A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

O1UE9Q1CVPCO is a subproject for the "Spinal Root Stimulation for Restoration of Function in Lower-Limb Amputees" dataset.

SMIZGIPQ0D5H is a subproject for the "Thin, High-Density, High-Performance, Depth and Surface Microelectrodes for Diagnosis and Treatment of Epilepsy" dataset. This project contains the data for the publication Russman et al., "Constructing 2D Maps of Human Spinal Cord Activity and Isolating the Functional Midline with High-Density Microelectrode Arrays". It contains the raw spine surface microelectrode data files to test novel high resolution thin film surface electrodes for recording neural activity. The data set involves the intraoperative monitoring approaches such as examining motor or sensory evoked potentials (SSEPs and MEPs) to map functional circuits in the spine at high resolution. Contact: Shadi Dayeh (sdayeh@eng.ucsd.edu). If you use this data as a part of any publications, please use the following citation: Russman SM, Cleary DR, Tchoe Y, Bourhis AM, Stedelin B, Martin J, Brown EC, Zhang X, Kawamoto A, Ryu WHA, Raslan AM, Ciacci JD, Dayeh SA. Constructing 2D maps of human spinal cord activity and isolating the functional midline with high-density microelectrode arrays. Sci Transl Med. 2022 Sep 28;14(664):eabq4744. doi: 10.1126/scitranslmed.abq4744. Epub 2022 Sep 28. PMID: 36170445; PMCID: PMC9650777. The research subjects were patients undergoing an intramedullary spinal cord tumor resection or biopsy procedure at the cervical segment with IONM with both epidural and subdural spinal cord exposure. Subjects were recruited by the neurosurgery team involved in this study. All patients voluntarily participated after informed consent in accordance with the University of California Institutional Review Board (IRB). Subjects were informed that participating in the study would not affect the treatment they received. Participants could withdraw at any time. Recordings were acquired from 6 participants.

This dataset contains human single-neuron data recorded from the medial temporal lobe (MTL) during a set of experiments to explore the role of neurons that respond to cognitive boundaries. This dataset accompanies the paper cited below. Example code on how to plot this data can be found at https://github.com/rutishauserlab/cogboundary-zheng . Reference (to be updated upon publication): Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. Jie Zheng, Andrea Gómez Palacio Schjetnan, Mar Yebra, Clayton Mosher, Suneil Kalia, Taufik A. Valiante, Adam N. Mamelak, Gabriel Kreiman, Ueli Rutishauser. bioRxiv 2021.01.16.426538. [Nat Neuro, in press, 2022]

UXUF7822Z3JL is a subproject for the "Electrophysiological Biomarkers to Optimize DBS for Depression" dataset. This repository contains the electrophysiological, imaging, and facial expression data, and custom code.

We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent intracranial monitoring for epileptic activity. Subjects first performed a screening task (907 neurons), based on which we identified images for which highly selective cells were present in the medial temporal lobe. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images and, following a maintenance period, were asked if a probe was identical to one of the currently maintained images. This Neurodata Without Borders (NWB) formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the existence of concept cells and their persistent activity during working memory maintenance. This dataset provides a substantial amount of rare human single-neuron recordings and behavior, thereby enabling investigation of the neural mechanisms of working memory at the single-neuron level.

926SYIE0GS11 is a subproject for the "Thin, High-Density, High-Performance, Depth and Surface Microelectrodes for Diagnosis and Treatment of Epilepsy" dataset. This project contains the data for the publication Lee et al., "Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain". It contains the raw and preprocessed (epoched) intracranial EEG (iEEG) data files from human brain to test novel high resolution micro-stereo-electrodes for recording neural activity in the brain. The data set involves the use of direct electrical stimulation to examine effects of stimulation in the brain. Contact: Shadi Dayeh (sdayeh@eng.ucsd.edu) or Angelique C. Paulk (apaulk@mgh.harvard.edu). If you use this data as a part of any publications, please use the following citation: Lee, K., Paulk, A. C., Ro, Y. G., Cleary, D. R., Tonsfeldt, K. J., Kfir, Y., Pezaris, J. S., Tchoe, Y., Lee, J., Bourhis, A. M., Vatsyayan, R., Martin, J. R., Russman, S. M., Yang, J. C., Baohan, A., Richardson, R. M., Williams, Z. M., Fried, S. I., Hoi Sang, U, Raslan, A. M., Ben-Haim, S., Halgren, E., Cash, S.S., Shadi. A. Dayeh. Flexible, scalable, high channel count stereo-electrode for recording in the human brain. Nature Communications, 2024, 15(1), 218. https://doi.org/10.1038/s41467-023-43727-9. All patients (N=2) voluntarily participated after fully informed consent as monitored by the Partners Institutional Review Board covering Massachusetts General Hospital (MGH). Participants were informed that participation in the stimulation tests would not alter their clinical treatment in any way, and that they could withdraw at any time without jeopardizing their clinical care. In one participant, MicrosEEG recordings from the lateral temporal lobe were performed in the operating room with either baseline recordings or operating room from a not-awake participant (under general anesthesia) undergoing a resective surgery for tumor or epilepsy. In a second participant, MicrosEEG recordings from the lateral temporal lobe were performed in the operating room with either baseline recordings or operating room from an awake participant (under monitored anesthesia care) undergoing a resective surgery for tumor or epilepsy.

6281M47AHII3 is a subproject for the "A Biomimetic Approach towards a Dexterous Neuroprosthesis" dataset.

This study is recording invasive neurophysiology during deep brain stimulation surgery to study network level control of motor control. The team will obtain multi-focal cortical and basal ganglia recordings across three action suppression tasks (self-paced movement, Eriksen Flanker task, and stop signal) and will record across multiple scales: single unit activity, local field potentials, and fMRI.

Prolonged iEEG monitoring for SOZ localization does add to the risk of complications and may include serious issues, such as intracranial bleeding, meningoencephalitis, and eventually death. The intellectual merit of this project is to develop computational intelligence tools based on recent advances in sparse coding and unsupervised machine learning techniques to investigate stereotyped high frequency oscillations (HFOs) in long-term iEEG and test the hypothesis whether the automated detection of HFOs will yield accurate and fast identification of SOZ.

This intracranial data set is included in the below publication investigating single pulse stimulation responses in the human brain: Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, et al. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022. 15: 15:491–508. Available from: https://www.sciencedirect.com/science/article/pii/S1935861X22000456 Highlights of the publication include: •Intracranial single pulse electrical stimulation (SPES) response increases with increased pulse duration mostly near the stimulation site. •SPES response varies nonlinearly with injected current with an effect of distance from the stimulation site. •SPES near the grey-white boundary and 90° to the nearest cortical axis induces larger local responses, but white matter stimulation produces larger distant responses. •The relationship between SPES location and responses depends on brain region stimulated. The work was funded by NIH grants MH086400, DA026297, and EY017658 to ENE, MH109722, NS100548, and MH111872 to ASW, NS100548 to DDD, and ECOR, NINDS K24-NS088568 to SSC and Tiny Blue Dot Foundation to SSC, ACP, and RZ. A United States Department of Energy Computational Sciences Graduate Fellowship [DE-FG02-97ER25308] supported BC. Some of this research was sponsored by the U.S. Army Research Office and Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0045 issued by ARO contracting office in support of DARPA's SUBNETS Program. The views and conclusions contained in this document are those of the authors and do not represent the official policies, either expressed or implied, of the funding sources. The raw data included here also includes data from two other previous publications: Basu I, Robertson MM, Crocker B, Peled N, Farnes K, Vallejo-Lopez DI, Deng H, Thombs M, Martinez-Rubio C, Cheng JJ, McDonald E, Dougherty DD, Eskandar EN, Widge AS, Paulk AC, Cash SS (2019) Consistent Linear and Non-Linear Responses to Electrical Brain Stimulation Across Individuals and Primate Species. Brain Stimulation. 12: 877-892. Crocker B, Ostrowski L, Williams ZM, Dougherty DD, Eskandar EN, Widge AS, Chu CJ, Cash SS, Paulk AC. (2021) Local and Distant responses to single pulse electrical stimulation reflect different forms of connectivity. NeuroImage. 237:118094.

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.

4KKN28OD1I87 is a subproject for the "Spinal Root Stimulation for Restoration of Function in Lower-Limb Amputees" dataset.

JNC5SX9FVNBU is a subproject for the "Technology development for closed-loop deep brain stimulation to treat refractory neuropathic pain" dataset. First-in-human prediction of chronic pain state using intracranial neural biomarkers - LINK: https://www.nature.com/articles/s41593-023-01338-z