Back

A systematically optimized awake mouse fMRI paradigm

Xu, W.; Pei, M.; Zhang, K.; Tong, C.; Bo, B.; Feng, J.; Zhang, X.-Y.; Liang, Z.

2022-11-16 neuroscience
10.1101/2022.11.16.516376 bioRxiv
Show abstract

Functional magnetic resonance imaging (fMRI) has been increasingly utilized in mice. Due to the non-negligible effects of anesthetics on mouse fMRI, it is becoming more common to perform fMRI in the awake mice. However, high stress level and head motion in awake mouse fMRI remain to be fully addressed, which limits its practical applications. Therefore, here we presented a systematically optimized awake mouse fMRI paradigm as a practical and open-source solution. First, we designed a soundproof habituation chamber in which multiple mice can be habituated simultaneously and independently. Then, combining corticosterone, body weight and behavioral measurements, we systematically evaluated the potential factors that may contribute to animals stress level for awake imaging. Among many factors, we found that the restraining setup allowing forelimbs freely moving and head tilted at 30-degree was optimal for minimizing stress level. Importantly, we implemented multiband simultaneous multi-slice imaging to enable ultrafast fMRI acquisition in awake mice. Compared to conventional single-band EPI, faster acquisition enabled by multiband imaging were more robust to head motion and yielded higher statistical power. Thus, more robust resting-state functional connectivity was detected using multiband acquisition in awake mouse fMRI, compared to conventional single-band acquisition. In conclusion, we presented an awake mouse fMRI paradigm that is highly optimized in both awake mice habituation and fMRI acquisition, and such paradigm minimized animals stress level and provided more resistance to head motion and higher statistical power.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.