ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2020
Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's disease.Neuroimage. Reports · 2026Article
- Exploring Dynamic Alpha Band Connectivity in Parkinson's Disease: A Novel Approach to Postural Control Assessment Using the BioVRSea Paradigm.Brain topography · 2026Article
- Article
- Bifurcations and bursting in the Epileptor.PLoS computational biology · 2024Article
- The impact of paediatric epilepsy and co-occurring neurodevelopmental disorders on functional brain networks in wake and sleep.PloS one · 2024Article
- Treatment effects in epilepsy: a mathematical framework for understanding response over time.Frontiers in network physiology · 2024Article
- Electroencephalography derived connectivity informing epilepsy surgical planning: Towards clinical applications and future perspectives.NeuroImage. Clinical · 2024Review
- Low-density scalp electrical source imaging of the ictal onset zone network using source coherence maps.Frontiers in neurology · 2024Article
- Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings.Scientific reports · 2022Article
- Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery.Nature communications · 2022Article
- Dynamical Network Models From EEG and MEG for Epilepsy Surgery-A Quantitative Approach.Frontiers in neurology · 2022Review
- A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex.NeuroImage · 2021Article
- A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.PLoS computational biology · 2021Article
- A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2021Article
- Dynamics of a neuron-glia system: the occurrence of seizures and the influence of electroconvulsive stimuli : A mathematical and numerical study.Journal of computational neuroscience · 2020Article
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7 authors.
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Abstract
objectiveThe effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation.
methodsWe use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network's ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome.
resultsThe framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test).
conclusionsOur results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. SIGNIFICANCE: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.
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