CHCP Overview:The human behavior and brain are shaped by genetic, environmental and cultural interactions. Recent advances in neuroimaging integrate multimodal imaging data from a large population and start to explore the large-scale structural and functional connectomic architectures of the human brain. One of the major pioneers is the Human Connectome Project (HCP) that developed sophisticated imaging protocols and has built a collection of high-quality multimodal neuroimaging, behavioral and genetic data from US population. A large-scale neuroimaging project parallel to the HCP, but with a focus on the East Asian population, will allow comparisons of brain-behavior associations across different ethnicities and cultures. The Chinese Human Connectome Project (CHCP) is launched in 2017 and led by Professor Jia-Hong GAO at Peking University, Beijing, China. CHCP aims to provide large sets of multimodal neuroimaging, behavioral and genetic data on the Chinese population that are comparable to the data of the HCP. The CHCP protocols were almost identical to those of the HCP, including the procedure for 3T MRI scanning, the data acquisition parameters, and the task paradigms for functional brain imaging. The CHCP also collected behavioral and genetic data that were compatible with the HCP dataset. The first public release of the CHCP dataset is in 2022. CHCP dataset includes high-resolution structural MR images (T1W and T2W), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion MR images (dMRI) of the human brain as well as behavioral data based on Chinese population. The unprocessed "raw" images of CHCP dataset (about 1.85 TB) have been released on the platform and can be downloaded. Considering our current cloud-storage service, sharing full preprocessed images (up to 70 TB) requires further construction. We will be actively cooperating with researchers who contact us for academic request, offering case-by-case solution to access the preprocessed data in a timely manner, such as by mailing hard disks or a third-party trusted cloud-storage service. V2 Release (Date: January 16, 2023):Here, we released the seven major domains task fMRI EVs files, including: 1) visual, motion, somatosensory, and motor systems; 2) category specific representations; 3) working memory/cognitive control systems; 4) language processing (semantic and phonological processing); 5) social cognition (Theory of Mind); 6) relational processing; and 7) emotion processing.V3 Release (Date: January 12, 2024):This version of data release primarily discloses the CHCP raw MRI dataset that underwent “HCP minimal preprocessing pipeline”, located in CHCP_ScienceDB_preproc folder (about 6.90 TB). In this folder, preprocessed MRI data includes T1W, T2W, rfMRI, tfMRI, and dMRI modalities for all young adulthood participants, as well as partial results for middle-aged and older adulthood participants in the CHCP dataset. Following the data sharing strategy of the HCP, we have eliminated some redundant preprocessed data, resulting in a final total size of the preprocessed CHCP dataset is about 6.90 TB in zip files. V4 Release (Date: December 4, 2024):In this update, we have fixed the issue with the corrupted compressed file of preprocessed data for subject 3011, and removed the incorrect preprocessed results for subject 3090. Additionally, we have updated the subject file information list. Additionally, this release includes the update of unprocessed "raw" images of the CHCP dataset in CHCP_ScienceDB_unpreproc folder (about 1.85 TB), addressing the previously insufficient anonymization of T1W and T2W modalities data for some older adulthood participants in versions V1 and V2. For more detailed information, please refer to the data descriptions in versions V1 and V2.CHCP Summary:Subjects:366 healthy adults (Chinese Han)Imaging Scanner:3T MR (Siemens Prisma)Institution:Peking University, Beijing, ChinaFunding Agencies:Beijing Municipal Science & Technology CommissionChinese Institute for Brain Research (Beijing)National Natural Science Foundation of ChinaMinistry of Science and Technology of China CHCP Citations:Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from CHCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao) funded by the Beijing Municipal Science & Technology Commission, Chinese Institute for Brain Research (Beijing), National Natural Science Foundation of China, and the Ministry of Science and Technology of China."

Hi All!Following instructions gathered via twitter and the HCP message board, I've projected the new HCP-MMP1.0 parcellation onto fsaverage. I have attached the annotation files here and detailed my process below:1. Download the correct data from BALSA:https://balsa.wustl.edu/WN562. Download the correct spheres from the HCP github page:this is detailed here: https://www.mail-archive.com/hcp-users%40humanconnectome.org/msg02890.html3. Using workbench, convert parcellation dabel.nii file label.gii file:wb_command -cifti-separate Q1-Q6_RelatedParcellation210.L.CorticalAreas_dil_Colors.32k_fs_LR.dlabel.nii COLUMN -label CORTEX_LEFT Q1-Q6_RelatedParcellation210.L.CorticalAreas_dil_Colors.32k_fs_LR.label.giiwb_command -cifti-separate Q1-Q6_RelatedParcellation210.R.CorticalAreas_dil_Colors.32k_fs_LR.dlabel.nii COLUMN -label CORTEX_RIGHT Q1-Q6_RelatedParcellation210.R.CorticalAreas_dil_Colors.32k_fs_LR.label.gii4. Using workbench, convert label.gii file to fsaverage space:wb_command -label-resample Q1-Q6_RelatedParcellation210.L.CorticalAreas_dil_Colors.32k_fs_LR.label.gii L.sphere.32k_fs_LR.surf.gii fs_L-to-fs_LR_fsaverage.L_LR.spherical_std.164k_fs_L.surf.gii BARYCENTRIC left.fsaverage164.label.giiwb_command -label-resample Q1-Q6_RelatedParcellation210.R.CorticalAreas_dil_Colors.32k_fs_LR.label.gii R.sphere.32k_fs_LR.surf.gii fs_R-to-fs_LR_fsaverage.R_LR.spherical_std.164k_fs_R.surf.gii BARYCENTRIC right.fsaverage164.label.gii5. Using freesurfer, convert files from gii to annot:mris_convert --annot left.fsaverage164.label.gii fs_L-to-fs_LR_fsaverage.L_LR.spherical_std.164k_fs_L.surf.gii lh.HCP-MMP1.annotmris_convert --annot right.fsaverage164.label.gii fs_R-to-fs_LR_fsaverage.R_LR.spherical_std.164k_fs_R.surf.gii rh.HCP-MMP1.annot6. Rejoice and share!

We in our group received with great interest the publication of the HCP-MMP 1.0 parcellation by Glasser et al. (Nature) created using data from the Human Connectome Project earlier this year. Often in our connectivity pipelines we use volume files for parcellation in native space, so we decided to try and convert the Connectome Workbench files to volume masks in native structural space to try out in future studies.We were happy to find that someone had already gone through the trouble of generating FreeSurfer annotation files projected on fsaverage, so all we had to do was find a way to transform these annot files to each subject’s space and convert them to volume masks.So we wrote this Linux shell script that performs a series of conversion and transformation steps using only FreeSurfer commands. It first converts the annotation files (lh.HCPMMP1.annot and rh.HCPMMP1.annot, downloaded from https://figshare.com/articles/HCP-MMP1_0_projected_on_fsaverage/3498446) to labels using mri_annotation2label, then takes each label from fsaverage to each subject’s space with mri_label2label, converts transformed labels back to annotation with mri_label2annot, and finally converts these to volume files (nii.gz) with mris_label2annot. Seems like too many steps, but this is how we (who are not FreeSurfer experts) got satisfactory results.The default final file consists of a single .nii.gz volume containing the cortical HCP-MMP1.0 regions plus the subcortical regions from the FreeSurfer segmentation, each region assigned a unique voxel value. It should be noted that the HCP-MMP1.0 parcellation includes 180 regions per hemisphere - 179 cortical and one subcortical (hippocampus). In the final volume file, left-hemisphere cortical HCP-MMP1.0 regions will have values between 1001 and 1181, whereas right-sided regions will have values between 2001 and 2181. The correspondence between each specific region and its voxel value is given in a look-up table that is saved in each subject’s output folder. To identify the hippocampi (and other subcortical structures), one needs to check the corresponding voxel value given in the FreeSurferColorLUT.txt file provided with FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT), as they are generated based on the original aseg parcellation*.*In the previous version of the script, a few perihippocampal cortical voxels were ascribed values (1121 and 2121) that should correspond to the hippocampus in the HCPMMP1.0 parcellation. Since only the cortical regions from this parcellation are generated, these voxels are now assigned the values corresponding to the hippocampus as defined by the automatic FreeSurfer subcortical segmentation (17 and 53).Optionally, one can choose to also generate individual volume files for each cortical and/or subcortical parcellation region. This option requires FSL. If the user chooses to create individual subcortical masks, the FreeSurferColorLUT.txt must also be available in the base ($SUBJECTS_DIR/) folder.By default, the script also generates tables with anatomical information for each cortical region (number of vertices, area, volume, mean thickness, etc.).Instructions on how to use the script can be found in the script itself, or here:https://cjneurolab.org/2016/11/22/hcp-mmp1-0-volumetric-nifti-masks-in-native-structural-space/Hope this can be of use!

A diffusion fibre orientation template created from 100 unrelated subjects of the Human Connectome Project scanned at 3 Tesla

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Superficial Chauvel's human white matter atlas.

The left and right superficial white matter atlases of the human brain were built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans from the Human Connectome Project (HCP), registered on a template space (the MNI ICBM152 2009c non-linear asymmetric template). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, https://framagit.org/cpoupon/gkg). The atlas can be visualized using the BrainVISA/Anatomist viewer available at https://brainvisa.info/web/download.html. The left hemisphere atlas is composed of 733 superficial white matter bundles and the right hemisphere atlas of 632 superficial white matter bundles.

The 38 cortical regions considered for the left and right hemispheres were : the anterior/middle/posterior superior frontal gyrus (aSFG/mSFG/pSFG) ; the anterior and posterior middle frontal gyrus (aMFG/pMFG) ; the anterior/middle/posterior inferior frontal gyrus (aIFG/mIFG/pIFG) ; the medial lateral orbitofrontal cortex (mOFC/ lOFC) ; the superior, middle, inferior precentral gyrus (sPrCG / mPrCG / iPrCG) ; the Paracentral Lobule (PCL) ; the anterior and posterior insula (alns / plns) ; the anterior, posterior superior temporal gyrus (aSTG/pSTG) ; the anterior, posterior middle temporal gyrus (aMTG/pMTG) ; the anterior and posterior inferior temporal gyrus (aITG, pITG) ; the anterior and posterior fusiform gyrus (aFFG/pFFG) ; the superior, middle and inferior postcentral gyrus (sPoCG/mPoCG/iPoCG) ; the superior parietal lobule (SPL) ; the supramarginal gyrus (SMG) ; the angular gyrus (AnG) ; the Precuneus (PCun) ; the cuneus (Cun) ; the Lingual Gyrus (LG) ; the superior, middle, inferior occipital gyrus (sOG / mOG/ iOG) ; the entorhinal Cortex (EnC) ; the parahippocampal gyrus (PHC).

The atlas is provided using the Anatomist .bundles/.bundlesdata format for which metainformation can be found in the *.bundles file among which: - the labels of the different white matter bundles ('labels' entry) following the syntactic rule "_", - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key, - the total number of white matter bundles ('item_count' entry), - the total number of streamlines ('curves_count' entry)

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

The human connectome project's group level multimodal parcellation (MMP; Glasser et al. [2016] Nature) is here projected from surface coordinates into volumetric space using registration fusion. 329 participants from two previously published studies (N=88 and N=241) contributed their individual alignments for registration fusion, all of which were computed by running fmriprep with default parameters and --recon-all to produce freesurfer surface segmentations and alignments. By passing the surface atlas through the subject specific transformations we obtain subject specific projections of the atlas onto two standard templates in widespread use, MNI152NLin6Asym (FSL/HCP "standard" space) and MNI152NLin2009cAsym (fmriprep's default space). Probabilistic parcel labels were derived based on individual subject alignments. Data is available as probability maps for each parcel, and also as labeled parcellations obtained using winner-take-all label assignments and a 0.2 probability threshold, in addition to individual subject segmentations in template space.File dictionary:glasser_MNI152NLin[2009c|6]Asym_atlas.nii.gz - probabilistic maps projected to two templates' spacesglasser_atlas_labels.txt - labels of parcels above, line order corresponds to volume order.glasser_MNI152NLin[2009cA|6]Asym_labeled_p20.nii.gz - winner-takes-all parcellation derived from probabilistic maps (p > 0.2) with a connectome workbench compatible color map.[lr]h_[bmrk5|paingen]_MNI152NLin[2009c|6]Asym.nii.gz - subject specific parcellations projected in templates' space. One per hemisphere[rl]h.HCP.MMP1 - glasser atlas resampled to fsaverage surfacesmethods - informal methods description (but still pretty formal)Refer to methods.txt for scanning protocols and participant details.For a comparison with https://figshare.com/articles/dataset/HCP-MMP1_0_projected_on_MNI2009a_GM_volumetric_in_NIfTI_format/3501911 please see README here:https://github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation

The BigBrainWarp Support dataset contains MSM spherical transformations to resample labels and fields between the BigBrain surface and 3 other major reference surfaces: FreeSurfer's fsaverage, Human Connectome Project's (HCP) fs_LR, and CIVET's MNI152. The BigBrain dataset is a digitized reconstruction of high-resolution histological sections of the brain of a 65 year old man with no history of neurological or psychiatric diseases. The BigBrain dataset is the result of a collaborative effort between the teams of Dr. Katrin Amunts and Dr. Karl Zilles (Forschungszentrum Jülich) and Dr. Alan Evans (Montreal Neurological Institute). For more information please visit the BigBrain Project website [https://bigbrainproject.org]

SummaryThis is folder containing all the files necessary to create a pycortex subject for the fsaverage brain from freesurfer (Dale, Fischl, & Sereno, 1999; Fischl, Dale & Sereno, 1999; Fischl, 2012) in pycortex (Gao et al, 2015). This means that any data mapped to the MNI brain or to the vertices of the fsaverage surface can be displayed with pycortex on the surface provided in this dataset. For usage of pycortex, see the pycortex git page (https://github.com/gallantlab/pycortex), the pycortex documentation page (https://gallantlab.github.io/), and the pycortex gallery (https://gallantlab.github.io/auto_examples/index.html).Once you have installed pycortex, the files for fsaverage can be automatically downloaded from this site by calling the following at a python command prompt: import cortexcortex.download_subject('fsaverage')If you automatically downloaded this dataset using the command above, you can find the files by calling the following at the command prompt:import cortexfile_store = cortex.options.config.get('basic', 'filestore')file_path = os.path.join(file_store, 'fsaverage', 'overlays.svg')print(file_path)The surface has labeled regions of interest (ROIs) for V1, V2, V3, V3A, V3B, V4, LO1, LO2, hMT, MST, VO1, VO2, IPS0, IPS1, IPS2, IPS3, IPS4, IPS5, SPL1, OFA, FFA, and PPA, defined according to multiple sources, including the Wang et al (2015) probabilistic atlas, Human Connectome Project 7T retinotopy data (Benson et al 2018), and cross-subject probabilistic maps of FFA and PPA (from Weiner et al 2017, 2018). The data that provides the basis for these ROIs can be viewed in the layers of the overlays.svg file included in this dataset. ContributionsNB performed the initial import of the fsaverge subject from freesurfer and created and transforms to various atlas resolutionsML re-flattened the brain, curated and projected the regions of interest onto the brain, and manually defined the regions of interest and sulci according to (). ReferencesDale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9(2), 179–194. https://doi.org/10.1006/nimg.1998.0395Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 9(2), 195–207. https://doi.org/10.1006/nimg.1998.0396Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/J.NEUROIMAGE.2012.01.021Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015). Pycortex: an interactive surface visualizer for fMRI. Frontiers in Neuroinformatics, 9. https://doi.org/10.3389/fninf.2015.00023Wang, L., Mruczek, R. E. B., Arcaro, M. J., & Kastner, S. (2015). Probabilistic Maps of Visual Topography in Human Cortex. Cerebral Cortex, 25(10), 3911–3931. https://doi.org/10.1093/cercor/bhu277Weiner, K.S., Barnett, M.A., Lorenz, S., Caspers, J., Stigliani, A., Amunts, K., Zilles, K., Fischl, B., and Grill-Spector, K. (2017). The Cytoarchitecture of Domain-specific Regions in Human High-level Visual Cortex. Cereb. Cortex 27, 146–161.Weiner, K.S., Barnett, M.A., Witthoft, N., Golarai, G., Stigliani, A., Kay, K.N., Gomez, J., Natu, V.S., Amunts, K., Zilles, K., et al. (2018). Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation. Neuroimage 170, 373–384.