Probabilistic landscape of seizure semiology localizing values

Abstract

Semiology describes the evolution of symptoms and signs during epileptic seizures and contributes to the evaluation of individuals with focal drug-resistant epilepsy for curative resection. Semiology varies in complexity from elementary sensorimotor seizures arising from primary cortex to complex behaviours and automatisms emerging from distributed cerebral networks. Detailed semiology interpreted by expert epileptologists may point towards the likely site of seizure onset, but this process is subjective. No study has captured the variances in semiological localising values in a data-driven manner to allow objective and probabilistic determinations of implicated networks and nodes. We curated an open dataset from the epilepsy literature, in accordance with PRISMA guidelines, linking semiology to hierarchical brain localisations. A total of 11230 datapoints were collected from 4643 patients across 309 articles, labelled using ground-truths (postoperative seizure-freedom, concordance of imaging and neurophysiology, and/or invasive EEG) and a designation method that distinguished between semiologies arising from a predefined cortical region and descriptions of neuroanatomical localisations responsible for generating a particular semiology. This allowed us to mitigate temporal lobe publication bias by filtering studies that preselected patients based on prior knowledge of their seizure-foci. Using this dataset, we describe the probabilistic landscape of semiological localising values as forest plots at the resolution of seven major brain regions: temporal, frontal, cingulate, parietal, occipital, insula, and hypothalamus, and five temporal subregions. We evaluated the intrinsic value of any one semiology over all other ictal manifestations. For example, epigastric auras implicated the temporal lobe with 83% probability when not accounting for the publication bias that favoured temporal lobe epilepsies. Unbiased results for a prior distribution of cortical localisations revised the prevalence of temporal lobe epilepsies from 66% to 44%. Therefore, knowledge about the presence of epigastric auras updates localisation to the temporal lobe with an odds ratio (OR) of 2.4 (CI95% [1.9, 2.9]; and specifically, mesial temporal structures OR 2.8[2.3, 2.9]), attesting the value of epigastric auras. As a further example, although head version is thought to implicate the frontal lobes, it did not add localising value compared to the prior distribution of cortical localisations (OR 0.9[0.7, 1.2]). Objectification of the localising values of the twelve most common semiologies provides a complementary view of brain dysfunction to that of lesion-deficit mappings, as instead of linking brain regions to phenotypic-deficits, semiological phenotypes are linked back to brain sources. This work enables coupling of seizure-propagation with ictal-manifestations, and clinical support algorithms for localising seizure phenotypes.

Software tool for visualization of a probabilistic map of the epileptogenic zone from seizure semiologies

“…In this section, we demonstrate a case study example of our SVT for a retrospective analysis of a patient with epilepsy who underwent resective surgery at the National Hospital for Neurology and Neurosurgery (NHNN) (Queen Square, London, UK). For all analyzes, we use the querying module, which uses the Semio2Brain database (Alim-Marvasti et al, 2022a , b ).…”

“…The usage examples in this paper use the Semio2Brain database and the corresponding developed software to query the database (Alim-Marvasti et al, 2022a , b ), which were defined according to the Neuromorphometrics atlas parcellation scheme 5 .…”

“…It would be useful for clinicians to be able to quickly and easily assess relevant studies in the literature for a specific semiology. Such a method has been proposed using a large number of studies analyzed via the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework (Page et al, 2021 ) to generate Semio2Brain , a database of entries mapping seizure semiologies to brain regions (Alim-Marvasti et al, 2022b ). Clinicians and researchers would also benefit from a easy-to-use and intuitive graphical user interface (GUI) to such a database.…”

Development of a Seizure Matching System for Clinical Decision Making in Epilepsy Surgery

“…Seizure similarity serves as the initial step towards achieving patient similarity; it is necessary to consider other factors such as seizure semiology, 42 MRI findings, 4 etiology, interictal biomarkers, 35 implantation scheme, etc., to have a comprehensive understanding of patient similarity and facilitate the phenotyping of patients into groups with shared characteristics, aiding the search for improved personalized treatments. The current study serves a proof-of-principle that a seizure matching system can be implemented in the current clinical context of epilepsy surgery.…”

EpiSemoLLM: A Fine-tuned Large Language Model for Epileptogenic Zone Localization Based on Seizure Semiology with a Performance Comparable to Epileptologists

“…These selected articles documented over 900 epilepsy cases, detailing seizure semiology across various surgically validated EZ locations. These EZ locations (i.e., ground truth) were labeled based on postoperative outcomes (defined as seizure freedom after surgery), concordance of imaging and neurophysiology, or available stereoelectroencephalography (sEEG) findings [20]. Cases with indeterminate EZs or nonspecific semiology were excluded.…”