Brain最新文献

{"title":"EBV-specific T-cell responses are telling us something important about multiple sclerosis.","authors":"Gavin Giovannoni","doi":"10.1093/brain/awaf027","DOIUrl":"https://doi.org/10.1093/brain/awaf027","url":null,"abstract":"","PeriodicalId":9063,"journal":{"name":"Brain","volume":" ","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiomic analyses direct hypotheses for Creutzfeldt-Jakob disease risk genes.","authors":"Fahri Küçükali, Elizabeth Hill, Tijs Watzeels, Holger Hummerich, Tracy Campbell, Lee Darwent, Steven Collins, Christiane Stehmann, Gabor G Kovacs, Michael D Geschwind, Karl Frontzek, Herbert Budka, Ellen Gelpi, Adriano Aguzzi, Sven J van der Lee, Cornelia M van Duijn, Pawel P Liberski, Miguel Calero, Pascual Sanchez-Juan, Elodie Bouaziz-Amar, Jean-Louis Laplanche, Stéphane Haïk, Jean-Phillipe Brandel, Angela Mammana, Sabina Capellari, Anna Poleggi, Anna Ladogana, Dorina Tiple, Saima Zafar, Stephanie Booth, Gerard H Jansen, Aušrinė Areškevičiūtė, Eva Løbner Lund, Katie Glisic, Piero Parchi, Peter Hermann, Inga Zerr, Jiri Safar, Pierluigi Gambetti, Brian S Appleby, John Collinge, Kristel Sleegers, Simon Mead","doi":"10.1093/brain/awaf032","DOIUrl":"10.1093/brain/awaf032","url":null,"abstract":"<p><p>Prions are assemblies of misfolded prion protein that cause several fatal and transmissible neurodegenerative diseases, with the most common phenotype in humans being sporadic Creutzfeldt-Jakob disease (sCJD). Aside from variation of the prion protein itself, molecular risk factors are not well understood. Prion and prion-like mechanisms are thought to underpin common neurodegenerative disorders meaning that the elucidation of mechanisms could have broad relevance. Herein we sought to further develop our understanding of the factors that confer risk of sCJD using a systematic gene prioritization and functional interpretation pipeline based on multiomic integrative analyses. We integrated the published sCJD genome-wide association study (GWAS) summary statistics with publicly available bulk brain and brain cell type gene and protein expression datasets. We performed multiple transcriptome and proteome-wide association studies (TWAS & PWAS) and Bayesian genetic colocalization analyses between sCJD risk association signals and multiple brain molecular quantitative trait loci signals. We then applied our systematic gene prioritization pipeline on the obtained results and nominated prioritized sCJD risk genes with risk-associated molecular mechanisms in a transcriptome and proteome-wide manner. Genetic upregulation of both gene and protein expression of syntaxin-6 (STX6) in the brain was associated with sCJD risk in multiple datasets, with a risk-associated gene expression regulation specific to oligodendrocytes. Similarly, increased gene and protein expression of protein disulfide isomerase family A member 4 (PDIA4), involved in the unfolded protein response, was linked to increased disease risk, particularly in excitatory neurons. Protein expression of mesencephalic astrocyte derived neurotrophic factor (MANF), involved in protection against endoplasmic reticulum stress and sulfatide binding (linking to the enzyme in the final step of sulfatide synthesis, encoded by sCJD risk gene GAL3ST1), was identified as protective against sCJD. In total 32 genes were prioritized into two tiers based on the level of evidence and confidence for further studies. This study provides insights into the genetically-associated molecular mechanisms underlying sCJD susceptibility and prioritizes several specific hypotheses for exploration beyond the prion protein itself and beyond the previously highlighted sCJD risk loci through the newly prioritized sCJD risk genes and mechanisms. These findings highlight the importance of glial cells, sulfatides and the excitatory neuron unfolded protein response in sCJD pathogenesis.</p>","PeriodicalId":9063,"journal":{"name":"Brain","volume":" ","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"White matter connections within the central sulcus subserving the somato-cognitive action network","authors":"Georgios P Skandalakis, Luca Viganò, Clemens Neudorfer, Marco Rossi, Luca Fornia, Gabriella Cerri, Kelsey P Kinsman, Zabiullah Bajouri, Armin D Tavakkoli, Christos Koutsarnakis, Evgenia Lani, Spyridon Komaitis, George Stranjalis, Gelareh Zadeh, Jessica Barrios-Martinez, Fang-Cheng Yeh, Demitre Serletis, Michael Kogan, Constantinos G Hadjipanayis, Jennifer Hong, Nathan Simmons, Evan M Gordon, Nico U F Dosenbach, Andreas Horn, Lorenzo Bello, Aristotelis Kalyvas, Linton T Evans","doi":"10.1093/brain/awaf022","DOIUrl":"https://doi.org/10.1093/brain/awaf022","url":null,"abstract":"The somato-cognitive action network (SCAN) consists of three nodes interspersed within Penfield’s motor effector regions. The configuration of the somato-cognitive action network nodes resembles the one of the ‘plis de passage’ of the central sulcus: small gyri bridging the precentral and postcentral gyri. Thus, we hypothesize that these may provide a structural substrate of the somato-cognitive action network. Here, using microdissections of sixteen human hemispheres, we consistently identified a chain of three distinct plis de passage with increased underlying white matter, in locations analogous to the somato-cognitive action network nodes. We mapped localizations of plis de passage into standard stereotactic space to seed fMRI connectivity across 9,000 resting-state fMRI scans, which demonstrated the connectivity of these sites with the somato-cognitive action network. Intraoperative recordings during direct electrical central sulcus stimulation further identified inter-effector regions corresponding to plis de passage locations. This work provides a critical step towards improved understanding of the somato-cognitive action network in both structural and functional terms. Further, our work has the potential to guide the development of refined motor cortex stimulation techniques for treating brain disorders, and operative resective techniques for complex surgery of the motor cortex.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"35 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of resective epilepsy surgery on the brain network: evidence from post-surgical imaging","authors":"Lucas E Sainburg, Dario J Englot, Victoria L Morgan","doi":"10.1093/brain/awaf026","DOIUrl":"https://doi.org/10.1093/brain/awaf026","url":null,"abstract":"Resective epilepsy surgery can be an effective treatment for patients with medication-resistant focal epilepsy. Epilepsy resection consists of the surgical removal of an epileptic focus to stop seizure generation and disrupt the epileptic network. However, even focal surgical resections for epilepsy lead to widespread brain network changes. Understanding the impact of epilepsy surgery on the brain is crucial to improve surgical outcomes for patients. Here we provide a summary of studies imaging the postsurgical effects of epilepsy resection on the brain. We focus on MRI and PET studies of temporal lobe and pediatric epilepsy, reflecting the current literature. We discuss three potential mechanisms for surgery-induced brain changes: damage and degeneration, recovery, and reorganization. We additionally review the postsurgical brain correlates of surgical outcomes as well as the potential to predict the impact of surgery on an individual patient’s brain. A comprehensive characterization of the impact of surgery on the brain and precise methods to predict these brain network changes could lead to more personalized surgeries that improve seizure outcomes and reduce neuropsychological deficits after surgery.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"47 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning analyses of splicing variants identify the link of PCP4 with amyotrophic lateral sclerosis","authors":"Xuelin Tang, Yan Chen, Yongfei Ren, Wanli Yang, Wendi Yu, Yu Zhou, Jingyan Guo, Jiali Hu, Xi Chen, Yuqi Gu, Chuyi Wang, Yi Dong, Hong Yang, Christine Sato, Ji He, Dongsheng Fan, Linya You, Lorne Zinman, Ekaterina Rogaeva, Yelin Chen, Ming Zhang","doi":"10.1093/brain/awaf025","DOIUrl":"https://doi.org/10.1093/brain/awaf025","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease, with most sporadic cases lacking clear genetic causes. Abnormal pre-mRNA splicing is a fundamental mechanism in neurodegenerative diseases. For example, TAR DNA-binding protein 43 (TDP-43) loss-of-function (LOF) causes widespread RNA mis-splicing events in ALS. Additionally, splicing mutations are major contributors to neurological disorders. However, the role of intronic variants driving RNA mis-splicing in ALS remains poorly understood. To address this, we developed Spliformer to predict RNA splicing. Spliformer is a transformer-based deep learning model trained and tested on splicing events from the GENCODE database, as well as RNA-seq data from blood and central nervous system tissues. We benchmarked Spliformer against SpliceAI and Pangolin using testing datasets and paired whole-genome sequencing (WGS) with RNA-seq data. We further developed the Spliformer-motif model to identify splicing regulatory motifs. We analyzed Clinvar dataset to identify the link of splicing variants with disease pathogenicity. Additionally, we analyzed WGS data of ALS patients and controls to identify common intronic splicing variants linked to ALS risk or disease phenotypes. We also profiled rare intronic splicing variants in ALS patients to identify known or novel ALS-associated genes. Minigene assays were employed to validate candidate splicing variants. Finally, we measured spine density in neurons with a specific gene knockdown or those expressing a TDP-43 disease-causing mutant. Spliformer accurately predicts the possibilities of a nucleotide within a pre-mRNA sequence being a splice donor, acceptor, or neither. Spliformer outperformed SpliceAI and Pangolin in both speed and accuracy in tested splicing events and/or paired WGS/RNA-seq data. Spliformer-motif successfully identified canonical and novel splicing regulatory motifs. In Clinvar dataset, splicing variants are highly related to disease pathogenicity. Genome-wide analyses of common intronic splicing variants nominated one variant linked to ALS progression. Deep learning analyses of WGS data from 1,370 ALS patients revealed rare splicing variants in reported ALS genes (such as PTPRN2 and CFAP410, validated through minigene assays and RNA-seq), and TDP-43 LOF related RNA mis-splicing genes (such as PTPRD). Further genetic analysis and minigene assays nominated PCP4 and TMEM63A as ALS-associated genes. Functional assays demonstrated that PCP4 is critical for maintaining spine density and can rescue spine loss in neurons expressing a disease-causing TDP-43 mutant. In summary, we developed Spliformer and Spliformer-motif that accurately predict and interpret pre-mRNA splicing. Our findings highlight an intronic genetic mechanism driving RNA mis-splicing in ALS and nominate PCP4 as an ALS-associated gene.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"58 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence","authors":"Ezequiel Gleichgerrcht, Erik Kaestner, Reihaneh Hassanzadeh, Rebecca W Roth, Alexandra Parashos, Kathryn A Davis, Anto Bagić, Simon S Keller, Theodor Rüber, Travis Stoub, Heath R Pardoe, Patricia Dugan, Daniel L Drane, Anees Abrol, Vince Calhoun, Ruben I Kuzniecky, Carrie R McDonald, Leonardo Bonilha","doi":"10.1093/brain/awaf020","DOIUrl":"https://doi.org/10.1093/brain/awaf020","url":null,"abstract":"Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as “non-lesional” (i.e., MRI negative or MRI–) based on visual assessment by human experts. MRI– patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI– patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that may be too subtle for the human eye to detect. This signature pattern could be successfully translated into clinical use via artificial intelligence (AI) advances in computer-aided MRI interpretation, thereby improving the detection of brain “lesional” patterns associated with TLE. Here, we tested this hypothesis by employing a three-dimensional convolutional neural network (3D CNN) applied to a dataset of 1,178 scans from 12 different centers. 3D CNN was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6) and whole-brain (78.3% ± 3.3) volumes. Our analysis subsequently focused on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI– patients from this cohort were accurately identified as TLE 82.7% ± 0.9 of the time, an encouraging finding since clinically these were all patients considered to be MRI– (i.e., not radiographically different than controls). The saliency maps from the CNN revealed that limbic structures, particularly medial temporal, cingulate, and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI+ and MRI– TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI– patients are on the same continuum common across all TLE patients. As such, AI can identify TLE lesional patterns and AI-aided diagnosis has the potential to greatly enhance the neuroimaging diagnosis of TLE and redefine the concept of “lesional” TLE.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"14 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell-type-specific networks during hippocampal seizures at the micro- and macroscale","authors":"Jiaoyang Wang, Jiaqing Yan, Donghong Li, Shipei He, Xiaonan Li, Yue Xing, Huanling Lai, Yue Gui, Nannan Zhang, Wenyao Huang, Xiaofeng Yang","doi":"10.1093/brain/awaf024","DOIUrl":"https://doi.org/10.1093/brain/awaf024","url":null,"abstract":"Epilepsy is a network disorder, involving neural circuits at both the micro- and macroscale. While local excitatory-inhibitory imbalances are recognized as a hallmark at the microscale, the dynamic role of distinct neuron types during seizures remain poorly understood. At the macroscale, interactions between key nodes within the epileptic network, such as the central median thalamic nucleus (CMT), are critical to the, hippocampal epileptic process. However, precise mechanisms underlying these interactions remain unclear. In this study, we investigated the microcircuit dynamics within the seizure onset zone and secondary spreading regions, as well as the network connectivity between the hippocampus and the CMT, using a 4-aminopyridine (4-AP) induced hippocampal seizure model. Rats were allocated into three experimental groups. The first group used a 3D tetrode array to monitor hippocampal seizure activity and microcircuit dynamics, including seizure propagation across the macroscale network. In the second group, a chemical lesion was induced in the CMT to assess its impact on hippocampal seizures. In the third group, chemogenetic techniques were used to selectively suppress pyramidal neurons in the CMT and observe changes in neural network connectivity between the CMT and hippocampus during seizures. Offline single-unit sorting was performed using KlustaKwik and further analysis was conducted with CellExplorer. At seizure onset, the narrow interneurons exhibited increased firing rates, initiating recruitment of other neurons, followed by increased activity in pyramidal neuron. Wide interneurons also showed heightened activity subsequent to pyramidal neurons. Interneurons played a more prominent role in the microcircuit during seizures compared to baseline. The CMT exhibited characteristic seizure activity and a decrease in narrow interneuron activity, whereas the cortex did not display seizure activity during hippocampal seizures. Lesioning the CMT resulted in the loss of the tonic component of hippocampal seizures and reduced overall neuronal activity in the hippocampal. Selective suppression of CMT pyramidal neurons resulted in shortened hippocampal seizures while preserving the tonic component. Narrow interneuron activity remained unchanged, while pyramidal neuron and wide interneuron activity significantly decreased. Our findings underscore the critical role of interneurons in the micronetwork of the seizure onset zone and secondary spreading region. Narrow interneurons were particularly vital in seizure initiation, whereas wide interneurons may contribute to seizure termination within the onset zone but not in the secondary spreading region. Pyramidal neurons in the CMT influence hippocampal seizures by modulating of both hippocampal pyramidal neurons and wide interneurons.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"25 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct roles of mTORC2 in excitatory and inhibitory neurons in inflammatory and neuropathic pain.","authors":"Wei He,Xin Ge,Ru-Rong Ji","doi":"10.1093/brain/awaf004","DOIUrl":"https://doi.org/10.1093/brain/awaf004","url":null,"abstract":"","PeriodicalId":9063,"journal":{"name":"Brain","volume":"28 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning","authors":"Marie-Sophie von Braun, Kristin Starke, Lucas Peter, Daniel Kürsten, Florian Welle, Hans Ralf Schneider, Max Wawrzyniak, Daniel P O Kaiser, Gordian Prasse, Cindy Richter, Elias Kellner, Marco Reisert, Julian Klingbeil, Anika Stockert, Karl-Titus Hoffmann, Gerik Scheuermann, Christina Gillmann, Dorothee Saur","doi":"10.1093/brain/awaf013","DOIUrl":"https://doi.org/10.1093/brain/awaf013","url":null,"abstract":"The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in NIHSS at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers like thrombectomy success, final infarct localization, and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalised linear model. Our deep-learning model showed significant superiority, with a mean Dice score of 0.48 on internal (n = 50) and 0.52 on external (n = 51) test data, versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischemia. We believe this method holds significant potential to enhance personalised therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"52 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Composite microRNA-genetic risk score model links to migraine and implicates its pathogenesis","authors":"Shih-Pin Chen, Ya-Hsuan Chang, Yen-Feng Wang, Hsuan-Yu Chen, Shuu-Jiun Wang","doi":"10.1093/brain/awaf005","DOIUrl":"https://doi.org/10.1093/brain/awaf005","url":null,"abstract":"The neurobiological mechanisms driving the ictal-interictal fluctuations and the chronification of migraine remain elusive. We aimed to construct a composite genetic-microRNA model that could reflect the dynamic perturbations of the disease course and inform the pathogenesis of migraine. We prospectively recruited four groups of participants, including interictal episodic migraine (i.e., headache-free for &amp;gt; 72 hrs apart from prior and subsequent attacks), ictal episodic migraine (i.e., during moderate to severe migraine attacks), chronic migraine, and controls in the discovery cohort. Next-generation sequencing (NGS) was used for microRNA profiling. The candidate microRNAs were validated with quantitative PCR (qPCR) in an independent validation cohort. Biological pathways associated with the microRNA regulome and interaction networks were explored. In addition, all participants received genotyping with the Axiom Genome-Wide Array TWB chip. A composite model was established, combining disease-associated microRNAs and genetic risk scores (GRS) indicative of genetic susceptibility, with the objective of differentiating migraine from controls using a binary outcome. From a total of 120 participants in the discovery cohort and 197 participants in the validation cohort, we identified disease-state microRNA signatures (including miR-183, miR-25, and miR-320) that were ubiquitously higher or lower in patients with migraine compared to controls. We have also validated four disease-activity miRNA signatures (miR-1307-5p, miR-6810-5p, let-7e, and miR-140-3p) that were differentially expressed only during the ictal stage of episodic migraine. Functional analysis suggested that prolactin and estrogen signaling pathways might play important roles in the pathogenesis. Moreover, the composite microRNA-GRS model differentiated patients from controls, achieving a positive predictive value of over 90%. To conclude, we developed a composite microRNA-genetic risk score model, which may serve as a predictive tool for identifying high-risk individuals. Our findings may help illuminate potential pathogenic mechanisms underlying the dysfunctional allostasis of migraine and pave the way for future precision medicine.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"30 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}