{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:08:14Z","timestamp":1789049294247,"version":"build-2803163510"},"reference-count":52,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["British Journal of Pain"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clinical heterogeneity in fibromyalgia (FM) results in highly variable therapeutic responses to transcranial direct current stimulation (tDCS). Identifying predictors and temporal response patterns is crucial to transform personalized neuromodulation strategies. This secondary analysis integrated supervised prediction, pain-trajectory modeling, and causal inference to characterize individual response profiles to anodal tDCS in women with FM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Secondary analysis of a randomized, controlled, triple-blind clinical trial including 35 women allocated to active anodal M1 tDCS (2\u00a0mA, 20\u00a0min, 10 sessions) or sham stimulation. Pain (VAS) was assessed at baseline, D10, D30, and D90; functional, fatigue, mood, and QoL measures at baseline and D30. Six supervised algorithms were trained toclassify responders (\u226530% VAS reduction). Pain-trajectory clusters were derived using Dynamic Time Warping with k-means. A T-learner framework estimated Conditional Average Treatment Effects (CATE) to detect treatment-effect modifiers. The Research Ethics Committee of the Centro Universit\u00e1rio da Grande Dourados under registration number Certificado de Apresenta\u00e7\u00e3o de Aprecia\u00e7\u00e3o \u00c9tica approved this research:36444920.5.0000.5159. The study was registered in The Brazilian Registry of Clinical Trials with the identifier RBR-8wc8rjq.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Active tDCS elicited greater pain reduction than sham at D10, D30, and D90 (\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; .001). Predictive models demonstrated stable discrimination (AUC 0.78\u20130.84). WHOQOL-Physical and WHOQOL-Psychological domains were the strongest positive predictors, while higher FIQ and antidepressant use lowered response probability. Three exclusive pain-trajectory phenotypes were identified\u2014rapid responders, progressive responders, and non-responders\u2014with distinct sustainability at D90. CATE analyses indicated higher treatment advantage among participants with higher psychological QoL, moderate baseline VAS (6\u20138), and no antidepressant use.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of machine-learning models, longitudinal trajectory analysis, and causal estimation revealed heterogeneous response dynamics and specific clinical profiles with higher likelihood of therapeutic gain from tDCS. These findings may support refinement of personalized stimulation protocols, early monitoring strategies, and targeted candidate selection in FM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20494637261487997","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:17:26Z","timestamp":1788877046000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning\u2013based modeling of response patterns to transcranial direct current stimulation in women with fibromyalgia"],"prefix":"10.1177","author":[{"given":"Michele de Moura","family":"Gon\u00e7alves","sequence":"first","affiliation":[{"name":"Universidade Federal da Grande Dourados - UFGD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefany Vilani","family":"Gomes","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Pedro Ferreira","family":"Zanatta","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo Henrique","family":"Rocha","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovana Mendes Wolf","family":"Viegas","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabela de Souza","family":"Santos","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariana Mendes","family":"Freire","sequence":"additional","affiliation":[{"name":"Universidade Federal da Grande Dourados - UFGD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gisele da Silva Peixoto","family":"Zandona","sequence":"additional","affiliation":[{"name":"Universidade Federal da Grande Dourados - UFGD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7821-1145","authenticated-orcid":false,"given":"Eduardo Henrique","family":"Loreti","sequence":"additional","affiliation":[{"name":"Centro Universit\u00e1rio da Grande Dourados - UNIGRAN"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/medicina60020272"},{"key":"e_1_3_5_3_2","first-page":"2616","article-title":"Structural and functional thalamocortical connectivity in female fibromyalgia","volume":"11","author":"Kim D","year":"2021","unstructured":"Kim D, Lim M, Kim J, et al. Structural and functional thalamocortical connectivity in female fibromyalgia. Sci Rep 2021; 11: 2616.","journal-title":"Sci Rep"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2025.01.048"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/cns.70500"},{"key":"e_1_3_5_6_2","first-page":"892","article-title":"Primary motor cortex tDCS modulates temporal summation of the nociceptive withdrawal reflex in healthy subjects","volume":"19","author":"Hughes S","year":"2018","unstructured":"Hughes S, Grimsey S, Strutton P. Primary motor cortex tDCS modulates temporal summation of the nociceptive withdrawal reflex in healthy subjects. Pain Med 2018; 19: 892\u2013903.","journal-title":"Pain Med"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/17434440.2020.1816168"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpain.2022.1005634"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00018"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.06.076"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.631838"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep22180"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12111569"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpain.2025.1593746"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurom.2022.11.007"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2017.00685"},{"key":"e_1_3_5_17_2","unstructured":"Silva 2027 \u2014 falta o t\u00edtulo para completar."},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-30127-z"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-023-03445-7"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnmol.2024.1269636"},{"key":"e_1_3_5_21_2","first-page":"739","article-title":"Analgesic effect of tDCS in fibromyalgia: systematic review and meta-regression","volume":"25","author":"Teixeira P","year":"2022","unstructured":"Teixeira P, Pacheco-Barrios K, Branco L, et al. Analgesic effect of tDCS in fibromyalgia: systematic review and meta-regression. Neuromodulation 2022; 25: 739\u2013751.","journal-title":"Neuromodulation"},{"key":"e_1_3_5_22_2","doi-asserted-by":"crossref","first-page":"e0288830","DOI":"10.1371\/journal.pone.0288830","article-title":"Neural network modulation and symptom correlation in fibromyalgia: home-based tDCS randomized trial","volume":"19","author":"Alves RL","year":"2024","unstructured":"Alves RL, Zortea M, Serrano P, et al. Neural network modulation and symptom correlation in fibromyalgia: home-based tDCS randomized trial. PLoS One 2024; 19: e0288830.","journal-title":"PLoS One"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1097\/AUD.0000000000001246"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.brs.2023.05.020"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-34724-5"},{"key":"e_1_3_5_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.brs.2020.10.001"},{"key":"e_1_3_5_27_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-02064-z"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.1111\/add.16226"},{"key":"e_1_3_5_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-86686-5"},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-94782-9"},{"key":"e_1_3_5_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-024-01185-7"},{"key":"e_1_3_5_32_2","first-page":"18206","article-title":"Multi-CATE: multi-accurate conditional average treatment-effect estimation","volume":"2405","author":"Kern C","year":"2024","unstructured":"Kern C, Kim M, Zhou A. Multi-CATE: multi-accurate conditional average treatment-effect estimation. arXiv 2024; 2405: 18206.","journal-title":"arXiv"},{"key":"e_1_3_5_33_2","first-page":"e078378","article-title":"TRIPOD+AI: updated reporting guidelines for ML prediction models","volume":"385","author":"Collins GS","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI: updated reporting guidelines for ML prediction models. Br Med J 2024; 385: e078378.","journal-title":"Br Med J"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.add4391"},{"key":"e_1_3_5_35_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-21557-x"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12993-019-0156-0"},{"key":"e_1_3_5_37_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awf238"},{"key":"e_1_3_5_38_2","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.2003.049916"},{"key":"e_1_3_5_39_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.70162"},{"key":"e_1_3_5_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm14041337"},{"key":"e_1_3_5_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-67401-2"},{"key":"e_1_3_5_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125369"},{"key":"e_1_3_5_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2025.01.007"},{"key":"e_1_3_5_44_2","doi-asserted-by":"publisher","DOI":"10.1038\/s12276-025-01409-0"},{"key":"e_1_3_5_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurot.2024.e00464"},{"key":"e_1_3_5_46_2","doi-asserted-by":"publisher","DOI":"10.36076\/ppj.2012\/15\/1"},{"key":"e_1_3_5_47_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12010025"},{"key":"e_1_3_5_48_2","first-page":"ade189","article-title":"Failure modes and mitigations for Bayesian optimization of neuromodulation parameters","volume":"22","author":"Rijn E","year":"2025","unstructured":"Rijn E, Widge A. Failure modes and mitigations for Bayesian optimization of neuromodulation parameters. J Neural Eng 2025; 22: ade189.","journal-title":"J Neural Eng"},{"key":"e_1_3_5_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2016.10.087"},{"key":"e_1_3_5_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpain.2020.01.003"},{"key":"e_1_3_5_51_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci14121162"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.12786\/bn.2024.17.e21"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering12060656"}],"container-title":["British Journal of Pain"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20494637261487997","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20494637261487997","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20494637261487997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:17:22Z","timestamp":1789046242000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20494637261487997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":52,"alternative-id":["10.1177\/20494637261487997"],"URL":"https:\/\/doi.org\/10.1177\/20494637261487997","relation":{},"ISSN":["2049-4637","2049-4645"],"issn-type":[{"value":"2049-4637","type":"print"},{"value":"2049-4645","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]},"article-number":"20494637261487997"}}