A Toolbox for Representational Similarity Analysis

By Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William D. Marslen-Wilson, Nikolaus Kriegeskorte. 2014

932 citations recorded by the source corpus.

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

Subfields: Cognitive Neuroscience

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Citations in the corpus

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “Wewould also like to mention a toolbox speciﬁcally created for carrying out representational similarity analysis (RSA tool-box, Nili et al., 2014) which can serve as a standard for this type of analysis.”
Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning (2019, methodology): “We use RSA Matlab toolbox [29] for RSA related analysis 4 .”
Inferring brain-computational mechanisms with models of activity measurements (2016, methodology): “4), which indicate the expected performance of the true model given the noise and intersubject variability (Nili et al. 2014).”
Neural network models and deep learning - a primer for biologists (2019, methodology): “In another approach, called representational similarity analysis (Kriegeskorte et al., 2008; Nili et al., 2014; Kriegeskorte and Diedrichsen, 2016), each representation in brain and model is characterized by a representational dissimilarity matrix.”
Deep Neural Networks as a Computational Model for Human Shape Sensitivity (2016, methodology): “Again, as done in [70], only the upper triangle was used for correlations.”
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence (2021, background): “It is thus quite different from, for example, noise ceiling metrics computed for group average representational dissimilarity matrices 83 .”
On the distribution of cross-validated Mahalanobis distances (2016, methodology): “In this paper, we focus exclusively on one particular distance measure: The cross-validated Mahalanobis distance [1,22].”
Object-Specific Semantic Coding in Human Perirhinal Cortex (2014, methodology): “An RSA searchlight mapping procedure (Kriegeskorte et al., 2006) was implemented using the RSA toolbox (Nili et al., 2014) and custom MATLAB functions to determine whether object dissimilarity predicted by theoretical models was significantly related to dissimilarity defined by local fMRI activity…”
Reliability of dissimilarity measures for multi-voxel pattern analysis (2016, positive): “In this paper, we estimate the crossvalidated measures (LDC and LDt) in a leave-one-run-out crossvalidation, where one run was assigned to dataset A, and the remaining runs to dataset B.”
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation (2014, methodology): “Representational similarity analysis (RSA) RSA enables us to relate representations obtained from different modalities (e.g. computational models and fMRI patterns) by comparing the dissimilarity patterns of the representations.”
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, methodology): “…Hanson, et al., 2009; Hanke, Halchenko, Sederberg, Olivetti, et al., 2009), MNE-python (Gramfort et al., 2013)—and the Matlab language—PRoNTO (Schrouff et al., 2013), Search-might (Pereira & Botvinick, 2011), the RSA toolbox (Nili et al., 2014) and The Decoding Toolbox (Hebart et al., 2014).”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, background): “For each classification task and each region, there might thus be a different noise ceiling that limits the maximum possible accuracy given the noise in the data (Kay et al., 2008; Nili et al., 2014).”
Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, methodology): “…for comparing internal representations in neural population codes between models and brains enable us to test models as theories of brain information processing (Dumoulin & Wandell 2008; Kay et al. 2008; Kriegeskorte et al. 2008a,b, 2013; Kriegeskorte 2011; Mitchell et al. 2008; Nili et al. 2014).”
Neural Coding of Cognitive Control: The Representational Similarity Analysis Approach. (2020, result): “By contrast, in its original formulation, RSA was developed for the condition-rich naturalistic experimental designs that are common in visual neuroscience [16,57].”