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122 results for “connectomes”

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zenodo28/100

Template connectome harmonics for FreeSurfer template cortical surfaces using Gibbs Tractography Dataset

The folder contains connectome harmonics for template surface meshes cvs_avg35_inMNI152, fsaverage45 and fsaverage5 from Freesurfer, using the Gibbs connectome tractography streamlines. The connectome harmonics framework is integrated to the SCRIPTS pipeline, and the files present here use default parameters from Table 1 in Naze et al. 2020. Each .mat file include: - graph Laplacian (L) - connectome harmonics (H) - connectome harmonics projected in the Desikan-Killiany atlas (H_DSK) - local connectivity adjacency matrix (A_local) - long-range connectivity adjacency matrix (A_ctx) - vertices and faces of the cortical surface mesh (white matter -  gray matter boundary) - degree matrix (of combined adjacency matrices) - eigenvalues of the eigendecomposition - r, the ratio of local connections over all connections (local_vs_global_ratio) - average (mu_cc) and standard deviation (sigma_cc) of the long-range connectome - z_C, the weight threshold applied to the high resolution conectome to obtain its adjacency matrix (ta_zsc) Reference: Naze S., Proix T., Atasoy S. & Kozloski J.R. (2020) Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes. Neuroimage.

opencc-by-4.0Sep 2020View details →

dryad28/100

Data from: Mapping and analysis of the connectome of sympathetic premotor neurons in the rostral ventrolateral medulla of the rat using a volumetric brain atlas

Spinally projecting neurons in the rostral ventrolateral medulla (RVLM) play a critical role in the generation of vasomotor sympathetic tone and are thought to receive convergent input from neurons at every level of the neuraxis; the factors that determine their ongoing activity remain unresolved. In this study we use a genetically restricted viral tracing strategy to definitively map their spatially diffuse connectome. We infected bulbospinal RVLM neurons with recombinant rabies variant that drives reporter expression in monosynaptically connected input neurons and mapped their distribution using a MRI-based volumetric atlas and a novel image alignment and visualization tool that efficiently translates the positions of neurons captured in conventional photomicrographs to Cartesian coordinates. We identified prominent inputs from well-established neurohumoral and viscero-sympathetic sensory actuators, medullary autonomic and respiratory subnuclei, and supramedullary autonomic nuclei. The majority of inputs lay within the brainstem (88 – 94%), and included putative respiratory neurons in the pre-Bötzinger Complex and post-inspiratory complex that are therefore likely to underlie respiratory-sympathetic coupling. We also discovered a substantial and previously unrecognized input from the region immediately ventral to nucleus prepositus hypoglossi. In contrast, RVLM sympathetic premotor neurons were only sparsely innervated by suprapontine structures including the paraventricular nucleus, lateral hypothalamus, periaqueductal grey and superior colliculus, and we found almost no evidence of direct inputs from the cortex or amygdala. Our approach can be used to quantify, standardize and share complete neuroanatomical datasets, and therefore provides researchers with a platform for presentation, analysis and independent analysis of connectomic data.

opencc-zeroDec 2016View details →

dryad28/100

Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project

Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10.5061/dryad.4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al., 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc. In summary, Diffantom is intended for unit testing of novel methods, cross-comparison of established methods, and integration testing of partial or complete processing flows to extract connectivity networks from dMRI.

opencc-zeroDec 2015View details →

dryad28/100

Data from: Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.

opencc-zeroDec 2016View details →

zenodo28/100

FlyWire Whole-brain Connectome Connectivity Data

This repository contains the connectivity data for the FlyWire Connectome release. Currently, the latest release is version 783 (see also codex.flywire.ai). The synapses represent a combination of four different data releases, which are combined by the FlyWire whole-brain connectome release. The synapses (as points in space) were detected and published by <a href="https://www.nature.com/articles/s41592-021-01183-7">Buhmann et al., 2021</a> who made use of a cleft segmentation produced by <a href="https://link.springer.com/chapter/10.1007/978-3-030-00934-2_36">Heinrich et al., 2018</a>. The neurotransmitter for these synapses was then predicted and released by <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6">Eckstein, Bates et al., 2024</a>. The segmentation and neuron IDs (= root IDs) were proofread by the FlyWire consortium and are released by <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a> as part of the <a href="https://www.nature.com/collections/hgcfafejia">FlyWire connectome paper package</a>.  Because multiple methods were involved in the production of this resource, the description of the methods is distributed across these manuscripts. We provide a summary in Dorkenwald et al., 2024 (Methods->Synaptic connections). Some of the files made available use feather as a file format. See a code example for reading these files, including a chunk-wise streaming to handle the large file. <h3>flywire_synapses_783.feather</h3> a pandas dataframe with all ~130 million synapses, their locations, neurotransmitter predictions and pre and postsynaptic partners (=root ids). This table contains all synapses that passed the thresholds (see methods section in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>), but not all synapses were associated with proofread neurons (e.g., see Discussion->Limitations of our reconstruction in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>). Hence, not all root IDs in this table will have a match in the proofread root IDs array. This table is provided for completeness, and to allow calculations about total synaptic input and output of neurons independent of other limitations.  Columns: <ul> <li>id: synapse ID</li> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>connection_score: score assigned by Buhmann et al.; higher is better. We did not use this score to threshold synapses in any analysis</li> <li>cleft_score: score derived from the cleft segmentation by Heinrich et al.; higher is better. We used a threshold of 50 for all analyses and the released dataset. Synapses with lower score are not made available but can be made available on demand.</li> <li>gaba: probability for neurotransmitter=GABA</li> <li>ach: probability for neurotransmitter=Acetylcholine</li> <li>glut: probability for neurotransmitter=Glutamate</li> <li>oct: probability for neurotransmitter=Octopamine</li> <li>ser: probability for neurotransmitter=Serotonin</li> <li>da: probability for neurotransmitter=Dopamine</li> <li>neuropil: the name of the neuropil associated with this synapse. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>post_pt_position_{x,y,z}: Coordinate within the postsynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> <li>pre_pt_position_{x,y,z}: Coordinate within the presynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> </ul>   <h3>per_neuron_neuropil_count_post_783.feather</h3> a pandas dataframe containing the number of postsynapses per neuropil and segment id, i.e. this is a summarized version of flywire_synapses_783.feather Columns: <ul> <li>post_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For a mapping to long names see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>per_neuron_neuropil_count_pre_783.feather</h3> a pandas dataframe containing the number of presynapses per neuropil and segment id, i.e. this is a summarized version of flywire_synapses_783.feather Columns: <ul> <li>pre_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>proofread_root_ids_783.npy</h3> an array of all proofread neuron ids (=root ids)   <h3>proofread_connections_783.feather</h3> a pandas dataframe containing the proofread subset from flywire_synapses_783.feather and summarized per neuron-neuron pair and neuropil, i.e. this table contains one entry per neuron-neuron pair and neuropil if there is 1 or more synapses for a given combination Columns: <ul> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>syn_count: number of synapses between these two neurons in this neuropil</li> <li>gaba_avg: average probability across the synapses for neurotransmitter=GABA</li> <li>ach_avg: average probability across the synapses for neurotransmitter=Acetylcholine</li> <li>glut_avg: average probability across the synapses for neurotransmitter=Glutamate</li> <li>oct_avg: average probability across the synapses for neurotransmitter=Octopamine</li> <li>ser_avg: average probability across the synapses for neurotransmitter=Serotonin</li> <li>da_avg: average probability across the synapses for neurotransmitter=Dopamine</li> </ul>   <h3>Code for reading and streaming feather files</h3> Read feather files with pandas: <code>import pandas as pd</code> <code>df = pd.read_feather(path)</code>   Stream large feather files in chunks: <code>import pyarrow.feather as feather</code> <code>table = feather.read_table(path)</code> <code># Total number of rows in the Feather file</code> <code>num_rows = table.num_rows</code> <code># Define chunk size</code> <code>chunk_size = 1000</code> <code># Read and process the data in chunks</code> <code>for i in range(0, num_rows, chunk_size):</code> <code>    end_row = min(i + chunk_size, num_rows)</code> <code>    chunk = table.slice(i, end_row - i)  # Slice the table from i to end_row</code> <code>    </code> <code>    # Convert to pandas DataFrame if needed</code> <code>    df_chunk = chunk.to_pandas()</code> <code>    </code> <code>    # Now you can process each chunk DataFrame as needed</code> <code>    print(df_chunk.head())</code>

opencc-by-4.0Jun 2024View details →

zenodo28/100

Structural and functional connectome from 70 young healthy adults

Data Acquisition Informed written consent in accordance with institutional guidelines (protocol approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005) was obtained for all subjects. Data provided are fully anonymized. A total of 70 healthy participants (age 28.8 +- 9.1 years, 27 females) were scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The session protocol was comprised 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 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, 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). Gray 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). Within each voxel, the starting points were spatially random. For each starting point, a fiber streamline was grown in two opposite directions with a fixed step of 1 mm. Once the fiber entered a new voxel, the fiber growth continued along the ODF maximum direction that produces the least curvature for the fiber (i.e., was most similar to the trajectory of the fiber to that point). Fibers were stopped if the change in direction was greater than 60 degrees/mm. The process was complete when both ends of the fiber left the white matter mask. 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. Functional Connectivity Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al., 2009; Power et al., 2012). fMRI volumes were corrected for physiological variables, including regression of white matter, cerebrospinal fluid, as well as motion (three translations and three rotations, estimated by rigid body co-registration). BOLD time series were then subjected to a lowpass filter (temporal Gaussian filter with full width half maximum equal to 1.92 s). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. Motion ‘‘scrubbing’’ was performed as described by (Power et al., 2012). A group-average functional connectivity matrix was constructed from the fMRI BOLD time series by concatenating the regional time series from all participants and estimating a single correlation matrix. To threshold this matrix, we sampled at random 276 points from the concatenated times series and calculated a full correlation matrix from these points. We repeated this analysis 1,000 times. From these bootstrapped samples, we estimated confidence intervals for the correlation magnitude between every pair of brain regions. Pairs whose correlation was consistently positive or negative across the 1,000 samples were retained (along with the sign and weight of the correlation) as putative functional connections.

opencc-by-4.0May 2019View details →

ClinicalTrials.gov28/100

Reducing Adolescent Suicide Risk: Safety, Efficacy, and Connectome Phenotypes of Intravenous Ketamine

ClinicalTrials.gov study NCT04613453. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov28/100

SVR III: Brain Connectome and Neurodevelopmental Outcomes

ClinicalTrials.gov study NCT02692443. IPD Sharing: YES. Countries: 0. Publications: 1.

controlledIPD-YESFeb 2026View details →

dryad28/100