Downloads and reads data from Human 'Connectome' Project 
    <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 
    'S3' buckets.

A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.

Contains functions to query and visualize the Neuroimaging features 
  associated with genetically regulated gene expression (GReX). The primary utility,
  neuroimaGene(), relies on a list of user-defined genes and returns a table of
  neuroimaging features (NIDPs) associated with each gene. This resource 
  is designed to assist in the interpretation of genome-wide and transcriptome-wide
  association studies that evaluate brain related traits. Bledsoe (2024) 
  <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization 
  functions that generate summary plots and 2-dimensional visualizations of regional 
  brain measures. Mowinckel (2020).

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites.

Generates functional Magnetic Resonance Imaging (fMRI) 
             time series or 4D data. Some high-level 
             functions are created for fast data generation with only 
             a few arguments and a diversity of functions to define 
             activation and noise. For more advanced users it is possible 
             to use the low-level functions and manipulate the arguments.
             See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.