[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100655131":3},{"organization":4,"outcomesModule":7,"designInfo":63,"detailedDescription":62,"studyPopulation":66,"armGroups":67,"interventions":77,"overallOfficials":62,"centralContacts":82,"locations":88,"responsibleParty":108,"collaborators":62,"id":110,"slug":111,"hasResults":112,"nctId":113,"briefTitle":114,"officialTitle":114,"acronym":62,"eligibilityCriteria":115,"healthyVolunteers":116,"sex":117,"minAge":118,"maxAge":119,"enrollmentInfo":120,"targetDuration":62,"studyType":123,"phases":62,"briefSummary":124,"conditions":125,"keywords":62,"overallStatus":91,"whyStopped":62,"lastUpdateSubmitDate":127,"lastUpdatePostDateStruct":128,"startDateStruct":131,"completionDateStruct":133,"leadSponsor":135,"locationsCount":136},{"fullName":5,"class":6},"Qilu Hospital of Shandong University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":62},[9],{"measure":10,"description":11,"timeFrame":12},"Visual Analogue Scale","Uses a 0-10 cm linear scale, with 0 representing no pain and 10 representing the worst imaginable pain; participants mark their current pain level based on subjective feeling","baseline, pre-intervention",[14,17,20,23,26,29,32,35,38,41,44,47,50,53,56,59],{"measure":15,"description":16,"timeFrame":12},"Pittsburgh Sleep Quality Index (PSQI)","Consists of 19 self-rated items assessing 7 components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction); total score 0-21; higher scores indicate poorer sleep quality",{"measure":18,"description":19,"timeFrame":12},"Shoulder Pain and Disability Index (SPADI)","Consists of two subscales: pain (5 items, 0-10 each, total 0-50) and disability (8 items, 0-10 each, total 0-80), with a total score of 0-130 expressed as a percentage (total\u002F130 × 100%); higher percentages indicate more severe shoulder pain and disability",{"measure":21,"description":22,"timeFrame":12},"Neck Disability Index (NDI)","Consists of 10 items, each scored 0-5, with a total score of 0-50; the score is expressed as a percentage (total\u002F50 × 100%), with higher percentages indicating more severe disability due to neck pain",{"measure":24,"description":25,"timeFrame":12},"Brain MRI","Uses MRI to evaluate brain structure, used for coregistration with MEG source localization",{"measure":27,"description":28,"timeFrame":12},"Oswestry Disability Index (ODI)","Consists of 10 items, each scored 0-5, with a total score of 0-50; the score is expressed as a percentage (total\u002F50 × 100%), with higher percentages indicating more severe disability due to low back pain",{"measure":30,"description":31,"timeFrame":12},"Central Sensitization Inventory (CSI)","Part A consists of 25 items, each scored 0-4, with a total score of 0-100; higher scores indicate more severe central sensitization. Part B records previously diagnosed related syndromes",{"measure":33,"description":12,"timeFrame":34},"American Orthopaedic Foot & Ankle Society (AOFAS)","Consists of 3 subscales: pain (40 points), function (50 points), and alignment (10 points), with a total score of 0-100; higher scores indicate better ankle-hindfoot function",{"measure":36,"description":37,"timeFrame":12},"SF-36 Health Survey","Consists of 8 dimensions (physical functioning, role-physical, bodily pain, general health, vitality, social functioning, role-emotional, mental health), with each dimension score transformed to a 0-100 scale; higher scores indicate better health status",{"measure":39,"description":40,"timeFrame":12},"nternational Knee Documentation Committee Subjective Knee Form","Covers multiple aspects including knee symptoms, functional activities, and sports ability; total score 0-100, with higher scores indicating better knee function; 90-100 represents normal knee function",{"measure":42,"description":43,"timeFrame":12},"Magnetoencephalography","Uses atomic spin magnetometers in a magnetically shielded room to record spontaneous and task-evoked magnetic signals from the brain, and analyzes neural oscillatory power spectra and functional connectivity as brain network features",{"measure":45,"description":46,"timeFrame":12},"Disabilities of the Arm, Shoulder and Hand Scale","Consists of 30 items, each scored 1-5; the total score is calculated using the formula: (total\u002F30 - 1) × 25, yielding a score range of 0-100; higher scores indicate more severe upper limb disability",{"measure":48,"description":49,"timeFrame":12},"Hamilton Anxiety Scale","Consists of 14 items, each scored 0-4, with a total score of 0-56; higher scores indicate more severe anxiety symptoms; 0-17: mild, 18-24: moderate, ≥25: severe",{"measure":51,"description":52,"timeFrame":12},"Electroencephalography","Uses scalp electrodes to record brain electrical activity and assess brain function",{"measure":54,"description":55,"timeFrame":12},"Hamilton Depression Scale","Consists of 17 items, each scored 0-4, with a total score of 0-52; higher scores indicate more severe depressive symptoms; 0-7: normal\u002Fvery mild, 8-13: mild, 14-18: moderate, ≥19: severe",{"measure":57,"description":58,"timeFrame":12},"Western Ontario and McMaster Universities Osteoarthritis Index","Consists of 3 subscales: pain (5 items, 0-20), stiffness (2 items, 0-8), and physical function (17 items, 0-68), with a total score of 0-96; higher scores indicate more severe osteoarthritis symptoms",{"measure":60,"description":61,"timeFrame":12},"McGill Pain Questionnaire","Includes pain quality score (20 adjectives, 0-3 each, total 0-78) and pain intensity score (0-5), with a total score of 0-83; higher scores indicate more severe pain",null,{"allocation":62,"interventionModel":62,"interventionModelDescription":62,"primaryPurpose":62,"observationalModel":64,"timePerspective":65,"maskingInfo":62},"COHORT","PROSPECTIVE","Pain patients constitute the core longitudinal follow-up cohort of this study. Their role is to provide baseline MEG brain network features and clinical data for model training, and to supply longitudinal outcome data through follow-up visits, which will be used to establish a prognostic model for pain outcomes and to screen for candidate objective neural biomarkers.\n\nHealthy controls serve as a baseline reference group and do not participate in longitudinal follow-up. Their role is to establish normative reference features of brain networks in healthy individuals, providing a normal benchmark for assessing the degree of abnormality in pain patients.",[68,73],{"label":69,"type":62,"description":70,"interventionNames":71},"Pain group","This is a prospective cohort that continuously enrolls pain patients aged 18-70 years who meet the diagnostic criteria for muscle pain, have a VAS score ≥3, and present with lesions in the soft tissues of the neck, shoulders, or low back accompanied by tender points. Patients with a history of previous cervical, shoulder, or lumbar disorders, those taking medications that significantly affect central nervous system excitability with unstable disease control, those with contraindications to MEG\u002FMRI, those with severe systemic diseases, and those with cognitive or communication impairments are excluded. After undergoing baseline MEG, MRI, and multi-dimensional scale assessments, participants receive routine clinical treatment according to standard care pathways (the study does not impose any additional intervention), and are followed up regularly via telephone or electronic means. Treatment exposure and outcome changes are dynamically documented throughout follow-up, and the collected d",[72],"Device: Magnetoencephalography",{"label":74,"type":62,"description":75,"interventionNames":76},"Healthy Control Group","This group serves as a baseline reference cohort, comprising healthy volunteers aged 18-70 years without metallic implants in the head or neck region. After signing informed consent, participants complete a single baseline assessment consisting of MEG, MRI, and health questionnaire evaluations. No intervention is administered, and participants are not included in the longitudinal follow-up. Data from this group are used to establish reference characteristics of brain networks in healthy individuals, providing a baseline comparative standard for assessing the degree of brain network abnormalities in the pain group.",[72],[78],{"type":79,"name":42,"description":80,"armGroupLabels":81,"otherNames":62},"DEVICE","MEG is a technique that passively detects weak magnetic field signals generated by intracellular currents in the brain, using superconducting or atomic magnetometers within a magnetically shielded room. By co-registering the MEG signals with MRI for source localization, and performing power spectral analysis across different frequency bands, it enables non-invasive, high temporal-resolution assessment of brain function.",[74,69],[83],{"name":84,"role":85,"phone":86,"phoneExt":62,"email":87},"Xiaofei Zhang","CONTACT","86-15563139097","xfeizhang2025@163.com",[89],{"facility":90,"status":91,"city":92,"state":93,"zip":94,"country":95,"countryCode":96,"cosmosGeoPoint":97,"geoPoint":102,"contacts":103},"Qilu hospital, Jinan, Shangdong","RECRUITING","Jinan","Shandong","250000","China","CN",{"type":98,"coordinates":99},"Point",[100,101],116.99722,36.66833,{"lat":101,"lon":100},[104],{"name":105,"role":85,"phone":106,"phoneExt":62,"email":107},"Xiangxin Xing","86-18560086949","xingxiangxin123@163.com",{"type":109,"investigatorFullName":62,"investigatorTitle":62,"investigatorAffiliation":62,"oldNameTitle":62,"oldOrganization":62},"SPONSOR","100655131","brain-network-characteristics-of-pain-patients-based-on-magnetoencephalography-100655131",false,"NCT07809542","Brain Network Characteristics of Pain Patients Based on Magnetoencephalography","Inclusion Criteria:\n\n1. Meet the diagnostic criteria for muscle pain as specified in the Clinical Diagnosis Guidelines: Volume of Physical Medicine and Rehabilitation\n2. VAS score ≥ 3 points.\n3. Aged 18-70 years.\n4. Lesions located in the soft tissues of the neck, shoulder, or lumbodorsal region, accompanied by tender points.\n5. Able to understand and cooperate in completing the scales and undergoing MEG and MRI examinations.\n6. Has signed the informed consent form\n\nExclusion Criteria:\n\n1. History of previous related disorders in the neck, shoulder, or lumbodorsal region.\n2. Recent use of medications that significantly affect central excitability and inability to stabilize the condition.\n3. Contraindications to MEG or MRI.\n4. Presence of ferromagnetic metals implanted in the body (e.g., metal dentures, aneurysm clips, pacemakers, stents, steel plates, etc.), claustrophobia that prevents MEG examination, or failed data acquisition.\n5. Concurrent severe diseases of the heart, brain, kidney, or other major organs.\n6. History of muscle diseases such as tuberculosis, rheumatism, or infection.\n7. Obvious cognitive impairment or communication difficulties that prevent cooperation with this study.",true,"ALL","18 Years","70 Years",{"count":121,"type":122},1200,"ESTIMATED","OBSERVATIONAL","This is a single-center prospective cohort study. We plan to enroll at least 1,000 patients with muscle pain (VAS ≥ 3) and 200 healthy controls. The study aims to investigate neural oscillatory and functional connectivity characteristics in muscle pain patients using magnetoencephalography (MEG), and to analyze their associations with clinical phenotypes including pain intensity, emotional status, sleep quality, disease duration, and sex. Furthermore, we aim to develop a prognostic model for pain outcome. All pain patients will undergo baseline assessments including MEG (with working-memory task, resting-state, and stimulus-evoked paradigms), brain MRI, and a series of clinical scales (VAS, MPQ, HAMA, HAMD, PSQI, ODI, NDI, SPADI, IKDC, WOMAC, DASH, etc.). They will then be followed up at 1, 3, and 6 months, with repeated collection of pain and functional outcome measures. Multivariate regression, mixed-effects models, and penalized regression will be employed to build prediction models. Model performance will be evaluated using AUC, calibration curves, and bootstrap internal validation. We aim to identify candidate objective neural biomarkers to provide a scientific basis for objective pain assessment and stratified management.",[126],"Muscle Pain","2026-09-02",{"date":129,"type":130},"2026-09-09","ACTUAL",{"date":132,"type":130},"2026-05-26",{"date":134,"type":122},"2028-12-31",{"name":5,"class":6},1]