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Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Drug-resistant epilepsy can be treated by surgically removing the seizure onset zone (SOZ), but localizing this zone is challenging because no clinically accepted biomarker of the SOZ exists. A novel computational biomarker, neural resonance, utilizes control theory to model the brain\u2019s response to stimulation. However, model validation is limited, and it is necessary to further contextualize the relationship between the model\u2019s prediction and the brain\u2019s true response to stimulation.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Single-pulse electrical stimulation (SPES) provides an opportunity to bridge this gap as Bode plots derived from dynamical network models built from SPES data can be directly compared to the spectral response of the SPES-evoked waveform, the cortico-cortical spectral response (CCSR). In this study, we tested whether neural resonance, predicted by dynamical network models, reflects the SPES-evoked CCSR.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Using SPES data from 33 patients, we constructed dynamical network models and compared the model-predicted magnitude and frequency behavior (Bode plot) to the CCSR curve measured for each patient. Across patients, Bode plots correlated best with the CCSR curves taken at the time of the N\n                    <jats:sub>2<\/jats:sub>\n                    component of the evoked response and taken as the average post-stimulus CCSR response (median Pearson correlation coefficient,\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mi>r<\/mml:mi>\n                          <mml:mo>=<\/mml:mo>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    0.45 and 0.60, respectively), and significantly better than CCSR curves from randomly selected brain regions (Wilcoxon signed-rank test,\n                    <jats:italic>p<\/jats:italic>\n                    &lt;\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mn>1<\/mml:mn>\n                          <mml:mo>\u00d7<\/mml:mo>\n                          <mml:mrow>\n                            <mml:msup>\n                              <mml:mn>10<\/mml:mn>\n                              <mml:mrow>\n                                <mml:mo>\u2212<\/mml:mo>\n                                <mml:mn>5<\/mml:mn>\n                              <\/mml:mrow>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ). We also found that resonant frequencies in the Bode plot were significantly more co-expressed in CCSR curves coinciding with the time of the N1 and N\n                    <jats:sub>2<\/jats:sub>\n                    components of the evoked response, features associated with early direct connectivity and delayed indirect connectivity, respectively (Wilcoxon rank-sum test, both\n                    <jats:italic>p<\/jats:italic>\n                    &lt;\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mn>1<\/mml:mn>\n                          <mml:mo>\u00d7<\/mml:mo>\n                          <mml:mrow>\n                            <mml:msup>\n                              <mml:mn>10<\/mml:mn>\n                              <mml:mrow>\n                                <mml:mo>\u2212<\/mml:mo>\n                                <mml:mn>3<\/mml:mn>\n                              <\/mml:mrow>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ).\n                    <jats:italic>Significance.<\/jats:italic>\n                    These findings validate the use of dynamical network models to predict neural connectivity and further support the use of neural resonance as a biomarker helping to localize the SOZ through guiding stimulation to induce seizures.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/aea59c","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T22:49:51Z","timestamp":1789080591000},"page":"056037","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamical network models predict spectral response to single-pulse electrical stimulation"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9384-9538","authenticated-orcid":true,"given":"Helen E","family":"Brinyark","sequence":"first","affiliation":[{"name":"University of Alabama at 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Birmingham"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-7986","authenticated-orcid":false,"given":"Benjamin C","family":"Cox","sequence":"additional","affiliation":[{"name":"University of Alabama at Birmingham"},{"name":"Birmingham VA Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0987-4834","authenticated-orcid":true,"given":"Arie","family":"Nakhmani","sequence":"additional","affiliation":[{"name":"University of Alabama at 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