; T0\u0026thinsp;=\u0026thinsp;baseline; T1\u0026thinsp;=\u0026thinsp;follow-up after 12 weeks of atogepant treatment; R\u0026thinsp;=\u0026thinsp;right hemisphere; K\u0026thinsp;=\u0026thinsp;cluster extent; CI\u0026thinsp;=\u0026thinsp;confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026ndash; insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here \u0026ndash;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between rs-fMRI connectivity and clinical scales\u003c/h2\u003e \u003cp\u003eThe correlation analysis of longitudinal changes in ROI-to-ROI FC with longitudinal differences in clinical variables showed a significant association. Greater longitudinal increases in FC between the right PCC and the left ACC were associated with larger reductions over the 12-week treatment period in MDD (ΔMDD; r= -0.52, p\u0026thinsp;=\u0026thinsp;0.05), MAM (ΔMAM; r= -0.56, p\u0026thinsp;=\u0026thinsp;0.03), and ASC-12 scores (ΔASC-12 r= -0.60, p\u0026thinsp;=\u0026thinsp;0.02). No significant correlations were found between longitudinal changes in neurotransmitter-related FC clusters and clinical variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we observed that 12-week atogepant treatment in EM patients is associated with significant changes in the brain\u0026rsquo;s FC at rest and dopamine-related FC within the mesocorticolimbic system, accompanied by a sizable clinical improvement, in both MMD and MAM, consistently with atogepant efficacy data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, we also observed a concomitant decrease in allodynia, a marker of central sensitization. This is partly consistent with previous findings which showed marked decrease in ASC-12, [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], for example with erenumab [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], where this effect was attributed to prolonged suppression of peripheral nociceptive input. Our findings therefore may suggest that atogepant may similarly modulate central sensitization mechanisms.\u003c/p\u003e \u003cp\u003eLongitudinal ROI-to-ROI analyses showed a pattern of increased connectivity at T1, involving the right SFG, bilateral putamen, left pallidum, left ACC, and bilateral PCC. Importantly, greater the longitudinal increase in ACC-PCC connectivity, the larger the improvement in MDD, MAM, and allodynia scores after treatment, linking FC modulation with clinical response. All these regions participate in different aspects of the neural processing of the pain experience, with either pain-facilitating or pain-inhibiting functions and show functional alterations in migraine patients [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SFG emerged as the node showing the largest number of post-treatment connectivity changes with other ROIs, indicating a central role in the network reorganization. This finding should be considered alongside previous data [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reporting reduced connectivity between the dorsolateral prefrontal cortex and several cortical and basal ganglia regions, including the putamen, in EM patients. Given the critical involvement of SFG in pain modulation, both findings point to a dysfunction of descending pain-control pathways both interictally and in relation to treatment-related reorganization. In this framework, the increased frontal-putaminal connectivity observed after atogepant may reflect a strengthening of interictal fronto-striatal coupling and a reinforcement of top-down pain modulation mechanisms.\u003c/p\u003e \u003cp\u003eThe SFG also showed longitudinally increased connectivity with the ACC, which in turn exhibited enhanced connectivity with the PCC, suggesting an atogepant-related strengthening of frontal-anterior cingulate-posterior cingulate interactions. Within this axis, the ACC plays a central role: it integrates the sensory, emotional, and motivational components of the pain experience, contributes to autonomic regulation, and acts as a key node of descending pain-control pathways [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The PCC, on the other hand, is a core hub of the default mode network, involved in the conscious experience of pain [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Together with the superior frontal cortex, these regions form a network implicated in higher-order modulation of nociceptive processing. Findings from migraine cohorts further highlight the relevance of these midline regions. Consistent structural and functional alterations were reported in the prefrontal cortex, ACC, and PCC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These functional abnormalities include reduced interictal metabolism in both cingulate regions, particularly in the ACC, where lower metabolic activity correlates with longer disease duration and higher attack frequency [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] as well as altered FC. In particular, stronger coupling between the PCC, ACC, and medial prefrontal regions has been associated with more efficient conditioned pain modulation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], whereas pain catastrophizing corresponds to reduced activation of these same nodes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Resting-state ACC-PCC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and PCC [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] connectivity has also been negatively correlated with pain intensity, and patients with cutaneous allodynia exhibit heightened cingulate responses to painful stimulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], reinforcing the role of this pathway is critical for endogenous pain regulation and central sensitization in migraine.\u003c/p\u003e \u003cp\u003eIn this context, the increase in frontal-ACC-PCC connectivity observed in our cohort, and the ACC-PCC FC association with reductions in MMD, MAM, and allodynia, suggests that atogepant may help restore the functional balance of this midline regulatory system.\u003c/p\u003e \u003cp\u003eInterestingly, the regions identified in our study (i.e. frontal areas, cingulate cortex, and putamen) overlap with those showing increased connectivity in response to painful stimulation in patients who responded to an 8-week erenumab treatment compared with non-responders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering that erenumab and atogepant targets both the CGRP receptor, these converging findings raise the possibility of a similar, albeit likely indirect, central mechanism of action underlying therapeutic response [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith respect to neurotransmitter-related FC, we observed increased dopamine-related connectivity in the mesocorticolimbic dopaminergic system (D1 receptor and DAT transporter), in a cluster located in the medial orbitofrontal cortex, a key node region of the mesocorticolimbic circuit, that contributes to pain modulation by integrating the affective and motivational dimensions of painful stimuli and by exerting inhibitory coupling with regions involved in nociceptive information processing [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Alterations of this dopaminergic pathway have already been described in migraine, where mesocorticolimbic FC alterations associated with dopamine availability have been previously reported in EM patients, suggesting a dopaminergic contribution to pain processing and affective response to pain [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Importantly, similar patterns have been observed across chronic pain conditions. Reduced dopamine synthesis and release, together with gray matter volume loss of its major nodes, have been observed [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Likewise, disrupted mesocorticolimbic connectivity has been associated with central pain sensitization and chronification in conditions such as chronic low back pain [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Based on these observations, the dopamine-related FC increases observed here may reflect a normalization of altered mesocorticolimbic dynamics, suggesting that atogepant may exert part of its therapeutic effect by directly or indirectly normalizing disrupted dopaminergic connectivity patterns, restoring adaptive pain processing mechanisms.\u003c/p\u003e \u003cp\u003eAlthough atogepant antagonism of the CGRP receptor is well established, the extent to which atogepant crosses the BBB remains uncertain. Both gepants and mAbs exhibit limited BBB permeability and are therefore believed to exert their therapeutic effects outside the central nervous system (CNS) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For mAbs, the prevailing view is that their reduction of peripheral activation afferents, may secondarily influence central brain regions involved in pain perception, salience processing, and descending inhibitory control [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. On the other hand, even small quantities reaching the CNS may still contribute to treatment response [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although slightly higher in BBB penetration, a similar mechanism is plausible even for gepants.\u003c/p\u003e \u003cp\u003eIn this framework, the clinical improvements observed in our cohort together with the longitudinal increases in connectivity across regions involved in multiple dimensions of pain processing, including the mesocorticolimbic dopaminergic system, may reflect indirect central modulation mediated by atogepant resulting from reduced peripheral nociceptive drive. Further work is needed to clarify whether atogepant, and gepants broadly, exert central actions directly or predominantly through peripheral CGRP-receptor blockade and comparative studies with mAbs will be crucial to clarify exact mechanisms of anti-CGRP therapies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur findings should be considered in light of some limitations. \u003cem\u003eFirst\u003c/em\u003e, our sample size of EM patients was modest, although homogeneous, reflecting the explorative nature of the study. \u003cem\u003eSecond\u003c/em\u003e, the follow-up period was limited to 12 weeks. While this interval aligns with standard clinical practice, longer observation may help clarify the extent and durability of atogepant CNS effects. \u003cem\u003eThird\u003c/em\u003e, this was a cohort study, and therefore we cannot completely exclude that the observed variations in brain connectivity are the solely effect of atogepant administration. Studies with a control condition could further explore the specific neuromodulation activity of atogepant.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we provide the first evidence of longitudinal FC changes in brain regions involved in pain processing and within the dopaminergic mesocorticolimbic system in EM patients after 12 weeks of atogepant treatment. These findings may reflect direct or indirect central neurofunctional adaptations associated with clinical improvement. A deeper understanding of such central changes could offer new insights into the mechanism underlying successful preventive therapies for migraine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData may be made available upon reasonable request to corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors are grateful to the GARR consortium for the high-performance infrastructure used for the analyses at Neuroradiology Unit of Fondazione IRCCS Istituto Neurologico Carlo Besta (Milan, Italy). DAM has received personal fee as speaker and travel grant from: AbbVie, Lundbeck, Pfizer, TEVA Pharm Ind. LG has received consultancy and advisory fees from: Abbvie, EliLilly, Lundbeck, Organon, Pfizer, TEVA Pharm Ind.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. AR is associate editor of The Journal of Headache and Pain. He was not involved in the journal’s peer review process of, or decisions related to, this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Fondazione IRCCS Istituto Neurologico Carlo Besta (approval CET 48/24 BST 61/23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fondazione Cariplo (Biomedical Research conducted by Young Researchers 2022) research grant MESH 2022 − 0610 (to AN) and RRC (to MG). GC has been funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 3.3 - Call for tender No. 117 of 02/03/2023 of Italian Ministry of University and Research funded by the European Union - NextGenerationEU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDAM, LG, AN, DF, and GD conceived and designed the study; DAM and LG performed patient recruitment and data curation; DAM, LG, GD, AP, AR, AM, and GR performed patient clinical evaluation; AN, DF, GC, JPMC, and GD contributed to neuroimaging data collection and curation; AE, DF and GC performed neuroimaging data clinical assessment; DF, GC, and JPMC performed statistical and neuroimaging data analyses; DAM, DF, AN, GD, and GC drafted the manuscript; JPMC, EC, AE, MG, AM, GR, AP, AR, and LG conducted a rigorous review of the manuscript and provided critical feedback; AN and MG contributed to funding acquisition; MG provided MRI facility resources; AR, AN, LG, MG supervised the project. All authors contributed toward the revision and writing of the final draft and approved the final version of the manuscript before submission. DF performed manuscript submission and author correspondence.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRaggi A, Leonardi M, Arruda M et al (2024) Hallmarks of primary headache: part 1 - migraine. J Headache Pain 25:189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-024-01889-x\u003c/span\u003e\u003cspan address=\"10.1186/s10194-024-01889-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwedt TJ, Chiang C-C, Chong CD, Dodick, David W (2015) Functional Magnetic Resonance Imaging of Migraine. Lancet Neurol 14:81\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1474-4422(14)70193-0\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(14)70193-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchramm S, B\u0026ouml;rner C, Reichert M et al (2023) Functional magnetic resonance imaging in migraine. A systematic review\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue T, Yuan K, Cheng P et al (2013) Alterations of regional spontaneous neuronal activity and corresponding brain circuit changes during resting state in migraine without aura. NMR Biomed 26:1051\u0026ndash;1058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/nbm.2917\u003c/span\u003e\u003cspan address=\"10.1002/nbm.2917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai M, Liu J, Wang X et al (2023) Spontaneous brain activity abnormalities in migraine: A meta-analysis of functional neuroimaging. Hum Brain Mapp 44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hbm.26085\u003c/span\u003e\u003cspan address=\"10.1002/hbm.26085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue T, Yuan K, Zhao L et al (2012) Intrinsic Brain Network Abnormalities in Migraines without Aura Revealed in Resting-State fMRI. PLoS ONE 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0052927\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0052927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaghdoost F, Puledda F, Garcia-Azorin D et al (2023) Evaluating the efficacy of CGRP mAbs and gepants for the preventive treatment of migraine: A systematic review and network meta-analysis of phase 3 randomised controlled trials. Cephalalgia 43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/03331024231159366\u003c/span\u003e\u003cspan address=\"10.1177/03331024231159366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePozo-Rosich P, Ailani J, Ashina M et al (2023) Atogepant for the preventive treatment of chronic migraine (PROGRESS): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(23)01049-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01049-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshina M, Tepper SJ, Reuter U et al (2023) Once-daily oral atogepant for the long-term preventive treatment of migraine: Findings from a multicenter, randomized, open-label, phase 3 trial. Headache 63:79\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/head.14439\u003c/span\u003e\u003cspan address=\"10.1111/head.14439\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipton RB, Pozo-Rosich P, Blumenfeld AM et al (2022) Rates of Response to Atogepant for Migraine Prophylaxis among Adults: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 5:E2215499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2022.15499\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2022.15499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwedt TJ, Nikolova S, Dumkrieger G et al (2022) Longitudinal changes in functional connectivity and pain-induced brain activations in patients with migraine: a functional MRI study pre- and post- treatment with Erenumab. J Headache Pain 23:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-022-01526-5\u003c/span\u003e\u003cspan address=\"10.1186/s10194-022-01526-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzabo E, Bolo NR, Borsook D et al (2025) Peripherally acting anti-CGRP monoclonal antibodies attenuate cortical resting-state connectivity in migraine patients. Cephalalgia 45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/03331024241313377\u003c/span\u003e\u003cspan address=\"10.1177/03331024241313377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasedau H, Sturm LM, Mehnert J et al (2022) Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target \u0026ndash; an fMRI study. Elife 11:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.77146\u003c/span\u003e\u003cspan address=\"10.7554/eLife.77146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegeler C, Mehnert J, Asmussen K, May A (2020) Central effects of erenumab in migraine patients: An event-related functional imaging study. Neurology 95:E2794\u0026ndash;E2802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000010740\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000010740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRecober A (2021) Pathophysiology of Migraine. Continuum (Minneap Minn) 27:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/CON.0000000000000983\u003c/span\u003e\u003cspan address=\"10.1212/CON.0000000000000983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillal\u0026oacute;n CM, VanDenBrink AM (2017) The Role of 5-Hydroxytryptamine in the Pathophysiology of Migraine and its Relevance to the Design of Novel Treatments. Mini-Reviews Med Chem 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1389557516666160728121050\u003c/span\u003e\u003cspan address=\"10.2174/1389557516666160728121050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeadache Classification Comittee of the International Headache Society (IHS) (2018) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38:1\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102417738202\u003c/span\u003e\u003cspan address=\"10.1177/0333102417738202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteban O, Birman D, Schaer M et al (2017) MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0184661\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0184661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieto-Castanon A, Whitfield-Gabrieli S (2020) CONN functional connectivity toolbox: RRID SCR_009550, release 20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDipasquale O, Selvaggi P, Veronese M et al Receptor-Enriched Analysis of functional connectivity by targets (REACT): A novel, multimodal analytical approach informed by PET to study the pharmacodynamic response of the brain under MDMA. Neuroimage 195:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2019.04.007\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2019.04.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeliveau V, Ganz M, Feng L et al (2017) A high-resolution in vivo atlas of the human brain\u0026rsquo;s serotonin system. J Neurosci 37:120\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.2830-16.2016\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.2830-16.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen JY, Shafiei G, Markello RD et al (2022) Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci 25:1569\u0026ndash;1581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41593-022-01186-3\u003c/span\u003e\u003cspan address=\"10.1038/s41593-022-01186-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManca R, De Marco M, Soininen H et al (2025) Changes in neurotransmitter-related functional connectivity along the Alzheimer\u0026rsquo;s disease continuum. Brain Commun 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/braincomms/fcaf008\u003c/span\u003e\u003cspan address=\"10.1093/braincomms/fcaf008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrutti AC, Fontanesi L, Mulder MJ et al (2021) A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data. Brain Struct Funct 226:1155\u0026ndash;1167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00429-021-02231-w\u003c/span\u003e\u003cspan address=\"10.1007/s00429-021-02231-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammers A, Allom R, Koepp MJ et al (2003) Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp 19:224\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hbm.10123\u003c/span\u003e\u003cspan address=\"10.1002/hbm.10123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGousias IS, Rueckert D, Heckemann RA et al (2008) Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. NeuroImage 40:672\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2007.11.034\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2007.11.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaillenot I, Heckemann RA, Frot M, Hammers A (2017) Macroanatomy and 3D probabilistic atlas of the human insula. NeuroImage 150:88\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2017.01.073\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.01.073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWild HM, Heckemann RA, Studholme C, Hammers A (2017) Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases. PLoS ONE 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0180866\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0180866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontisano DA, Giossi R, Canella M et al (2024) Reducing the Impact of Headache and Allodynia Score in Chronic Migraine: An Exploratory Analysis from the Real-World Effectiveness of Anti-CGRP Monoclonal Antibodies Compared to Onabotulinum Toxin A (RAMO) Study. Toxins (Basel) 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/toxins16040178\u003c/span\u003e\u003cspan address=\"10.3390/toxins16040178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026iacute;nguez-Olaondo A, Quintas S, Moroll\u0026oacute;n S\u0026aacute;nchez-Mateos N et al (2022) Cutaneous Allodynia in Migraine: A Narrative Review. Front Neurol 12:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2021.831035\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2021.831035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipton RB, Burstein R, Buse DC et al (2021) Efficacy of erenumab in chronic migraine patients with and without ictal allodynia. Cephalalgia 41:1152\u0026ndash;1160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/03331024211010305\u003c/span\u003e\u003cspan address=\"10.1177/03331024211010305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartucci KT, Mackey SC (2018) Neuroimaging of Pain: Human Evidence and Clinical Relevance of Central Nervous System Processes and Modulation. Anesthesiology 128:1241\u0026ndash;1254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ALN.0000000000002137\u003c/span\u003e\u003cspan address=\"10.1097/ALN.0000000000002137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMungoven TJ, Marciszewski KK, Macefield VG et al (2022) Alterations in pain processing circuitries in episodic migraine. J Headache Pain 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-021-01381-w\u003c/span\u003e\u003cspan address=\"10.1186/s10194-021-01381-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeissner F, Meissner K, Bar K-J et al (2013) The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function. J Neurosci 33:10503\u0026ndash;10511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1103-13.2013\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1103-13.2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBushnell MC, Čeko M, Low LA (2013) Cognitive and emotional control of pain and its disruption in chronic pain. Nat Rev Neurosci 14:502\u0026ndash;511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn3516\u003c/span\u003e\u003cspan address=\"10.1038/nrn3516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenarroch EE (2020) Physiology and Pathophysiology of the Autonomic Nervous System. Contin Lifelong Learn Neurol 26:12\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/CON.0000000000000817\u003c/span\u003e\u003cspan address=\"10.1212/CON.0000000000000817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu D, Yuan K, Luo L et al (2017) Abnormal functional integration across core brain networks in migraine without aura. Mol Pain 13:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1744806917737461\u003c/span\u003e\u003cspan address=\"10.1177/1744806917737461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaleki N, Becerra L, Brawn J et al (2012) Concurrent functional and structural cortical alterations in migraine. Cephalalgia 32:607\u0026ndash;620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102412445622\u003c/span\u003e\u003cspan address=\"10.1177/0333102412445622\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin C, Yuan K, Zhao L et al (2013) Structural and functional abnormalities in migraine patients without aura. NMR Biomed 26:58\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/nbm.2819\u003c/span\u003e\u003cspan address=\"10.1002/nbm.2819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCahill CM, Cook C, Pickens S et al (2014) Migraine and Reward System\u0026mdash;Or Is It Aversive? Curr Pain Headache. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11916-014-0410-y\u003c/span\u003e\u003cspan address=\"10.1007/s11916-014-0410-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Reports 2014 185 18:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JH, Kim S, Suh SI et al (2010) Interictal metabolic changes in episodic migraine: A voxel-based FDG-PET study. Cephalalgia 30:53\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1468-2982.2009.01890.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1468-2982.2009.01890.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgaman Y, Kisler LB, Granovsky Y et al (2020) The Endogenous Analgesia Signature in the Resting Brain of Healthy Adults and Migraineurs. J Pain 21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpain.2019.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jpain.2019.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathur VA, Moayedi M, Keaser ML et al (2016) Frontiers | High Frequency Migraine Is Associated with Lower Acute Pain Sensitivity and Abnormal Insula Activity Related to Migraine Pain Intensity, Attack Frequency, and Pain Catastrophizing. Front Hum Neurosci 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnhum.2016.00489\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2016.00489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusso A, Esposito F, Conte F et al (2017) Functional interictal changes of pain processing in migraine with ictal cutaneous allodynia. Cephalalgia 37:305\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102416644969\u003c/span\u003e\u003cspan address=\"10.1177/0333102416644969\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker S, Gandhi W, Pomares F et al (2017) Orbitofrontal cortex mediates pain inhibition by monetary reward. Soc Cogn Affect Neurosci 12:651\u0026ndash;661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/scan/nsw173\u003c/span\u003e\u003cspan address=\"10.1093/scan/nsw173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DJ, Jassar H, Lim M et al (2021) Dopaminergic regulation of reward system connectivity underpins pain and emotional suffering in migraine. J Pain Res 14:631\u0026ndash;643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/JPR.S296540\u003c/span\u003e\u003cspan address=\"10.2147/JPR.S296540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Boudier-Rev\u0026eacute;ret M, Choo YJ, Chang MC (2020) Association between chronic pain and alterations in the mesolimbic dopaminergic system. Brain Sci 10:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/brainsci10100701\u003c/span\u003e\u003cspan address=\"10.3390/brainsci10100701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, Li W, Shen W et al (2020) Impaired mesocorticolimbic connectivity underlies increased mechanical pain sensitivity in chronic low back pain. NeuroImage 218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2020.116969\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.116969\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdvinsson L, Warfvinge K (2019) Recognizing the role of CGRP and CGRP receptors in migraine and its treatment. Cephalalgia 39:366\u0026ndash;373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102417736900\u003c/span\u003e\u003cspan address=\"10.1177/0333102417736900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEftekhari S, Salvatore CA, Johansson S et al (2015) Localization of CGRP, CGRP receptor, PACAP and glutamate in trigeminal ganglion. Relation to the blood-brain barrier. Brain Res 1600:93\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brainres.2014.11.031\u003c/span\u003e\u003cspan address=\"10.1016/j.brainres.2014.11.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasedau H, Peng KP, Schellong M, May A (2024) Double-blind, randomized, placebo-controlled study to evaluate erenumab-specific central effects: an fMRI study. J Headache Pain 25:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-023-01709-8\u003c/span\u003e\u003cspan address=\"10.1186/s10194-023-01709-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Tommaso M, Vecchio E, Quitadamo SG et al (2021) Pain-related brain connectivity changes in migraine: A narrative review and proof of concept about possible novel treatments interference. Brain Sci 11:1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/brainsci11020234\u003c/span\u003e\u003cspan address=\"10.3390/brainsci11020234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atogepant, resting-state fMRI, migraine, functional connectivity, neurostransmitter, gepant","lastPublishedDoi":"10.21203/rs.3.rs-8406852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8406852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpisodic Migraine (EM) is a highly prevalent and disabling neurological disorder. Recent therapeutic advances have targeted the calcitonin gene-related peptide (CGRP), a neuropeptide implicated in the pathophysiology of migraine. Promising outcomes have been obtained with monoclonal antibodies and, more recently, with a new class of drugs consisting of small-molecule CGRP receptor antagonists, known as gepants. Atogepant, an oral agent of the gepant class, has demonstrated efficacy in migraine prevention, yet its effects on central brain activity and neurotransmitter circuits remain largely unclear. This study investigates changes in brain functional connectivity and changes related to key neurotransmitter systems, following 12 weeks of treatment with atogepant in a group of EM patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is a prospective single-arm cohort study with a pre\u0026ndash;post neuroimaging evaluation of the effects of atogepant. We enrolled patients diagnosed with EM according to ICHD-3 criteria, without prior exposure to anti-CGRP therapies. Participants underwent clinical assessments (monthly migraine days MMD, acute drugs intake MAM) and resting-state functional MRI (rs-fMRI) before and after 12 weeks treatment of daily atogepant 60mg administration. Longitudinal functional connectivity analyses were performed at the whole-brain level with a region-of-interest analysis. Additionally, longitudinal neurotransmitter-related functional connectivity was investigated within the serotoninergic and the dopaminergic systems.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 15 patients completed the evaluation. Following 12 weeks of atogepant treatment, all patients showed a sizable reduction in MMD (T=-7.09, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and MAM, (T=-6.35, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), accompanied by a reduction of allodynia symptoms. Patients exhibited significant longitudinally increased functional connectivity, involving the right superior frontal gyrus, bilateral putamen, left pallidum, left anterior and bilateral posterior cingulate cortex. Greater the longitudinal increase in cingulate cortices connectivity, the larger the improvement in MDD, MAM, and allodynia symptoms after treatment. Additionally, longitudinal connectivity changes were observed within the orbitofrontal cortex in the mesocorticolimbic dopaminergic system.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSignificative clinical improvement after atogepant treatment is associated with functional connectivity adaptations in EM patients. These findings may reflect either direct or indirect central modulation mediated by atogepant. A deeper understanding of the observed central changes may help to clarify the mechanisms underlying anti-CGRP therapies for migraine.\u003c/p\u003e","manuscriptTitle":"Atogepant modulates brain connectivity in episodic migraine: a longitudinal cohort fMRI study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:51:11","doi":"10.21203/rs.3.rs-8406852/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed06661a-b438-4ac6-babd-22eae36745aa","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-03T08:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:51:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8406852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8406852","identity":"rs-8406852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}