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autohrf
View on CRAN: Click
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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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.autohrf . https://github.com/demsarjure/autohrf/issues . https://github.com/demsarjure/autohrf . autohrf results . autohrf.pdf . autohrf_1.1.3.tar.gz . autohrf_1.1.3.zip . autohrf_1.1.3.zip . autohrf_1.1.3.zip . autohrf_1.1.3.tgz . autohrf_1.1.3.tgz . autohrf_1.1.3.tgz . autohrf_1.1.3.tgz . autohrf archive . https://CRAN.R-project.org/package=autohrf .
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:
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Other R packages that autohrf depends,
imports, suggests or enhances
Complete documentation for autohrf
Functions, R codes and Examples using
the autohrf R package
Some associated functions: autohrf-datasets . autohrf . convolve_events . convolve_hrf . create_boynton_hrf . create_child . create_first_generation . create_new_generation . create_spm_hrf . downsample . evaluate_model . fit_to_constraints . get_best_models . get_parents . plot_best_models . plot_events . plot_fitness . plot_model . run_model .
Some associated R codes: data.R . events.R . hrf.R . modelling.R . signal.R . Full autohrf package functions and examples
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