Dataset

This dataset was created by syed sajid

Contents

This database contains the connectivity matrices of the resting-state functional MRI scans that were collected in two databases of the Human Connectome Project, Young Adult and Aging. These matrices contain the functional connectivity between brain regions (here, several different brain atlases were used, leading to several different connectivity matrices for each subject). The connectivity matrices are symmetrical n x n matrices. Here, n indicates the number of regions present in the atlas, and any number n_i,j in the matrix is generated by calculating a simple Pearson correlation coefficient between the functional time series that describe the functional activation of regions i and j throughout the resting-state functional scan. The matrices presented in this database are present as .pconn.nii files (which can be handled using software like wb_command) or as .txt file.

A full explanation of the database and the brain atlases used here, as well as all the scripts used to generate these connectivity matrices can be found on the GitHub page of this project: floristijhuis/HCP-rfMRI-repository (github.com).

This data set contains structural and functional connectivity and region-average fMRI time series from 785 participants of the Human Connectome Project (HCP) Young Adult S900 data set. HCP builds network maps (connectomes) of the human brain and provides data for research into brain disorders, but also seeks to understand healthy brains and developmental processes. HCP uses non-invasive imaging technologies: resting-state fMRI, task-based fMRI, MEG, diffusion MRI and also performs behavioral and genetic testing. Data was acquired using customized sequences with high spatial and temporal resolution and state of the art distortion correction. HCP's preprocessing pipelines were used for preprocessing, involving distortion removal, surface mapping, registration, and alignment to standard space. Correction of EPI and eddy-current distortions uses phase-encoding direction- reversed images for each diffusion direction. Region-parcellation is based on HCP's Glasser/MMP1 atlas. Probabilistic tractography with MRtrix yielded structural connectomes. Disclaimer: when using this data set, please take the regularly updated quality control results from HCP into consideration. For an overview publication over the HCP please refer to Van Essen et al. (2013). SC, fMRI and FC have been extracted from HCP's S900 release. Please find additional information on the data set under this URL: https://www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release. For parcellating the brain into regions HCP's Glasser et al. multimodal brain parcellation was used.

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."

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.

this graph was created in code R :

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We develop a data-driven fiber-cluster atlas utilizing ultra-high-field 7T structural and diffusion MRI data from 171 Human Connectome Project (HCP) participants. We cluster streamlines connecting seven cortical networks and nine subcortical regions using cosine k-means clustering alongside two-level consensus filtering. The resulting atlas comprises 33,256 clusters for a seven-network scheme and 65,184 clusters for a seventeen-network scheme, encompassing both deep and superficial white matter.

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

Deep Chauvel's human white matter atlas.

The deep white matter atlas of the human brain was built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans shared by 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.

This atlas is composed of 39 white matter bundles including : - symmetrically on both hemispheres, the anterior, superior and posterior thalamic radiations (optic radiations), the arcuate, dorsal and ventral cingulum, the cortico-spinal tract, the fornix, the frontal aslants, the inferior fronto-occipital fascicle, the inferior longitudinal fasciculus, the middle longitudinal fascicle, the optic radiations, the uncinate fascicle and the visual occipito-temporal fibers (also called ventral visual stream), - interhemispheric bundles such as the anterior commissure and the Witelson's subdivisions of the corpus callosum (I, II, III, IV, V, VI, VII), - cerebellar bundles, such as the hypothamic-subthalamic fibers and the cortico-ponto-cerebellar fibers, The atlas is provided using the Anatomist .bundles/.bundlesdata format for which meta-information can be found in the *.bundles file among which: - the labels of the different white matter bundles ('labels' entry), - 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)

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.