Abstracts

Rationale:
Intracranial electroencephalography (icEEG) is used in epilepsy surgery evaluation to define the seizure onset zone. icEEG targets are chosen by clinicians after interpretation of non-invasive data, particularly seizure semiology, scalp EEG and MRI. There are variations in implantation strategies between centres, based on views regarding the relationship between semiological features and brain regions involved. The aim of our research is to create an objective, evidence-based dataset of seizure semiology and the neuroanatomical correlates to inform optimal icEEG implantation strategies. We present an interactive visualisation tool to display the link between clinical semiology and cerebral regions involved.
Method:
A literature review of 1208 manuscripts was used to generate a Semiology-to-Brain database called ‘SemioBrain’, containing 13285 data points from 4454 patients. Each data point corresponds to one patient presenting a specific seizure semiology, associated to a certain brain region, based on one or more of the following criteria: postoperative seizure freedom, icEEG monitoring, electrical stimulation, concordance of neuroimaging and neurophysiology. We developed a Python module for the 3D Slicer software platform that references our database to visualise brain regions associated with a set of user-specified semiologies. To search the database, the user can enter custom semiology terms or select from a list of 47 pre-defined terms. Each query can be altered to filter manuscripts according to the ground-truth criteria used to establish the epileptogenic zone and the inclusion criteria for the study, to reduce publication bias (Fig. 1). Once the corresponding brain regions are identified, a visualisation is displayed (Fig. 2). An MNI template subdivided into the Neuromorphometrics atlas labels with the geodesic information flows algorithm is used for visualisation. The search result is shown in a table containing each brain region and the corresponding number of patients, presenting the pre-selected semiology localising to that region. For visualisation, a score is computed for each region by combining data points across semiologies and is linearly mapped to a colour scheme in which brighter colours and higher opacity correspond to more data points. Brain regions with no involvement in the semiologies are shown either in grey (3D) or not displayed (2D). For more details on the individual structures, the user can place the mouse cursor over the region to visualise the name of the region and corresponding score (Fig. 2). If there is more than one semiological feature, the implicated anatomy can be displayed separately or combined into a composite figure.
Results:
Code and documentation are available at https://github.com/thenineteen/Semiology-Visualisation-Tool. Fig. 2 shows the result of querying the database with the semiology term ‘Tonic right’. In this case, the brain region with the highest score is the left supplementary motor cortex.
Conclusion:
We present a data-driven open-source tool to visualise brain regions associated with a set of seizure semiologies. This tool can be used as a clinical decision support system to determine the appropriate strategy for icEEG implantations.
Funding:
:This work is supported by the UCL EPSRC Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z). This publication represents in part independent research commissioned by the Wellcome Trust Health Innovation Challenge Fund (WT106882). The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust.