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.

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8.59 score 58 stars 3 dependents 247 scripts 806 downloads

rrobot - Robust Outlier Detection for Diverse Distributions

Provides robust outlier detection techniques for identifying anomalies in multivariate data, with a focus on methods that remain effective under non-Gaussian distributions. For more details see Saluja, Parlak, and Mejia (2026+) <doi:10.48550/arXiv.2505.11806>.

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5.98 score 2 stars 16 scripts 499 downloads

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>.

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5.47 score 38 stars 26 scripts 594 downloads

fMRIscrub - Scrubbing and Other Data Cleaning Routines for fMRI

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

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5.24 score 6 stars 2 dependents 24 scripts 565 downloads

fMRItools - Routines for Common fMRI Processing Tasks

Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in 'CIFTI', 'GIFTI' and 'NIFTI' data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).

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5.12 score 2 stars 5 dependents 44 scripts 226 downloads

BayesBrainMap - Estimate Brain Networks and Connectivity with Population-Derived Priors

Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods.  Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.

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4.74 score 2 stars 12 scripts 217 downloads

templateICAr - Estimate Brain Networks and Connectivity with ICA and Empirical Priors

Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.

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4.49 score 11 stars 28 scripts 660 downloads

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.

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4.29 score 3 stars 13 scripts 186 downloads