Other packages > Find by keyword >

autohrf  

Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis
View on CRAN: Click here


Download and install autohrf package within the R console
Install from CRAN:
install.packages("autohrf")

Install from Github:
library("remotes")
install_github("cran/autohrf")

Install by package version:
library("remotes")
install_version("autohrf", "1.1.3")



Attach the package and use:
library("autohrf")
Maintained by
Jure Demšar
[Scholar Profile | Author Map]
First Published: 2022-07-21
Latest Update: 2023-02-15
Description:
Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf'
How to cite:
Jure Demšar (2022). autohrf: Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis. R package version 1.1.3, https://cran.r-project.org/web/packages/autohrf. Accessed 29 Apr. 2025.
Previous versions and publish date:
1.0.3 (2022-07-21 14:00), 1.0.4 (2022-09-30 11:20), 1.1.0 (2022-11-19 19:20), 1.1.2 (2023-02-15 12:50)
Other packages that cited autohrf R package
View autohrf citation profile
Other R packages that autohrf depends, imports, suggests or enhances
Complete documentation for autohrf
Downloads during the last 30 days
03/3003/3104/0104/0204/0304/0404/0504/0604/0704/0804/0904/1004/1104/1204/1304/1404/1504/1604/1704/1804/1904/2004/2104/2204/2304/2404/2504/2604/2704/28Downloads for autohrf051015202530TrendBars

Today's Hot Picks in Authors and Packages

NBBDesigns  
Neighbour Balanced Block Designs (NBBDesigns)
Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. T ...
Download / Learn more Package Citations See dependency  
knockoff  
The Knockoff Filter for Controlled Variable Selection
The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when perf ...
Download / Learn more Package Citations See dependency  
phecodemap  
Visualization for PheCode Mapping with ICD-9 and ICD-10-CM Codes
To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classi ...
Download / Learn more Package Citations See dependency  
PCAmatchR  
Match Cases to Controls Based on Genotype Principal Components
Matches cases to controls based on genotype principal components (PC). In order to produce better r ...
Download / Learn more Package Citations See dependency  
bigGP  
Distributed Gaussian Process Calculations
Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

24,142

R Packages

207,311

Dependencies

65,176

Author Associations

24,143

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA