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Nikolaus Kriegeskorte; Representational similarity analysis — relating hi-res fMRI to other modalities and to computational models. Journal of Vision 2007;7(15):44. doi: 10.1167/7.15.44.
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© ARVO (1962-2015); The Authors (2016-present)
High-resolution functional magnetic resonance imaging (hi-res fMRI) promises to help bridge the gap of spatial scales between human low-resolution neuroimaging and animal invasive electrophysiology. The fine-scale neuronal-pattern information present in hi-res fMRI data can be exploited for neuroscientific insight by means of multivariate analysis. The novel approach of representational similarity analysis allows us (1) to combine evidence across brain space and experimental conditions to sensitively detect neuronal pattern information and (2) to relate results (a) between different modalities of brain-activity measurement, (b) between different species, and (c) between brain-activity data and computational models of brain information processing. This approach is illustrated by a study combining human and monkey data from hi-res fMRI and single-cell recordings, respectively. We investigated response patterns elicited by the same 92 photographs of isolated real-world objects in inferotemporal (IT) cortex of both species. Within each species, we compute a matrix of response-pattern similarities (one similarity value for each pair of images). We find a striking match of the resulting similarity matrices for man and monkey. This finding suggests very similar categorical IT representations and provides some hope that data from single-cell recording and fMRI, for all their differences, may consistently reveal neuronal representations when subjected to massively multivariate analyses of response-pattern information.
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