Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources.

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.


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Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Bin He Mar 19, 2020 The patient data was recorded in XLTeK amplifier in Mayo Clinic which is a typical software in clinical data collection. We used Curry 7/8 to view, select and filter the data and all other data analysis was done in MATLAB (2013b and 2018b). Details are presented in Methods and Code Availability sections.
Custom Software developed in MATLAB; Code is shared (https://github.com/bfinl/FAST-IRES) -All other toolboxes used are mentioned in the paper (EEGLab version 14.1.1b, specifically the infomax algorithm in this toolbox for independent component analysis, eConnectome version 1.0 beta for connectivity analysis, and custom code developed in MATLAB 2013b and also tested in 2018b, particularly a mathematical algorithm called FISTA was implemented by us which is available in the shared code -more details in Supplementary Methods). Detailed explanations are given in the manuscript under Code Availability.
The data that support the findings of this study are available upon reasonable request from the corresponding author (B.H.). A partial subset of data has been deidentified and is available at (https://doi.org/10.35092/yhjc.11996931) for the benefit of the scientific community. Other data are not publicly available due to them containing information that could compromise research participant privacy/consent.

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Life sciences study design
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Detailed information is provided in the Methods section of the paper. To achieve localization errors that were comparable to ECoG grid size, i.e. distictively smaller than10mm, given our expectation and experience of localization errors of 5 mm for our approach, we determined a sample size of 25 to be adequate to guarantee a statistical power of 0.9 (alpha=0.05) in distinguishing the means of these distributions. Based on our experience and general trend on the field, analyzing the data of 20-25 patients gives us robust and reasonable results. We used all the patients that satisfied the inclusion criteria (described in Methods). We had 36 patients' data for analysis.
Patients inclusion criteria are explained in Methods section. No data point was discarded.
Many patients (36 patients) were analyzed. Each patient has many tens of spikes and multiple seizures. The analysis was performed on these many instances and worked consistently.
No randomization were used. The post-hoc statistics analysis was based on patients' seizure outcome.
Blinding was not performed, because data included de-identified clinical reports of the patients, which was used for validation. These information were not used for analysis so blinding is irrelevant in this study.
Detailed information about inclusion criteria is brought in the Methods section of the paper, additionally, patients' (de-identified) information are brought in details in Supplementary Tables 4 and 5.
The patients were referred to Mayo Clinic, Rochester for treatment. Focal epilepsy patients who underwent intra-cranial recordings and/or had surgery were included in the study (details in the paper). This procedure is part of the medical routine performed at Mayo Clinic by Dr. Worrell's team. No known bias is identified in treating and collecting data from these patients.
The patients participated in the study willingly and gave written consent. The institutional review boards of Mayo Clinic, Carnegie Mellon University and University of Minnesota approved the study and all research was conducted within the regulations set by these IRBs.