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26 results for “resting EEG”
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
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EEG: Depression rest
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EEG: 3-Stim Auditory Oddball and Rest in Parkinson's
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EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI
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Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease
<p>This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.</p> <p><strong>[AD: i108, MCI: i100, SCD: i090, HC: s055]</strong></p> <p> </p> <p><strong>Participants & Settings</strong></p> <p>In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.</p> <p>The full dataset includes:</p> <p><strong>Healthy Controls Elders (60+ years old)</strong>: 33 participants</p> <p><strong>Subjective Cognitive Decline:</strong> 34 participants</p> <p><strong>Mild Cognitive Impairment</strong>: 79 participants</p> <p><strong>Alzheimer's Disease</strong>: 48 participants</p> <p><strong>Healthy Young (25-40 years old):</strong> 36 participants</p> <p>The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.</p> <p>Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. </p> <p><strong>Resting-State EEG Recording</strong></p> <p>Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.</p> <p><strong>EEG Data Acquisition</strong></p> <p>The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.</p> <p> </p> <p><strong>Full Dataset Access</strong></p> <p>More information about the sample dataset and access to the full dataset can be available after request via e-mail:</p> <p><strong>Ioulietta Lazarou</strong> BSc, MSc, PhD candidate</p> <p>Neuropsychologist - Clinical Research Associate </p> <p>Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)</p> <p>6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece</p> <p>E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a></p>
Resting-state EEG simulations
<p>Cortical-level activity was generated using a flexible neural mass model framework, named COALIA. This multi-population neural mass model enables the simulation of brain-scale electrophysiological activity while accounting for the macro- (between regions) and micro-circuitry (within a single region) of the brain, with one neural mass representing the local field potential of one Desikan-Killiany atlas region [for details, readers may refer to <a href="https://paperpile.com/c/vi4R1W/FXx0">(Bensaid et al. 2019)</a>].</p> <p>The simulated cortical networks (DMN and DAN) each included six regions based on the Desikan-Killiany atlas <a href="https://paperpile.com/c/vi4R1W/Rmev">(Desikan et al. 2006)</a> in terms of region parcellation. The DMN consisted of the right and left posterior cingulate cortex (PCC), medial orbitofrontal (MOF) gyrus, and inferior parietal lobe (IPL). Regarding the DAN, this network consisted of the right and left inferior parietal lobe (IPL), caudal middle frontal gyrus (cMFG), and superior parietal lobe (SPL).</p> <p>Activity in the alpha band ([8-12] Hz) was attributed to the regions belonging to reference RSNs, while background activity was assigned to remaining cortical regions. A variability between simulated data segments was introduced at the subject level, as well as at the level of epochs per subject. Each “virtual subject” had different connectivity matrices provided to the model, while each epoch for the same subject had a different input noise (mean =90, standard deviation = 30) set within the model. More specifically, for each subject, a different fractional anisotropy matrix of the HCP dataset was used <a href="https://paperpile.com/c/vi4R1W/XVSq">(Van Essen et al. 2013)</a>, and the weights corresponding to a RSN-connection were modified and set to a value of (1 ± 20%). A corresponding scaling of the matrices followed in accordance with COALIA’s requisites and the type of each input matrix (inhibitory/excitatory). A total of 50 “virtual subjects”, 4 epochs per subject (i.e., 200 data segments) were simulated; with a duration of 40 seconds each and a sampling rate of 2048 Hz. The time delay between NMMs was determined by the euclidean distance between the centroids of Desikan-Killainy’s regions divided by the velocity of action potentials propagation, which was set as 100 cm/s.</p> <p>Scalp EEG signals can be estimated from simulated cortical activity by solving the forward problem.</p> <p> </p> <p><a href="http://paperpile.com/b/vi4R1W/FXx0">Bensaid, Siouar, Julien Modolo, Isabelle Merlet, Fabrice Wendling, and Pascal Benquet. 2019. “COALIA: A Computational Model of Human EEG for Consciousness Research.” Frontiers in Systems Neuroscience 13: 1–18.</a></p> <p><a href="http://paperpile.com/b/vi4R1W/Rmev">Desikan, Rahul S., Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, et al. 2006. “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans into Gyral Based Regions of Interest.” NeuroImage 31: 968–80.</a></p> <p><a href="http://paperpile.com/b/vi4R1W/XVSq">Van Essen, David C., Stephen M. Smith, Deanna M. Barch, Timothy E. J. Behrens, Essa Yacoub, Kamil Ugurbil, and WU-Minn HCP Consortium. 2013. “The WU-Minn Human Connectome Project: An Overview.” NeuroImage 80 (October): 62–79.</a></p> <p> </p>
EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans
<p>Scripts used to analyze data for the manuscript submitted for publication in EJN</p> <p><strong>Script_Curve_Fitting_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com </p> <p><span>We therefore considered this continuous change in power as an extraneous variable </span><em><span>y<sub>k</sub>(x)</span></em><span> impacting the measured power spectrum </span><em><span>Pow(E<sub>k</sub>)</span></em><span>, and modeled it with a binomial equation that best fit the data, where the coefficients in <em>p<sub>i</sub></em> are in descending powers, and the length of <em>p</em> is <em>(n+1), k </em>is trial number (<em>k = 1 to 10</em>):</span></p> <p><strong><em><span>y<sub>k</sub>(x) = p<sub><span>1 </span></sub>. x<sup><span>2</span></sup><span><span> </span></span>+ p<sub><span>2 </span></sub>. x<span> </span>+ p<sub><span>3</span></sub></span></em></strong></p> <p><span>In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (</span><em><span>p<sub>1</sub></span></em><span>, </span><em><span>p<sub>2</sub></span></em><span> and </span><em><span>p<sub>3</sub></span></em><span>) of the binomial equation using the Matlab function <em>polyfit.m.</em> Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula:</span></p> <p><strong><em><span>PowFit(E<sub><span>k</span></sub>) = Pow (E<sub><span>k</span></sub>) – </span></em></strong><strong><em><span>y<sub>k</sub>(x)</span></em></strong></p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Power_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Meziane: hbmeziane@gmail.com<br>This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results</p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com<br>This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
Dataset from "Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis"
<p>Denoised and Preprocessed data from <em>EEG: First Episode Psychosis vs. Control Resting Task 1</em> as described in <em>Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis.</em></p> <p>Original dataset: <a href="https://doi.org/10.18112/openneuro.ds003944.v1.0.1">doi:10.18112/openneuro.ds003944.v1.0.1</a></p>
Resting-state EEG recordings in 20-30-year-old and 65-75-year-old healthy humans
<p>Raw EEG recordings of resting-state brain activity (six one minute baseline recordings, alternating eyes-closed and eyes-open for one minute each), down-sampled offline to 512 Hz using the Biosemi decimator tool and exported from BrainAnalyzer after renaming the markers. No other changes applied.</p> <p>BrainAnalyzer generates three files for each subject, needed to open the data in BA, EEGlab… A text header file (.vhdr) containing meta data, a text marker file (.vmrk) containing information about events in the data and a binary data file (.eeg) containing the voltage values of the EEG.</p> <p>For acronyms within the recordings:</p> <p>- the start of each eye closed segment = BegF1 (or BegRSf for a few files)</p> <p>- the end of each eye closed segment = EndF1 (or EndRSf for a few files) </p> <p>- the start of each eye open segment = BegO1 (or BegRSo for a few files) </p> <p>- the end of each eye open segment = EndO1 (or EndRSo for a few files) </p>
Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP
ClinicalTrials.gov study NCT05949528. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.
Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
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Measuring and Modulating Changes in EEG Resting State Functional Connectivity During Short-term and Long-term Pain
ClinicalTrials.gov study NCT05158309. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Resting-state EEG Gamma Oscillations in Alzheimer's Disease
ClinicalTrials.gov study NCT05989087. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effects of CBD/CBD-A Oral Extract on Resting-state EEG and Neuropathic Pain Symptoms After SCI
ClinicalTrials.gov study NCT05630235. IPD Sharing: NO. Countries: 1. Publications: 0.
RCT Study on the Therapeutic Effect of Escitalopram Loaded With XingpiJieyu Formula on Resting State EEG Energy Abnormalities in Severe Liver Depression and Spleen Deficiency Type MDD
ClinicalTrials.gov study NCT06758167. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of Cognitive Reserve Disorder Affecting Depression in Aged Related Hearing Loss Via Rest State EEG
ClinicalTrials.gov study NCT05892731. IPD Sharing: NO. Countries: 1. Publications: 0.
Rest EEG recordings in healthy subjects during wakefulness, sleep and anesthesia with ketamine, propofol, and xenon
<p><strong>Resting state EEG recording in healthy subjects:</strong></p> <p><strong>N = 5 during wakefulness eyes open and ketamine anesthesia;</strong></p> <p><strong>N = 5 during wakefulness eyes closed and propofol anesthesia;</strong></p> <p><strong>N = 5 during wakefulness eyes closed and xenon anesthesia;</strong></p> <p><strong>N = 4 during sleep (stage N3).</strong></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.