Blog
About

2,550
views
0
recommends
+1 Recommend
1 collections
    22
    shares
    • Record: found
    • Abstract: found
    • Article: found
    Is Open Access

    Intersubject information mapping: revealing canonical representations of complex natural stimuli

    Read this article at

    Bookmark
        There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

        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.

        Related collections

        Most cited references 24

        • Record: found
        • Abstract: found
        • Article: not found

        Thresholding of statistical maps in functional neuroimaging using the false discovery rate.

        Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments. (C)2002 Elsevier Science (USA).
          Bookmark
          • Record: found
          • Abstract: not found
          • Article: not found

          Nonparametric permutation tests for functional neuroimaging: A primer with examples

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Information-based functional brain mapping.

            The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
              Bookmark

              Author and article information

              Affiliations
              [1 ]Medical Research Council, Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge, CB2 7EF, UK
              Author notes
              [* ]Corresponding author's e-mail address: nikolaus.kriegeskorte@ 123456mrc-cbu.cam.ac.uk
              Contributors
              (View ORCID Profile)
              Journal
              SOR-SOCSCI
              ScienceOpen Research
              ScienceOpen
              2199-1006
              26 March 2015
              : 0 (ID: 5d34869b-8fc4-4033-8dfe-e9047380b6d5 )
              : 0
              : 1-9
              2605:XE
              10.14293/S2199-1006.1.SOR-SOCSCI.APDIXF.v1
              © 2015 N. Kriegeskorte.

              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 .

              Counts
              Figures: 6, Tables: 0, References: 24, Pages: 9
              Product
              Categories
              Original article

              Comments

              Comment on this article