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30 results for “Brain oscillation”
Data from: Identification of neural oscillations and epileptiform changes in human brain organoids.
<p>Seurat object containing processed single-cell RNA sequencing data described in:</p> <p>Samarasinghe, R.A., Miranda, O.A., Buth, J.E. <em>et al.</em> Identification of neural oscillations and epileptiform changes in human brain organoids. <em>Nat Neurosci</em> <strong>24, </strong>1488–1500 (2021). <a href="https://doi.org/10.1038/s41593-021-00906-5">https://doi.org/10.1038/s41593-021-00906-5</a></p> <p>Additional raw and processed data can be accessed at the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165577">GSE165577</a>. </p> <p> </p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
Brain waves would include equipotential fluctuations in addition to oscillations as sine waves
<p>Raw 1200 LFP data sets of 60s are in /60s_125(250,500,1000)Hz_20h/12XX/eeg1_t0(1-1199) .txt. Results of tau and burst, whose state is identified by existing method (0:awake, 1:NREM, 2:REM), are in /60s_125-1000Hz_20h/12XX_m2 or S1/tau-burst_m2(s1)_thX.XXX.txt. th=threshold. Column 1:trial No., 2:state, 3:Ntau, 4:Mtau, 5:ratio of total tau, 6:number of burst, 7:mean burst, 8:Abst. Those identified by Ntau method are in /60s_125-1000Hz_20h/4state (mean per state), 4state_m2, or 4states _s1/12XX/tauburst-m2_4state_thx.XXX.txt.The state is listed in third column as 2:awake, 10:REM, 20:NREM, 30:light sleep. Data of SEF95 are in /60s_sef_4state/12XX_SEF4state_125(250,500,1000) Hz.txt.</p> <p>Raw EEG data sets of 64s are in /dogEEG/older(younger)_sev/20150XXX/50Hz_nonMovAve/20150XXX_sevX.X_int0(1,2)_cts2.txt. Results of tau and burst are contained in /dogEEG/older(younger)_sev/20150XXX/sratio_peak/non-movave/20150XXX_sratio_peak_tau-burst_thXX.XX.txt. Column 1:sevoflurane concentration, 2:int, 3:Ntau, 4:Mtau, 5:ratio of total tau, 6:number of burst, 7:mean burst, 8:Abst. Results of SEF95 are in /dogEEG/older(younger)_sev/20150XXX/50Hz_nonMovAve/20150XXX_sef95.txt. Data of maximum Ntau, minimum Abst, or Mtau=2.3, 2.5 sample interval are in /dogEEG/THnt_250Hz.</p>
Examining the Effect of Lateralization Imagery Training on EEG Brain Oscillations in Individuals With Chronic Neck Pain
ClinicalTrials.gov study NCT06679335. IPD Sharing: NO. Countries: 1. Publications: 36.
Stroke Therapy With Brain Oscillation Synchronized Stimulation
ClinicalTrials.gov study NCT05005780. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Induction and Stabilization of Gamma Oscillations With 40 Hz External Brain Stimulation
ClinicalTrials.gov study NCT06680154. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Brain Oscillation-synchronized Stimulation of the Frontal Cortex in Major Depressive Disorder
ClinicalTrials.gov study NCT06345651. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clonazepam Effects on Brain Oscillations and Cognition in Schizophrenia
ClinicalTrials.gov study NCT03061136. IPD Sharing: NO. Countries: 1. Publications: 5.
Brain-Oscillation-Synchronized Stimulation to Enhance Motor Recovery in Early Subacute Stroke
ClinicalTrials.gov study NCT05600374. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Back-tracking during navigation shows enhanced anterior cingulate activity and suppression of alpha oscillations and 'default-mode' brain activity
Successful navigation can require realising the current path choice was a mistake and the best strategy is to retreat along the recent path: 'back-tracking'. Despite the wealth of studies on the neural correlates of navigation little is known about back-tracking. To explore the neural underpinnings of back-tracking we tested humans during functional magnetic resonance imaging (fMRI) on their ability to navigate to a set of goal locations in a virtual desert island riven by lava which constrained path that could be taken. We found that on a subset of trials, participants spontaneously chose to back-track and that the majority of these choices were optimal. During back-tracking, activity increased in frontal regions and the dorsal anterior cingulate cortex, while activity was suppressed in regions associated with the core default-mode network. Using the same task, magnetoencephalography (MEG) and a separate group of participants we found that power in the alpha band was significantly decreased immediately prior to such back-tracking events. These results highlight the importance for navigation of brain networks previously identified in processing internally-generated errors and that such error-detection responses may involve shifting the brain from default-mode states to aid successful spatial orientation.
Brain-Oscillation Synchronized Stimulation of the DMPFC
ClinicalTrials.gov study NCT06942559. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Back-tracking during navigation shows enhanced anterior cingulate activity and suppression of alpha oscillations and ‘default-mode’ brain activity
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Circadian dependent transcriptional oscillation in the olfactory and brain tissues of Aedes and Anopheles mosquitoes regulate perireceptor events and olfactory sensitivity
GEO Series GSE238168. Anopheles culicifacies; Aedes aegypti. 47 samples. Type: Expression profiling by high throughput sequencing.
Identification of neural oscillations and epileptiform changes in human brain organoids
GEO Series GSE165577. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Data from: Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anesthesia depth
Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.
Pharmacological Modulation of Brain Oscillations in Memory Processing
ClinicalTrials.gov study NCT05594017. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Multicenter Randomized Controlled Study of Non-invasive Brain Oscillation Modulation in the Treatment of AD-MCI Patients
ClinicalTrials.gov study NCT06470074. IPD Sharing: NO. Countries: 1. Publications: 0.
Exercise Target Brain Oscillations in Psychosis
ClinicalTrials.gov study NCT05338424. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of Brain Oscillation-Dependent TMS on Motor Function
ClinicalTrials.gov study NCT03288220. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection and Amelioration of Gamma Oscillation Abnormalities in Blast-Related Brain Injury
ClinicalTrials.gov study NCT03836976. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.