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Resolving human object recognition in space and time

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Nature neuroscience

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      A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here, we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively later. Using representational similarity analysis, we combine human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing, with sources in V1 and IT., Finally, human MEG signals were correlated to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision.

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        Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. This form of analysis can be likened to a "fishing expedition." As we become more knowledgeable about the structure-function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.
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            Author and article information

            [1 ] Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
            [2 ] McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
            Author notes
            CORRESPONDING AUTHOR Radoslaw Martin Cichy Science and Artificial Intelligence Laboratory MIT 32-D430 Cambridge, MA, USA Phone: +1 617 253 1428 rmcichy@
            Nat Neurosci
            Nat. Neurosci.
            Nature neuroscience
            4 December 2014
            26 January 2014
            March 2014
            10 December 2014
            : 17
            : 3
            : 455-462



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