Showing 11 of total 11 results (show query)
cwatson
brainGraph:Graph Theory Analysis of Brain MRI Data
A set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures.
Maintained by Christopher G. Watson. Last updated 1 years ago.
brain-connectivitybrain-imagingcomplex-networksconnectomeconnectomicsfmrigraph-theorymrinetwork-analysisneuroimagingneurosciencestatisticstractography
20.0 match 188 stars 7.86 score 107 scripts 3 dependents
natverse
nat:NeuroAnatomy Toolbox for Analysis of 3D Image Data
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf format. Traced neurons can be imported from and written to SWC and 'Amira' LineSet and SkeletonGraph formats. These data can then be visualised in 3D via 'rgl', manipulated including applying calculated registrations, e.g. using the 'CMTK' registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the 'nat.nblast' extension package).
Maintained by Gregory Jefferis. Last updated 5 months ago.
3dconnectomicsimage-analysisneuroanatomyneuroanatomy-toolboxneuronneuron-morphologyneurosciencevisualisation
10.0 match 67 stars 9.94 score 436 scripts 2 dependents
muschellij2
neurohcp:Human 'Connectome' Project Interface
Downloads and reads data from Human 'Connectome' Project <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 'S3' buckets.
Maintained by John Muschelli. Last updated 4 years ago.
7.8 match 9 stars 5.35 score 50 scripts
mandymejia
ciftiTools:Tools for Reading, Writing, Viewing and Manipulating CIFTI Files
CIFTI files contain brain imaging data in "grayordinates," which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 'ciftiTools' provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing.
Maintained by Amanda Mejia. Last updated 1 months ago.
3.7 match 47 stars 8.90 score 176 scripts 4 dependents
tmsalab
edmdata:Data Sets for Psychometric Modeling
Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) <doi:10.1111/bmsp.12307>, Culpepper, S. A., & Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>, Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>, Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>, Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>, Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>, Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>, Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>, and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.
Maintained by James Joseph Balamuta. Last updated 5 months ago.
cognitive-diagnostic-modelsdataedm
5.0 match 5 stars 4.18 score 7 scripts 1 dependents
shaelebrown
TDApplied:Machine Learning and Inference for Topological Data Analysis
Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.
Maintained by Shael Brown. Last updated 5 months ago.
3.1 match 16 stars 6.60 score 8 scripts
psychelzh
cpmr:Connectome Predictive Modelling in R
Connectome Predictive Modelling (CPM) (Shen et al. (2017) <doi:10.1038/nprot.2016.178>) is a method to predict individual differences in behaviour from brain functional connectivity. 'cpmr' provides a simple yet efficient implementation of this method.
Maintained by Liang Zhang. Last updated 5 months ago.
5.4 match 1 stars 3.65 score 4 scripts
alexchristensen
NetworkToolbox:Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Maintained by Alexander Christensen. Last updated 2 years ago.
2.4 match 23 stars 6.99 score 101 scripts 4 dependentsmandymejia
BayesfMRI:Spatial Bayesian Methods for Task Functional MRI Studies
Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.
Maintained by Amanda Mejia. Last updated 7 days ago.
2.3 match 26 stars 5.77 score 19 scripts
ggseg
ggsegGlasser:Glasser datasets for the ggseg-plotting tool
This is a support package for the ggseg, and ggseg3d packages. It contains the Glasser atlases to plot using functions from those two packages.
Maintained by Athanasia Mo Mowinckel. Last updated 3 years ago.
3.8 match 6 stars 3.44 score 92 scriptsmandymejia
hrf:Hemodynamic Response Function
Computes the hemodynamic response function (HRF) for task functional magnetic resonance imaging (fMRI) data. Also includes functions for constructing a design matrix from task fMRI event timings, and for comparing multiple design matrices in a general linear model (GLM). A wrapper function is provided for GLM analysis of CIFTI-format data. Lastly, there are supporting functions which provide visual summaries of the HRFs and design matrices.
Maintained by Amanda Mejia. Last updated 1 months ago.
2.3 match 1 stars 4.18 score