<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Dickinson AH</submitter><funding>Foundation for Angelman Syndrome Therapeutics Australia</funding><pagination>103865</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12409793</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>48</volume><pubmed_abstract>&lt;h4>Objectives&lt;/h4>Biomarkers of atypical brain development are crucial for advancing clinical trials and guiding therapeutic interventions in Angelman syndrome (AS). Electroencephalography (EEG) captures well-characterized developmental changes in peak alpha frequency (PAF) that reflect underlying neural circuit maturation and may provide a sensitive metric for mapping atypical neural trajectories in AS.&lt;h4>Method&lt;/h4>We analyzed 159 EEG recordings from 95 children with AS (ages 1-15 years) and 185 age-matched typically developing (TD) controls. PAF was quantified using a well-established curve-fitting method applied to 1/f-corrected power spectra. To validate robustness, we further evaluated PAF using an alternative prominence-based peak detection approach across varying detection thresh</pubmed_abstract><journal>NeuroImage. Clinical</journal><pubmed_title>Atypical alpha oscillatory EEG dynamics in children with Angelman syndrome.</pubmed_title><pmcid>PMC12409793</pmcid><funding_grant_id>FT2002-003</funding_grant_id><pubmed_authors>Hosamane NS</pubmed_authors><pubmed_authors>Dickinson AH</pubmed_authors><pubmed_authors>Chu CJ</pubmed_authors><pubmed_authors>Youngkin AE</pubmed_authors><pubmed_authors>Ryan DP</pubmed_authors><pubmed_authors>Sidorov MS</pubmed_authors><pubmed_authors>Bowen-Kauth MS</pubmed_authors><pubmed_authors>Shide JJ</pubmed_authors><pubmed_authors>McNair CA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Atypical alpha oscillatory EEG dynamics in children with Angelman syndrome.</name><description>&lt;h4>Objectives&lt;/h4>Biomarkers of atypical brain development are crucial for advancing clinical trials and guiding therapeutic interventions in Angelman syndrome (AS). Electroencephalography (EEG) captures well-characterized developmental changes in peak alpha frequency (PAF) that reflect underlying neural circuit maturation and may provide a sensitive metric for mapping atypical neural trajectories in AS.&lt;h4>Method&lt;/h4>We analyzed 159 EEG recordings from 95 children with AS (ages 1-15 years) and 185 age-matched typically developing (TD) controls. PAF was quantified using a well-established curve-fitting method applied to 1/f-corrected power spectra. To validate robustness, we further evaluated PAF using an alternative prominence-based peak detection approach across varying detection thresh</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-07-15T14:20:44.163Z</modification><creation>2026-07-05T03:12:22.238Z</creation></dates><accession>S-EPMC12409793</accession><cross_references><pubmed>40882406</pubmed><doi>10.1016/j.nicl.2025.103865</doi></cross_references></HashMap>