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Neuroimaging\u2010based models can help evaluate how electrode placement impacts improvement, aiming to reduce the burden of programming. However, most existing models have been developed to explain differences between patients rather than differences between contacts within the same patient, leaving the clinical relevance of image\u2010guided programming unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed data from patients with Parkinson's disease treated with subthalamic deep brain stimulation to develop and validate a neuroimaging\u2010informed model of motor improvement measured by the Unified Parkinson's Disease Scale. Five approaches were tested: active contact coordinates, electric fields, tract activations, as well as structural and functional networks. All approaches were integrated into a combined ridge regression model and validated using 2 hold\u2010out datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sample included 236 patients (604 stimulation sites), divided into a training cohort (N\u2009=\u2009129), a retrospective validation cohort (N\u2009=\u200989), and a prospectively acquired validation cohort (N\u2009=\u200921 electrodes). Consistent with expectations, our model explained approximately 12% of the variance in unseen group\u2010level data (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.12,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001). At the individual level, the model identified the optimal clinical contact or its neighboring contact in all but one case (mixed\u2010effects\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.31,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20093.67\u2009\u00d7\u200910\n                      <jats:sup>\u221210<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>An imaging\u2010informed model explained the expected variance at the group level and demonstrated potential for guiding stimulation programming, suggesting that image\u2010guided approaches may improve clinical decision making while reducing the need for lengthy postoperative testing. ANN NEUROL 2026;100:22\u201335<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ana.78206","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:25:15Z","timestamp":1776414315000},"page":"22-35","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease"],"prefix":"10.1002","volume":"100","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7831-8796","authenticated-orcid":false,"given":"Patricia","family":"Zvarova","sequence":"first","affiliation":[{"name":"Movement Disorder and Neuromodulation Unit, Department of Neurology Charit\u00e9\u2010Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"},{"name":"Einstein Center for Neurosciences Berlin, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin  Berlin 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