Project ID: R01NS062092

Investigators: Angelique c Paulk

Dataset Size: 2.86 GB

Files: 1076

Description:

This data set is for the execution of a public protocol on protocols.io for the localization of intracranial implanted electrodes in the brain. The purpose of this data set and protocol is to provide a data for a set of modular methodologies and a combination of available software tools. This pipeline involves taking pre- and post-operative images to localized and visualize electrode locations in the case of implanted intracranial electrodes, whether the electrodes penetrate the brain parenchyma (depth, stereoelectroencephalography, sEEG electrodes) or are on the surface of the cortex (electrocorticography, ECoG grid or strip electrodes). The pipeline uses a number of different packages in a modular approach that allows use of a subset or all parts of the pipeline depending on need. The methodology has been used to identify electrode locations in 260 patients to date. The protocol is described in detail in the following online protocols.io site: https://www.protocols.io/view/modular-reconstruction-and-co-registration-of-imag-5qpvornedv4o/v2 Daniel J Soper, Alex Ross, Dustine Reich, Sydney S. Cash, Ishita Basu, Noam Peled, Angelique C. Paulk 2023. Modular Reconstruction and Co-registration of Imaging from Implanted ECoG and SEEG Electrodes. protocols.iohttps://dx.doi.org/10.17504/protocols.io.5qpvornedv4o/v2 Further details are included in the README.txt file. Data Description The preoperative and postoperative MRI and CT imaging are included following the Brain Imaging Data Structure specification, following the iEEG BIDS formatting (https://www.nature.com/articles/s41597-019-0105-7). A community-driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ The remaining folder contains the outputs from the protocol, described in the README and below. CoregistrationInfo: Each patient has a folder with a text file. That text file has the values, in degrees, which the post-operative image must be transformed (rotated or translated) in order for it to be aligned with the pre-operative image. These values were determined using Freeview. Freesurfer: Each patient has a “SurferOutput” folder. This folder was created by running FreeSurfer’s “recon-all -all” command on the pre-operative T1 imaging with the “-localGI” flag. MMVT: Each patient has a folder for MMVT output. These output were created using the “anatomy.py” and “electrodes.py” commands from the MMVT software. Within this folder, there are several subfolders, including: Electrodes: The locations, labels, and coloring for each electrode contact as well as the output from the electrode labeling algorithm which determines the brain regions for each electrode contact. Labels: The parcellations files for each cortical region used in the labeling algorithm. Subcortical: The segmentation files for each subcortical region used in the labeling algorithm. Surf: The surface file output from FreeSurfer ParcellatedVolumes: Each patient has a folder with all the .stl files for each brain region which can be output from 3DSlicer or Freesurfer. These can be used to render the volumes of each area. ReconImages: Each patient has a folder with the output from our protocol visualization pipeline. This would be visuals of the 3D brain with the electrodes overlaid, and MRI images with individual contacts or entire electrode shanks overlaid. This data is given in the derivatives folder in three ways: 1) a folder filled with the PNG images, 2) an uncompressed PDF of all the images together, and 3) a compressed PDF of all the images together. Schematic: Each patient has a folder with a powerpoint files that shows the approximate placement of each electrode in the patient’s brain. For depths, these locations represent the location on the cortex where each depth was inserted, not the target. Funding and support This work was supported by the Tiny Blue Dot foundation, https://www.tinybluedotfoundation.org/ , NIH grants NINDS R01- NS062092, 1K24NS088568, R01-NS079533, R01-NS072023, NIMH grant 1 R21 MH127009-01A1, and Massachusetts General Hospital Executive Committee on Research (MGH-ECOR). Some of this research was sponsored by the U.S. Army Research Office and Defense Advanced Research Projects Agency (DARPA), https://www.darpa.mil/, under Cooperative Agreement Number W911NF-14-2-0045 issued by ARO contracting office in support of DARPA’s SUBNETS Program and the DoD (CDMRP FY21 Epilepsy Research Program W81XWH-22-1-0315). The views and conclusions contained in this associated documentation with this repository and publications and do not represent the official policies, either expressed or implied, of the funding sources. The funders did not have a role in study design, data collection and analysis, decision to publish, or preparation of the associated publications.

For questions about this dataset, contact apaulk@mgh.harvard.edu

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