{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T15:48:10Z","timestamp":1785167290831,"version":"3.55.0"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271161"],"award-info":[{"award-number":["82271161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071041"],"award-info":[{"award-number":["82071041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020732","name":"Innovative Research Team of High-level Local University in Shanghai","doi-asserted-by":"publisher","award":["SHSMU\u2010ZLCX20211702"],"award-info":[{"award-number":["SHSMU\u2010ZLCX20211702"]}],"id":[{"id":"10.13039\/100020732","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Central auditory processing (CAP) is crucial for speech perception and is also fundamental for cognitive function. This study investigated whether gap detection threshold (GDT) could serve as an early marker for identifying individuals with cognitive impairment (CI) at high risk of dementia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Sixty\u2010four older adults underwent peripheral auditory, cognitive, and CAP assessments. Machine learning and resting state electroencephalography (EEG)\/event\u2010related potential (ERP) analyses explored predictors and neural correlates of CI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      GDT was significantly higher in those with CI (mean\u00a0\u00b1\u00a0standard deviation: 8.25\u00a0\u00b1\u00a06.14\u00a0versus 5.98\u00a0\u00b1\u00a03.44\u00a0ms, respectively,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.034), and negatively correlated with cognitive test scores (e.g., Addenbrooke's Cognitive Examination III:\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a0\u20130.40,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.001). GDT emerged as a key predictor. EEG showed altered auditory connectivity and ERP revealed reduced N1\/N2 amplitudes in high\u2010GDT individuals (false discovery rate corrected\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>GDT may reflect early neurophysiological changes in individuals with CI and has potential as a non\u2010invasive biomarker.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Central auditory processing (CAP) test scores were found to be significantly correlated with cognitive tests.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>By machine learning, the best variable gap detection threshold (GDT) for predicting cognitive impairment was screened out.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>GDT subgroup analysis was performed within the normal control (NC) group. Compared to the low GDT subgroup, the high GDT subgroup had lower amplitudes of the cognitive components of the event\u2010related potential and many differences in functional connectivity, indicating that GDT has predictive value for changes in cognitive function.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70189","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T08:50:39Z","timestamp":1758876639000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Can auditory processing dysfunction indicate early cognitive decline?"],"prefix":"10.1002","volume":"17","author":[{"given":"Xinrong","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Shanghai Jiao Tong University Affiliated Sixth People's 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