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Neural Eng."],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Anterior nucleus of the thalamus deep brain stimulation (ANT-DBS) is an effective therapeutic option for drug-resistant epilepsy (DRE); however, substantial inter-individual variability in treatment response limits its clinical optimization. This study aimed to develop an objective and explainable preoperative prediction model for ANT-DBS outcomes using nonlinear dynamical features derived from preoperative N2 sleep electroencephalography (EEG).\n                    <jats:italic>Approach.<\/jats:italic>\n                    Artifact-free N2 sleep EEG segments were retrospectively collected from 26 patients with DRE who underwent ANT-DBS and were classified as responders or non-responders according to postoperative seizure reduction. Nonlinear dynamical features, including conditional entropy, robust permutation entropy (RPE), and their multiscale variants, were extracted across six frequency bands (\n                    <jats:italic>\u03b4, \u03b8, \u03b1, \u03c3, \u03b2<\/jats:italic>\n                    , and low-\n                    <jats:italic>\u03b3<\/jats:italic>\n                    ). Statistically significant features were identified using the Mann\u2013Whitney\n                    <jats:italic>U<\/jats:italic>\n                    test with false discovery rate correction (\n                    <jats:italic>q<\/jats:italic>\n                    &lt; 0.05). These features were subsequently integrated into a multidimensional feature space to construct a support vector machine (SVM) classifier. Model interpretability was further evaluated using the SHapley additive explanations (SHAP) framework.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Non-responders exhibited significantly higher complexity in the\n                    <jats:italic>\u03b4<\/jats:italic>\n                    and\n                    <jats:italic>\u03c3<\/jats:italic>\n                    bands (\n                    <jats:italic>q<\/jats:italic>\n                    &lt; 0.05), indicating impaired thalamocortical rhythmic fidelity and disrupted synchronization stability. In contrast, responders demonstrated significantly higher\n                    <jats:italic>\u03b1<\/jats:italic>\n                    -band RPE (\n                    <jats:italic>q<\/jats:italic>\n                    &lt; 0.01), reflecting greater cortical functional flexibility and a richer dynamical repertoire. The SVM classifier achieved an area under the curve of 0.933 on an independent validation set, with 100% sensitivity for responder identification. SHAP-based attribution further revealed that the most influential features were strongly associated with physiologically meaningful alterations in thalamocortical network dynamics.\n                    <jats:italic>Significance.<\/jats:italic>\n                    These findings demonstrate that preoperative N2 sleep EEG complexity may serve as a non-invasive and interpretable biomarker for predicting ANT-DBS outcomes in DRE. This framework provides a clinically accessible decision-support tool for personalized patient selection and precision neuromodulation.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae94b2","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T22:52:06Z","timestamp":1785883926000},"page":"046053","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear neurodynamics of N2 sleep EEG predict outcomes of anterior nucleus of the thalamus deep brain stimulation in epilepsy: a pilot study"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3177-3362","authenticated-orcid":true,"given":"Zhihong","family":"Zhou","sequence":"first","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1331-3340","authenticated-orcid":true,"given":"Yun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chongqing Polytechnic University of Electronic Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyi","family":"He","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2990-6100","authenticated-orcid":true,"given":"Qingling","family":"Xia","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4183-4242","authenticated-orcid":true,"given":"Gen","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5218-7805","authenticated-orcid":true,"given":"Bowen","family":"Yang","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Tiantan Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7514-0652","authenticated-orcid":true,"given":"Bin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"jneae94b2bib1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.seizure.2022.01.004","type":"journal-article","article-title":"Analysis of power spectrum and phase lag index changes following deep brain stimulation of the anterior nucleus of the thalamus in patients with drug-resistant epilepsy: a retrospective study","volume":"96","author":"Tong","year":"2022","journal-title":"Seizure"},{"key":"jneae94b2bib2","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/S0140-6736(18)32596-0","type":"journal-article","article-title":"Epilepsy in adults","volume":"393","author":"Thijs","year":"2019","journal-title":"The Lancet"},{"key":"jneae94b2bib3","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1056\/NEJM200002033420503","type":"journal-article","article-title":"Early identification of refractory epilepsy","volume":"342","author":"Kwan","year":"2000","journal-title":"New Engl. 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