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      Intersubject information mapping: revealing canonical representations of complex natural stimuli

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          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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          Most cited references 17

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          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.
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            Matching categorical object representations in inferior temporal cortex of man and monkey.

            Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.
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              Estimating Mutual Information

              We present two classes of improved estimators for mutual information \(M(X,Y)\), from samples of random points distributed according to some joint probability density \(\mu(x,y)\). In contrast to conventional estimators based on binnings, they are based on entropy estimates from \(k\)-nearest neighbour distances. This means that they are data efficient (with \(k=1\) we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to non-uniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of \(k/N\) for \(N\) points. Numerically, we find that both families become {\it exact} for independent distributions, i.e. the estimator \(\hat M(X,Y)\) vanishes (up to statistical fluctuations) if \(\mu(x,y) = \mu(x) \mu(y)\). This holds for all tested marginal distributions and for all dimensions of \(x\) and \(y\). In addition, we give estimators for redundancies between more than 2 random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation.
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                Author and article information

                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
                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
                Article
                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 .

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                Figures: 6, Tables: 0, References: 24, Pages: 9
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