{"doi":"10.1101/2020.12.19.20248557","title":"Intracranial EEG biomarkers for seizure lateralization in rapidly-bisynchronous epilepsy after laser corpus callosotomy","abstract":"Abstract Objective It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci. Methods We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callostomy (CC) in patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA) and line length (LL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG. Results Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA and LL metrics. Conclusions While quantitative metrics of IED rate and ictal HGA, PHLG and LL all accurately lateralize based on post-CC iEEG, only IED rate consistently does so based on pre-CC data. Significance Quantitative analysis of IEDs may be useful in localizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis. Highlights We evaluated intracranial EEG biomarkers in corpus callostomy patients with bisynchronous seizures pre-operatively. Despite testing more contemporary metrics, only interictal epileptiform discharge counting consistently lateralized seizure foci. High-frequency metrics, especially high-frequency oscillation counting, appear to be sensitive to parameter selection.","journal":"medRxiv","year":2020,"id":132004,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":586925,"name":"Sean Hwang","orcid":"0000-0002-7004-6464","position":1,"is_corresponding":false},{"id":255638,"name":"Ashesh D. Mehta","orcid":"0000-0001-7293-1101","position":2,"is_corresponding":false},{"id":586924,"name":"Simon Khuvis","orcid":"0000-0003-2506-2606","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-18T23:16:07.542484Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}