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Neurosci."],"accepted":{"date-parts":[[2025,10,5]]},"published-print":{"date-parts":[[2025,12,10]]},"abstract":"<jats:p>\n                    Epilepsy constitutes a clinically manifest excitability disorder that is characterized by aberrant electrophysiological activity in the electroencephalogram (EEG). The correct identification of the seizure onset zone relies on the visual detection of pathological waveforms and the assessment of their morphology, rhythmicity, and density. Recent advances in quantitative EEG analyses indicated that aperiodic EEG background activity might provide complementary information to traditional qualitative methods. Importantly, aperiodic activity, and specifically the slope of the 1\/\u0192\n                    <jats:italic>\n                      <jats:sup>\u03c7<\/jats:sup>\n                    <\/jats:italic>\n                    decay function of the power spectrum, might constitute a biomarker of the underlying population excitability dynamics. Hence, in the context of epileptic activity, an altered spectral slope is often considered as a signature of pathological excitability. To date, it remained unclear if this straightforward interpretation also applies to states of manifest seizure activity. To address this question, we recorded intracranial electroencephalography (iEEG) during focal seizures from patients diagnosed with pharmacoresistant epilepsy (18 patients, 11 females). The results demonstrate that the spectral slope successfully delineates seizure activity. However, the spectral slope was sensitive to the presence and waveform shape of distinct epileptic components. By combining iEEG recordings with simulations, we demonstrate that epileptic spiking activity and associated slow-wave components differentially impact spectral slope estimates. These results offer a more parsimonious explanation for the biophysical origins of aperiodic activity as compared with the concept of an underlying balance between excitation and inhibition.\n                  <\/jats:p>","DOI":"10.1523\/jneurosci.0146-25.2025","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:55:18Z","timestamp":1762800918000},"page":"e0146252025","update-policy":"https:\/\/doi.org\/10.1523\/jneurosci.crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Aperiodic Activity Reflects Pathologic Waveform Shapes in Focal Epilepsy"],"prefix":"10.1523","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0182-3980","authenticated-orcid":false,"given":"Laura F.","family":"Heidiri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silke","family":"Ethofer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Naros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8291-652X","authenticated-orcid":false,"given":"Frank J.","family":"van Schalkwijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-3111","authenticated-orcid":false,"given":"Randolph F.","family":"Helfrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"2025121011002122000_45.50.e0146252025.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2024.04.016"},{"key":"2025121011002122000_45.50.e0146252025.2","doi-asserted-by":"crossref","first-page":"906038","DOI":"10.1155\/2014\/906038","article-title":"Role of EEG as biomarker in the early detection and classification of dementia","volume":"2014","author":"Al-Qazzaz","year":"2014","journal-title":"Sci World J"},{"key":"2025121011002122000_45.50.e0146252025.3","doi-asserted-by":"crossref","unstructured":"Ameen MS , Jacobs J , Schabus M , Hoedlmoser K , Donoghue T (2024) The temporal dynamics of aperiodic neural activity track changes in sleep architecture. bioRxiv. 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