A leading software suite for analysis and display of multiple MRI modalities.
AFNI (Analysis of Functional NeuroImages) is a powerful, open-source software suite designed for the analysis and visualization of neuroimaging data. Developed primarily in C, Python, and R, it supports multiple MRI modalities, including anatomical, functional MRI (fMRI), and diffusion-weighted (DW) data. The software is built to run on Unix-based systems with X11 and Motif displays, with precompiled binaries available for MacOS and Linux distributions like Fedora, CentOS/Red Hat, and Ubuntu (including Windows Subsystem for Linux). AFNI incorporates advanced data analysis techniques, including inter-subject correlation group analysis, atlas and template support, and cortical surface-based functional imaging analysis via the SUMA program. SUMA allows real-time rendering of functional imaging data in Slice, Graph, Volume, and Surface modes, with direct links between them, and supports tractographic reconstructions from DTI models. AFNI integrates with the NIfTI standard for neuroimaging data exchange, promoting interoperability.
AFNI (Analysis of Functional NeuroImages) is a powerful, open-source software suite designed for the analysis and visualization of neuroimaging data.
Explore all tools that specialize in analyze fmri data. This domain focus ensures AFNI (Analysis of Functional NeuroImages) delivers optimized results for this specific requirement.
Explore all tools that specialize in perform statistical analysis. This domain focus ensures AFNI (Analysis of Functional NeuroImages) delivers optimized results for this specific requirement.
Explore all tools that specialize in statistical analysis. This domain focus ensures AFNI (Analysis of Functional NeuroImages) delivers optimized results for this specific requirement.
Allows for group-level analysis by correlating time series data across subjects to identify consistent patterns of brain activity.
Provides tools for visualizing and analyzing fMRI data mapped onto 3D cortical surface models.
Incorporates a wide selection of atlases, standard templates, and template spaces for spatial normalization and region-of-interest (ROI) analysis.
Includes tools for analyzing DWI data, including tractographic reconstruction of white matter pathways.
Simultaneously renders functional imaging data in Slice, Graph, Volume, and Surface modes with direct links between them.
1. Download the appropriate binary package for your operating system (MacOS, Linux).
2. Install necessary dependencies, including X11 and Motif libraries.
3. Set up the AFNI environment by defining the AFNI_DIR environment variable.
4. Download and configure the AFNI_atlas_spaces.niml file for atlas and template support.
5. Explore the example datasets provided to familiarize yourself with the software's functionality.
6. Consult the AFNI documentation and online resources for detailed usage instructions.
7. Utilize the 'afni' command to launch the main AFNI GUI.
8. Use 'suma' command for surface-based analysis.
All Set
Ready to go
Verified feedback from other users.
"AFNI is a highly regarded neuroimaging software known for its flexibility, comprehensive features, and strong community support, but it has a steep learning curve."
Post questions, share tips, and help other users.
EViews offers financial institutions, corporations, government agencies, and academics access to powerful statistical, time series, forecasting, and modeling tools.
The fundamental foundation for scientific computing and multi-dimensional array processing in Python.
A comprehensive Python distribution for scientific and analytic computing.
On-demand access to a global network of PhD-level scientists and research experts.
Democratizing Data Science through an AI-powered UI for R and reproducible analytics.
An open-source JavaScript graphing library for creating interactive data visualizations.
