Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
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TLDR
A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.Abstract:
A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g. single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement, and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices, which characterize the information carried by a given representation in a brain or model. We propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing representational dissimilarity matrices. We demonstrate RSA by relating representations of visual objects as measured with fMRI to computational models spanning a wide range of complexities. We argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.read more
Citations
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TL;DR: These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing, and adoption of these techniques is enabling a new generation of experiments and analyses that could transform the understanding of some of the most complex—and distinctly human—signals in the brain.
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J. Swaroop Guntupalli,Michael Hanke,Yaroslav O. Halchenko,Andrew C. Connolly,Peter J. Ramadge,James V. Haxby,James V. Haxby +6 more
TL;DR: A linear model of shared representational spaces in human cortex is presented that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions.
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Distributed representations of dynamic facial expressions in the superior temporal sulcus
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Shared understanding of narratives is correlated with shared neural responses.
TL;DR: The more similar two people's interpretations of the abstract shapes animation were, the more similar were their neural responses in regions of the default mode network (DMN) and fronto‐parietal network, suggesting a network of high‐level regions that are sensitive to subtle individual differences in narrative interpretation during naturalistic conditions, but also resilient to large differences in the modality of the narrative.
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Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex
Philip A. Kragel,Michiko Kano,Lukas Van Oudenhove,Huynh Giao Ly,Patrick Dupont,Amandine Rubio,Amandine Rubio,Chantal Delon-Martin,Chantal Delon-Martin,Bruno Bonaz,Bruno Bonaz,Stephen B. Manuck,Peter J. Gianaros,Marta Ceko,Elizabeth A. Reynolds Losin,Choong-Wan Woo,Thomas E. Nichols,Tor D. Wager +17 more
TL;DR: Assessing person-level human brain maps across 18 fMRI studies, the authors identify separable representations of pain, cognitive control, and negative emotion in the medial frontal cortex that generalize across different studies and tasks.
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