Introduction
Over the past thirty years, advances in brain imaging have allowed the development of techniques to study brain activity (1,2). In the absence of a task, the brain, far from being inactive, presents a basal activity called Resting-state (RS) (3–5).
The analysis of brain activity during the Resting-state makes it possible to establish connectivity maps and to highlight Resting-state networks (RSN). These RSNs reflect the activation patterns of functionally and anatomically distinct brain areas (5,6).
In the context of mental rotation, task-related functional imagery has shown that certain brain areas are associated with better performance (7–9). Based on this finding, it is possible that RSNs can provide predictive cues for performance in mental rotation.
Method
To test this hypothesis, we used Resting-state functional magnetic resonance imaging (rs-fMRI) to search for connections associated with mental rotation performance. For this purpose, we acquired RS data from 28 healthy participants and had them perform mental hand rotation exercises. The chronometric results of the exercises allowed us to split the participants into two distinct performance groups. We then selected regions of interest (ROIs) known to be involved in mental rotation tasks (10) and compared the connections between the two groups using ROI-to-ROI analysis
Results and discussion
We were able to identify 17 differing connections between the two performance groups. However, when applying correction for false discovery rate (FDR), the results were not significant.
One possible explanation is that the initial study (11), on which this project is based, was designed for another type of analysis, not aiming precisely at studying Resting-state connectivity. In addition, it seems that the number of participants was insufficient to obtain robust results with our parameters (12,13).
Conclusion
By comparing the Resting-state connectivity networks of groups distinguished by their level of performance in mental rotation of hands, we were unable to identify areas significantly associated with better performance in this task.
Approaching Resting-state data to try to identify regions predictive of certain skills could be extended to research areas other than mental rotation. On the other hand, it now seems clear to us that an appropriate design is essential to obtain conclusive results. In our case, it would be interesting to replicate the experiment with a larger number of participants and using research parameters specifically targeting Resting-state analysis.