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122 results for “connectomes”
Infrequent strong connections constrain connectomic predictions of neuronal function (1/3)
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Supplemental data S1 to accompany Marin et al 2020 "Connectomics analysis reveals first, second, and third order thermosensory and hygrosensory neurons in the adult Drosophila brain"
<p>Neuronal skeletons and meshes. Related to STAR Methods. marin2020-skeletons contains files describing the morphology of each neuron in this study (in FAFB14 space, .swc format). glomeruli_meshes contains the glomerular meshes (in FAFB14 space, .stl format). neuropil_meshes contains the glomerular meshes (in FAFB14 space, .stl format).</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>
Connectomes across development reveal principles of brain maturation
<p>These data sets belong to the following publication:</p> <p>Witvliet, D., Mulcahy, B., Mitchell, J.K. <em>et al.</em> Connectomes across development reveal principles of brain maturation. <em>Nature</em> <strong>596, </strong>257–261 (2021). https://doi.org/10.1038/s41586-021-03778-8</p> <p>Please read the README.md file before using these data sets.</p>
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
<p>This whole-brain in vivo diffusion MRI dataset was acquired at 760 µm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. It was acquired using state-of-the-art acquisition hardware and advanced reconstruction to achieve high SNR at such resolution, including a high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition, and parallel imaging reconstruction with advanced ghost reduction algorithms. With its unprecedented high resolution, SNR and image quality, it could help explore the fine-scale structures of in vivo human brain, and further advance the understanding of human brain connectivity. This dataset can also be used as a test bed for further technical development of new modeling, sub-sampling strategies, denoising and processing algorithms for in vivo high resolution dMRI. Whole brain anatomical T<sub>1</sub>-weighted and T<sub>2</sub>-weighted images at submillimeter scale, field maps and the code for preprocessing pipeline are also made available in the repository.</p>
Connectome of memristive nanowire networks through graph theory - Dataset
<p>This is the dataset of "Connectome of memristive nanowire networks through graph theory"</p>
Dataset for Spatial Variations in the Osteocyte Lacuno-canalicular Network Density and Analysis of the Connectomic Parameters
<p>This dataset is a representative case of the loaded tibia of a C57BL/6 mouse at the mid-shaft. The image pixel size is 0.303 by 0.303 um, and the z-depth is 0.296 um. </p> <p>To generate, analyse, and quantify the osteocyte lacuno-canalicular network, it requires 'Tool for Image and Network Analysis (TINA)' which can be acqruied from https://gitlab.mpikg.mpg.de/rummler/TINA.git. A demonstration has been included on using TINA.</p>
Data from: Structural and functional brain connectome in motor neuron diseases: a multicenter MRI study
Objective. To investigate structural and functional neural organization in amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS) and progressive muscular atrophy (PMA) patients. Methods. 173 ALS, 38 PLS, 28 PMA sporadic patients and 79 healthy controls were recruited from three Italian centers. Subjects underwent clinical, neuropsychological and brain MRI evaluations. Using graph analysis and connectomics, global and lobar topological network properties and regional structural and functional brain connectivity were assessed. The association between structural and functional network organization and clinical/cognitive data was investigated. Results. Compared to healthy controls, ALS and PLS patients showed altered structural global network properties, as well as local topological alterations and decreased structural connectivity in sensorimotor, basal ganglia, frontal and parietal areas. PMA patients showed preserved global structure. Patient groups did not show significant alterations of functional network topological properties relative to controls. Increased local functional connectivity was observed in ALS patients in the precentral, middle and superior frontal areas, and in PLS patients in the sensorimotor, basal ganglia and temporal networks. In both ALS and PLS patients, structural connectivity alterations correlated with motor impairment, while functional connectivity disruption was closely related to executive dysfunctions and behavioral disturbances. Conclusions. This multicenter study showed widespread motor/extra-motor network degeneration in ALS and PLS, suggesting that graph analysis and connectomics might represent a powerful approach to detect upper motor neuron degeneration, extra-motor brain changes and network reorganization associated with the disease. Network-based advanced MRI provides an objective in vivo assessment of motor neuron diseases, delivering potential prognostic markers.
Dataset: A multi-scale probabilistic atlas of the human connectome
<p>This repository complements the paper submitted to <strong>Scientific Data</strong> named <em>A multi-scale probabilistic atlas of the human connectome</em>.</p> <p><strong>Introduction</strong></p> <p>The assessment of the networks underlying brain processes is key to understand brain-related disorders. However, groundbreaking connectomics research is highly demanding in terms of equipment and expertise. The aim of this work is to create a multiscale probabilistic atlas of the human white matter (WM) to carry out network analyses in the context of clinical research, particularly when diffusion data is not directly available.</p> <p><strong>Methods</strong></p> <p>Sixty six subjects from the Human Connectome Project (HCP) database (29 males, age: 22-36 years old) were used to build the WM probabilistic atlas. MRI acquisition protocols are described in (Van Essen et al, 2012). Besides T1-, T2- and diffusion-weighted (DW) images, the HCP database provides the FreeSurfer outputs (Glasser et al, 2016) namely cortical surfaces (pial and white), subcortical segmentation and a cortical surface parcellation containing 34 structures for each hemisphere (Desikan et al, 2006).</p> <p>For each subject, the DWIs were employed to segment each thalamus in seven nuclei (Battistella et al, 2016) and to estimate the WM streamlines distribution. The constrained spherical deconvolution (Tournier et al, 2007) algorithm was used to compute the intravoxel fiber distribution functions for the anatomically-constrained particle-filter tractography approach (Descoteaux et al, 2009) to compute the WM streamlines.</p> <p>Subcortical, thalamic and multiscale cortical (Cammoun et al, 2012) parcellations were gathered to obtain four individual gray matter (GM) parcellations. Finally, for each scale, individual fiber bundles, were created by selecting the streamlines connecting each pair of GM regions (Figure 1a).</p> <p><em>Atlas construction</em></p> <p>The T1 and T2 images were non-linearly warped to their corresponding MNI templates (Evans et al, 2012, mni_icbm152_tal_nlin_asym_09c version) using ANTs (Avants et al, 2010). The resulting spatial transformations were applied to warp the individual fiber bundles to stereotactic space and the normalized tract density images (TDIs) were created. In these images, each voxel contains the number of streamlines passing through it. Finally, the spatial probability map for each bundle was obtained by binarizing and averaging the bundle TDIs across the subjects (Figure. 1b).</p> <p>Different views of the developed probabilistic multi-scale connectome atlas are shown in Figure 2.</p>
datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"
<p>This dataset accompanies the manuscript:<br> "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset."<br> DOI: (https://doi.org/10.1101/2022.10.29.22281666)</p> <p>by</p> <p>Flavia Venetucci Gouveia1,2,3*†,Jürgen Germann4,5†, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14, Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15*</p> <p>1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada <br> 2. Sunnybrook Research Institute; Toronto, Canada<br> 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil<br> 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada<br> 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada<br> 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada<br> 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,<br> Medellín, Colombia<br> 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia<br> 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain<br> 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia<br> 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,<br> Colombia<br> 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil<br> 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;<br> São Paulo, Brazil.<br> 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,<br> Colombia<br> 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada<br> 16. Department of Psychiatry, University of Toronto; Toronto, Canada<br> 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica<br> Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia.<br> 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada<br> 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada<br> 20. Institute of Medical Science, University of Toronto; Toronto, Canada<br> † Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship.</p> <p>* Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca<br> * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca</p> <p>It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file.<br> The case numbers are identical to Table 1 in the manuscript.</p>
Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis
<p>Test-retest functional connectomes for 32 Healthy Controls and 30 First Episode of Psychosis patients. This dataset was originally used in the following article:</p> <p><strong>(Preprint)</strong> Tepper, Ángeles and Núñez, Javiera Vásquez and Ramirez-Mahaluf, Juan Pablo and Aguirre, Juan Manuel and Barbagelata, Daniella and Maldonado, Elisa and Dellarossa, Camila Díaz and Nachar, Ruben and Gonzalez-Valderrama, Alfonso and Undurraga, Juan and Goñi, Joaquín and Crossley, Nicolas, Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis. Available at SSRN: <a href="https://ssrn.com/abstract=4241607">https://ssrn.com/abstract=4241607</a> or <a href="http://dx.doi.org/10.2139/ssrn.4241607">http://dx.doi.org/10.2139/ssrn.4241607</a></p> <p>More details and python code used for analyses can be found in this<strong> <a href="https://github.com/angietep/Inter-and-Intra-Indiv-Variability">GitHub repository</a></strong></p>
Tomography of memory engrams in self-organizing nanowire connectomes - Dataset
<p>This is the dataset of "Tomography of memory engrams in self-organizing nanowire connectomes"</p>
Neural Mechanisms of Meditation Training in Healthy and Depressed Adolescents: An MRI Connectome Study
ClinicalTrials.gov study NCT04254796. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
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Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART II)
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Data and code from: Spatial and morphological organization of mitochondria in neurons across a connectome
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Data from: Structural and functional brain connectome in motor neuron diseases: a multicenter MRI study
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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>
Data for arXiv:1702.04117: "The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone"
<p>This dataset contains the raw confocal image stacks of the osteocyte lacuno-canalicular network in woven bone from mouse and fibrolamellar bone from sheep as described in "The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone" (https://arxiv.org/abs/1702.04117). See the methods section of the manuscript for further detail. MATLAB code to reproduce the data plots in the figures can be downloaded from https://github.com/phi-max/OCY_connectomics.</p>
Human Connectome Project (HCP) Intrahemispheric FC
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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.