Article published in Journal of neuroscience methods

We are proud to announce that our research article “Probabilistic neuroanatomical assignment of intracranial electrodes using the ELAS toolbox” was officially published online in 'Journal of neuroscience methods 327 (2019)​'.

Intracranial electroencephalography (iEEG) is increasingly used in neuroscientific research. However, the position of the implanted electrodes varies greatly between patients, which makes group analyses particularly difficult. Therefore, an assignment procedure is needed that enables the neuroanatomical information to be obtained for each individual electrode contact.

New method
Here, we present a MATLAB-based electrode assignment approach for iEEG electrode contacts, implemented in the open-source toolbox ELAS, that allows a hierarchical probabilistic assignment of individual electrode contacts to cytoarchitectonically-defined brain areas. The here presented ELAS consists of two major steps: (I) a pre-assignment to the cerebral lobes and (II) a following probabilistic assignment based on lobe-specific probability maps of the SPM Anatomy Toolbox.

We analyzed iEEG data obtained in 14 epilepsy patients with a total of 783 intracranial electrode contacts. The neuroanatomical assignment to cortical brain areas was possible in 72.5% of the electrode contacts that were located on the lateral cortical convexity.

Comparison with existing methods
This assignment procedure is to our knowledge the first approach that combines both individual macro-anatomical and cytoarchitectonic probabilistic information. Due to the integration of information about individual anatomical landmarks, incorrect assignments could be avoided in approx. 7% of electrode contacts.

The present study demonstrates how probabilistic assignment procedures developed for the analysis of neuroimaging data can be adapted to iEEG, which is especially helpful for group analyses. The presented assignment approach is freely available via the open-source toolbox ELAS, including a 3D visualization and a file export for virtual reality setups.

Behncke J.*, Kern M.*, Ruescher J.*, Schulze-Bonhage A. and Ball T., *equally contributing. "Probabilistic neuroanatomical assignment of intracranial electrodes using the ELAS toolbox". Journal of neuroscience methods (2019). doi.org/10.1016/j.jneumeth.2019.108396. ELAS toolbox: github.com/TNTLFreiburg/elas.

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