Average rating: | Rated 3 of 5. |
Level of importance: | Rated 3 of 5. |
Level of validity: | Rated 2 of 5. |
Level of completeness: | Rated 2 of 5. |
Level of comprehensibility: | Rated 4 of 5. |
Competing interests: | None |
Keywords: | intersubject information mapping, canonical, complex natural stimuli, Intersubject correlation mapping |
The manuscript looked for the brain regions whose signal is correlated between subjects using intersubject information mapping (IIM). Compared to intersubject correlation mapping (ICM), the manuscript claimed that IIM is less sensitive to the imperfect spatial coregistration from each individual to template. Emperial results are demonstrated.
Comments:
1. While using IIM to investigate the intersubject synchronization is interesting, the benefit of IIM over ICM should be further addressed and quantified. For example, Figure 5 demonstrated the IIM but no ICM was provided for comparison.
2. Figure 6 is the only figure that provides the comparison between IIM and ICM. However, to make the comparison more informative, more works need to be done. First, it’s confusing to see the statement “low intersubject information implies low intersubject correlation” in page 8, while a number of green dots shown in Figure 6, indicating that low intersubject information may not imply low intersubject correlation. Second, it would be helpful if the amount ratio of red and green dots can be quantified.
3. What is the case when the intersubject synchronization can be detected by intersubject information but not by intersubject correlation? Figure 3 addressed this issue using diagrammatic illustration. Neverheless, the link between the diagrams and real case is still unclear. A plot that demonstrates few emperial examples of this case will be helpful.
4. The manuscript mentioned that “To remove activation fluctuations in each subject, we consider each time point in turn and subtract the regional-average from each voxel’s value
at that time point”. However, this is a bit counter-intuitive. Take the extreme case for example. Assuming all the voxels and subjects are perfectly synchronized, the intersubject correlation will be high but the intersubject information may be very low due to that subtraction. In other words, IIM fails to measure the intersubject synchronization in the highly synchronized case. Please explain.