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Review of 'Intersubject information mapping: revealing canonical representations of complex natural stimuli'

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3
somewhat interesting but more emperical and quantitative results are needed
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    Rated 3 of 5.
Level of importance:
    Rated 3 of 5.
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    Rated 2 of 5.
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    Rated 2 of 5.
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    Rated 4 of 5.
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Intersubject information mapping: revealing canonical representations of complex natural stimuli

 Nikolaus Kriegeskorte (corresponding) (2015)
Abstract Real-world time-continuous stimuli such as video promise greater naturalism for studies of brain function. However, modeling the stimulus variation is challenging and introduces a bias in favor of particular descriptive dimensions. Alternatively, we can look for brain regions whose signal is correlated between subjects, essentially using one subject to model another. Intersubject correlation mapping (ICM) allows us to find brain regions driven in a canonical manner across subjects by a complex natural stimulus. However, it requires a direct voxel-to-voxel match between the spatiotemporal activity patterns and is thus only sensitive to common activations sufficiently extended to match up in Talairach space (or in an alternative, e.g. cortical-surface-based, common brain space). Here we introduce the more general approach of intersubject information mapping (IIM). For each brain region, IIM determines how much information is shared between the subjects' local spatiotemporal activity patterns. We estimate the intersubject mutual information using canonical correlation analysis applied to voxels within a spherical searchlight centered on each voxel in turn. The intersubject information estimate is invariant to linear transforms including spatial rearrangement of the voxels within the searchlight. This invariance to local encoding will be crucial in exploring fine-grained brain representations, which cannot be matched up in a common space and, more fundamentally, might be unique to each individual – like fingerprints. IIM yields a continuous brain map, which reflects intersubject information in fine-grained patterns. Performed on data from functional magnetic resonance imaging (fMRI) of subjects viewing the same television show, IIM and ICM both highlighted sensory representations, including primary visual and auditory cortices. However, IIM revealed additional regions in higher association cortices, namely temporal pole and orbitofrontal cortex. These regions appear to encode the same information across subjects in their fine-grained patterns, although their spatial-average activation was not significantly correlated between subjects.
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    Review information

    10.14293/S2199-1006.1.SOR-SOCSCI.APDIXF.v1.RQQITL

    This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

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    Review text

    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.

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