A toolbox for representational similarity analysis.
A toolbox for representational similarity analysis.
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DOI：
10.1371/journal.pcbi.1003553
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发表时间：
2014-04
影响因子：
4.3
通讯作者：
Kriegeskorte N
中科院分区：
文献类型：
--
作者：
Nili H;Wingfield C;Walther A;Su L;Marslen-Wilson W;Kriegeskorte N
神经元群体编码越来越多地被研究与多元模式信息分析。一个关键的挑战是使用测量的大脑活动模式来测试大脑信息处理的计算模型。解决这个问题的一种方法是表征相似性分析（RSA），它通过一组刺激引起的反应模式的距离矩阵来表征大脑或计算模型中的表征。表征距离矩阵封装了在表征中强调刺激之间的哪些区别以及不强调哪些区别。通过将模型预测的代表性距离矩阵与测量的大脑区域的距离矩阵进行比较来测试模型。RSA还使我们能够比较给定大脑或模型内处理阶段之间的表征，大脑和行为数据之间的表征，以及个体和物种之间的表征。在这里，我们介绍了一个用于RSA的Matlab工具箱。该工具箱支持同时由数据和假设驱动的分析方法。它旨在帮助将广泛的计算模型集成到由现代功能成像和神经元记录技术提供的多通道脑活动测量的分析中。可视化和推理工具使用户能够将模型集与大脑区域集相关联，并使用非参数推理方法对模型进行统计测试和比较。该工具箱支持基于探照灯的RSA，以连续映射测量的脑体积，以搜索具有特定几何形状的神经元群体代码。最后，我们引入了线性判别t值作为代表性判别能力的衡量标准，弥补了线性解码分析和RSA之间的差距。为了证明工具箱的功能，我们将其应用于模拟和真实的fMRI数据。关键功能同样适用于其他形式的大脑活动测量。该工具箱根据开放源码许可协议免费提供给社区（http：//www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/）。
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/).
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影响因子：
9.8
作者：
Haushofer, Johannes;Livingstone, Margaret S.;Kanwisher, Nancy
通讯作者：
Kanwisher, Nancy
影响因子：
16.2
作者：
Kriegeskorte N;Mur M;Ruff DA;Kiani R;Bodurka J;Esteky H;Tanaka K;Bandettini PA
通讯作者：
Bandettini PA
影响因子：
5.7
作者：
Aguirre, Geoffrey Karl
通讯作者：
Aguirre, Geoffrey Karl
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1016/j.cub.2011.09.025
发表时间：
2011-11-08
期刊：
Current biology : CB
影响因子：
--
作者：
Carlin JD;Calder AJ;Kriegeskorte N;Nili H;Rowe JB
通讯作者：
Rowe JB