ArticleEpilepsia open2021
Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy.
Article in Epilepsia open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm.bioRxiv : the preprint server for biology · 2025Article
- Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.Journal of imaging · 2024Article
- Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation.Brain communications · 2024Article
- Efficient volume-based localization and automatic labeling of intracranial depth electrodes.Annals of translational medicine · 2023Article
- A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy.Human brain mapping · 2022Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI.Journal of neuroscience methods · 2022Article
- Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy.Epilepsia open · 2021Article
- The FAST graph: A novel framework for the anatomically-guided visualization and analysis of cortico-cortical evoked potentials.Epilepsy research · 2020Article
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveStereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.
methodStereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.
resultsAnatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method. SIGNIFICANCE: The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.