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Connectome

The connectome refers to the comprehensive map of neural connections in the brain.
It provides a detailed picture of the structural and functional wiring of the brain, allowing researchers to study brain architecture, connectivity, and information processing.
Mapping the connectome is a key focus in neuroscience, as it can yield insights into brain development, plasticity, and disorders.
Cutting-edge techniques like diffusion imaging and optogenetics enable researchers to visualize and analyze the connectome in unprecedented detail.
This rich dataset can inform a wide range of neuroscientific inquiries, from understanding cognition to developing targeted therapies for neurological conditions.
Explore the connectome and unlock the brain's myriad mysteries with the power of PubCompare.ai.

Most cited protocols related to «Connectome»

1
Multimodal Cortical Parcellation Comparison
Once the parcellation has been created, parcellated representations of data from each modality can be generated using either the group parcellation or the individual subject parcellations. For the statistical cross-validation, we created parcellated myelin, cortical thickness, task fMRI, and resting state functional connectivity datasets using the semi-automated multimodal group parcellation (see Supplementary Methods 7.1). For myelin and cortical thickness, we simply averaged the values of the dense individual subject maps within each area. For task fMRI, we averaged the time series within each area prior to computing task statistics (to benefit from the CNR improvements of parcellation demonstrated in Fig. 4e). For the same reason, we averaged resting state time series within each parcel prior to computing functional connectivity to form a parcellated functional connectome.
For each pair of areas that shared a border in the parcellation, we computed a paired samples two-tailed t-test across subjects on these parcellated data for each feature (ignoring tests that involved the diagonal in the resting state parcellated functional connectome). We thresholded these tests at the Bonferroni-corrected significance level of P < 9 × 10−8 (number of area pairs across both hemispheres (1,050) × number of features (266) × number of tails (2) × 0.05) and an effect size threshold of Cohen’s d > 1. We grouped the features into 4 independent categories (cortical thickness, myelin, task fMRI, and resting state fMRI) to determine for each area pair whether it showed robust and statistically significant differences across multiple modalities. For more details, see Supplementary Methods 7.2.
Glasser M.F., Coalson T.S., Robinson E.C., Hacker C.D., Harwell J., Yacoub E., Ugurbil K., Andersson J., Beckmann C.F., Jenkinson M., Smith S.M, & Van Essen D.C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
Publication 2016
Connectome Cortex, Cerebral fMRI Microtubule-Associated Proteins Multimodal Imaging Myelin Sheath Tail
2
Multimodal Cortical Parcellation Comparison
Once the parcellation has been created, parcellated representations of data from each modality can be generated using either the group parcellation or the individual subject parcellations. For the statistical cross-validation, we created parcellated myelin, cortical thickness, task fMRI, and resting state functional connectivity datasets using the semi-automated multimodal group parcellation (see Supplementary Methods 7.1). For myelin and cortical thickness, we simply averaged the values of the dense individual subject maps within each area. For task fMRI, we averaged the time series within each area prior to computing task statistics (to benefit from the CNR improvements of parcellation demonstrated in Fig. 4e). For the same reason, we averaged resting state time series within each parcel prior to computing functional connectivity to form a parcellated functional connectome.
For each pair of areas that shared a border in the parcellation, we computed a paired samples two-tailed t-test across subjects on these parcellated data for each feature (ignoring tests that involved the diagonal in the resting state parcellated functional connectome). We thresholded these tests at the Bonferroni-corrected significance level of P < 9 × 10−8 (number of area pairs across both hemispheres (1,050) × number of features (266) × number of tails (2) × 0.05) and an effect size threshold of Cohen’s d > 1. We grouped the features into 4 independent categories (cortical thickness, myelin, task fMRI, and resting state fMRI) to determine for each area pair whether it showed robust and statistically significant differences across multiple modalities. For more details, see Supplementary Methods 7.2.
Glasser M.F., Coalson T.S., Robinson E.C., Hacker C.D., Harwell J., Yacoub E., Ugurbil K., Andersson J., Beckmann C.F., Jenkinson M., Smith S.M, & Van Essen D.C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
Publication 2016
Connectome Cortex, Cerebral fMRI Microtubule-Associated Proteins Multimodal Imaging Myelin Sheath Tail
3
Voxel-based and Surface-based fMRI Analysis

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Publication 2013
Brain Connectome Cortex, Cerebral Gray Matter Microtubule-Associated Proteins
4
Resting-state fMRI Data Analysis Protocol
To demonstrate the visualization effects of this toolbox on real data, we analyzed a published resting-state fMRI dataset. The dataset was downloaded from the 1000 Functional Connectomes Project (www.nitrc.org/projects/fcon_1000/), which is a worldwide multi-site project with fMRI data sharing for the imaging community. The resting-state images were acquired from 198 healthy right-handed volunteers (males, 76; females, 122; age, 18 - 26 years) at the scanning site of Beijing Normal University. The data for one subject were removed because of an orientation error during scanning. Each participant provided written informed consent before initiating scanning. The study was approved through the Institutional Review Board of the Beijing Normal University Imaging Center for Brain Research.
Xia M., Wang J, & He Y. (2013). BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS ONE, 8(7), e68910.
Publication 2013
Brain Connectome Ethics Committees, Research Females fMRI Healthy Volunteers Males
5
Interactive Ligand-Receptor Network Visualization
The interactive visualization and query tool for ligand–receptor networks was developed using custom and open source tools. The vector graphic visualization was generated using the D3.js visualization library61 (link) (http://d3js.org/). The application interface was developed using the AngularJS web application framework (https://angularjs.org/) and the twitter bootstrap front-end framework (http://getbootstrap.com/).
The visualization interface takes the expression files generated in this study along with other metadata in tabular format to generate the network/hive visualization as shown in Fig. 5. An online version of the resource is located at: http://fantom.gsc.riken.jp/5/suppl/Ramilowski_et_al_2015/vis/ and mirrored at http://forrest-lab.github.io/connectome. The source code is under MIT license and is available at: https://github.com/Hypercubed/connectome/ (version: /tree/v0.1.0).
Ramilowski J.A., Goldberg T., Harshbarger J., Kloppman E., Lizio M., Satagopam V.P., Itoh M., Kawaji H., Carninci P., Rost B, & Forrest A.R. (2015). A draft network of ligand–receptor-mediated multicellular signalling in human. Nature Communications, 6, 7866.
Publication 2015
Cloning Vectors Connectome Ligands Trees Urticaria

Most recents protocols related to «Connectome»

1
Structural Connectivity and Clinical Outcomes
Network Based Statistics (NBS) (Zalesky et al., 2010 (link)) was used to assess the correlation between the strength of structural connectivity with the clinical scores at discharge. NBS is a validated nonparametric statistical method to evaluate group differences or relationships between variables in large networks, whilst dealing with multiple comparisons problem. It first univariately tests every connection within the matrix, and then identifies any connected structures (components) above the specified test-statistic threshold. The p-values are then assigned to suprathreshold components by indexing their size with the null distribution of maximal component size through permutation testing, controlling for the family-wise error rate. (Zalesky et al., 2010 (link)).
Using the NBS toolbox, (NITRC: Network-Based Statistic (NBS) (2022) ) we conducted a linear regression between the structural connectivity presented as mean FA values in the connectomes and the two clinical scores at discharge (DRS and CRS-R) while controlling for the patient’s age, sex, and scanning acquisition parameters (the number of diffusion gradient directions, echo time, repetition time, and interslice gap). As the acquisition parameters showed high inter-dependence, we reduced the multicollinearity by aggregating them into one variable using principal component analysis. The scores derived from the loadings of the first component were then used as a single nuisance covariate in the NBS analysis. A detailed description of this step is given in the Supplementary material.
Each subject’s matrix consisted of 95×95 nodes presenting the cortical and subcortical regions as defined by the Lausanne 2018 atlas, (Cammoun et al., 2012 (link)) and each element of the matrix (edge) presented the connectivity strength of the respective nodal pair. Statistical significance of correlation between the edges and the clinical score at discharge was assessed through t-test for correlation coefficient, by specifying the corresponding contrast, with 10.000 permutations, at the P-value of 0.05 (as defined with the NBS method). We evaluated the presence of significant correlations at the primary t-value threshold of 3.5, which corresponded to P =.001 at 39 degrees of freedom. As the test-statistic threshold influences the extent of the returned subnetwork, and its value has not been standardly defined, it has been suggested to assess the extent of subnetwork using different thresholds. (Zalesky et al., 2010 (link)) Therefore, in addition to the primary threshold of t = 3.5, we estimated the extent of the significant network by increasing and decreasing this threshold.
Pozeg P., Alemán-Goméz Y., Jöhr J., Muresanu D., Pincherle A., Ryvlin P., Hagmann P., Diserens K, & Dunet V. (2023). Structural connectivity in recovery after coma: Connectome atlas approach. NeuroImage : Clinical, 37, 103358.
Publication 2023
Connectome Cortex, Cerebral Diffusion ECHO protocol Patient Discharge Patients
2
Multi-scale Structural Connectome Atlas
Structural connectomes were calculated on the basis of the multi-scale probabilistic atlas of human connectome. (Alemán-Gómez et al., 2022 (link)) This atlas was derived from the diffusion data of 66 healthy adult subjects included in the Human Connectome Project. It models white matter connectivity between cortical and subcortical grey matter regions, parcellated at 4 different scales (Lausanne 2018 parcellation). (Alemán-Gómez et al., 2022 (link)) Each normalized FA image was overlaid with the probabilistic tractography atlas and the mean FA values were calculated for each bundle connecting each pair of regions of the scale 1 (95×95 regions). The connectivity strength in the structural connectome thus presented the mean FA values along the voxels belonging to the bundle connecting each pair of regions within the selected parcellation scheme. To exclude voxels with low probability from the selected bundle, the calculations were limited only to the connections present in 80% of the population, and to the voxels belonging to the bundle in 90% of the subjects. (Alemán-Gómez et al., 2022 (link)).
Pozeg P., Alemán-Goméz Y., Jöhr J., Muresanu D., Pincherle A., Ryvlin P., Hagmann P., Diserens K, & Dunet V. (2023). Structural connectivity in recovery after coma: Connectome atlas approach. NeuroImage : Clinical, 37, 103358.
Publication 2023
Adult Connectome Cortex, Cerebral Diffusion Gray Matter Healthy Volunteers Human Connectome White Matter
3
Robust Brain Segmentation Model
The proposed method is trained only on synthetic data (no real images) generated from a set of brain segmentation maps. Here, we use 1,020 maps obtained from 1 mmT1-weighted scans: 20 from the OASIS database (43 (link)), 500 from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (7 (link)), and 500 from the Human Connectome Project (HCP) (44 (link)). These segmentations contain labels for 31 brain structures, obtained manually (OASIS) or with FreeSurfer (HCP and ADNI) (45 ). Moreover, we complement each map with 11 automated labels for extra-cerebral regions (46 ) and 68 FreeSurfer labels for cortex parcellation. We emphasize that, while HCP subjects are young and healthy, ADNI contains aging and diseased subjects, who frequently exhibit large atrophy and white matter lesions. Thus, using such a diverse population enables us to build robustness across a wide range of morphologies.
Billot B., Magdamo C., Cheng Y., Arnold S.E., Das S, & Iglesias J.E. (2023). Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2216399120.
Publication 2023
Alzheimer's Disease Atrophy Brain Brain Mapping Connectome Cortex, Cerebral CREB3L1 protein, human Microtubule-Associated Proteins Radionuclide Imaging White Matter
4
Multidimensional Neural Network for Heading Dynamics
We extended the single population network dynamics, Eq. 4, to encompass five populations: a HD population, which we designed to track HD estimate and certainty with its bump parameter dynamics; two angular velocity populations (AV+ and AV-), which are tuned to HD and are differentially modulated by angular velocity input; an inhibitory population (INH); and a population that mediates external input (EXT), corresponding to HD observations. The network parameters were tuned such that the activity profile in the HD population tracks the dynamics of the circKF quadratic approximation, in the same way as for the single-population network, Eq. 4. To limit the degrees of freedom, we further constrained the connectivity structure between HD and AV+/- and INH populations by the known connectome of the Drosophila HD system (hemibrain dataset in ref. 42 ) and tuned only across-population connectivity weights. For further details on the network dynamics and with- and across-population connectivity weights, please consult SI Appendix.
Kutschireiter A., Basnak M.A., Wilson R.I, & Drugowitsch J. (2023). Bayesian inference in ring attractor networks. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2210622120.
Publication 2023
Connectome Drosophila Psychological Inhibition
5
Genetic Associations of Psychosis and Brain Phenotypes
Generalized linear models with gamma family and log link were run using the glm package in R version 3.6.3 to test the association between C4 GREx and continuous measures of psychotic experiences (i.e., PQ-Bsym, PQ-Bsev), as these variables demonstrated a right-skewed distribution. The association between C4 GREx and PPbin was tested via logistic regression with binomial family using the glm package in R. Fixed effects included predicted C4A and C4B GREx, age, socioeconomic status (SES; taken as the average of parent education and income at the baseline time point), four genetic principal components, sex, and site ID. Statistical models run in related individuals using family ID as a random effect failed to converge; thus, analyses were restricted to one randomly selected subject from each family.
Models testing multivariable phenome-wide associations were also restricted to one individual per family. For normally distributed variables, linear models were fitted, while for non-normally distributed variables, generalized linear models with gamma distribution and log link were fitted. C4A and C4B GREx, age, SES, four genetic principal components, sex, and site ID were specified as fixed effects. Benjamini-Hochberg false discovery rate (FDR) correction was used to account for the number of variables tested at each time point (i.e., baseline, 1-year follow-up, 2-year follow-up). The significance of covariates of interest was assessed using the likelihood ratio test.
To test the association between C4 GREx and neuroimaging indices of brain volume, cortical thickness, and surface area, linear mixed models were run via the lme4 package in R. C4A and C4B GREx, age, four genetic principal components, SES, and sex were entered as fixed effects; MRI device serial number and family ID were entered as random effects. To control for global brain effects, whole brain volume, mean cortical thickness, or mean surface area were also included as covariates for respective models. The significance of covariates of interest was assessed using the likelihood ratio test, and FDR correction was used to account for the number of variables tested within each imaging measure (i.e., VOL, CT, SA).
Sex differences in the effects of C4 GREx on brain and behavioral phenotypes were tested by including an interaction term between C4A and C4B GREx and sex in statistical models. Follow-up analyses were performed in each sex separately.
The total number of participants in each analysis (i.e., those with complete PQ-Bsym, PQ-Bsev, PPbin, phenotype, and/or brain-imaging data) is provided in Additional file 1.
Plots were generated using R packages ggpubr, ggplot, and ggesg. PheWAS results were plotted in R by modifying publicly available code from Dr. Yoonjung Joo (https://rpubs.com/helloyjjoo/pheWAS_connectome).
Hernandez L.M., Kim M., Zhang P., Bethlehem R.A., Hoftman G., Loughnan R., Smith D., Bookheimer S.Y., Fan C.C., Bearden C.E., Thompson W.K, & Gandal M.J. (2023). Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth. Genome Biology, 24, 42.
Publication 2023
Brain Connectome Cortex, Cerebral cytokine receptor, GLM-R Gamma Rays Gene Components Medical Devices Mental Disorders Parent Phenotype

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1
Connectome skyra by Siemens
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The Connectome Skyra is a high-performance magnetic resonance imaging (MRI) system designed for advanced neuroimaging research. It features a powerful magnetic field strength, enabling detailed visualization and analysis of the brain's intricate connections, known as the connectome. The Connectome Skyra provides researchers with the necessary tools to study the complex neural networks and their functions.
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Matlab by MathWorks
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Connectome skyra scanner by Siemens
The Connectome-Skyra scanner is a magnetic resonance imaging (MRI) system designed for advanced neuroimaging applications. It is a high-performance, wide-bore MRI scanner that provides exceptional image quality and powerful gradient performance. The Connectome-Skyra scanner is optimized for the comprehensive assessment of brain structure and function, enabling researchers to investigate the complex connections and pathways within the human brain.
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32 channel head coil by Siemens
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The 32-channel head coil is a key component in magnetic resonance imaging (MRI) systems. It is designed to acquire high-quality images of the human head, enabling detailed visualization and analysis of brain structure and function.
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The 3-T connectome-Skyra scanner is a magnetic resonance imaging (MRI) system manufactured by Siemens. It operates at a magnetic field strength of 3 Tesla, which allows for high-resolution imaging of the human brain and its neural connections. The scanner is designed to capture detailed anatomical and functional data, enabling researchers and clinicians to study the brain's structure and connectivity.
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The Tim Trio is a versatile laboratory equipment that serves as a temperature-controlled magnetic stirrer. It features three independent stirring positions, allowing simultaneous operation of multiple samples. The device maintains a consistent temperature range to ensure accurate and reliable results during experimental procedures.
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The 3T Connectome Scanner is a powerful magnetic resonance imaging (MRI) system designed for advanced brain imaging research. It operates at a magnetic field strength of 3 Tesla, providing high-resolution images of the brain's structural and functional connectivity. The scanner is capable of acquiring data for diffusion-weighted imaging, which enables the study of white matter tracts and the brain's neural networks.
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Connectome scanner by Siemens
The Connectome scanner is a specialized medical imaging device designed for high-resolution mapping of the brain's neural connections. It utilizes advanced magnetic resonance imaging (MRI) technology to capture detailed images of the brain's intricate network of neurons and their interconnections. The Connectome scanner's core function is to provide researchers and clinicians with comprehensive data on the structural and functional connectivity of the human brain.

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