# Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates

## TL;DR

This paper introduces a new method to correct MRI artifacts in awake non-human primates, improving the accuracy and reliability of brain activation measurements.

## Contribution

A dynamic off-resonance correction method is proposed to enhance fMRI data quality in awake non-human primates.

## Key findings

- Dynamic correction reduced bias and improved temporal stability in time-series data.
- Sensitivity to functional activation increased at both individual and group levels.
- Statistical parameter estimates became more reliable with the new correction method.

## Abstract

Use of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data.

We correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction.

Applying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates.

Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.