Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls
Authors: Shakeshaft Amy , Laiou Petroula , Abela Eugenio , Stavropoulos Ioannis , Richardson Mark P , Pal Deb K ,   BIOJUME Consortium, Orsini Alessandro , Howell Alice , Hyde Alison , McQueen Alison , Duran Almu , Gaurav Alok , Collingwood Amber , Kitching Amy , Shakeshaft Amy , Papathanasiou Anastasia , Clough Andrea , Gribbin Andrew , Swain Andrew , Needle Ann , Hall Anna , Smith Anna , Macleod Anne , Chhibda Asyah , Fonferko-Shadrach Beata , Camara Bintou , Petrova Boyanka , Stuart Carmel , Hamilton Caroline , Peacey Caroline , Campbell Carolyn , Cotter Catherine , Edwards Catherine , Picton Catie , Busby Charlotte , Quamina Charlotte , Waite Charlotte , West Charlotte , Ng Ching Ching , Giavasi Christina , Backhouse Claire , Holliday Claire , Mewies Claire , Thow Coleen , Egginton Dawn , Dickerson Debbie , Rice Debbie , Mullan Dee , Daly Deirdre , Mcaleer Dympna , Gardella Elena , Stephen Elma , Irvine Eve , Sacre Eve , Lin Fan , Castle Gail , Mackay Graham , Salim Halima , Cock Hannah , Collier Heather , Cockerill Helen , Navarra Helen , Mhandu Hilda , Crudgington Holly , Hayes Imogen , Stavropoulos Ioannis , Daglish Jacqueline , Smith Jacqueline , Bartholomew Jacqui , Cotta Janet , Ceballos Javier Peña , Natarajan Jaya , Crooks Jennifer , Quirk Jennifer , Bland Jeremy , Sidebottom Jo , Gesche Joanna , Glenton Joanne , Henry Joanne , Davis John , Ball Julie , Selmer Kaja K , Rhodes Karen , Holroyd Kelly , Lim Kheng Seang , O’Brien Kirsty , Thrasyvoulou Laura , Makawa Linetty , Charles Lisa , Richardson Lisa , Nelson Liz , Walding Lorna , Woodhead Louise , Ehiorobo Loveth , Hawkins Lynn , Adams Lynsey , Connon Margaret , Home Marie , Baker Mark , Mencias Mark , Richardson Mark P , Sargent Mark , Syvertsen Marte , Milner Matthew , Recto Mayeth , Chang Michael , O'Donoghue Michael , Young Michael , Ray Munni , Panjwani Naim , Ghaus Naveed , Sudarsan Nikil , Said Nooria , Pickrell Owen , Easton Patrick , Frattaroli Paul , McAlinden Paul , Harrison Rachel , Swingler Rachel , Wane Rachel , Ramsay Rebecca , Møller Rikke S , McDowall Robert , Clegg Rosie , Uka Sal , White Sam , Truscott Samantha , Francis Sarah , Tittensor Sarah , Sharman Sarah-Jane , Chung Seo-Kyung , Patel Shakeelah , Ellawela Shan , Begum Shanaz , Kempson Sharon , Raj Sonia , Bayley Sophie , Warriner Stephen , Kilroy Susan , MacFarlane Susan , Brown Thomas , Samakomva Tinashe , Nortcliffe Tonicha , Calder Verity , Collins Vicky , Parker Vicky , Richmond Vivien , Stern William , Haslam Zena , Šobíšková Zuzana , Agrawal Amit , Whiting Amy , Pratico Andrea , Desurkar Archana , Saraswatula Arun , MacDonald Bridget , Fong Choong Yi , Beier Christoph P , Andrade Danielle , Pauldhas Darwin , Greenberg David A , Deekollu David , Pal Deb K , Jayachandran Dina , Lozsadi Dora , Galizia Elizabeth , Scott Fraser , Rubboli Guido , Angus-Leppan Heather , Talvik Inga , Takon Inyan , Zarubova Jana , Koht Jeanette , Aram Julia , Lanyon Karen , Irwin Kate , Hamandi Khalid , Yeung Lap , Strug Lisa J , Rees Mark , Reuber Markus , Kirkpatrick Martin , Taylor Matthew , Maguire Melissa , Koutroumanidis Michalis , Khan Muhammad , Moran Nick , Striano Pasquale , Bala Pronab , Bharat Rahul , Pandey Rajesh , Mohanraj Rajiv , Thomas Rhys , Belderbos Rosemary , Slaght Seán J , Delamont Shane , Sastry Shashikiran , Mariguddi Shyam , Kumar Siva , Kumar Sumant , Majeed Tahir , Jegathasan Uma , Whitehouse William 
Published: July 2022

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 et al. (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 density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2–5 Hz) (P = 0.0007, hedges’ g = 0.55) and low-alpha (6–9 Hz) (P = 2.9 × 10−8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10−8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.