{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T12:10:53Z","timestamp":1793189453036,"version":"4.5.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"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. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The relevance of electroencephalographic (EEG) biomarkers is increasing, as advancements in spectral analysis enable computational decomposition of complex neural signals into quantitative EEG (qEEG) parameters. Especially the differentiation of periodic and aperiodic components can reveal insights into neural function, disease biomarkers, and therapeutic efficacy. The aim of these analyses from real-world clinical routine EEG recordings was to provide normative values of physiological age-related oscillatory (periodic) and non-rhythmic (aperiodic) activity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 532 physiological EEGs of patients between 8 and 92\u202fyears of age. EEG segments were preprocessed, and the power spectrum was computed using a multitaper method. We decomposed the power spectrum into periodic (peak power, frequency, and bandwidth) and aperiodic (intercept and exponent) components. Linear regression models were used to investigate age-related changes in these parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We observed significant global age-related changes in the periodic alpha (\u22120.015\u202fHz\/year) and gamma (+0.013 to +0.031\u202fHz\/year) peak frequency as well as in the aperiodic exponent (\u22120.003 to \u22120.004\u202f\u03bcV\n                      <jats:sup>2<\/jats:sup>\n                      \/Hz\/year). In the other parameters there were solely regional or no significant age-related changes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Decomposing the power spectrum into periodic and aperiodic components allows for the characterization of age-related changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>This study provides the first spectrum-wide normative characterization of age-related changes in periodic and aperiodic activity, relevant for non-invasive brain stimulation with alternating current targeting ongoing oscillatory activity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2025.1540040","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T01:13:33Z","timestamp":1744334013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Normative characterization of age-related periodic and aperiodic activity in resting-state real-world clinical EEG recordings"],"prefix":"10.3389","volume":"17","author":[{"given":"Sophie","family":"Leroy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktor","family":"Bublitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Falk","family":"von Dincklage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daria","family":"Antonenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Fleischmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.neuroimage.2015.03.001","article-title":"Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging","volume":"112","author":"Caplan","year":"2015","journal-title":"NeuroImage"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"119810","DOI":"10.1016\/j.neuroimage.2022.119810","article-title":"Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age","volume":"268","author":"Cesnaite","year":"2023","journal-title":"NeuroImage"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. 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