{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:44:30Z","timestamp":1780616670865,"version":"3.54.1"},"reference-count":56,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007150","name":"United Therapeutics Corporation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007150","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007631","name":"Canadian Institute for Advanced Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007631","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100020951","name":"Fonds de recherche du Qu\u00e9bec","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100020951","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Canada Research Chair in Neural Computations and Interfacing"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. Neurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety. Building such personalized protocols would benefit from accumulated information in increasingly large datasets of other individuals\u2019 responses. <jats:italic>Approach<\/jats:italic>. To address that need, we propose a meta-learning family of algorithms to conduct few-shot optimization of key fitting parameters of physiological and neural responses in new individuals. While our method is agnostic to neurostimulation setting, here we demonstrate its effectiveness on the problem of physiological modeling of fiber recruitment during vagus nerve stimulation (VNS). Using data from acute VNS experiments, the mapping between amplitudes of stimulus-evoked compound action potentials (eCAPs) and physiological responses, such as heart rate and breathing interval modulation, is inferred. <jats:italic>Main results<\/jats:italic>. Using additional synthetic data sets to complement experimental results, we demonstrate that our meta-learning framework is capable of directly modeling the physiology-eCAP relationship for individual subjects with much fewer individually queried data points than standard methods. <jats:italic>Significance<\/jats:italic>. Our meta-learning framework is general and can be adapted to many input\u2013response neurostimulation mapping problems. Moreover, this method leverages information from growing data sets of past patients, as a treatment is deployed. It can also be combined with several model types, including regression, Gaussian processes with Bayesian optimization, and beyond.<\/jats:p>","DOI":"10.1088\/1741-2552\/ad17f4","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T10:18:10Z","timestamp":1703240290000},"page":"016004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation"],"prefix":"10.1088","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4488-8796","authenticated-orcid":true,"given":"Ximeng","family":"Mao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0340-4652","authenticated-orcid":true,"given":"Yao-Chuan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stavros","family":"Zanos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2730-7291","authenticated-orcid":false,"given":"Guillaume","family":"Lajoie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"jnead17f4bib1","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1111\/papr.13008","article-title":"ECAP-controlled closed-loop spinal cord stimulation efficacy and opioid reduction over 24-months: final results of the prospective, multicenter, open-label avalon study","volume":"21","author":"Brooker","year":"2021","journal-title":"Pain Pract."},{"key":"jnead17f4bib2","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1056\/NEJMct1208070","article-title":"Deep-brain stimulation for Parkinson\u2019s disease","volume":"367","author":"Okun","year":"2012","journal-title":"New Engl. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-07-21","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-12-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-01-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}