{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T14:58:48Z","timestamp":1777993128492,"version":"3.51.4"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Headache Pain"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s10194-024-01812-4","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T23:02:06Z","timestamp":1718924526000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine"],"prefix":"10.1186","volume":"25","author":[{"given":"Heng-Le","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Sheng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng-Yao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang-Ping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junrong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"issue":"10258","key":"1812_CR1","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","volume":"396","author":"GBD 2019 Diseases and Injuries Collaborators","year":"2020","unstructured":"GBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396(10258):1204\u20131222","journal-title":"Lancet"},{"issue":"1","key":"1812_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/s10194-020-01208-0","volume":"21","author":"TJ Steiner","year":"2020","unstructured":"Steiner TJ, Stovner LJ, Jensen R, Uluduz D, Katsarava Z (2020) Migraine remains second among the world\u2019s causes of disability, and first among young women: findings from GBD2019. J Headache Pain 21(1):137","journal-title":"J Headache Pain"},{"issue":"3","key":"1812_CR3","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/00325481.2021.1985351","volume":"134","author":"R Patel","year":"2022","unstructured":"Patel R, Dickenson AH (2022) Neuropharmacological basis for multimodal analgesia in chronic pain. Postgrad Med 134(3):245\u2013259","journal-title":"Postgrad Med"},{"issue":"6","key":"1812_CR4","doi-asserted-by":"publisher","first-page":"3018","DOI":"10.3390\/ijms23063018","volume":"23","author":"I Simonetta","year":"2022","unstructured":"Simonetta I, Riolo R, Todaro F, Tuttolomondo A (2022) New insights on metabolic and genetic basis of migraine: novel impact on management and therapeutical approach. Int J Mol Sci 23(6):3018","journal-title":"Int J Mol Sci"},{"issue":"2","key":"1812_CR5","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/S1474-4422(17)30435-0","volume":"17","author":"A Charles","year":"2018","unstructured":"Charles A (2018) The pathophysiology of migraine: implications for clinical management. Lancet Neurol 17(2):174\u2013182","journal-title":"Lancet Neurol"},{"issue":"1","key":"1812_CR6","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s10194-020-01132-3","volume":"21","author":"DB Clemow","year":"2020","unstructured":"Clemow DB, Johnson KW, Hochstetler HM, Ossipov MH, Hake AM, Blumenfeld AM (2020) Lasmiditan mechanism of action-review of a selective 5-HT(1F) agonist. J Headache Pain 21(1):71","journal-title":"J Headache Pain"},{"issue":"19","key":"1812_CR7","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1056\/NEJMra1915327","volume":"383","author":"M Ashina","year":"2020","unstructured":"Ashina M (2020) Migraine. N Engl J Med 383(19):1866\u20131876","journal-title":"N Engl J Med"},{"key":"1812_CR8","doi-asserted-by":"publisher","first-page":"363","DOI":"10.3389\/fphar.2019.00363","volume":"10","author":"D Scuteri","year":"2019","unstructured":"Scuteri D, Adornetto A, Rombola L, Naturale MD, Morrone LA, Bagetta G et al (2019) New trends in migraine pharmacology: targeting calcitonin gene-related peptide (CGRP) with monoclonal antibodies. Front Pharmacol 10:363","journal-title":"Front Pharmacol"},{"key":"1812_CR9","doi-asserted-by":"publisher","first-page":"107969","DOI":"10.1016\/j.neuropharm.2020.107969","volume":"166","author":"L Agrawal","year":"2020","unstructured":"Agrawal L, Korkutata M, Vimal SK, Yadav MK, Bhattacharyya S, Shiga T (2020) Therapeutic potential of serotonin 4 receptor for chronic depression and its associated comorbidity in the gut. Neuropharmacology 166:107969","journal-title":"Neuropharmacology"},{"issue":"8","key":"1812_CR10","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1177\/2047487319848105","volume":"27","author":"A Cabassi","year":"2020","unstructured":"Cabassi A, Tedeschi S, Perlini S, Verzicco I, Volpi R, Gonzi G et al (2020) Non-steroidal anti-inflammatory drug effects on renal and cardiovascular function: From physiology to clinical practice. Eur J Prev Cardiol 27(8):850\u2013867","journal-title":"Eur J Prev Cardiol"},{"key":"1812_CR11","doi-asserted-by":"publisher","first-page":"114147","DOI":"10.1016\/j.bcp.2020.114147","volume":"180","author":"S Bindu","year":"2020","unstructured":"Bindu S, Mazumder S, Bandyopadhyay U (2020) Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: a current perspective. Biochem Pharmacol 180:114147","journal-title":"Biochem Pharmacol"},{"issue":"8","key":"1812_CR12","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S1474-4422(19)30185-1","volume":"18","author":"M Ashina","year":"2019","unstructured":"Ashina M, Hansen JM, Do TP, Melo-Carrillo A, Burstein R, Moskowitz MA (2019) Migraine and the trigeminovascular system-40 years and counting. Lancet Neurol 18(8):795\u2013804","journal-title":"Lancet Neurol"},{"issue":"2","key":"1812_CR13","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1152\/physrev.00034.2015","volume":"97","author":"PJ Goadsby","year":"2017","unstructured":"Goadsby PJ, Holland PR, Martins-Oliveira M, Hoffmann J, Schankin C, Akerman S (2017) Pathophysiology of migraine: a disorder of sensory processing. Physiol Rev 97(2):553\u2013622","journal-title":"Physiol Rev"},{"issue":"7","key":"1812_CR14","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1111\/head.13860","volume":"60","author":"A Russo","year":"2020","unstructured":"Russo A, Silvestro M, Trojsi F, Bisecco A, De Micco R, Caiazzo G et al (2020) Cognitive networks disarrangement in patients with migraine predicts cutaneous allodynia. Headache 60(7):1228\u20131243","journal-title":"Headache"},{"issue":"34","key":"1812_CR15","doi-asserted-by":"publisher","first-page":"6236","DOI":"10.2174\/0929867325666180406115427","volume":"26","author":"A Russo","year":"2019","unstructured":"Russo A, Silvestro M, Tessitore A, Tedeschi G (2019) Functional neuroimaging biomarkers in migraine: diagnostic, prognostic and therapeutic implications. Curr Med Chem 26(34):6236\u20136252","journal-title":"Curr Med Chem"},{"issue":"4","key":"1812_CR16","first-page":"e14243","volume":"13","author":"H Kesserwani","year":"2021","unstructured":"Kesserwani H (2021) Migraine triggers: an overview of the pharmacology, biochemistry, atmospherics, and their effects on neural networks. Cureus 13(4):e14243","journal-title":"Cureus"},{"issue":"7","key":"1812_CR17","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1038\/nm0795-658","volume":"1","author":"C Weiller","year":"1995","unstructured":"Weiller C, May A, Limmroth V, Juptner M, Kaube H, Schayck RV et al (1995) Brain stem activation in spontaneous human migraine attacks. Nat Med 1(7):658\u2013660","journal-title":"Nat Med"},{"issue":"10","key":"1812_CR18","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1111\/j.1526-4610.2012.02209.x","volume":"52","author":"M Maizels","year":"2012","unstructured":"Maizels M, Aurora S, Heinricher M (2012) Beyond neurovascular: migraine as a dysfunctional neurolimbic pain network. Headache 52(10):1553\u20131565","journal-title":"Headache"},{"key":"1812_CR19","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3389\/fnins.2018.00507","volume":"12","author":"Y Xiang","year":"2018","unstructured":"Xiang Y, Wang Y, Gao S, Zhang X, Cui R (2018) Neural mechanisms with respect to different paradigms and relevant regulatory factors in empathy for pain. Front Neurosci 12:507","journal-title":"Front Neurosci"},{"issue":"7","key":"1812_CR20","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1097\/j.pain.0000000000000176","volume":"156","author":"DJ Hodkinson","year":"2015","unstructured":"Hodkinson DJ, Khawaja N, O\u2019Daly O, Thacker MA, Zelaya FO, Wooldridge CL et al (2015) Cerebral analgesic response to nonsteroidal anti-inflammatory drug ibuprofen. Pain 156(7):1301\u20131310","journal-title":"Pain"},{"issue":"7","key":"1812_CR21","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1038\/s41593-020-0632-8","volume":"23","author":"T Hua","year":"2020","unstructured":"Hua T, Chen B, Lu D, Sakurai K, Zhao S, Han BX et al (2020) General anesthetics activate a potent central pain-suppression circuit in the amygdala. Nat Neurosci 23(7):854\u2013868","journal-title":"Nat Neurosci"},{"key":"1812_CR22","doi-asserted-by":"publisher","first-page":"953182","DOI":"10.3389\/fnins.2022.953182","volume":"16","author":"JR Bumgarner","year":"2022","unstructured":"Bumgarner JR, Becker-Krail DD, White RC, Nelson RJ (2022) Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors. Front Neurosci 16:953182","journal-title":"Front Neurosci"},{"key":"1812_CR23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","volume":"64","author":"M Khosla","year":"2019","unstructured":"Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR (2019) Machine learning in resting-state fMRI analysis. Magn Reson Imaging 64:101\u2013121","journal-title":"Magn Reson Imaging"},{"issue":"9","key":"1812_CR24","doi-asserted-by":"publisher","first-page":"e163875","DOI":"10.1371\/journal.pone.0163875","volume":"11","author":"Q Zhang","year":"2016","unstructured":"Zhang Q, Wu Q, Zhang J, He L, Huang J, Zhang J et al (2016) Discriminative analysis of migraine without aura: Using functional and structural MRI with a multi-feature classification approach. PLoS One 11(9):e163875","journal-title":"PLoS One"},{"issue":"9","key":"1812_CR25","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1177\/0333102419839975","volume":"39","author":"B Zhu","year":"2019","unstructured":"Zhu B, Coppola G, Shoaran M (2019) Migraine classification using somatosensory evoked potentials. Cephalalgia 39(9):1143\u20131155","journal-title":"Cephalalgia"},{"key":"1812_CR26","doi-asserted-by":"publisher","first-page":"588207","DOI":"10.3389\/fneur.2020.588207","volume":"11","author":"T Yin","year":"2020","unstructured":"Yin T, Sun G, Tian Z, Liu M, Gao Y, Dong M et al (2020) The spontaneous activity pattern of the middle occipital gyrus predicts the clinical efficacy of acupuncture treatment for migraine without aura. Front Neurol 11:588207","journal-title":"Front Neurol"},{"key":"1812_CR27","doi-asserted-by":"publisher","first-page":"953921","DOI":"10.3389\/fneur.2022.953921","volume":"13","author":"S Cheng","year":"2022","unstructured":"Cheng S, Zhang X, Zheng H, Jiang N, Zhou J, Li X et al (2022) Efficacy prediction of acupuncture treatment for migraine without aura based on multimodal MRI: a study protocol. Front Neurol 13:953921","journal-title":"Front Neurol"},{"issue":"1","key":"1812_CR28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1002\/hbm.24372","volume":"40","author":"J Liu","year":"2019","unstructured":"Liu J, Mu J, Chen T, Zhang M, Tian J (2019) White matter tract microstructure of the mPFC-amygdala predicts interindividual differences in placebo response related to treatment in migraine patients. Hum Brain Mapp 40(1):284\u2013292","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"1812_CR29","doi-asserted-by":"publisher","first-page":"e227021","DOI":"10.1371\/journal.pone.0227021","volume":"15","author":"XZ Jia","year":"2020","unstructured":"Jia XZ, Sun JW, Ji GJ, Liao W, Lv YT, Wang J et al (2020) Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level. PLoS One 15(1):e227021","journal-title":"PLoS One"},{"issue":"9","key":"1812_CR30","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1177\/0333102413485658","volume":"33","author":"Headache Classification Committee of the International Headache Society (IHS)","year":"2013","unstructured":"Headache Classification Committee of the International Headache Society (IHS) (2013) The International Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia 33(9):629\u2013808","journal-title":"Cephalalgia"},{"issue":"3","key":"1812_CR31","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1111\/j.1475-1313.2004.00180.x","volume":"24","author":"JS Pointer","year":"2004","unstructured":"Pointer JS (2004) A novel visual analogue scale (VAS) device: an instrument based on the VAS designed to quantify the subjective visual experience. Ophthal Physl Opt 24(3):181\u2013185","journal-title":"Ophthal Physl Opt"},{"issue":"4","key":"1812_CR32","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3988\/jcn.2008.4.4.158","volume":"4","author":"HE Shin","year":"2008","unstructured":"Shin HE, Park JW, Kim YI, Lee KS (2008) Headache Impact Test-6 (HIT-6) scores for migraine patients: their relation to disability as measured from a headache diary. J Clin Neurol 4(4):507","journal-title":"J Clin Neurol"},{"issue":"1","key":"1812_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s12883-020-01646-y","volume":"20","author":"D Rodriguez-Almagro","year":"2020","unstructured":"Rodriguez-Almagro D, Achalandabaso A, Rus A, Obrero-Gaitan E, Zagalaz-Anula N, Lomas-Vega R (2020) Validation of the Spanish version of the migraine disability assessment questionnaire (MIDAS) in university students with migraine. Bmc Neurol 20(1):67","journal-title":"Bmc Neurol"},{"issue":"1","key":"1812_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s10194-015-0583-8","volume":"16","author":"J Seo","year":"2015","unstructured":"Seo J, Park S (2015) Validation of the Generalized Anxiety Disorder-7 (GAD-7) and GAD-2 in patients with migraine. J Headache Pain 16(1):1\u20137","journal-title":"J Headache Pain"},{"issue":"1","key":"1812_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s10194-015-0552-2","volume":"16","author":"J Seo","year":"2015","unstructured":"Seo J, Park S (2015) Validation of the Patient Health Questionnaire-9 (PHQ-9) and PHQ-2 in patients with migraine. J Headache Pain 16(1):1\u20137","journal-title":"J Headache Pain"},{"key":"1812_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5546484","volume":"2021","author":"CL Larche","year":"2021","unstructured":"Larche CL, Plante I, Roy M, Ingelmo PM, Ferland CE (2021) The Pittsburgh Sleep Quality Index: reliability, factor structure, and related clinical factors among children, adolescents, and young adults with chronic pain. Sleep Disord 2021:1\u20138","journal-title":"Sleep Disord"},{"issue":"14","key":"1812_CR37","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1016\/j.scib.2019.05.008","volume":"64","author":"XZ Jia","year":"2019","unstructured":"Jia XZ, Wang J, Sun HY, Zhang H, Liao W, Wang Z et al (2019) RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. Sci Bull (Beijing) 64(14):953\u2013954","journal-title":"Sci Bull (Beijing)"},{"issue":"4","key":"1812_CR38","doi-asserted-by":"publisher","first-page":"100452","DOI":"10.1016\/j.patter.2022.100452","volume":"3","author":"Y Ning","year":"2022","unstructured":"Ning Y, Ong M, Chakraborty B, Goldstein BA, Ting D, Vaughan R et al (2022) Shapley variable importance cloud for interpretable machine learning. Patterns (N Y) 3(4):100452","journal-title":"Patterns (N Y)"},{"issue":"10","key":"1812_CR39","doi-asserted-by":"publisher","first-page":"e1002570","DOI":"10.1371\/journal.pbio.1002570","volume":"14","author":"P Tetreault","year":"2016","unstructured":"Tetreault P, Mansour A, Vachon-Presseau E, Schnitzer TJ, Apkarian AV, Baliki MN (2016) Brain connectivity predicts placebo response across chronic pain clinical trials. Plos Biol 14(10):e1002570","journal-title":"Plos Biol"},{"issue":"14","key":"1812_CR40","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.2174\/1389450123666220819141827","volume":"23","author":"S Kelkar","year":"2022","unstructured":"Kelkar S, Nailwal N, Bhatia NY, Doshi G, Sathaye S, Godad AP (2022) An update on proficiency of voltage-gated ion channel blockers in the treatment of inflammation-associated diseases. Curr Drug Targets 23(14):1290\u20131303","journal-title":"Curr Drug Targets"},{"issue":"8","key":"1812_CR41","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1097\/j.pain.0000000000001897","volume":"161","author":"S Shiers","year":"2020","unstructured":"Shiers S, Price TJ (2020) Molecular, circuit, and anatomical changes in the prefrontal cortex in chronic pain. Pain 161(8):1726\u20131729","journal-title":"Pain"},{"key":"1812_CR42","doi-asserted-by":"publisher","first-page":"757241","DOI":"10.3389\/fneur.2022.757241","volume":"13","author":"D De Ridder","year":"2022","unstructured":"De Ridder D, Vanneste S, Smith M, Adhia D (2022) Pain and the triple network model. Front Neurol 13:757241","journal-title":"Front Neurol"},{"issue":"9","key":"1812_CR43","doi-asserted-by":"publisher","first-page":"3598","DOI":"10.1093\/brain\/awad181","volume":"146","author":"NB Dadario","year":"2023","unstructured":"Dadario NB, Sughrue ME (2023) The functional role of the precuneus. Brain 146(9):3598\u20133607","journal-title":"Brain"},{"issue":"1","key":"1812_CR44","first-page":"49","volume":"89","author":"A Mohan","year":"2016","unstructured":"Mohan A, Roberto AJ, Mohan A, Lorenzo A, Jones K, Carney MJ et al (2016) The significance of the default mode network (DMN) in neurological and neuropsychiatric disorders: a review. Yale J Biol Med 89(1):49\u201357","journal-title":"Yale J Biol Med"},{"issue":"21","key":"1812_CR45","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1212\/WNL.0000000000008525","volume":"93","author":"EE Benarroch","year":"2019","unstructured":"Benarroch EE (2019) Insular cortex: functional complexity and clinical correlations. Neurology 93(21):932\u2013938","journal-title":"Neurology"},{"issue":"6","key":"1812_CR46","doi-asserted-by":"publisher","first-page":"5736","DOI":"10.3390\/ijms24065736","volume":"24","author":"C Labrakakis","year":"2023","unstructured":"Labrakakis C (2023) The role of the insular cortex in pain. Int J Mol Sci 24(6):5736","journal-title":"Int J Mol Sci"},{"key":"1812_CR47","doi-asserted-by":"publisher","first-page":"109385","DOI":"10.1016\/j.mehy.2019.109385","volume":"133","author":"B Menon","year":"2019","unstructured":"Menon B (2019) Towards a new model of understanding - the triple network, psychopathology and the structure of the mind. Med Hypotheses 133:109385","journal-title":"Med Hypotheses"},{"issue":"9","key":"1812_CR48","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1523\/JNEUROSCI.1976-21.2022","volume":"43","author":"G Mastria","year":"2023","unstructured":"Mastria G, Scaliti E, Mehring C, Burdet E, Becchio C, Serino A et al (2023) Morphology, connectivity, and encoding features of tactile and motor representations of the fingers in the human precentral and postcentral gyrus. J Neurosci 43(9):1572\u20131589","journal-title":"J Neurosci"},{"key":"1812_CR49","volume-title":"Neuroanatomy, postcentral gyrus","author":"J Diguiseppi","year":"2024","unstructured":"Diguiseppi J, Tadi P (2024) Neuroanatomy, postcentral gyrus"},{"issue":"4","key":"1812_CR50","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1006\/nimg.2000.0715","volume":"13","author":"P Morosan","year":"2001","unstructured":"Morosan P, Rademacher J, Schleicher A, Amunts K, Schormann T, Zilles K (2001) Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. Neuroimage 13(4):684\u2013701","journal-title":"Neuroimage"},{"issue":"3","key":"1812_CR51","doi-asserted-by":"publisher","first-page":"368","DOI":"10.3390\/brainsci12030368","volume":"12","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Cui X, Wang B, Xue J, Niu Y et al (2022) Functional integration and segregation in a multilayer network model of patients with Schizophrenia. Brain Sci 12(3):368","journal-title":"Brain Sci"},{"issue":"1","key":"1812_CR52","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s10194-020-01210-6","volume":"22","author":"M Lim","year":"2021","unstructured":"Lim M, Jassar H, Kim DJ, Nascimento TD, Dasilva AF (2021) Differential alteration of fMRI signal variability in the ascending trigeminal somatosensory and pain modulatory pathways in migraine. J Headache Pain 22(1):4","journal-title":"J Headache Pain"},{"key":"1812_CR53","doi-asserted-by":"publisher","first-page":"102027","DOI":"10.1016\/j.nicl.2019.102027","volume":"24","author":"YH Tsai","year":"2019","unstructured":"Tsai YH, Liang X, Yang JT, Hsu LM (2019) Modular organization of brain resting state networks in patients with classical trigeminal neuralgia. Neuroimage Clin 24:102027","journal-title":"Neuroimage Clin"},{"issue":"1","key":"1812_CR54","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.pain.2012.09.016","volume":"154","author":"J Liu","year":"2013","unstructured":"Liu J, Hao Y, Du M, Wang X, Zhang J, Manor B et al (2013) Quantitative cerebral blood flow mapping and functional connectivity of postherpetic neuralgia pain: a perfusion fMRI study. Pain 154(1):110\u2013118","journal-title":"Pain"},{"issue":"11","key":"1812_CR55","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1007\/s11427-017-9108-6","volume":"61","author":"J Liu","year":"2018","unstructured":"Liu J, Zhang F, Liu X, Zhuo Z, Wei J, Du M et al (2018) Altered small-world, functional brain networks in patients with lower back pain. Sci China Life Sci 61(11):1420\u20131424","journal-title":"Sci China Life Sci"},{"issue":"1","key":"1812_CR56","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s13023-021-01709-4","volume":"16","author":"K Peng","year":"2021","unstructured":"Peng K, Karunakaran KD, Labadie R, Veliu M, Cheung C, Lee A et al (2021) Suppressed prefrontal cortex oscillations associate with clinical pain in fibrodysplasia ossificans progressiva. Orphanet J Rare Dis 16(1):54","journal-title":"Orphanet J Rare Dis"},{"key":"1812_CR57","volume-title":"Brain morphologic abnormalities in migraine patients: an observational study","author":"L Bonanno","year":"2020","unstructured":"Bonanno L, Lo BV, De Salvo S, Ruvolo C, Torre V, Bramanti V et al (2020) Brain morphologic abnormalities in migraine patients: an observational study"},{"issue":"1","key":"1812_CR58","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s10194-020-01176-5","volume":"21","author":"HL Wei","year":"2020","unstructured":"Wei HL, Chen J, Chen YC, Yu YS, Guo X, Zhou GP et al (2020) Impaired effective functional connectivity of the sensorimotor network in interictal episodic migraineurs without aura. J Headache Pain 21(1):111","journal-title":"J Headache Pain"},{"key":"1812_CR59","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00473","volume-title":"Altered brain structure and functional connectivity of primary visual cortex in optic neuritis","author":"J Huang","year":"2018","unstructured":"Huang J, Duan Y, Liu S, Liang P, Ren Z, Gao Y et al (2018) Altered brain structure and functional connectivity of primary visual cortex in optic neuritis"},{"key":"1812_CR60","doi-asserted-by":"publisher","first-page":"102137","DOI":"10.1016\/j.nicl.2019.102137","volume":"25","author":"A Jimenez-Marin","year":"2020","unstructured":"Jimenez-Marin A, Rivera D, Boado V, Diez I, Labayen F, Garrido I et al (2020) Brain connectivity and cognitive functioning in individuals six months after multiorgan failure. Neuroimage Clin 25:102137","journal-title":"Neuroimage Clin"},{"key":"1812_CR61","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s11556-019-0217-2","volume":"16","author":"F Herold","year":"2019","unstructured":"Herold F, Torpel A, Schega L, Muller NG (2019) Functional and\/or structural brain changes in response to resistance exercises and resistance training lead to cognitive improvements: a systematic review. Eur Rev Aging Phys Act 16:10","journal-title":"Eur Rev Aging Phys Act"},{"issue":"4","key":"1812_CR62","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1002\/hbm.20115","volume":"25","author":"SM Grieve","year":"2005","unstructured":"Grieve SM, Clark CR, Williams LM, Peduto AJ, Gordon E (2005) Preservation of limbic and paralimbic structures in aging. Hum Brain Mapp 25(4):391\u2013401","journal-title":"Hum Brain Mapp"},{"issue":"7","key":"1812_CR63","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1097\/j.pain.0000000000000159","volume":"156","author":"N Maleki","year":"2015","unstructured":"Maleki N, Barmettler G, Moulton EA, Scrivani S, Veggeberg R, Spierings E et al (2015) Female migraineurs show lack of insular thinning with age. Pain 156(7):1232\u20131239","journal-title":"Pain"},{"key":"1812_CR64","volume-title":"Her versus his migraine: multiple sex differences in brain function and structure","author":"N Maleki","year":"2012","unstructured":"Maleki N, Linnman C, Brawn J, Burstein R, Becerra L, Borsook D (2012) Her versus his migraine: multiple sex differences in brain function and structure"},{"issue":"8","key":"1812_CR65","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1097\/j.pain.0000000000002871","volume":"164","author":"TA Melum","year":"2023","unstructured":"Melum TA, Vangberg TR, Johnsen LH, Steingrimsdottir OA, Stubhaug A, Mathiesen EB et al (2023) Gray matter volume and pain tolerance in a general population: the Tromso study. Pain 164(8):1750\u20131758","journal-title":"Pain"},{"key":"1812_CR66","doi-asserted-by":"publisher","first-page":"110722","DOI":"10.1016\/j.biopha.2020.110722","volume":"131","author":"N Tsagareli","year":"2020","unstructured":"Tsagareli N, Tsiklauri N, Kvachadze I, Tsagareli MG (2020) Endogenous opioid and cannabinoid systems contribute to antinociception produced by administration of NSAIDs into the insular cortex of rats. Biomed Pharmacother 131:110722","journal-title":"Biomed Pharmacother"},{"key":"1812_CR67","doi-asserted-by":"publisher","first-page":"937453","DOI":"10.3389\/fnins.2022.937453","volume":"16","author":"C Fu","year":"2022","unstructured":"Fu C, Zhang Y, Ye Y, Hou X, Wen Z, Yan Z et al (2022) Predicting response to tVNS in patients with migraine using functional MRI: a voxels-based machine learning analysis. Front Neurosci 16:937453","journal-title":"Front Neurosci"},{"issue":"1","key":"1812_CR68","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s10194-019-0986-z","volume":"20","author":"MJ Lee","year":"2019","unstructured":"Lee MJ, Park BY, Cho S, Kim ST, Park H, Chung CS (2019) Increased connectivity of pain matrix in chronic migraine: a resting-state functional MRI study. J Headache Pain 20(1):29","journal-title":"J Headache Pain"},{"issue":"6","key":"1812_CR69","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1001\/jamaneurol.2016.0653","volume":"73","author":"TV Salomons","year":"2016","unstructured":"Salomons TV, Iannetti GD, Liang M, Wood JN (2016) The, \u201cpain matrix\u201d in pain-free individuals. Jama Neurol 73(6):755\u2013756","journal-title":"Jama Neurol"},{"issue":"1","key":"1812_CR70","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.pneurobio.2010.10.005","volume":"93","author":"V Legrain","year":"2011","unstructured":"Legrain V, Iannetti GD, Plaghki L, Mouraux A (2011) The pain matrix reloaded: a salience detection system for the body. Prog Neurobiol 93(1):111\u2013124","journal-title":"Prog Neurobiol"},{"key":"1812_CR71","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1146\/annurev-psych-010213-115146","volume":"66","author":"NI Eisenberger","year":"2015","unstructured":"Eisenberger NI (2015) Social pain and the brain: controversies, questions, and where to go from here. Annu Rev Psychol 66:601\u2013629","journal-title":"Annu Rev Psychol"},{"issue":"Pt 5","key":"1812_CR72","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1093\/brain\/awg102","volume":"126","author":"J Lorenz","year":"2003","unstructured":"Lorenz J, Minoshima S, Casey KL (2003) Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation. Brain 126(Pt 5):1079\u20131091","journal-title":"Brain"},{"issue":"7","key":"1812_CR73","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1097\/j.pain.0000000000001541","volume":"160","author":"J Kim","year":"2019","unstructured":"Kim J, Mawla I, Kong J, Lee J, Gerber J, Ortiz A et al (2019) Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain. Pain 160(7):1594\u20131605","journal-title":"Pain"},{"issue":"22","key":"1812_CR74","doi-asserted-by":"publisher","first-page":"e2550","DOI":"10.1212\/WNL.0000000000007577","volume":"92","author":"G Coppola","year":"2019","unstructured":"Coppola G, Di Renzo A, Petolicchio B, Tinelli E, Di Lorenzo C, Parisi V et al (2019) Aberrant interactions of cortical networks in chronic migraine: a resting-state fMRI study. Neurology 92(22):e2550\u2013e2558","journal-title":"Neurology"},{"key":"1812_CR75","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.00111","volume-title":"Baseline brain gray matter volume as a predictor of acupuncture outcome in treating migraine","author":"XJ Yang","year":"2020","unstructured":"Yang XJ, Liu L, Xu ZL, Zhang YJ, Liu DP, Fishers M et al (2020) Baseline brain gray matter volume as a predictor of acupuncture outcome in treating migraine"},{"key":"1812_CR76","volume-title":"Neurological mechanism and treatment effects prediction of acupuncture on migraine without aura: study protocol for a randomized controlled trial","author":"J Hong","year":"2022","unstructured":"Hong J, Sun J, Zhang L, Tan Z, Chen Y, Chen Q et al (2022) Neurological mechanism and treatment effects prediction of acupuncture on migraine without aura: study protocol for a randomized controlled trial"},{"key":"1812_CR77","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.798695","volume-title":"The use of neuroimaging for predicting Sumatriptan treatment response in patients with migraine","author":"J Wu","year":"2022","unstructured":"Wu J, Lai PY, Chen YL, Wang YF, Lirng JF, Chen ST et al (2022) The use of neuroimaging for predicting Sumatriptan treatment response in patients with migraine"},{"issue":"4","key":"1812_CR78","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1177\/1745691620970586","volume":"16","author":"MI Eronen","year":"2021","unstructured":"Eronen MI, Bringmann LF (2021) The theory crisis in psychology: how to move forward. Perspect Psychol Sci 16(4):779\u2013788","journal-title":"Perspect Psychol Sci"},{"key":"1812_CR79","doi-asserted-by":"publisher","first-page":"9850248","DOI":"10.34133\/2022\/9850248","volume":"2022","author":"B Hu","year":"2022","unstructured":"Hu B, Qian K, Zhang Y, Shen J, Schuller BW (2022) The inverse problems for computational psychophysiology: opinions and insights. Cyborg Bionic Syst 2022:9850248","journal-title":"Cyborg Bionic Syst"}],"container-title":["The Journal of Headache and Pain"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s10194-024-01812-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s10194-024-01812-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s10194-024-01812-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T23:02:12Z","timestamp":1718924532000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejournalofheadacheandpain.biomedcentral.com\/articles\/10.1186\/s10194-024-01812-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":79,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1812"],"URL":"https:\/\/doi.org\/10.1186\/s10194-024-01812-4","relation":{},"ISSN":["1129-2377"],"issn-type":[{"value":"1129-2377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"14 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was obtained through the Ethics Committee of the Affiliated Jiangning Hospital of Nanjing Medical University (20180285, 2020\u201303-026-K01, and 2023\u201303-010-K01). Informed consent was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"104"}}