Step 1: Retrieve images, 5–10 minutes

Following implant, the pre-operative T1-weighted MRI before contrast is acquired along with the post-operative CT or MRI with electrodes implanted. These images do not have to be anonymized or named anything in particular at this step.

Step 2: Freesurfer reconstruction output, 3–10 Hours

To reconstruct the brain regions and parcellate/segment the brain regions from the MRI, we used the recon-all function from the FreeSurfer scripts (http://surfer.nmr.mgh.harvard.edu) on the pre-operative T1-weighted MRI [41,42,44–61] followed by functions which subdivided brain regions based on the Desikan-Killiany-Tourville Cortical Atlas (DKT) using default parameters [42]. This step is time-consuming and is not necessary if only the 2D visualizations from Step 6 are desired. However, there are different paths for different outputs associated with this pipeline which are detailed both in Table 1 and in two flow charts (Figs 1 and S1). For the output from the “Basic” or “Advanced” pipelines, we find starting Step 2 as soon as possible, even before the initial electrode implant, can offset the amount of time the FreeSurfer pipeline takes.

Step 3: Imaging co-registration with freeview, 5 minutes

The pre-operative T1-weighted MRI is co-registered with a post-operative CT using volumetric image co-registration procedures using FreeSurfer tools (http://surfer.nmr.mgh.harvard.edu), [41,42,44–61] specifically using Freeview and default parameters. These procedures rely on linear transformations of the post-operative imaging onto the pre-operative imaging. A key point is that all the imaging is registered to a single identified pre-operative T1-weighted MRI scan. As such, if multiple CT scans are performed, each can be registered to the single pre-operative T1-weighted MRI scan sequentially such that the coordinates can be mapped to the same space. A short video of how this is performed is included in the protocols.io publication [39]. Optionally, we have also found we could use Mango [62–64] or LeadDBS [29] for this alignment step, since the coordinates just need to be in the native space of the pre-operative MRI (S1 Fig).

Step 4: Identifying RAS coordinates, 0.5–2 hours

Electrode coordinates are manually determined from the overlaid electrodes (from the post-operative CT or MRI) in the patient’s native space [5]. The length of time this step takes depends on many factors including: the resolution of the post-operative scans, the number of electrodes, knowledge of the relative locations for each electrode, and the skill level of the user. There is a short video example of identifying these coordinates in the protocols.io publication [39].

Step 5: Snapping the electrode grid, 30 minutes

When grid and strip electrodes on the surface of the brain are implanted, there is a shift and compression in the brain as a result of the surgery, which can lead to the electrodes being incorrectly mapped relative to the pre-operative MRI. Various researchers have identified algorithms to shift the electrodes to the surface of the brain while constraining the relationships between contacts, though these details are reported and validated elsewhere [21,65,66]. We use the extracted and smoothed surface of the pre-operative MRI as a target for snapping the grids and strips, specifically using the method detailed in Dykstra et al. [5]. This method was selected for its ease of use with the pipeline, but many other pipelines can handle this step as well if desired (Table 2, S1 Fig) [5,21,65,66].

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Table 2. Features of this pipeline and alternatives.

https://doi.org/10.1371/journal.pone.0287921.t002

Step 6: Creating images of the MRI with the electrode overlay, 30 minutes

To communicate the electrode locations relative to the MRI among the interested teams, we generate a.pdf (Adobe Acrobat) document (S2 Fig) which includes per-page, individual contact locations relative to the 3D brain and the MRI axes (coronal, sagittal, and horizontal sections). Further, we visualize the length of the depth electrode in a re-sliced coronal view along the long axis of the depth electrode. This step can be run with only 2D output (the electrode overlaid onto the MRI slice) as well, which does not require 3D surfaces (which are produced from FreeSurfer). This step uses a custom MATLAB script that we developed and which is included in the associated GitHub page (https://github.com/Center-For-Neurotechnology/Reconstruction-coreg-pipeline) and detailed in the online protocol [39].

Step 7: Multi-Modality Visualization Tool-lite (MMVT-lite), 10 minutes

The Multi-Modality Visualization Tool (MMVT) is an open-source python package developed by NP that converts the FreeSurfer reconstruction output into a Blender (blender.org) [67] file for three-dimensional visualization of electrode locations which can incorporate MRI, Freesurfer output, RAS mapping, and even evoked potentials into the visualization (https://mmvt.mgh.harvard.edu/) [30]. The original MMVT output has been highlighted in a number of publications and journal covers in the past few years [17,31,68,69]; however, the full MMVT installation requires a considerable number of dependencies for installation and has not been updated to use the latest version of blender.

As such, for the purposes of our protocol, we introduce MMVT-lite (https://github.com/pelednoam/mmvt_lite), which incorporates FreeSurfer commands that include parcellating the brain, mapping to different brain atlases, and the electrode labelling algorithm (ELA, (https://github.com/pelednoam/ieil)) [40] but does not output a blender file. MMVT-lite requires fewer dependencies than the original MMVT version, can be installed on any Mac or Linux machine (or Windows virtual machine) and uses ELA for automatic labelling of electrodes relative to different brain regions. NP developed ELA to identify the nearest brain region label by identifying the probability of overlap of an expanding area (cylinder) around each electrode with identified brain structure labels corresponding to grey matter (whether cortical or subcortical) using purely anatomical approaches. We map electrodes to regions in a given location which can be flexibly chosen within FreeSurfer, using the DKT 40 atlas in combination with a subcortical mapping [41,42,52,61]. As the ELA method involves arriving at probabilities that the contacts or the bipolar pair of electrodes are in that labelled brain region based on proximity to labelled nearby surfaces of the brain structures, a single contact can have multiple probabilities above zero if the contact is near different areas. This data is output in the form of a spreadsheet (.xls or.csv) with every brain region given a probability that each electrode contact is located in that region. Notably, parcellating the brain, which involves mapping brain structures onto the brain using common brain atlases, can also be independently done of MMVT-lite using FreeSurfer or SPM tools [41,42,46,60,70].

Step 8: iEEG BIDS formatting of channel labels and file formats, 30–40 minutes

Another feature we have incorporated into our pipeline involves converting the neural and electrode labelling into the intracranial electroencephalography Brain Imaging Data Structure (iEEG BIDS) format and folder structure including listing the electrodes based on iEEG BIDS formats [43]. As such, we included our modified code from the Bids Starter Kit code (https://bids-standard.github.io/bids-starter-kit/) which includes reformatting the neural, imaging, and electrode localization data into the Brain Vision format with the BIDS folder structure (https://bids-standard.github.io/bids-starter-kit/tutorials/ieeg.html).

Step 9: Extra measurements of electrode location relative to brain features, 20–30 minutes

The next step involves running custom MATLAB code developed by ACP that uses the FreeSurfer output, RAS file, and MMVT file output structure to perform different calculations regarding electrode location, label electrodes based on DKT or other selected brain atlas maps and produce output that matches the iEEG BIDS formatting. This also includes using a second approach for labelling each electrode based on the brain region location.

A second, novel, optional type of labelling that we developed for this pipeline for further electrode location validation, labelled Electrode Volume Labelling (EVL), involves mapping electrode locations relative to brain volumes circumscribed by the parcellated brain region maps. To do this, we export the FreeSurfer volume parcellations into 3D files (.stl files) using 3DSlicer (https://www.slicer.org/). The steps involve importing files from the FreeSurfer output folder and then saving the volumes per brain label: 1) load the brain.mgz and aparc.DKTatlas+aseg.mgz files from the SurferOutput folder; 2) save the output volumes of the parcellations: save (export) the.stl file to the selected folder. The resultant saved volumes are imported into MATLAB (MATLAB 2020b). Then the MATLAB function alphaShape is used to generate enclosing volumes per brain region label. The second MATLAB function, inshape, is then used to determine if an electrode was within the brain region volume (such as a cortical ribbon for a cortical label). Notably, for this step, we check that the T1 used in the original coregistration (Step 3) is in the same orientation and location as the FreeSurfer output.

For identification of electrode location relative to grey and white matter, we measure the orthogonal Euclidean distance from the center of each bipolar pair of electrodes to the nearest reconstructed vertex of the pial and white matter surfaces generated from FreeSurfer tools following colocalization [41,42,44–61]. Inter-contact distances between each contact is re-calculated using Euclidean measures. We classify electrode sites as in the grey matter, subcortical regions, white matter, and pial surface by identifying the colocalized location if the location is within the grey matter volume, the white matter volume, and the reconstructed subcortical volumes in the participants’ native space. The classification of the sites relative to the surfaces is done using the MATLAB inpolyhedron function [14,71]. In addition, we use the MATLAB functions alphaShape and inshape to identify electrodes on the pial surface but not within the cortex.

Step 10: Defacing the MRIs and CTs for deidentified data sharing, 10–15 minutes

For sharing data following FAIR data practices [72], it is essential that the data be de-identified to protect the identity of the patients. A number of algorithms can be used to de-face the scans [1,5,19,27,48,49,51,73]. However, as there can be errors in removing facial features from scans while allowing the scans to be useable for future reconstructions [74], we use the fieldtrip de-facer function for manual defacing of each scan [25,27].

Ethics statement

All imaged patients voluntarily participated after fully informed consent as monitored by the Partners Institutional Review Board covering Brigham and Women’s Hospital (BWH) and Massachusetts General Hospital (MGH). The full consent process is both verbal and written in that the initiation of consent starts with a conversation and the final consent is given through a written form. Participants were informed that participation would not alter their clinical treatment in any way, and that they could withdraw at any time without jeopardizing their clinical care.

Electrode localization relative to brain regions

There are a large number of ways to visualize electrode locations in the brain [1,19–30,75,76]. Some pipelines involve transforming the brain regions and electrode locations into a common map (MNI) and visualize activity in a single brain [1,28,29]. Other approaches involve projecting activity onto the surface or onto an inflated (flattened) cortical map [75,76]. Further, when transforming the patient brain into a common atlas, the regions of interest can be represented as voxels using a volumetric approach or as surfaces using the surface-based approach. Typically, surface-based approaches are used for labeling cortical areas because of the inherent variability across cortices, and volume-based approaches are better suited to more homogeneous subcortical labeling [60]. As such, we developed two separate pipelines, one mostly surface based and one purely volume based, for localizing electrodes to brain regions in commonly used atlases per electrode contact or pairs of electrodes (Figs 3 and 4). The two methods are 1) the Electrode Labelling Algorithm (ELA) using the Multi-Modal Visualization Tool (MMVT; Fig 3) also briefly described elsewhere [6,30,40] and 2) Electrode Volume Labeling (EVL; Fig 4), described here for the first time [39].

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Fig 3. The electrode labelling algorithm.

A. The electrode labeling algorithm (ELA) involves identifying, in a reconstructed MRI, the location of a depth electrode (red dots overlaid on the MRI, black line in inset) and all the surrounding voxels such that their distance from the electrode is smaller than a given threshold area (red). B. Illustration of the intersection of the volume relative to the labelled cortical or subcortical surface created from the FreeSurfer output, indicating contacts with low and high probabilities of being in that brain region. C. Example spreadsheet output from ELA of probabilities in contact volumes intersecting with surfaces. D. Electrode locations color-coded by log probability of being in nearest grey matter/white matter. E. The electrode brain region labelling approach involves labelling the electrode location relative to the highest grey matter (non-white matter) probability. F. Electrodes color coded to brain region locations with the corresponding color-coded surfaces exported from FreeSurfer.

https://doi.org/10.1371/journal.pone.0287921.g003

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Fig 4. Electrode Volume Labelling and electrode location measurements.

A. Electrode Volume Labelling (EVL) steps, which involve exporting the aparc+aseg volume brain region labelling from FreeSurfer (such as from the DKT 40 map) to.stl files, which can be imported into MATLAB. Next, electrode contacts are labelled per brain region if they are contained within these volumes, which can contain the thickness of the cortical volume. B. Volumetric categorization of electrode contacts as white matter, cortex, subcortical structures, and outside the brain. C. Euclidean measurements of distances between the recording contacts and the nearest anatomical features. D. Further measurements of electrode contacts relative to different features of the nearest cortical structure. E. Categorization of electrode locations relative to neuroanatomical structures (cortical grey matter, subcortical structures, white matter, and outside brain) for depth electrodes with different ending trajectories for different brain regions. Counts of electrode locations relative to different classifications are across individuals (up to 27 patients).

https://doi.org/10.1371/journal.pone.0287921.g004

The ELA is a combined volume and surface approach which involves sampling the volume of space around electrode contacts to identify the nearest surface reconstructed from FreeSurfer, arriving at a probability that the electrode location is near that brain region surface (Fig 3). As such, for each contact, there can be multiple probability values above zero of that contact being in white matter or intersecting with the nearest grey matter surface (Fig 3). In our current approach, we take the highest probability value in the non-white matter labels to determine the brain region location for each contact (Fig 3), though further information is accessible in the ELA output [40]. Finally, after identifying the maximal grey matter probabilities per contact, we can then color code the contacts with the same labels in a common brain with the same atlas (e.g. with the DTK atlas; Fig 3F). We provide this code in our pipeline (https://github.com/Center-For-Neurotechnology/Reconstruction-coreg-pipeline) for use. ELA is currently incorporated into the MMVT-lite pipeline for processing the data but can be used separately [40]. Not only can we identify the brain region of each contact, but the pipeline also provides localizations as probabilities for better statistical rigor (and supplies the maximum likelihood value). This localization is now applied to almost all research projects performed by our lab as well as other researchers [6,11,13,14,18,31,34–36,38].

The second method developed de novo for this protocol, Electrode Volume Labeling (EVL), uses a purely volumetric approach (Fig 4). The steps involve exporting the volumetric aparc segmentations in FreeSurfer [42] using 3DSlicer [77] into.stl files which can be imported into MATLAB or Python for further mapping. Using MATLAB functions, we then classify electrode locations as within different volumes (including white matter volumes). This approach takes into account the thickness of various structures including grey matter. EVL is likely more relevant to depth electrodes than surface arrays (ECoG grids and strips), which is partly why we use both methods for identifying brain region locations per contact.

We have taken measures to validate our ELA and EVL output, though there is much more room for validation in this space. First, for ELA, we provided the 2D electrode localizations (as visualized with the.pdf output) of 20 patients relative to the MRI images to a neurologist and a psychiatrist with training in identifying brain regions with the brain labels. They confirmed the locations through a visual inspection of the localization. For N>200 patients, we have run ELA and found that the locations detected automatically match with the locations found in the MRI upon visual inspection. A limitation here is that these are not trained neuro-radiologists, though we consider these checks be a meaningful indication of accuracy. Further, in comparing EVL versus ELA, we find the methods for identifying brain region per contact are generally consistent between methods, with differences in identification of contacts which are fully in white matter versus grey matter which is may be expected with a volume versus surface based approach (N = 23).

Electrode characterization and measurements relative to anatomical structures

In parallel to electrode localization relative to brain regions, there are a number of different measures which could be relevant to further study, including localization relative to grey and white matter, contact size, and contact spacing. The organization of the FreeSurfer folders and RAS coordinates allows us to perform automatic calculations of these metrics using basic MATLAB functions without additional toolboxes. An essential step for identifying electrode locations relative to brain regions involves algorithmically and automatically labeling electrode contacts in the patient’s native space using purely anatomical landmarks. These measurements include locations relative to anatomical features such as the nearby grey matter, the nearest grey-white boundary, whether contacts are in white or grey matter, or the angles relative to the nearest cortical column. Some of the measures involve volumetric localization (Fig 4B). Other measures involve calculating the closest points (vertices) of the nearest imported brain surfaces (Fig 4C and 4D). As this approach allows across-patient comparisons automatically, we are able to calculate coverage of electrode contacts in grey matter, white matter, subcortical regions, and outside the brain for different depth trajectories (Fig 4E). A depth trajectory is where the distal tip of the sEEG electrode is aimed during implantation. Along this trajectory, we then calculate the number of contacts classified as in grey matter, white matter, subcortical regions, and outside brain using volumetric measures (see Methods and protocol [39]). Across one data set (N = 27), the location of electrodes in white matter versus cortical grey matter varies depending on the depth electrode trajectory (Fig 4E). This information may be useful for designing novel electrodes for localizing grey matter along common trajectories [78–80]. Further, this localization information and measures of electrode location is instrumental for examining how anatomical features alter neural responses to stimulation or for seizure localization (Fig 5) [6,14].

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Fig 5. Pipeline research use cases for stimulation and seizure onset.

A. Electrode localization and labelling across patients. The color coding indicates common labels across participants (designated EPXX) for implanted leads. B. The pipeline allows automatic identification of electrode coverage across patients per brain label. Contact counts for different brain regions in individual participant (top) and across multiple participants (bottom, N = 30). C. Demonstration of how stimulation electrode location relative to the grey-white boundary can affect responses in local contacts (less than 15 mm away) and in distant contacts (greater than 15 mm; [14]). D. Electrode labeling algorithm (ELA) used to determine the location of electrodes during seizure onset (colored dots represent seizure onsets). Using ELA output, phase-amplitude coupling was analyzed and found to have different dynamics in different brain regions. δ: Delta; θ: Theta; R: Ripple; FR: Fast ripple; PAC: Phase-amplitude coupling; a.u.: Arbitrary unit [6].

https://doi.org/10.1371/journal.pone.0287921.g005

Use-case: Across-patient mapping, data standardization, and data sharing

Aside from the initial co-registration and electrode localization, the remaining steps in electrode localization, brain region labeling, and mapping are automatic, including measurements of electrode locations relative to neuroanatomical markers (Figs 4E and 5A). Therefore, across patients, we are able to color code contacts paired to brain regions across participants (Fig 5A) and summarize counts of contacts per brain region to identify coverage in a data set (Fig 5B), allowing for standardization across data sets.

Relevant to this point, as many journals and funding agencies now require the data be shared, we implement de-identification (defacing) and data reformatting into the iEEG BIDS format (see Methods and protocol [39]) following FAIR practices [72] to ensure we can safely share data following Open Science practices such as on the Data Archive BRAIN Initiative (DABI; https://dabi.loni.usc.edu [81]). At this archive, we share two imaging data sets with their various outputs from the pipeline (https://dabi.loni.usc.edu/dsi/R01NS062092). We have already shared a large intracranial data set using this pipeline (N = 52; [13,14,35]; https://dabi.loni.usc.edu/dsi/W4SNQ7HR49RL). Further electrode localization steps described in the protocol can also be included in the file output in a standardized and well-documented approach (see protocol [39]).

Use-case: Planning for neuromodulation / stimulation

In addition to its clinical uses, this pipeline is also valuable for researchers. For example, this pipeline has been used in understanding how stimulation alters brain activity as well as probe activity in different states such as during sleep and under general anesthesia (N = 82, [11,13,14,33–36,38]), where the reconstructed localizations were used to inform selecting stimulation sites. Localizing stimulation sites is important because a key facet of stimulation is understanding the distance between stimulation sites and the brain areas that are targets for stimulation [13,14]. As such, simple calculations of Euclidean distance are crucial for understanding how the brain responds to stimulation (Fig 5C). Using the surfaces, localizations, 3D output, and measurements (Fig 4D), we can precisely show where and how stimulation will impact a certain area based on the stimulating contact’s location and orientation relative to the grey-white matter boundary (Fig 5C). We found that stimulation responses vary depending on the stimulation location relative to grey and white matter [14]. We found that stimulation in white matter induced larger distant effects while stimulation closer to the boundary between grey and white matter induced larger local responses (Fig 5C) [14]. These metrics and relationships with stimulation may have implications for the use of neuromodulation for therapeutic use and understanding brain connectivity, which is dependent on this pipeline [14].

Use-case: Localizing and characterizing seizures

Another project set which has relied on this pipeline involves seizure electrophysiology (Fig 5D). Understanding the epileptogenic network and the role of different structures in seizure onset, spread, and termination is critical to understanding the underlying mechanisms of epilepsy [6,32]. One way to study seizures is to differentiate them based on their region of onset and the regions to which they spread. Understanding these pathways provides insights into seizure dynamics and how they may eventually be disrupted. For example, we have studied seizures recorded from 43 patients, and we used ELA to determine the brain region in which each electrode registering the seizure onset was most likely to be located in these patients. The seizures were classified based on (a) their electrographic pattern (e.g., low-voltage fast activity) at the onset and (b) the region (e.g., hippocampus, lateral temporal, etc.) from which the seizures originated. The phase-amplitude coupling (PAC) analysis showed that seizures originating from different regions might have more distinct PACs, and therefore the region of onset may distinctively impact the dynamics of seizure initiation. For instance, seizures with low-voltage fast activity at their onset have different PAC dynamics if they originate in the hippocampus versus lateral temporal regions (Fig 5D) [6].

Limitations

This methodology and pipeline has a number of inherent limitations which users should be aware of before using this overall approach. First, the manual step of electrode picking can present challenges such as poor contrast, an overwhelming imaging artifact from the electrodes, and low-resolution imaging. Therefore, it is not entirely surprising that the inconsistencies between electrode contact distances presented here can show up in the localizations. One approach to handle this discrepancy and to validate the localization would be to utilize algorithms to automatically detect the ‘bright spots’ of a postoperative CT scan and use this information to identify the centers of the electrode contacts, an approach utilized by some software packages [19,20,29]. We do not apply this approach here, but it could be done in future work. A second way to validate the electrode localizations would be to have multiple users localize the contacts, providing cross-user validation (which we have done previously, S2 Fig) or compare reconstructions across software packages, though this latter step could be outside the scope of the current study.

Secondly, because this is not a single software package and we make use of multiple software packages (e.g. FreeSurfer, Blender, MATLAB) as well as rely on the command-line for execution of the steps in the pipeline, there is no unifying graphical user interface for controlling all aspects of the pipeline. Therefore, some knowledge of how to navigate between directories through the command-line and how to safely manipulate their contents is required. We reference guides and give tips in our protocol to assuage such fears. Nevertheless, this may be off-putting to potential users who would ideally like to control everything from a single application (such as MATLAB), to potential users with no previous experience controlling an operating system from the command-line, or to potential users who would feel more comfortable controlling everything from a single graphical user interface (such as Lead-DBS or Brainstorm [28,29]).

Thirdly, because the methodology entails the execution of commands in the bash shell—bash being a Unix shell—and preprocessing of the images is handled by FreeSurfer—an application that is only available for Mac OS and Linux OS—not every user or organization will be able to immediately take advantage of this pipeline. While we have suggested running the pipeline through an Ubuntu virtual machine should a Windows user be interested in executing the pipeline, this may be off-putting to people with no prior experience installing or working within virtual machines.

Finally, one major limitation in any electrode localization approach is that the post-operative scans and localizations are only ‘snapshots’ of the electrode locations. Tissue shifts during and after implant could result in some amount of error. As such, these electrode localizations are best-guess approximations and any analyses and interpretations should keep this issue in consideration. We recommend using the most up-to-date post-operative imaging in order to avoid this problem. The associated brain shifts with ECoG electrodes are a further example of how localization error can propagate as the methodology, as described above, is prone to error or requires assumptions.

This pipeline is an attempt to demonstrate a longstanding collection of methodologies that take advantage of the strengths of different software packages generating all the output described above, with alternatives given along the way (S1 Fig). While each software piece may or may not be the perfect tool for each job, we find parts of the pipeline are sufficient for many use cases, and that there are alternatives in part or in whole to our pipeline. The comparison table (Table 2) and software pipeline with alternative options (S1 Fig) are quick guides on which of the popular software packages may best assist or even substitute for our pipeline methodology for a given feature.

Future directions

In the future, we anticipate the implementation and possible incorporation of new analysis pipelines into this overall pipeline. This includes white matter tract mapping using such approaches as TRACULA [82,83]. Since seizure disorders like epilepsy are understood to be disorders of the network [84], having more information about how these networks are constructed would be an invaluable tool. This can be used by research teams to answer questions regarding white matter connectivity and its relation to seizure disorders.

Another example of a future tool to add to this pipeline is the inclusion of additional subcortical atlases used in pipelines like LeadDBS [29]. This is a pipeline similar to ours, but where we focus on cortical areas, LeadDBS focuses on subcortical areas with an emphasis on the thalamus. The combination or integration of these two pipelines would also be a major advance for both clinicians and researchers, as both cortical and deep brain structures could be accurately localized and visualized.

While no pipeline like this will be perfect for all use cases, this protocol represents a reproducible pipeline followed by multiple users and applied in hundreds of instances for reconstructing a patient’s brain, visualizing implanted electrodes, algorithmically identifying the brain region for each implanted electrode, and organizing all this data into a sharable format. As this type of data becomes more common and widely used, we hope that each of these facets will become more robust, more user-friendly, and more clinically and scientifically impactful.

Associated content

dx.doi.org/10.17504/protocols.io.5qpvornedv4o/v2.