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122 results for “connectomes”
Connectome-based reservoir computing with the conn2res toolbox
Open the record for dataset details and reuse information.
Functional connectomes denoised with various strategies
<p>Dataset denoised with the following strategies:</p> <p>https://github.com/SIMEXP/fmriprep-denoise-benchmark/code/benchmark_strategies.json</p> <p>Atlas used:</p> <p>Schaefer 7 network 400 ROIs<br> <br> Preprocessing:</p> <p>fMRIPrep LTS 20.2.1</p> <pre><code class="language-bash">singularity run --cleanenv \ -B $SLURM_TMPDIR:/DATA \ -B /PATH/TO/templateflow:/templateflow \ -B /etc/pki:/etc/pki/ \ -B /PATH/TO/OUTPUT:/OUTPUT \ /PATH/TO/fmriprep-20.2.1lts.sif \ -w /DATA/fmriprep_work \ --participant-label pixar001 \ --bids-filter-file /OUTPUT/bids_filters.json \ --cifti-output 91k \ --use-aroma \ --output-spaces MNI152NLin2009cAsym MNI152NLin6Asym fsaverage5 \ --output-layout bids \ --notrack \ --skip_bids_validation \ --write-graph \ --omp-nthreads 8 \ --nprocs 16 \ --mem_mb 65536 \ --resource-monitor \ /DATA/ds000288 /DATA/ds000288/derivatives/fmriprep participant </code></pre> <p> </p>
GSDGM files generated by human connectome project s500 rs-fMRI (CONN atlas 132 ROIs) data
<p>Group Surrogate Data Generating Model (GSDGM) dataset generated by human connectome project s500 rs-fMRI (CONN atlas 132 ROIs) data.</p>
Data for 'Integrating direct electrical brain stimulation with the human connectome'
<p>Cortical and subcortical DES coordinates in MNI space, anonymized patients’ demographic data, and aggregated functional maps for the 12 functional categories described in the accompanying publication.</p> <p>Link to publication: https://doi.org/10.1093/brain/awad402</p> <p>If you use this data please do not forgeto to cite our work.</p>
Rewired connectome of an SSCX model with inhibitory targeting based on trends found in MICrONS data
<p>This is a rewired connectome of the internal synaptic connectivity of the model deposited under <a href="../records/7930275" target="_blank" rel="noopener">https://zenodo.org/records/7930275</a>.</p> <p>In the original model, local connectivity is based on axo-dendritic overlaps combined with a pruning rule, which together are known to be able to recreate biological trends in excitatory subnetworks. By comparison with the <a href="https://www.microns-explorer.org/cortical-mm3" target="_blank" rel="noopener">MICrONS</a> dataset we found that the inhibitory trends characterized by <a href="https://www.biorxiv.org/content/10.1101/2023.01.23.525290v3" target="_blank" rel="noopener">Schneider-Mizell et al. (2023)</a> can be reproduced with some additional, relatively simple pruning rules (see accompanying <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144" target="_blank" rel="noopener">anatomy publication</a>), and created a rewired version of the connectome in <a href="https://github.com/AllenInstitute/sonata" target="_blank" rel="noopener">SONATA</a> format (edges.h5) that reproduces these trends. We refer to this as the Schneider-Mizell compatible SM-connectome in the accompanying <a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology publication</a>.</p> <p>Note that for technical reasons, all <em>afferent_segment_...</em> and <em>efferent_...</em> synapse properties (which are not required for running simulations) were set to zero in the rewired pathways.</p> <p><strong>[Update 08/05/2024 - v3]:</strong> Conductances of individual synapses were recalibrated to preserve pathway-specific reference PSP amplitudes. The <em>afferent_section_type</em> and <em>efferent_section_type</em> synapse properties were previously off by 1 and were fixed. Missing synaptic input compensation was recalibrated to obtain target firing rates and added to this release.</p> <p><strong>[Update 28/02/2024 - v2]:</strong> Source m-type L1_NGC-SA included</p>
Comparative basolateral amygdala connectomics reveals dissociable single-neuron projection patterns to frontal cortex in macaques and mice
<p>Zeisler et al., (2024) <em>Current Biology</em></p> <p> </p> <p>This repository contains data and code necessary to generate the main figures contained in the paper.</p> <p> </p> <p>The macaque data used is available at: https://zenodo.org/records/8319819</p>
Rewired SSCx network model with 2nd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 2nd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 3rd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 3rd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 1st-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 1st-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 4th-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 4th-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Dataset related to "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations"
<p>This is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). It contains the resulting data of the manipulated SSCx network model, such as benchmarks, fitted stochastic models, manipulated connectomes, structural validations, as well as simulation data and analysis results.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <p><strong>Update 25/09/2024 (v2):</strong> Added benchmark results for assessing strong and weak scaling behavior of connectome rewiring.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Dataset: UL and HCP multiscale weighted human brain connectomes
<p>This repository contains the multi-scale weighted connectomes (edge list with fiber density) computed using diffusion weighted images from 2 different datasets. The first dataset contains a subsample of 44 subjects extracted from the test-retest dataset included in the Human Connectome Project (HCP). The second dataset is compound by 40 healty controls selected from Lausanne psychosis cohort (UL). The age of the subjects belonging to the HCP dataset ranges between 22 and 35 years old while the mean age for the subjects included in the UL dataset is around 25 years old.</p> <p> </p> <p>Files ending in '_nodes.txt' contain the structure code, the name and the center of mass coordinate for each gray matter structure.</p> <p>Files ending in '_weight_edgelist.txt' contain the structure code, the name and the center of mass coordinate for each gray matter structure.</p>
Consensus Structural and functional connectome from 70 young healthy adults
<p>Consensus Structural and functional connectome from 70 young healthy adults.</p>
Multimodal Connectome Study of Brain Tumor-operated Patients
ClinicalTrials.gov study NCT04163315. IPD Sharing: NO. Countries: 1. Publications: 4.
Connectome Characterization With Aging
ClinicalTrials.gov study NCT03109275. IPD Sharing: NO. Countries: 1. Publications: 1.
Measuring the Latency Connectome in the Central Nervous Systems Using Neuroimaging and Neurophysiological Techniques
ClinicalTrials.gov study NCT03223636. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study on Functional Connectome and rTMS Intervention of Cognitive Flexibility Impairment in Patients With Major Depressive Disorder
ClinicalTrials.gov study NCT07161492. IPD Sharing: NO. Countries: 1. Publications: 11.
Alterations in the Brain's Connectome After Severe Traumatic Brain Injury
ClinicalTrials.gov study NCT02424656. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Identification of a Resting Bold Connectome Associated with Cognitive Reserve - Data and code archive
Open the record for dataset details and reuse information.
The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T
<p><strong>Connectome dataset for the publication: 'The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T'<br> Wirsich et al. 2021, NeuroImage, doi: </strong>10.1016/j.neuroimage.2021.117864</p> <p><strong>Data description</strong></p> <p><em>eeg-fmri_connectomes_$atlas$_scrubbed_$dataset$.mat</em><br> datasets with filename truncTo4min58_5s hold static connectivities based on timeseries truncated<br> to 4min58.5s. 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 (imaginary part of the coherency: iCoh,<br> amplitude envelope correlation (orthongonaliyzed): hilb_Ortho, Amplitude envelope correlation<br> (not orthongonaliyzed): hilb_noOrtho)<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 or Destrieux)<br> subj.atlas.regions: number of regions</p> <p><br> Use the following code to convert connectivity vectors to a connectivity matrix for atlas(1):</p> <pre><code>regions = length(atlas(1).labels) mrtx = zeros(regions); count = 1; for r1 = 1:regions-1 for r2 = r1+1:regions mrtx(r1, r2) = subj.sess.fMRI(count); mrtx(r2, r1) = mrtx(r1, r2); count = count + 1; end end</code></pre> <p><br> <em>atlas_labels.mat</em></p> <p>atlas.name: atlas Name</p> <p>atlas.labels: labels of each atlas region</p> <p><br> </p>
ScienceDex guides
Understand access before you commit
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.