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126 results for “TEMPORAL LOBE”
3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe
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A role for the medial temporal lobe subsystem in guiding prosociality: the effect of episodic processes on willingness to help others
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A role for the medial temporal lobe subsystem in guiding prosociality: the effect of episodic processes on willingness to help others (Experiment 2)
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The medial temporal lobe supports mnemonic discrimination for event duration
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Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance
<p>Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy</p>
Data: Effects of anterior temporal lobe resection on cortical morphology
<p>Data used for analysis for the paper <a href="http://doi.org/10.48550/arXiv.2212.06529">Effects of anterior temporal lobe resection on cortical morphology</a>.</p> <p>Code used for the analysis can be found on github: <a href="https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology">https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology</a>.</p> <p>The folder "not_corrected" contains morphological data for each subject (pre and post surgery for individuals with TLE) and vertex before application of the gam correction, and corresponding meta data. File names indicate metrics (T=average cortical thickness, At=pial surface area, Ae=exposed surface area), hemispheres (lh=left hemisphere, rh=right hemisphere), and onset sides (RTLE=subjects with right onset TLE, LTLE=subjects with left onset TLE). Controls are included in each file, processed without the temporal lobe for rh_RTLE and lh_LTLE.</p> <p>The folder "age_sex_corrected" contains the data for subjects with TLE with age, sex, and scanning protocol effects removed. Both onset sides have been combined (RTLE hemispheres are switched), and the files contain data for both hemispheres pre- and postoperatively.</p>
Data: Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
<p>We make available all the brain network data, and metadata of 83 patients and 29 healthy controls included in our study.</p> <p>Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, and Peter N. Taylor "<em>Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy.</em>" Epilepsia 2021 <em>doi:10.1111/epi.16819</em>.</p> <p>Methodological details on MRI acquisition and data processing are provided in our manuscript. We request users to kindly cite our article and data appropriately.</p>
Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '
<p>Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '</p> <div> <div>For the linked publication see: <a href="https://doi.org/10.1162/netn_a_00362" target="_blank" rel="noopener">https://doi.org/10.1162/netn_a_00362</a></div> <div> </div> </div> <p><em>eeg-fmri_$dataset$_$group$_connectomes_desikan_scrubbed.mat</em><br>datasets with filename truncTo5min hold static connectivities based on timeseries truncated<br>to 5min. All other datasets are based on static connectivities derived from the total session<br>timeseries.</p> <p><br>subj: subject<br>subj.name: name of the subject<br>subj.sess: session<br>subj.sess.sess_name: name of the session<br>subj.sess.fMRI: vector of upper triangular of fMRI connectivity<br>subj.sess.EEG: EEG connectomes<br>subj.sess.EEG.name: name of connectivity measure used (corrected imaginary part of the coherency: iCoh)<br>subj.sess.EEG.bands: EEG frequency bands<br>subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma)<br>subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity<br>subj.atlas: Atlas<br>subj.atlas.name: name of atlas used (Desikan)<br>subj.atlas.regions: number of regions</p> <p><em>$dataset$_$group$_</em>particpants.tsv: Subject metadata</p> <p><em>$dataset$_$group$_spikes.mat: Interictal epileptoform discharges (IEDs) marked for each session of TLE patients</em></p> <p><em>aparc_aseg_yeoR7_68reg_eeg_nosubc_cmfg2dan.mat: mapping of Desikan regions to Yeo7-networks (Yeo et al. 2011, JNP)</em></p> <p><em>desi_coord_68.txt: MNI coordinates of region centers of the Desikan atlas<br></em></p> <p><em>For the related code to this dataset please clone: </em>https://github.com/jwirsich/eeg-fmri-tle or use the code provided in 'eeg-fmri-tle-code.zip'</p>
Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe
<p>Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.</p>
Data from: Functionally analogous body- and animacy-responsive areas in the dog (Canis familiaris) and human occipito-temporal lobe
<p>Comparing the neural correlates of socio-cognitive skills across species provides insights into the evolution of the social brain and has revealed face- and body-sensitive regions in the primate temporal lobe. Although from a different lineage, dogs share convergent visuo-cognitive skills with humans and a temporal lobe which evolved independently in carnivores. We investigated the neural correlates of face and body perception in dogs (<em>N </em>= 15) and humans (<em>N</em> = 40) using functional MRI. Combining univariate and multivariate analysis approaches, we found functionally analogous occipito-temporal regions involved in the perception of animate entities and bodies in both species and face-sensitive regions in humans. Though unpredicted, we also observed neural representations of faces compared to inanimate objects, and dog compared to human bodies in dog olfactory regions. These findings shed light on the evolutionary foundations of human and dog social cognition and the predominant role of the temporal lobe.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 15 dogs</li> <li>structural scans of the same dogs incl. brain masks & skull-stripped versions</li> <li>files containing the condition names, onsets and durations for each dog and task run</li> <li>files containing the motion regressors incl. motion scrubbing for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for detailed sample descriptives and group-level imaging data of both species (<a href="https://osf.io/kzcs2/">https://osf.io/kzcs2/</a>).</p>
Low Frequency Electrical Stimulation of the Fornix in Intractable Mesial Temporal Lobe Epilepsy (MTLE)
ClinicalTrials.gov study NCT02383407. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ECT Pulse Amplitude and Medial Temporal Lobe Engagement
ClinicalTrials.gov study NCT02999269. IPD Sharing: YES. Countries: 1. Publications: 2.
Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe
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Ex vivo Mesoscale human temporal lobe dataset
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Abundant γ-amino butyric acidergic synaptic boutons per granule cell in sclerotic hippocampi of sea lions with temporal lobe epilepsy
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Data for: Human subsystems of medial temporal lobes extend locally to amygdala nuclei and globally to an allostatic-interoceptive system
<p><strong>Description</strong></p> <p>Results from masked-ICA based on 20 components and dual regression at the local (medial temporal lobe including the amygdala: 'within-A-MTL', download mask here: <a href="https://osf.io/8j9ts/">https://osf.io/8j9ts/</a>) and global (whole brain) level in three different samples presented in the paper <a href="https://www.sciencedirect.com/science/article/pii/S1053811919309954?via%3Dihub">Human subsystems of medial temporal lobes extend locally to amygdala nuclei and globally to an allostatic-interoceptive system</a> (<a href="https://doi.org/10.1016/j.neuroimage.2019.116404">doi.org/10.1016/j.neuroimage.2019.116404</a>)</p> <p><strong>Content</strong></p> <ul> <li><strong>MaskedICA-Global_dual_regression_20_ICs_original</strong>: Twenty .nii files corresponding to 20 independent components (ICs) at the whole-brain level (hence, 'Global') after the 'dual regression' step, based on the 'original' sample data. See manuscript for IC numbering.</li> <li><strong>MaskedICA-Global_dual_regression_20_ICs_replication1</strong>: One .nii files containing all 20 ICs ('merged'), using the first replication (REP1) sample data.</li> <li><strong>MaskedICA-Global_dual_regression_20_ICs_replication2</strong>: Same as the previous one, but based on the second replication (REP2) sample data.</li> <li><strong>MaskedICA-Local_dual_regression_20_ICs_original</strong>: Twenty .nii files corresponding to 20 ICs at the level of the A-MTL (hence, 'Local') after dual regression, based on the original sample data.</li> <li><strong>MaskedICA-Local_dual_regression_20_ICs_replication1</strong>: One .nii (merged) file containing all ICs at the A-MTL level, using REP1 sample data.</li> <li><strong>MaskedICA-Local_dual_regression_20_ICs_replication2</strong>: Same as the previous one, but based on REP2 sample data.</li> <li><strong>MaskedICA-Peaks_20_ICs_original</strong>: Twenty .nii files; each file contains voxelwise t-values derived from the dual regression step in the original sample. These files allow finding the 'functional connectivity peak', i.e., the voxel with the highest t-value.</li> </ul>
Simulation results and lambda value data for structural connectome based simulations of temporal lobe epilepsy surgery.
<p>This data file contains a number of matlab matrices holding the results of simulations carried out using structural connectome data from healthy individuals and individuals with a diagnosis of temporal lobe epilepsy (TLE). The results are in the form of either time values, representing the time at which brain regions in the simulations 'escaped' into a seizure state, or corresponding node labels which represent the region that escaped at that time. Simulations were stopped after the first three nodes escaped, and then repeated over 100 iterations. There were 39 controls and 22 left TLE patients. Simulations were also carried out for altered structural connectomes simulating surgery influence on the time taken for nodes to escape. Clinical resection (clinres), subject-specific resections (subspecres) or random resections (ranres). 'Lmdas' shows the deviation from the control average of surface areas for each region in each subject, normalised to lie between 0 and 1. 'Names' is a cell array containing the node labels for the 82 regions.</p>
Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity
<p><strong>Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity '</strong><br><strong>Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.nbd.2023.106382</span></a></strong></p> <p><strong>Dataset description</strong></p> <p><em>Data\data2publish\sub- </em>: 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs,<em> </em>Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal </p> <p><em>Data\data2publish\EA_info.xlsx</em>: number of epileptiform activities automatically detected for each animal at d28 and d29</p> <p><em>Data\data2publish\derivatives\eeg_preprocessing: </em>results of the script A_EEG_preprocessing.m, for each animal and session</p> <p><em>Data\data2publish\derivatives\elec_layout: </em>different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4</p> <p><em>Data\data2publish\derivatives\network_metrics</em>_<em>wpli: </em>results of network analyses (script C_network_analyses.m)</p> <p><em>Data\data2publish\derivatives\wpli</em>: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit</p> <p><strong>Code for analyses available here: </strong> <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a></p> <p>Abbreviations:</p> <p>EEG= electroencephalography</p>
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.