{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:17:49Z","timestamp":1775110669034,"version":"3.50.1"},"reference-count":96,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Human cognition is derived from functional cortical long-range connectivity, as reflected by phase synchronization between electrode pairs of low-frequency electroencephalographic (EEG) activity &amp;lt;8\u202fHz related to cognitive tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We tested the hypothesis that such an EEG marker, combined with machine learning, can discriminate between Parkinson\u2019s disease (PD) with mild cognitive impairment (MCI) and dementia (D) and those with dementia with Lewy bodies (DLB). Event-related EEG delta (1\u20133.5\u202fHz) and theta (4\u20137\u202fHz) phase coherence were computed from EEG activity recorded during a visual oddball task in healthy controls (HC,\n                      <jats:italic>N<\/jats:italic>\n                      \u202f=\u202f24) and PD-MCI (\n                      <jats:italic>N<\/jats:italic>\n                      \u202f=\u202f20), PDD (\n                      <jats:italic>N<\/jats:italic>\n                      \u202f=\u202f18), and DLB (\n                      <jats:italic>N<\/jats:italic>\n                      \u202f=\u202f11) patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using delta-band coherence as input, the comparison between HC and PD-MCI yielded an AUC of approximately 0.79 and an accuracy of 86.4%. Higher discriminative performance was observed for HC versus PDD, reaching an AUC near 0.92 with an overall accuracy of 94.6%. In the classification of HC versus DLB participants, the model achieved 83.3% sensitivity and 88.9% specificity, with an AUC around 0.77. Theta-band models showed comparable results, with average AUC values of about 0.75 for HC vs. DLB and slightly above 0.80 for HC vs. PDD, while classification of HC vs. PD-MCI remained in a moderate range.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest that event-related EEG phase coherence at &amp;lt;8\u202fHz is a promising EEG correlate of cognitive deficits in patients with PDD and DLB, offering insights into disrupted network dynamics of cortical activity related to cognitive processes and potential biomarkers for testing new drugs for cognitive enhancement and disease monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnhum.2026.1696861","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T05:23:35Z","timestamp":1775107415000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Wavelet event-related EEG phase coherence as a discriminant biomarker of the cognitive status in Parkinson\u2019s and Lewy body disease"],"prefix":"10.3389","volume":"20","author":[{"given":"S\u00fcmeyye","family":"\u00d6zdemir","sequence":"first","affiliation":[{"name":"Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Department of Neuroscience, Graduate School of Health Science, Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ay\u015fenur","family":"Akan","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Department of Neuroscience, Graduate School of Health Science, Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuba","family":"Akt\u00fcrk","sequence":"additional","affiliation":[{"name":"Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University","place":["Maastricht, Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebru","family":"Y\u0131ld\u0131r\u0131m","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Neuroscience and Neurotechnology Center of Excellence (N\u00d6ROM), Gazi University","place":["Ankara, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nesrin","family":"Helvac\u0131 Y\u0131lmaz","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine, Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L\u00fctf\u00fc","family":"Hano\u011flu","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Department of Neurology, School of Medicine, Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Babiloni","sequence":"additional","affiliation":[{"name":"Department of Physiology and Pharmacology \u201cVittorio Erspamer\u201d, Sapienza University of Rome","place":["Rome, Italy"]},{"name":"Hospital San Raffaele Cassino","place":["Cassino, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bahar","family":"G\u00fcntekin","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Department of Biophysics, School of Medicine, Istanbul Medipol University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Front. 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