{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T10:14:25Z","timestamp":1778580865542,"version":"3.51.4"},"reference-count":36,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"crossref","award":["F32NS129627"],"award-info":[{"award-number":["F32NS129627"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 NS090913"],"award-info":[{"award-number":["R01 NS090913"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100013301","name":"Parkinson\u2019s Foundation","doi-asserted-by":"crossref","award":["PF-SF-JFA-1419797"],"award-info":[{"award-number":["PF-SF-JFA-1419797"]}],"id":[{"id":"10.13039\/100013301","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013937","name":"Thiemann Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013937","id-type":"DOI","asserted-by":"crossref"}]},{"name":"TUYF Charitable Trust Fund"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Adaptive deep brain stimulation (aDBS) for Parkinson\u2019s disease is a recently-approved therapy that adjusts stimulation in response to neurophysiologic biomarkers of motor-symptom state. Most real-time implementations of aDBS rely on instantaneous, noise-susceptible classifiers that apply simple thresholds to neurophysiologic biomarkers. We examined whether incorporating temporal history through Bayesian state-space modeling improved motor-state classification compared to instantaneous discriminant classifiers.\n                    <jats:italic>Approach<\/jats:italic>\n                    . We analyzed naturalistic neural data from three patients with Parkinson\u2019s disease chronically implanted with investigational sensing-enabled DBS systems, recording from both the subthalamic nucleus (STN) and sensorimotor cortex. Biomarkers were extracted across multiple window lengths and labeled using wearable-derived bradykinesia and dyskinesia scores. Classifier behavior was evaluated using two biomarkers (cortical stimulation-entrained gamma and STN beta oscillations) across a factorial combination of two conditions: (1) instantaneous discriminant analysis vs Bayesian time-history modeling via hidden Markov models (HMMs), and (2) single Gaussian vs Gaussian mixture modeling of each motor state\u2019s biomarker distribution. Performance metrics included\n                    <jats:italic>F<\/jats:italic>\n                    1 scores, accuracy, prediction smoothness, latency, and computational load.\n                    <jats:italic>Main Results<\/jats:italic>\n                    . Using entrained-gamma biomarkers, incorporating time history via HMMs significantly improved hyperkinetic-state detection (\n                    <jats:italic>F<\/jats:italic>\n                    1: +12.9 \u00b1 1.8%; accuracy: +30.0 \u00b1 2.7%; both\n                    <jats:italic>p<\/jats:italic>\n                    <jats:sub>adj<\/jats:sub>\n                    &lt; 0.001) with modest decreases in hypokinetic-state performance, yielding a net increase in average\n                    <jats:italic>F<\/jats:italic>\n                    1 (+4.7 \u00b1 0.9%,\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001). HMMs also yielded smoother and more accurate predictions for a given latency compared to simply increasing the window length used to extract neurophysiologic biomarkers. Entrained-gamma biomarkers outperformed STN beta biomarkers across all classifiers (average\n                    <jats:italic>F<\/jats:italic>\n                    1: +12.9% \u00b1 0.5%,\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001). All methods operated within sub-millisecond prediction times and demonstrated sublinear empirical computational scaling.\n                    <jats:italic>Significance<\/jats:italic>\n                    . Bayesian time-history modeling enhanced motor-state classification while preserving the low latency and computational efficiency required for real-time aDBS. These findings, derived from chronic at-home recordings, support the translational potential of Bayesian state-space models for next-generation aDBS systems.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae62a5","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:51:48Z","timestamp":1776811908000},"page":"036007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian time-history modeling enhances Parkinsonian motor state classification for adaptive deep brain stimulation"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6541-8056","authenticated-orcid":true,"given":"Brianna","family":"Leung","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Shcherbakova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7062-2508","authenticated-orcid":true,"given":"Jiaang","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-6791","authenticated-orcid":true,"given":"Stephanie","family":"Cernera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8451-7960","authenticated-orcid":true,"given":"Carina R","family":"Oehrn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amelia","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Little","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip A","family":"Starr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4045-490X","authenticated-orcid":true,"given":"Lauren H","family":"Hammer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"jneae62a5bib1","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1056\/NEJMoa060281","type":"journal-article","article-title":"A randomized trial of deep-brain stimulation for Parkinson\u2019s Disease","volume":"355","author":"Deuschl","year":"2006","journal-title":"N. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-12-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-04-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}