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Demo of how to overlay SPM-produced t-maps onto SUMA surfaces Link to MNI Templates - http://afni.nimh.nih.gov/pub/dist/tgz/suma_MNI_N27.tgz Link to related blog post - http://andysbrainblog.blogspot.com/2012/09/mapping-results-onto-suma-part-2.html Link to website - http://mypage.iu.edu/~ajahn/

I've posted a demo of AFNI's surface mapper program, SUMA, over here on my screencast account. Specifically, I talk about how to map volume...

I watched an episode of The Big Bang Theory last night where Dr. Sheldon Cooper was airing an episode of “Fun with Flags,” which gave me the idea for today’s blog post title. A while ago, I detailed how to create ROIs in AFNI using a variety of different methods. And I even included a […]

AFNI's 3dTcat is used to concatenate datasets. For example, after performing first- and second-level analyses, you may want to join several ...

The IMT School for Advanced Studies Lucca organizes an AFNI Bootcamp, as a satellite event of the upcoming Annual Meeting of the Organization for Human Brain Mapping (OHBM Rome 2019). The AFNI Bootcamp will be held in Lucca (Italy) on June 3-7, 2019. This is a course designed to teach

FatCAT: Functional And Tractographic Connectivity Analysis Toolbox Tools for combining FC and AC

The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: "fixed shape" hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and "varia …

Official AFNI source and documentation. Contribute to afni/afni development by creating an account on GitHub.

AFNI is an open source software package for the analysis and display of functional MRI data. It originated in 1994 to meet the specific needs of resea…

For those of you without access to a Unix-based platform, or whether you are just having a difficult time correctly installing your Python l...

Scripts used for fMRI data analysis in AFNI. Contribute to andrewjahn/AFNI_Scripts development by creating an account on GitHub.

Hi, I am currently writing some packages for AFNI/FSL/SPM for my favourite distro (Gentoo). I plan to use these toolkits via nipype. On the example of AFNI: I am currently placing everything into /...

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract: The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: “fixed shape ” hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and “variable shape ” analysis, where the temporal shape of the response model in each stimulus block class is allowed to vary separately in each voxel. These time series regressions were carried out both in the volume and on the original data projected to individual standardized cortical surface models. Intersubject analyses were carried out voxel-wise on the regression amplitudes obtained from these time series results, using a multi-way within-subject analysis of variance (ANOVA). Group analysis of the block design demonstrated a significant repetition suppression of the BOLD signal within blocks in the superior and middle temporal gyrus. This effect may represent differences in the response to the first stimulus following a period of silence compared to the remaining sentences in the block. Analyzing the event-related data, Brodmann area 31 showed significant sentence effect and consecutive-sentence repetition effect. However, no significant speaker effect was found; these results may be consistent with the instructions to the subjects that they would be tested on the sentence content. Sentence by speaker interaction effects were found in bilateral middle temporal gyrus, left inferior frontal, and left inferior

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Surface-based brain imaging analysis is increasingly being used for detailed analysis of the topology of brain activation patterns and changes in cerebral gray matter. Here we present SUMA, a new interface for visualizing and performing surfacebased brain imaging analysis that is tightly coupled to AFNI – a volume-based brain imaging analysis suite. The interactive part of SUMA is used for rapid and interactive surface and data visualization, access and manipulations with direct link to the volumetric data rendered in AFNI. The batch-mode part of SUMA allows for surface based operations such as geometry and data smoothing [1, 2], surface to volume domain mapping in both directions and node-based statistical and computational tools. We also present methods for mapping low resolution functional data onto the cortical surface while preserving the topological information present in the volumetric data and detail an efficient procedure for performing cross-subject, surfacebased analysis with minimal interpolation of the functional data. 1.

Scientific Reports - Effects of the antibiotic rifaximin on cortical...