Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Supplementary data to accompany Information flow, cell types and stereotypy in a full olfactory connectome

Supplemental file 1 Layers assigned by the probabilistic graph traversal model. bodyId refers to neurons’ unique ID in ne- uPrint. layer mean contains the mean layer after 10,000 iterations of the main model (Figure 2). layer - olf mean and layer th mean contain the mean layers from running the traversal model with ORNs and THN/HRNs, respectively (Figure S2). S1 hemibrain neuron layers.csv Supplemental file 2 Sensory meta-information related to each glomerulus. Columns: glomerulus (canonical name for one of the 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli), laterality (whether the glomerulus receives bilateral or only unilateral innervation from ALRNs), expected cit (a citation that describes the expected number of RNs in this glomerulus), expected RN female 1h (number of expected RNs in one hemi- sphere), expected RN female SD (standard deviation in the expected number of RNs), missing (qualitative assessment of glomeruli truncation), RN frag (if the RNs in that glomerulus are fragmented), receptor (the OR or IR expressed by cognate ALRNs (Bates et al., 2020; Task et al., 2020)), odour scenes (the general ‘odour scene(s)’ which this glomerulus may help signal (Mansourian and Stensmyr, 2015; Bates et al., 2020)), key ligand(the ligand that excites the cognate ALLRN or receptor the most, based on pooled data from multiple studies (Mu ̈nch and Galizia, 2016)), valence (the presumed valence of this odour chan- nel (Badel et al., 2016)). Exists as hemibrain glomeruli summary in our R package hemibrainr. S2 hemibrain olfactory information.csv Supplemental file 3 File listing all identified antennal lobe receptor neurons (ALRNs) in the hemibrain, including information shown in neuPrint. See above for column explanations. Exists as rn.info in our R package hemibrainr. S3 hemibrain ALRN meta.csv Supplemental file 4 All the hemibrain neurons we have classed as antennal lobe local neurons (ALLNs). See above for column explanations. Exists as alln.info in our R package hemibrainr. S4 hemibrain ALLN meta.csv Supplemental file 5 All the hemibrain neurons we have classed as antennal lobe projection neurons (ALPNs). See above for column explanations. In addition, across dataset cluster refers to the clustering with left and right FAFB PNs; is canonical indicates whether that ALPN is one of the well studied “canonical” uPNs. Exists as pn.info in our R package hemibrainr. 40 S5 hemibrain ALPN meta.csv Supplemental file 6 All the hemibrain neurons we have classed as third-order olfactory neurons (TOONs) including lateral horn neurons (LHNs), as well as wedge projection neurons (WEDPNs), lateral horn centrifugal neurons (LHCENT) and other projection neuron classes (Figure 1). See above for column explanations. Exists as ton.info in our R package hemibrainr. S6 hemibrain TOON meta.csv Supplemental file 7 All the hemibrain neurons we have classed as neurons that descend to the ventral nervous system (DNs). See above for column explanations. Exists as dn.info in our R package hemibrainr. S8 hemibrain DN meta.csv Supplemental file 8 The root point in hemibrain voxel space, for each hemibrain neuron. This is either the location of the soma, or the tip of a severed cell body fibre tract, where possible. Exists as hemibrain somas in our R package hemibrainr. S8 hemibrain root points.csv Supplemental file 9 The start points for different neuron compartments. Nodes downstream of this position in the 3D structure of the neuron indicated with bodyid, belong to the compartment type designated by Label. A product of running flow centrality on hemibrain neurons, exists as hemibrain splitpoints in our R package hemi- brainr. S9 hemibrain compartment startpoints.csv Supplemental file 10 3D triangle mesh for the hemibrain surface as a .obj file. This mesh was generated by first merging individual ROI meshes from neuPrint and then filling the gaps in between in a semi-manual process. It also exists as hemibrain.surf in our R package hemibrainr. S10 hemibrain raw.obj Supplemental file 11 3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALRN presynapses. 41 Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed). S11 hemibrain AL glomeruli meshes RN-based.zip Supplemental file 12 3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALPN presynapses. Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed). These meshes are also available as hemibrain al.surf in our R package hemibrainr. S12 hemibrain AL glomeruli meshes PN-based.zip

opencc-by-3.0-usFeb 2023View details →

Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025 In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected. The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files. Note: Data has been updated with post-publication files.

opencc-by-4.0Jul 2020View details →

A whole-cortex probabilistic diffusion tractography connectome

This is a collection of the results data for the eNeuro article of the same name, <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. Please cite this publication when using these data. Files with the .mat extension are matlab v7.3 files. The raw data from which these data were derived are available from <a href="https://db.humanconnectome.org">https://db.humanconnectome.org</a> and <a href="https://f-tract.eu">https://f-tract.eu</a>.

opencc-by-4.0Aug 2022View details →

Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)

Summary statistics for genome-wide association studies reported in: Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. Scientific Reports, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>.  Abstract Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.   Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.

opencc-by-4.0Dec 2020View details →

A disease-specific functional connectome of Parkinson's disease patients.

The contribution of the presented work is a data record featuring a disease-specific dataset of resting state functional magnetic resonance imaging (rs-fMRI) acquisitions of 75 Parkinson's disease (PD) patients prior to deep brain stimulation (DBS) implantation - the Tor-PD connectome. Specifically, the dataset comprises 77 matrices (in the folder entitled 'vol'), each containing blood-oxygen-level-dependent-signal (BOLD) signal values of every voxel in 77 corresponding rs-fMRI acquisitions in standard MNI152 NLIN 2009b space. BOLD signal time-series matrices are given as .mat files and are viewable in MATLAB. In addition to the matrices, we have also provided a mask in NIfTI-1 format corresponding to voxels in standard space where a BOLD signal could be calculated in >80% of the 77 matrices (NaN mask). The data records derived from this work can be obtained through Zenodo (https://zenodo.org/) and Lead DBS (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>). Crucially, the format of choice can be directly used without further conversion or preprocessing steps with openly available software (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>).

opencc-by-4.0Sep 2023View details →

Connectomics of (part of) the MICrONS mm3 dataset

This dataset provides the connectome of a large part of the IARPA MICrONS mm^3 dataset (<a href="https://www.microns-explorer.org/cortical-mm3">https://www.microns-explorer.org/cortical-mm3</a>). Specifically, it contains internal connectivity between most neurons of "portion 65" of the EM volume (see above link for details), i.e. synapses between neurons inside the volume, but no synapses from neurons extrinsic to the volume. The volume contains parts of the regions VISp, VISrl, VISal and VISlm. ---NEW VERSION 3.0.0--- This new version of the dataset is based on version 1412 of the MICrONS data.  We have not included some of the less used edge and vertex properties to shrink the file size. Notably, we have excluded the spatial locations of synapses. Additionally, we have excluded around 20,000 neurons in the periphery of the volume. Those neurons would be affected in terms of their connectivity by a strong, artificial edge effect. If you are interested in a version with all 72,000 neurons or with spatial synapse locations, please contact the maintainer of this dataset. We have added vertext properties that denote the neuron locations in micrometers in addition to the previously existing properties that listed them in nanometers. These are "x", "y", "z" in addition to "x_nm", "y_nm", "z_nm". For convenience we have included in this release a distance-dependent control of the data. For each pathways (combination of pre- and post-synaptic layer and synapse class) we fit an exponentially decaying connection probability profile. Then we generate a random instance according to those models.   ---NEW VERSION 2.0.0--- This new version of the dataset is based on a newer version of the data on the side of MICrONS. While 1.0.0 was based on v117 of the MICrONS data, 2.0.0 is based on v1181. Main differences are to my understanding:   - More proofreading: A larger number of neurons have been manually proofread.  - Cell type classification. A better cell type classification has been added. See below for details.   Details The file combines data from various tables of the "minnie65_public" release (see link above). v2.0.0 of the dataset ueses version 1181 of the following tables: - aibs_metamodel_mtypes_v661_v2 for neuron identifiers, tentative classes and soma locations - proofreading_status_and_strategy for information about axon / dendrite completeness - synapses_pni_2 for synaptic connection locations, sizes, source and target neurons The full description of those tables, as originally provided, can be found below. We converted location indicated in voxel indices in the original data to locations in nm, additionally we provide very tentative region annotations for neurons (but see below!). The main utility of this release lies in its formatting for the Connectome-utilities python package (<a href="https://github.com/BlueBrain/ConnectomeUtilities">https://github.com/BlueBrain/ConnectomeUtilities</a>). As such, you can easily load it and use Connectome-utilities functionality for various analyses. Exemplary notebooks are provided. The file contains two representations of the connectome: - "full" represent multiple synapses between neurons as multiple directed edges. - "condensed" only has (at most) a single edge between neurons, but it is associated with a property "count" that specifies the number of synapses. Other synapse properties (such as their locations) are mostly lost in the condensed representation, only the mean and sum of the "size" property is provided. You can also get the condensed version from the full version by using the .condense() function of Connectome-utilities (see documentation). Some notes: - The raw data contained some neuron identifiers associated with multiple types. Manual inspection of their meshes indicated that they really are merges of several neurons. Since this only seemed to affect a few hundred of neurons, we simply filtered them out for this dataset. Getting started: To start, check the <a href="https://github.com/BlueBrain/ConnectomeUtilities#readme">documentation of Connectome-utilities</a> or just dive into the included exemplary jupyter notebooks. Contact: If you have questions or notes: <a href="mailto:conntility.645co@simplelogin.com">conntility.645co@simplelogin.com</a>   CREDIT All of this is based on <a href="https://www.microns-explorer.org/cortical-mm3">MICrONS</a>, with very little work by me. To give full credit, I will include below the original description of the datasets used. Many thanks to everyone mentioned below and everyone else that worked hard to provide that highly valuable data! aibs_metamodel_mtypes_v661_v2: This table contains Mtype predictions (Schneider-Mizell 2023) for cells throughout the entire dataset at materialization version 661. The predictions come from a soma and nucleus feature trained metamodel (Elabbady 2022). This is a reference table where id refers to the unique nucleus id in the "nucleus_detection_v0" table. Classification_system refers to the coarse class predictions (excitatory or inhibitory) and cell_type denotes neuronal mtype predictions. Errors, nonneurons, and soma-soma mergers have been filtered out. For questions please contact Leila Elabbady or Forrest Collman. proofreading_status_and_strategy: The proofreading status of neurons that have been manually cleaned, extended, or both. Axon and dendrite compartment status are marked separately under status_axon and status_dendrite, as proofreading effort was applied differently to the different compartments in some cells. status_axon and status_dendrite are TRUE if the compartment is at least clean, meaning the synapses are accurate but possibly incomplete. strategy_axon and strategy_dendrite represent the specific strategy used for each compartment, the full details of which are available at www.microns-explorer.org/manifests/mm3-proofreading. Uploaded and maintained by Bethanny Danskin, with the help of Forrest Collman and and Casey Schneider-Mizell. Proofreading collected in this table represents the work by many proofreaders.  synapses_pni_2: Automated synapse detection performed by Nick Turner from the Seung Lab.  size represents the number of (4x4x40 nm) voxels painted by the automated cleft segmentation, and the IDs reference the IDs of the cleft segmentation.  Ctr_pt reflects the centroid of the cleft segmentation. The cleft segmentation volume is located in the flat_segmentation_source field.

opencc-by-4.0May 2024View details →

Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome

<h2>Brief description</h2>   These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T1 weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T2* measurements. <h2>Animals</h2>   All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.   <h2>MR acquisition</h2>   MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were  acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm3, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm2, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm3, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms  (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm3, 24 averages, pulse angle shift 117°). <h2>Data structure</h2>   The repository contains the following data: -       READ_ME.txt: this file -       RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals. -       rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files: o   bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively. o   dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired. o   bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file. o   zerob: Text file containing the image numbers where images with no diffusion weighting were acquired. o   ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively. -       derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit. -       Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.

opencc-by-4.0Jul 2024View details →

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

Dataset linked to the article with the same title The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter  cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit. Analyzing the model The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook) Simulating the model To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch nbS1-2023, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a "special" executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where mod-files-from-released-circuit is replaced by the location of O1/mods on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (simulation_config.zip). Analyzing simulation results Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations. Version 2 Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1. -- The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

opencc-by-4.0Sep 2023View details →

Connectomes, APOE, Age, Sex, and Diet in Mouse Models of Aging

Brain networks and covariates for mouse models of aging. Includes APOE22/33/44 with and without HN, age, sex, and diet. Files: - connectomes.rda: a tensor of symmetric adjacency matrices corresponding to brain networks. - mice.rda: a dataframe containing mouse covariates. - mouse_anatomy.csv: a table containing scientific names for each brain region in connectomes.rda.

opencc-by-4.0Apr 2020View details →

Structural and functional connectomes from 27 schizophrenic patients and 27 matched healthy adults

Data Acquisition The cohort consists of a total of 27 healthy participants (age 35 ± 6.8 years) and 27 schizophrenic patients (age 41 ± 9.6), scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The schizophrenic patients are from the Service of General Psychiatry at the Lausanne University Hospital (CHUV). All of them were diagnosed with schizophrenic and schizoaffective disorders after meeting the DSM-IV criteria (American Psychiatric Association (2000): Diagnostic and Statistical Manual of Mental Disorders, 4th ed. DSM-IV-TR. American Psychiatric Pub, Arlington, VA22209, USA). The Diagnostic Interview for Genetic Studies assessment was used to recruits the healthy controls (Preisig et al. 1999). 24 out of the 27 schizophrenics were under medication with mean chlorpromazine equivalent dose (CPZ) of 431 ± 288 mg. The written consent was obtained for all subjects - in accordance with institutional guidelines of the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005). All subjects were fully anonymised. The session protocol consisted of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a Diffusion Spectrum Imaging (DSI) sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm2, 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TE 30 ms, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI). Data Pre-processing  Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci et al. 2012). Grey and white matter were segmented from the MPRAGE volume using freesurfer (Desikan et al. 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun et al. 2012). DSI data were reconstructed following the protocol described by (Wedeen et al. 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction. Structural Connectivity Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen et al. 2008). Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann et al. 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size. The number of fibers and fiber length were also included in the dataset. For the quantitative measure of structural connectivity, the generalised fractional anisotropy (gFA, Tuch et al. 2004) and average apparent diffusion coefficient (ADC, Sener et al. 2001) were also computed for each tract.   Functional Connectivity Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al. 2009, Power et al. 2012). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. The signal was linearly detrended and further physiological (white-matter and cerebrospinal fluid regressors) and motion artefacts (three translational and three rotational regressors) confounds were regressed. Then, the signal was spatially smoothed and bandpass-filtered between 0.01-0.1 Hz with Hamming windowed sinc FIR filter. To obtain the brain regions for different atlas scales the signal was linearly registered to the MPRAGE image and averaged within a given region (Jenkinson et al. 2012). Functional matrices were obtained by computing Pearson’s correlation between the individual pairs of regions. All of the above was carried out in subject’s native space (Daducci et al. 2012, Griffa et al. 2017). Brain cortical bert freesurfer rendering for the 5 scales of the Lausanne2008 atlas is available on <a href="https://github.com/jvohryzek/bert4lausanne2008">https://github.com/jvohryzek/bert4lausanne2008</a>.

opencc-by-4.0Apr 2020View details →

Numerically Perturbed Structural Connectomes from 100 individuals in the NKI Rockland Dataset

This dataset contains the derived connectomes, discriminability scores, and classification performance for structural connectomes estimated from a subset of the Nathan Kline Institute Rockland Sample dataset, and is associated with an upcoming manuscript entitled: Numerical Instabilities in Analytical Pipelines Compromise the Reliability of Network Neuroscience. The associated code for this project is publicly available at: <a href="https://github.com/gkpapers/2020ImpactOfInstability">https://github.com/gkpapers/2020ImpactOfInstability</a>. For any questions, please contact Gregory Kiar (gkiar07@gmail.com) or Tristan Glatard (tristan.glatard@concordia.ca). Below is a table of contents describing the contents of this dataset, which is followed by an excerpt from the manuscript pertaining to the contained data. <ul> <li>impactofinstability_connect_dset25x2x2x20_inputs.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with input perturbations.</li> <li>impactofinstability_connect_dset25x2x2x20_pipeline.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with pipeline perturbations.</li> <li>impactofinstability_discrim_dset25x2x2x20_both.csv : Discriminability scores for each grouping of the 25x2x2x20 dataset.</li> <li>impactofinstability_connect+feature_dset100x1x1x20_both.h5 : Connectomes and features derived from 100 subjects, 1 sessions, 1 subsamples, and 20 MCA simulations with both perturbation types.</li> <li>impactofinstability_classif_dset100x1x1x20_both.h5 : Classification performance results for the BMI classification task on the 100x1x1x20 dataset.</li> </ul> Dataset The Nathan Kline Institute Rockland Sample (NKI-RS) dataset [1] contains high-fidelity imaging and phenotypic data from over 1,000 individuals spread across the lifespan. A subset of this dataset was chosen for each experiment to both match sample sizes presented in the original analyses and to minimize the computational burden of performing MCA. The selected subset comprises 100 individuals ranging in age from 6 – 79 with a mean of 36.8 (original: 6 – 81, mean 37.8), 60% female (original: 60%), with 52% having a BMI over 25 (original: 54%). Each selected individual had at least a single session of both structural T1-weighted (MPRAGE) and diffusion-weighted (DWI) MR imaging data. DWI data was acquired with 137 diffusion directions; more information regarding the acquisition of this dataset can be found in the NKI-RS data release [1]. In addition to the 100 sessions mentioned above, 25 individuals had  a second session to be used in a test-retest analysis. Two additional copies of the data for these individuals were generated, including only the odd or even diffusion directions (64 + 9 B0 volumes = 73 in either case). This allows an extra level of stability evaluation to be performed between the levels of MCA and session-level variation. In total, the dataset is composed of 100 diffusion-downsampled sessions of data originating from 50 acquisitions and 25 individuals for in depth stability analysis, and an additional 100 sessions of full-resolution data from 100 individuals for subsequent analyses. Processing The dataset was preprocessed using a standard FSL [2] workflow consisting of eddy-current correction and alignment. The MNI152 atlas was aligned to each session of data, and the resulting transformation was applied to the DKT parcellation [3]. Downsampling the diffusion data took place after preprocessing was performed on full-resolution sessions, ensuring that an additional confound was not introduced in this process when comparing between downsampled sessions. The preprocessing described here was performed once without MCA, and thus is not being evaluated. Structural connectomes were generated from preprocessed data using two canonical pipelines from Dipy [4]: deterministic and probabilistic. In the deterministic pipeline, a constant solid angle model was used to estimate tensors at each voxel and streamlines were then generated using the EuDX algorithm [5]. In the probabilistic pipeline, a constrained spherical deconvolution model was fit at each voxel and streamlines were generated by iteratively sampling the resulting fiber orientation distributions. In both cases tracking occurred with 8 seeds per 3D voxel and edges were added to the graph based on the location of terminal nodes with weight determined by fiber count. Perturbations All connectomes were generated with one reference execution where no perturbation was introduced in the processing. For all other executions, all floating point operations were instrumented with Monte Carlo Arithmetic (MCA) [6] through Verificarlo [7]. MCA simulates the distribution of errors implicit to all instrumented floating point operations (flop). MCA can be introduced in two places for each flop: before or after evaluation. Performing MCA on the inputs of an operation limits its precision, while performing MCA on the output of an operation highlights round-off errors that may be introduced. The former is referred to as Precision Bounding (PB) and the latter is called Random Rounding (RR). Using MCA, the execution of a pipeline may be performed many times to produce a distribution of results. Studying the distribution of these results can then lead to insights on the stability of the instrumented tools or functions. To this end, a complete software stack was instrumented with MCA and is made available on GitHub through https://github.com/gkiar/fuzzy. Both the RR and PB variants of MCA were used independently for all experiments. As was presented in [8], both the degree of instrumentation (i.e. number of affected libraries) and the perturbation mode have an effect on the distribution of observed results. For this work, the RR-MCA was applied across the bulk of the relevant libraries and is referred to as Pipeline Perturbation. In this case the bulk of numerical operations were affected by MCA. Conversely, the case in which PB-MCA was applied across the operations in a small subset of libraries is here referred to as Input Perturbation. In this case, the inputs to operations within the instrumented libraries (namely, Python and Cython) were perturbed, resulting in less frequent, data-centric perturbations. Alongside the stated theoretical differences, Input Perturbation is considerably less computationally expensive than Pipeline Perturbation. All perturbations were targeted the least-significant-bit for all data (t=24and t=53in float32 and float64, respectively [7]). Simulations were performed between 10 and 20 times for each pipeline execution, depending on the experiment. A detailed motivation for the number of simulations can be found in [9]. Evaluation The magnitude and importance of instabilities in pipelines can be considered at a number of analytical levels, namely: the induced variability of derivatives directly, the resulting downstream impact on summary statistics or features, or the ultimate change in analyses or findings. We explore the nature and severity of instabilities through each of these lenses. Unless otherwise stated, all p-values were computed using Wilcoxon signed-rank tests.     Direct Evaluation of the Graphs The differences between simulated graphs was measured directly through both a direct variance quantification and a comparison to other sources of variance such as individual- and session-level differences. Quantification of Variability – Graphs, in the form of adjacency matrices, were compared to one another using three metrics: normalized percent deviation, Pearson correlation, and edgewise significant digits. The normalized percent deviation measure, defined in [8], scales the norm of the difference between a simulated graph and the reference execution (that without intentional perturbation) with respect to the norm of the reference graph. The purpose of this comparison is to provide insight on the scale of differences in observed graphs relative to the original signal intensity. A Pearson correlation coefficient was computed in complement to normalized percent deviation to identify the consistency of structure and not just intensity between observed graphs. Finally, the estimated number of significant digits for each edge in the graph was computed. The upper bound on significant digits is 15.7 for 64-bit floating point data. The percent deviation, correlation, and number of significant digits were each calculated within a single session of data, thereby removing any subject- and session-effects and providing a direct measure of the tool-introduced variability across perturbations. A distribution was formed by aggregating these individual results. Class-based Variability Evaluation – To gain a concrete understanding of the significance of observed variations we explore the separability of our results with respect to understood sources of variability, such as  subject-, session-, and pipeline-level effects. This can be probed through Discriminability [10], a technique similar to ICC which relies on the mean of a ranked distribution of distances between observations belonging to a defined set of classes. Discriminability can then be interpreted as the probability that an observation belonging to a given class will be more similar to other observations within that class than observations of a different class. It is a measure of reproducibility, and is discussed in detail in [10]. This definition allows for the exploration of deviations across arbitrarily defined classes which in practice can be any of those listed above. We combine this statistic with permutation testing to test hypotheses on whether differences between classes are statistically significant in each of these settings. With this in mind, three hypotheses were defined. For each setting, we state the alternate hypotheses, the variable(s) which will be used to determine class membership, and the remaining variables which may be sampled when obtaining multiple observations. Each hypothesis was tested independently for each pipeline and perturbation mode, and in every case where it is possible the hypotheses were tested using the reference executions alongside using MCA. <ol> <li>Individual Variation HA: Individuals are distinct from one another. Class definition: Subject ID. Experiments: Session (1 subsample), Direction (1 subsample), MCA (1 subsample, 1 session).</li> <li>Session Variation HA: Sessions within an individual are distinct. Class definition: Session ID | Subject ID. Experiments: Subsample, MCA (1 subsample).</li> <li>Subsample Variation HA: Direction subsamples within an acquisition are distinct. Class definition: Subsample | Subject ID, Session ID. Experiments: MCA.</li> </ol> As a result, we tested 3 hypotheses across 6 MCA experiments and 3 reference experiments on 2 pipelines and 2 perturbation modes, resulting in a total of 30 distinct tests.     Evaluating Graph-Theoretical Metrics While connectomes may be used directly for some analyses, it is common practice to summarize them with structural measures, which can then be used as lower-dimensional proxies of connectivity in so-called graph-theoretical studies [11]. We explored the stability of several commonly-used univariate (graphwise) and multivariate (nodewise or edgewise) features. The features computed and subsequent methods for comparison in this section were selected to closely match those computed in [12]. Univariate Differences – For each univariate statistic (edge count, mean clustering coefficient, global efficiency, modularity, assortativity, and mean path length) a distribution of values across all perturbations within subjects was observed. A Z-score was computed for each sample with respect to the distribution of feature values within an individual, and the proportion of "classically significant" Z-scores, i.e. corresponding to p < 0.05, was reported and aggregated across all subjects. The number of significant digits contained within an estimate derived from a single subject were calculated and aggregated. Multivariate Differences – In the case of both nodewise (degree distribution, clustering coefficient, betweenness centrality) and edgewise (weight distribution, connection length) features, the cumulative density functions of their distributions were evaluated over a fixed range and subsequently aggregated across individuals. The number of significant digits for each moment of these distributions (sum, mean, variance, skew, and kurtosis) were calculated across observations within a sample and aggregated.     Evaluating A Complete Analysis Though each of the above approaches explores the instability of derived connectomes and their features, many modern studies employ modeling or machine-learning approaches, for instance to learn brain-behavior relationships or identify differences across groups. We carried out one such study and explored the instability of its results with respect to the upstream variability of connectomes characterized in the previous sections. We performed the modeling task with a single sampled connectome per individual and repeated this sampling and modelling 20 times. We report the model performance for each sampling of the dataset and summarize its variance. BMI Classification – Structural changes have been linked to obesity in adolescents and adults [13]. We classified normal-weight and overweight individuals from their structural networks (using for overweight a cutoff of BMI > 25 [14]). We reduced the dimensionality of the connectomes through principal component analysis (PCA), and provided the first N-components to a logistic regression classifier for predicting BMI class membership, similar to methods shown in [14], [15]. The number of components was selected as the minimum set which explained > 90% of the variance when averaged across the training set for each fold within the cross validation of the original graphs; this resulted in a feature of 20 components. We trained the model using k-fold cross validation, with k = 2, 5, 10, and N (equivalent to leave-one-out; LOO).

opencc-by-4.0Dec 2021View details →

FlyWire: Online community for whole-brain connectomics

A ground truth dataset for 3D neuron reconstruction from electron microscopy (EM) images of the fly whole-brain, created for our project FlyWire: A human-AI collaboration to map the fly connectome. For more information, please visit <a href="https://flywire.ai/">https://flywire.ai/</a>.   Citation FlyWire: Online community for whole-brain connectomics Dorkenwald et al. bioRxiv 2020.08.30.274225; doi: https://doi.org/10.1101/2020.08.30.274225   Dataset description <ul> <li>cremi_realigned.tar.gz <ul> <li>cremi_{a,b,c}_realigned.h5: re-aligned CREMI volumes (<a href="https://cremi.org/">https://cremi.org/</a>)</li> <li>cremi_b_realigned_new.h5: re-aligned CREMI B volume with de-novo annotation</li> </ul> </li> <li>focused.tar.gz <ul> <li>A set of densely annotated volumes covering diverse structures in the fly brain that are underrepresented in the CREMI volumes.</li> </ul> </li> <li>sparse.tar.gz <ul> <li>A set of sparsely annotated volumes covering tricky failure modes in the initial segmentation attempt.  Here intracellular structures are often oversegmented conservatively to prevent merge errors at the expense of introducing some split errors. </li> </ul> </li> <li>glia.tar.gz <ul> <li>Semi-automatically generated ground truth for glia detction. A set of subvolumes from the FlyWire segmentation was sampled from which human experts classified automatically generated segments into either neurons or glia, while existing merge errors were excluded from annotation.</li> </ul> </li> </ul>

opencc-by-4.0Mar 2022View details →

InLang: Language functional connectomes

InLang is a functional MRI database, composed of 13 different language tasks performed cross-sectionally by 150 right-handed neurotypical adults. The fMRI protocols have been previously published and the MRI data have been acquired between 2010 and 2019 (7 Projects). The database is unique in that it covers a broad spectrum of language features: semantic and conceptual processing, decoding (phonology, sound), lexico-syntactic formulation (production), dialogality (social aspects of language), monitoring of self and others, and unintentional speech. fMRI Projects        Tasks                       Plast-LANG         Semantic categorization (SEM) N = 24                  Phoneme detection (PHON)                              Prosody detection (PROS) SEMVIE               Overt object naming (NAM) N = 30                  Overt categorical fluency (FLU) NeuroMod          Overt object naming (NAM) N = 28 NEREC                 Overt object naming (NAM) N = 13 Reorg-GEREC     Covert sentence generation (GENE) N = 20 Recovery            Overt monosyllabic word repetition (REP) N = 11                  Overt object naming (NAM)                              Word rhyme detection (RHYM) InnerSpeech        Speech perception (SP) of word’s definitions N =24                    Covert monologal sentence with self voice (MS)                               Covert monologal sentence with self voice (MO)                               Covert dialogal sentence with other voice (DO)                               Verbal mind-wandering (VMW) ALL         150        InLang.zip: In each "Project" folder are the scripts (codes) and fMRI signals extracted at the group level (derivatives). Data were extracted for each of the 13 InLang tasks, on the regions of Power's atlas (Power et al., 2011*) README files help navigate through the folders and subfolders of the database *Power et al. (2011). Functional Network Organization of the Human Brain. Neuron, 72, 665-678. doi: 10.1016/j.neuron.2011.09.006

opencc-by-4.0Oct 2018View details →

The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain

A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org

opencc-by-4.0Dec 2022View details →

Test-Retest Reliability of the Human Connectome: An OPM-MEG study

OPM-MEG data was acquired during naturalistic viewing of a 600s clip from the film "Dog Day Afternoon". Two sets of MEG data were acquired in each of the 10 scanned subjects. Defaced, T1 weighted MRIs are available for each subject and OPM sensor locations and orientations are given relative to subject anatomy to allow for source reconstruction. An example of MATLAB code used to analyse these data can be found on <a href="https://github.com/LukasRier/Rier2022_OPM_connectome_test-retest">GitHub</a>, which includes all code used to produce the results described in "Test-Retest Reliability of the Human Connectome: An OPM-MEG study" (<a href="https://biorxiv.org/cgi/content/short/2022.12.21.521184v1">biorxiv.org/cgi/content/short/2022.12.21.521184v1</a>) ______________________________________________________________ Updates: v1.0.1 Added missing meshes and AAL source location files v1.1.0 Added video file used in the experiment

opencc-by-4.0Sep 2021View details →

Co-activation probability between neurons in the largest brain connectome of the fruit fly

This is a data set containing the co-activation probability between neurons in the largest brain connectome of the fruit fly released by the FlyEM project. The co-activation probability is measured based on neural dynamics computation, where a standard leaky integrate-and-fire (LIF) model is applied on the connectome to generate neural dynamics. Please read the paper "Yang Tian, Pei Sun; Percolation may explain efficiency, robustness, and economy of the brain. Network Neuroscience 2022; 6 (3): 765–790. doi: <a href="https://doi.org/10.1162/netn_a_00246">https://doi.org/10.1162/netn_a_00246</a>" for more details. This is the newest version of the data set. The following is a list of variable information: (1) SomaLocation is a 23008*3 matrix that contains the three-dimensional coordinates of neurons; (2) LambdaVector is the vector of a vector of synaptic excitation-inhibition (E/I) balance (see "Percolation may explain efficiency, robustness, and economy of the brain" for detailed explanations). (3) DirectedCoactivationPattern is a cell of co-activation probability matrices generated under each E/I balance condition, which is used in "Percolation may explain efficiency, robustness, and economy of the brain" for computational experiments. The (i,j)-th element in the matrix is the probability for neuron i to activate neuron j under the corresponding E/I balance condition. Note that the (i,j)-th element can be differnt from the (j,i)-th element.  (4) SymmetricCoactivationPattern is a cell of symmetric co-activation probability matrices generated under each E/I balance condition. This is a new data that has not been used in "Percolation may explain efficiency, robustness, and economy of the brain" yet. The (i,j)-th element in the matrix is the probability for neurons i and j to be co-activated under the corresponding E/I balance condition. If we define D as the directed co-activation probability matrix and denote S as the symmetric co-activation probability matrix, then we have S(i,j)=S(j,i)=0.5*(D(i,j)+D(j,i)).  The earlist version of this data can be seen in https://zenodo.org/record/5497516, which may lack detailed explanations. The second version of this data can be seen in https://zenodo.org/record/7869532, where a small mistake is found while calculating the SymmetricCoactivationPattern. This mistake is resolved in the newest version.

opencc-zeroMay 2023View details →

Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the lifespan. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including non-linear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called Variational Autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting state data in healthy adults. Here, we demonstrated that non-linear brain features, i.e., latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, non-linear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.

publicJun 2025View details →

Infrequent strong connections constrain connectomic predictions of neuronal function (2/3)

Open the record for dataset details and reuse information.

publicMay 2023View details →

Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder

Open the record for dataset details and reuse information.