Abstract

The use of functional magnetic resonance imaging (fMRI) in alert non-human primates is of great potential for research in systems neuroscience. It can be combined with invasive techniques and afford better understanding of non-invasively acquired brain imaging signals in humans. However, the difficulties in optimal application of alert monkey fMRI are multi-faceted, especially at high magnetic fields where the effects of motion and of changes in B0 are greatly amplified. To overcome these difficulties, strict behavioral controls and elaborate animal-training are needed. Here, we introduce a number of hardware developments, quantify the effect of movements on fMRI data, and present procedures for animal training and scanning for well-controlled and artifact-reduced alert monkey fMRI at high magnetic field. In particular, we describe systems for monitoring jaw and body movements, and for accurately tracking eye movements. A link between body and jaw movement and MRI image artifacts is established, showing that relying on the immobilization of an animal’s head is not sufficient for high-quality imaging. Quantitative analysis showed that body and jaw movement events caused large instabilities in fMRI time series. On average, body movement events caused larger instabilities than jaw movement events. Residual baseline brain image position and signal amplitude shifts were observed after the jaw and body movement events ended. Based on these findings, we introduce a novel behavioral paradigm that relies on training the monkeys to stay still during long trials. A corresponding analysis method discards all data that were not obtained during the movement-free periods. The baseline position and amplitude shifts are overcome by motion correction and trial-by-trial signal normalization. The advantages of the presented method over conventional scanning and analysis are demonstrated with data obtained at 7 T. It is anticipated that the techniques presented here will prove useful for alert monkey fMRI at any magnetic field.

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