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

As the paper states in its introduction, one important way towards imaging studies of the behavior of the brain under natural conditions is the use of time continuous natural stimuli such as videos. However, this poses a data-analysis challenge since fully modelling such multidimensional stimuli is impossible. Inter-subject correlation (ISC, or intersubject correlation mapping (ICM)) is an already established technique of analysis of naturalistic stimulus data in fMRI (Hasson 2004). In this technique, subject’s bold responses are used to model other subjects’ bold responses. This ISC/ICM technique despite of its apparent simplicity has been shown to be surprisingly sensitive of detecting activations also with standard block design stimuli albeit the technique does not utilize any model of the stimulus (Pajula 2012). However, as the paper under review states, ISC mapping requires spatial matching of the subjects in Talairach space and therefore differences in individual functional anatomy (and the limited registration accuracy) limit its sensitivity particularly when studying higher order brain functions. This sensitivity problem is serious, I argue that it is more serious than in the traditional GLM based fMRI analysis, and often addressed by (excessive) smoothing. The paper under review proposes a much more sophisticated approach to solve the problem. Namely, it proposes to compute the first canonical correlations within search-light regions around each voxel. To me, this appears as very interesting idea and first results using this approach look promising. I only have few remarks about the paper, answering to which could further improve this very interesting work.

• I somehow managed to miss the number of subjects participating in the experiment.


• The paper does not make it clear if inter-subject information is measured between two subjects instead of n > 2 subjects . If it is measured between only two subjects, it would be interesting to obtain some insights how to do the analysis if there is a larger set of subjects. There are at least a few ways to combine pairwise inter subject correlation values (Hanson 2009, Lerner 2011, Kauppi 2014) to form a group statistic between n subjects. It would be interesting to obtain insights do these methods generalize to IIM.


• Although the connection of canonical correlation to mutual information is interesting, in addition it would be illuminating to mention that the first canonical correlation is equivalent to maximum correlation coefficient between linear combinations of time courses (each normalized to have unit variances) within the two search-lights (Johnson, Wichern 1992).


• I missed a figure about ICM results corresponding to IIM presented in figure 5.

References:

Hanson SJ, Gagliardi AD, Hanson C (2009) Solving the brain synchrony eigenvalue problem: Conservation of temporal dynamics (fmri) over subjects doing the same task. Journal of computational neuroscience 27: 103–114.

Hasson U, Nir Y, Levy I, Fuhrmann G, Malach R (2004) Intersubject synchronization of cortical activity during natural vision. Science 303: 1634–1640

Johnson, R. A., & Wichern, D. W. (1992). Applied multivariate statistical analysis. Englewood Cliffs, NJ: Prentice hall.

Kauppi JP, Pajula J, Tohka J. A versatile software package for inter-subject correlation based analyses of fMRI. Front Neuroinform. 2014 Jan 31;8:2.

Lerner, Y., Honey, C. J., Silbert, L. J., and Hasson, U. (2011). Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J. Neurosci. 31, 2906–2915.

Pajula, J., Kauppi, J.-P., and Tohka, J. (2012). Inter-subject correlation in fMRI: Method validation against stimulus-model based analysis. PLoS ONE 7:e41196.