Spanning stereo intracranial electroencephalography (SEEG) procedure requires the implantation of electrodes in about 1% of the brain. This procedure is very rare and is used to characterize the epileptogenic zone (EZ) in patients with pharmaco-resistant epilepsy, who require surgery to stop their seizures.
Each electrode has between 5 to 18 recording sites, and the precise location of each electrode contact can allow clinicians to relate the SEEG pathological signal identify the EZ. The accurate localization of each SEEG electrode is indeed crucial to correctly define the EZ. However, this procedure is not trivial and very time-consuming, as it requires a good knowledge and understanding of the implantation procedure, and some expertise in brain anatomy.
To support clinicians in this task, a number of semi-automated and automated methods have been proposed during the last decade (See references). These techniques typically leverage the hyper- or hypo-intensity of the electrode contact imaged after electrode implantation and work with pre- and post- implantation imaging dataset that could consist of T1 Magnetic Resonance Imaging (MRI) scans and/or Computed Tomography (CT), depending on the imaging protocol followed by the site in which the method was developed. While the developed techniques have shown to be performant in its specific set of imaging data, no existing solution could adapt to the data availability to the best of our knowledge.
In this project, we would like to kick-off the development of a new community-driven and open-source automated processing pipeline tool for the localization of SEEG electrodes contacts. This tool would aim to provide a universal pipeline in the BIDS App framework with modular workflows which would adapt to data availability (MRI, CT), minimize user interactions, and maximize its re-usability, portability, and reproducibility.
References
J.P. Princich, D. Wassermann, F. Latini, S. Oddo, A.O. Blenkmann, G. Seifer, S. Kochen. Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates. Front. Neurosci., 7 (2013), pp. 1-8, 10.3389/fnins.2013.00260.
A.O. Hebb, A.V. Poliakov. Imaging of deep brain stimulation leads using extended hounsfield unit CT Stereotact. Funct. Neurosurg., 87 (2009), pp. 155-160, doi:10.1159/000209296.
Arnulfo G, Narizzano M, Cardinale F, Fato MM, Palva JM. Automatic segmentation of deep intracerebral electrodes in computed tomography scans. BMC Bioinforma. 2015;16(1):99.
Narizzano M., Arnulfo G., Ricci S., Toselli B., Canessa A., Tisdall M., Fato M. M., Cardinale F. “SEEG Assistant: a 3DSlicer extension to support epilepsy surgery” BMC Bioinformatics (2017) 10.1186/s12859-017-1545-8.
IntrAnat Electrodes: A Free Database and Visualization Software for Intracranial Electroencephalographic Data Processed for Case and Group Studies (2018).
EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG, Journal of Neuroscience Methods, 2018.
Added as an issue for book keeping
Source: https://brainhack.ch/
Team Leaders:
Sebastien Tourbier
Spanning stereo intracranial electroencephalography (SEEG) procedure requires the implantation of electrodes in about 1% of the brain. This procedure is very rare and is used to characterize the epileptogenic zone (EZ) in patients with pharmaco-resistant epilepsy, who require surgery to stop their seizures.
Each electrode has between 5 to 18 recording sites, and the precise location of each electrode contact can allow clinicians to relate the SEEG pathological signal identify the EZ. The accurate localization of each SEEG electrode is indeed crucial to correctly define the EZ. However, this procedure is not trivial and very time-consuming, as it requires a good knowledge and understanding of the implantation procedure, and some expertise in brain anatomy.
To support clinicians in this task, a number of semi-automated and automated methods have been proposed during the last decade (See references). These techniques typically leverage the hyper- or hypo-intensity of the electrode contact imaged after electrode implantation and work with pre- and post- implantation imaging dataset that could consist of T1 Magnetic Resonance Imaging (MRI) scans and/or Computed Tomography (CT), depending on the imaging protocol followed by the site in which the method was developed. While the developed techniques have shown to be performant in its specific set of imaging data, no existing solution could adapt to the data availability to the best of our knowledge.
In this project, we would like to kick-off the development of a new community-driven and open-source automated processing pipeline tool for the localization of SEEG electrodes contacts. This tool would aim to provide a universal pipeline in the BIDS App framework with modular workflows which would adapt to data availability (MRI, CT), minimize user interactions, and maximize its re-usability, portability, and reproducibility.
References