Marieke Mur

Bio: Marieke Mur is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Temporal cortex & Fusiform face area. The author has an hindex of 15, co-authored 37 publications receiving 4933 citations. Previous affiliations of Marieke Mur include University of Cambridge & Maastricht University.

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

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.

Local discriminability determines the strength of holistic processing for faces in the fusiform face area.

Abstract: Recent evidence suggests that the Fusiform Face Area (FFA) is not exclusively dedicated to the interactive processing of face features, but also contains neurons sensitive to local features. This suggests the existence of both interactive and local processing modes, consistent with recent behavioral findings that the strength of interactive feature processing (IFP) engages most strongly when similar features need to be disambiguated. Here we address whether the engagement of the FFA into interactive versus featural representational modes is governed by local feature discriminability. We scanned human participants while they matched target features within face pairs, independently of the context of distracter features. IFP was operationalized as the failure to match the target without being distracted by distracter features. Picture-plane inversion was used to disrupt IFP while preserving input properties. We found that FFA activation was comparably strong, irrespective of whether similar target features were embedded in dissimilar contexts(i.e., inducing robust IFP) or dissimilar target features were embedded in the same context (i.e., engaging local processing). Second, inversion decreased FFA activation to faces most robustly when similar target features were embedded in dissimilar contexts, indicating that FFA engages into IFP mainly when features cannot be disambiguated at a local level. Third, by means of Spearman rank correlation tests, we show that the local processing of feature differences in the FFA is supported to a large extent by the Occipital Face Area, the Lateral Occipital Complex, and early visual cortex, suggesting that these regions encode the local aspects of face information. The present findings confirm the co-existence of holistic and featural representations in the FFA. Furthermore, they establish FFA as the main contributor to the featural/holistic representational mode switches determined by local discriminability.

Revealing representational content with pattern-information fMRI—an introductory guide

Abstract: Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, National Institutesof Health, Bethesda, MD, USAConventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detectingbrain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually takento indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patternsof activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can beinvestigated by pattern-information analysis. In this framework, a region’s multivariate pattern information is taken to indicaterepresentational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions,introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.

Abstract: The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2-dimensional arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

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.