biostudies-literatureUnknown4(4)Shakeshaft AAbnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury <i>et al</i>. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral Brain communicationsfcac180https://www.ebi.ac.uk/biostudies/studies/S-EPMC9301584biostudies-literatureHeterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.PMC9301584Gribbin AMullan DWest CSyvertsen MMariguddi SEllawela SMaguire MPratico ACockerill HTaylor MWane RGlenton JPatel SCollier HConnon MSwingler RHawkins LSaraswatula AGhaus NEaston PWhiting ADesurkar AQuirk JMcAlinden PLin FTalvik IYoung MSharman SJCalder VPal DKHome MClegg RShakeshaft AMcaleer DMencias MNavarra HRhodes KWhite SMewies CWaite CHall AMacleod AGaurav ADickerson DMacFarlane SClough AGreenberg DANeedle AFong CYWarriner SAram JNg CCPickrell OHenry JAngus-Leppan HKirkpatrick MEgginton DTittensor SThrasyvoulou LRichmond VDavis JCamara BSastry SJegathasan UParker VGesche JBusby CBall JKoutroumanidis MHyde ASidebottom JFrattaroli PHowell ANelson LDeekollu DLim KSBharat RPauldhas DHamilton CBegum SLozsadi DCastle GScott FLanyon KEhiorobo LHayes IIrvine ESelmer KKRubboli GKitching AHolliday CNatarajan JChung SKFrancis SMcQueen ASwain ASaid NZarubova JYeung LCollins VCrooks JPandey RTruscott SSlaght SJGiavasi CCollingwood ACotta JUka SRay MBIOJUME ConsortiumKilroy SOrsini APeacey CO'Brien KMajeed TMoran NDaly DSacre ECrudgington HBland JStrug LJRichardson LStern WSamakomva TDuran ACeballos JPRice DRichardson MPSalim HQuamina CHolroyd KSudarsan NDaglish JPanjwani NEdwards CThomas RMcDowall RCharles LKempson SChang MPapathanasiou AMilner MStriano PBala PAgrawal AWoodhead LBaker MAdams LNortcliffe TMoller RSHaslam ZFonferko-Shadrach BMohanraj RSobiskova ZDelamont SBayley SRees MMackay GCock HReuber MKumar SStavropoulos IMacDonald BAndrade DChhibda ARecto MMakawa LSargent MO'Donoghue MBartholomew JStephen EGalizia EMhandu HBackhouse CIrwin KStuart CWalding LJayachandran DRamsay RHarrison RBeier CPHamandi KBelderbos RLaiou PGardella ECotter CThow CPicton CRaj SSmith APetrova BSmith JAbela ETakon IKoht JKhan MWhitehouse WCampbell CBrown TfalseHeterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury <i>et al</i>. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral 2022-01-01T00:00:00Z20222025-04-05T15:05:20.032Z2025-02-19T03:52:57.58ZS-EPMC93015843587391810.1093/braincomms/fcac180