Date: 10 August, 2022 Dataset Title: Intracranial electroencephalographic (iEEG) dataset of targeted memory reactivation during sleep. Dataset creators: Jessica D. Creery, David J. Brang, Jason D. Arndt, Adrianna Bassard, Vernon L. Towle, James X. Tao, Shasha Wu, Sandra Rose, Peter C. Warnke, Naoum Issa, & Ken A. Paller Dataset Contact: Ken A. Paller kap@northwestern.edu Funding: This research was supported by the NSF (BCS-1829414 and BCS-2048681) and the NIH (T32NS047987 and T32HL007909). Key Points: - We provide a dataset obtained from iEEG - A total of 5 participants completed the tasks that involved an audio-visual spatial memory task with memory associated sounds played during sleep. - The data is fully preprocessed and ready for analysis in three unique frequency bands; Theta (4-8Hz), sigma (12-16Hz), and gamma (20-100Hz). We followed up by testing low gamma (20-50 Hz), mid-gamma (50-80 Hz), and high gamma (80-100 Hz) as well as a separate ripple analysis.   Research Overview: Here, we investigated overnight memory change by measuring electrical activity in and near the hippocampus. Electroencephalographic (EEG) recordings were made in five patients from electrodes implanted to determine whether a surgical treatment could relieve their seizure disorders. One night, while each patient slept in a hospital monitoring room, we recorded electrophysiological responses to 10-20 specific sounds that were presented very quietly, to avoid arousal. Half of the sounds had been associated with objects and their precise spatial locations that patients learned before sleep. After sleep, we found systematic improvements in spatial recall, replicating prior results. We assume that when the sounds were presented during sleep, they reactivated and strengthened corresponding spatial memories. Notably, the sounds also elicited oscillatory intracranial EEG activity, including increases in theta, sigma, and gamma EEG bands. Gamma responses, in particular, were consistently associated with the degree of improvement in spatial memory exhibited after sleep. We thus conclude that this electrophysiological activity in the hippocampus and adjacent medial temporal cortex reflects sleep-based enhancement of memory storage.   Methodology: A de-identified iEEG dataset obtained from 5 subjects during sleep after performing an audiovisual spatial memory task was collected from a hospital. The data is preprocessed according to the descriptions provided in the detailed description document. The data is provided in three distinct frequency bands; Theta (4-8Hz), sigma (12-16Hz), and gamma (20-100Hz). We followed up by testing low gamma (20-50 Hz), mid-gamma (50-80 Hz), and high gamma (80-100 Hz) as well as a separate ripple analysis. The task consisted of learning spatial locations of objects that were paired with a closely associated auditory stimulus. Electrode locations and their corresponding MNI vertices labelled according to freesurfer annotations at different resolutions (1mm, 2mm, 4mm, 10mm and 20mm) are included. Matlab codes to replicate the results in the accompanying manuscript are also included. Version 1.0.1. Instrument and/or Software specifications: Matlab 2019a or higher;  SleepSMG; EEGlab14_1_2b or higher ----------------------------------------------------------------------------- Files Contained Here: The dataset zip folder consists of three main sub-folders: 1) Electrodes: This folder provides details regarding the individual electrodes for each subject. The folder is structured in such a way that each subject consists of a separate subfolder within the master folder for each subject, containing the defaced T1 MRI and the electrode coordinates in the subject's RAS space. Each subject's directory also has the coordinates in MNI space (e.g., P1/MNI_Space/Real_LAT.dat). The first row of each .dat corresponds to the #1 contact on each electrode. We recommend overlaying these coordinates in freeview through Freesurfer (https://surfer.nmr.mgh.harvard.edu/), an open source neuroimaging toolkit for processing, analyzing, and visualizing human brain MR images.   2) Time Frequency: This folder contains preprocessed data for both of the key comparisons in the paper (in two folders: Cue vs Standard and TMR1 vs TMR2). Each folder contains the time frequency output described in the manuscript for the electrodes used in each comparison for individual subjects and group analysis. Images provided in the main manuscript are in the subfolder with the corresponding data and Matlab code used to produce that image. Matlab code is annotated to describe any user action needed to produce the images in the manuscript. The “Dependencies” folder contains Matlab scripts that need to be on path to run the Matlab codes. 1. Cue vs Standard is used for Figure 2 – 1.1. Step1_PatientAnalysis (folder) includes: 1.1.1. AnalysisWrapper_2to20.m, AnalysisWrapper_2to120lin.m AnalysisWrapper_2to120log.m – code to run time frequency analysis for the manuscript, with parameters used. 1.1.1.1. Input: “PtX_CvS.mat” preprocessed data for each patient (details of preprocessing can be found in the manuscript and in the OUTPUT structure in each .mat file) 1.1.1.2. Output: “PtX_2to20lin.mat”, “PtX_2to120lin.mat”, “PtX_2to120log.mat” (these files are stored in the Step2 folders as they are the inputs for Fig2_Step1_Average.m - recommend running one section at a time as you need to edit each section) 1.2. Step2_GroupAnalysis includes: 1.2.1. 2to120lin (folder): 1.2.1.1. Fig2_Step1_Average.m – 1.2.1.1.1. Input: “PtX_2to120lin.mat” (output of AnalysisWrapper_2to120lin.m above) 1.2.1.1.2. Output: averages the electrodes of interest to “avPtX_2to120linYtoZ.mat “(Y = index of first electrode in cluster, Z = index of last electrode in cluster) 1.2.1.2. Fig2c_BarGraph_Step2.m – 1.2.1.2.1. Input: “avPtX_2to120linYtoZ.mat” 1.2.1.2.2. Output: dB averages for Pts 1-5 in an excel file of each Cue and Standard for each frequency band of interest (annotated in .m script). 1.2.1.3. Fig2ab_2to20 (folder) includes 1.2.1.3.1. “PtX_2to20lin.mat” – Output of AnalysisWrapper_2to20.m in Step1_PatientAnalysis folder above 1.2.1.3.2. Figure2_Step1_Average2to20.m – 1.2.1.3.2.1. Input: “PtX_2to20lin.mat” 1.2.1.3.2.2. Output: averages the electrodes of interest to “avPtX_2to20linYtoZ.mat” (Y = index of first electrode in cluster, Z = index of last electrode in cluster) 1.2.1.3.3. Figure 2ab_TF_line 1.2.1.3.3.1. Input: “avPtX_2to20linYtoZ.mat” 1.2.1.3.3.2. Output: “2ab_2_20Hz.svg” 1.2.1.4. OutsideMTL (folder) includes the same input and outputs to determine averages for the cluster outside of the MTL 1.2.2. 2to120Log (folder) includes the same files to create a heatmap if you would prefer a figure with the log values (as in the supplementary materials). 2. TMR1 vs TMR2 (folder) includes: 2.1. Fig3_Step1.m 2.1.1. Input: “PtX_TMR1v2.mat” (hosted seperately outside of the TMR1 vs TMR2 folder) 2.1.2. Output: averages the electrodes of interest to “pc_PtX_TMR1v2YtoZ.mat” (Y = index of first electrode in cluster, Z = index of last electrode in cluster) 2.2. Fig3ab_TFandLinePlot.m 2.2.1. Input: all averaged .mat files starting with “pc_*” 2.2.2. Output: “3ab_MTL.svg” 2.3. Figure3c_BarGraph 2.3.1. Input: all averaged .mat files starting with “pc_*” 2.3.2. Output: “Fig3c_TMR1v2.xls” (see annotated version for reference) 3) Sleep: This folder contains the EEG from scalp electrodes used to do the sleep scoring for the whole night (Sleep Scoring Data) or just over the cue period (SleepMontage_DuringCues). All sleep scoring occurred in sleepSMG (available at sleepsmg.sourceforge.net/) 4) Memory: This folder contains: 1. behavioral data (PtX_memory.txt) and sleep codes (PtX_sleep.txt, PtX_indices.txt, PtX_standard.txt) for the 5 patients. 1.1. annotated_Pt3_memory.txt includes details of what is in each memory file 1.2. PtX_sleep.txt files contain a list of indices that correspond to objects sounds played, PtX_indices.txt are the cued objects and PtX_standard.txt are the standard objects). 2. easyEqualizeiEEG.m can be used to calculate distances. 3. Preprocessed data in the Excel file iEEGTMR_Memory_Fig1 was used to create Fig 1 in the manuscript. All distances are in cm. Please see the manuscript for more details. 3.1. Tab 1 includes the mean percent change & figure 1 3.2. Tab 2 shows how mean percent change was calculated for each patient (separately for cued and uncued objects: mean post-sleep test distance minus mean pre-sleep test distance divided by mean pre-sleep distance multiplied by 100). 3.3. Tab 3 includes the pre-sleep test distance, post-sleep test distance, error (post-sleep test distance minus pre-sleep test distance), and percent change distance for each object (post-sleep test distance minus pre-sleep test distance divided by pre-sleep distance multiplied by 100). Tab 3 also includes index number for each object and whether that object was cued (1) or not (0).   5) Dependencies: Matlab scripts used to process the data. Related publication(s): Creery JD, Brang D, Arndt JD, Bassard A, Towle VL, Tao JX, Wu S, Rose S, Warnke P, Issa NP, Paller KA (in press). Electrical Markers of Memory Consolidation in the Human Brain when Memories are Reactivated during Sleep. Proceedings of the National Academy of Sciences.   Use and Access: This data set is made available under a Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)   To Cite Data: Creery, J. D., Brang, D., Arndt, J. D., Bassard, A., Towle, V. L., Tao, J. X., Wu, S., Rose, S., Warnke, P. C., Issa, N., Paller, K. A. Dataset for "Electrophysiological markers of memory consolidation in the human brain when memories are reactivated during sleep" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/3cx8-3x86