9.2.1. Task FMRI processing: the MACAQUE_DEMO

Overview

Welcome to the homepage of the demo for processing task FMRI macaque data.

The demo dataset contains an anatomical dataset and several EPIs collected during a visual stimulus with four stimulus classes related to specific faces and objects shown to the subject.

The processing scripts include calculating nonlinear alignment between the subject’s anatomical (using AFNI’s @animal_warper) and the stereotaxic NMT template. Additionally, a full single subject processing pipeline is constructed with afni_proc.py, including an HRF appropriate for this EPI data that had been collected with MION.

These scripts are meant to providing and example and starting point of task processing. Many features can (and, in general, will) be adjusted for your own study’s design. Please feel free to ask question on the AFNI Message Board, even with regards to setting up your study.

Thanks go to Adam Messinger, Ben Jung and Jakob Seidlitz for both the accompanying macaque data set and many processing suggestions/advice.

Download the MACAQUE_DEMO

To get the demo, copy+paste:

@Install_MACAQUE_DEMO

This will both download and unpack the demo in the directory where the command is run.

There is a README.txt describing the contents.

Demo contents

Input datasets

The demo contains one subject’s initial data:

  • a standard, high-res anatomical (T1w) dataset

  • a set of task FMRI (EPI) datasets; these are raw EPIs, with “place correction” already performed

  • a set of stimulus timing files; these have already been adjusted for the TR removal that will be performed during processing

  • reference template and atlas: here, the stereotaxic NMT and the D99 template in that space

Processing: nonlinear alignment with @animal_warper

Some processed datasets are also included. In particular, AFNI’s @animal_warper command was run on the anatomical dataset to perform both skullstripping and nonlinear alignment to template space (here, the stereotaxic NMT template). This has been performed with the included s00*.tcsh script. Some of the functionality and output of this step can be seen in the automatically generated QC images:

Alignment check: warped anatomical overlaying reference base edges

../../_images/qc_00_e_temp+wrpd_inp.sag.png

Skullstripping check: native space anatomical underlaying brain mask

../../_images/qc_02_orig_inp+mask.sag.png

Atlas ROIs mapped to native space

../../_images/qc_03_ee_orig_inp+wrpd_atlas.sag.png

SUMA view of ROI parcellation in native space (some ROIs have been made nearly transparent to show show inner regions)

../../_images/d99.gif

Outputs from this step are included as inputs for afni_proc.py.


Processing: full single subject preprocessing with afni_proc.py

A full preprocessing script for the subject, including motion correction and regression modeling, is generated using afni_proc.py. The s01*.tcsh script contains an example of including 4 EPI datasets for processing, and the s11*.tcsh script runs over all 15 EPIs included in the demo (the latter takes notably longer to run).

The outputs of the the afni_proc.py-generated scripts are not included, but their automatically generated QC HTML files are.

QC blocks: anat-to-template and stat modeling (full F-stat)

QC block: motion and outliers

../../_images/apqc_va2t_vstat.png ../../_images/apqc_mot.png

QC block: warnings (collinearity, censoring, etc.)

QC block: regression (ideal response, DF counts, etc.)

../../_images/apqc_warns.png ../../_images/apqc_regr.png