Abstract
fMRI experiments with awake non-human primates (NHP) have seen a surge of applications in recent years. However, the standard fMRI analysis tools designed for human experiments are not optimal for analysis of NHP fMRI data collected at high fields. There are several reasons for this, including the trial-based nature of NHP experiments, with inter-trial periods being of no interest, and segmentation artefacts and distortions that may result from field changes due to movement. We demonstrate an approach that allows us to address some of these issues consisting of the following steps: 1) Trial-based experimental design. 2) Careful control of subject movement. 3) Computer-assisted selection of trials devoid of artefacts and animal motion. 4) Nonrigid between-trial and rigid within-trial realignment of concatenated data from temporally separated trials and sessions. 5) Linear interpolation of inter-trial intervals and high-pass filtering of temporally continuous data 6) Removal of interpolated data and reconcatenation of datasets before statistical analysis with SPM. We have implemented a software toolbox, fMRI Sandbox (http://code.google.com/p/fmri-sandbox/), for semi-automated application of these processing steps that interfaces with SPM software. Here, we demonstrate that our methodology provides significant improvements for the analysis of awake monkey fMRI data acquired at high-field. The method may also be useful for clinical applications with subjects that are unwilling or unable to remain motionless for the whole duration of a functional scan.
Highlights
In human fMRI studies, there are theoretically and experimentally established preprocessing procedures that prepare the data for subsequent statistical analysis
To suppress slowly varying trends and remove high-frequency noise, data are temporally filtered before running statistical tests. Using this conventional processing pipeline for awake non-human primate (NHP) data acquired at high magnetic field (7T) without specific adaptations is not appropriate for a variety of reasons, including the trial-based nature of NHP experiments, with inter-trial periods being of no interest, and the artefacts produced by field changes due to movement
Brain extraction For M1, the performance of our brain extraction algorithm is illustrated in Figure 6, comparing SPM analysis masks derived from different brain extraction approaches
Summary
In human fMRI studies, there are theoretically and experimentally established preprocessing procedures that prepare the data for subsequent statistical analysis. The realigned images are normalized to a template brain to prepare the data for later analysis at the group level. To suppress slowly varying trends and remove high-frequency noise, data are temporally filtered before running statistical tests. Using this conventional processing pipeline for awake non-human primate (NHP) data acquired at high magnetic field (7T) without specific adaptations is not appropriate for a variety of reasons, including the trial-based nature of NHP experiments, with inter-trial periods being of no interest, and the artefacts produced by field changes due to movement. In this paper we describe an approach to analysis of high-field data from NHPs, overcoming several limitations of standard automated processing packages
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