Representational geometry: integrating cognition, computation, and the brain

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure

Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

Decoding information in the human hippocampus: a user's guide

Multi-voxel pattern analysis (MVPA), or 'decoding', of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Affective iconic words benefit from additional sound–meaning integration in the left amygdala

Recent studies have shown that a similarity between sound and meaning of a word (i.e., iconicity) can help more readily access the meaning of that word, but the neural mechanisms underlying this beneficial role of iconicity in semantic processing remain largely unknown. In an fMRI study, we focused on the affective domain and examined whether affective iconic words (e.g., high arousal in both sound and meaning) activate additional brain regions that integrate emotional information from different domains (i.e., sound and meaning). In line with our hypothesis, affective iconic words, compared to their non‐iconic counterparts, elicited additional BOLD responses in the left amygdala known for its role in multimodal representation of emotions. Functional connectivity analyses revealed that the observed amygdalar activity was modulated by an interaction of iconic condition and activations in two hubs representative for processing sound (left superior temporal gyrus) and meaning (left inferior frontal gyrus) of words. These results provide a neural explanation for the facilitative role of iconicity in language processing and indicate that language users are sensitive to the interaction between sound and meaning aspect of words, suggesting the existence of iconicity as a general property of human language

Semantic data set construction from human clustering and spatial arrangement

Abstract Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pairwise judgments of words in isolation, or are limited to specific contexts and lexical stimuli. There are limitations with these approaches that either do not provide any context for judgments, and thereby ignore ambiguity, or provide very specific sentential contexts that cannot then be used to generate a larger lexical resource. Furthermore, similarity between more than two items is not considered. We provide a full description and analysis of our recently proposed methodology for large-scale data set construction that produces a semantic classification of a large sample of verbs in the first phase, as well as multi-way similarity judgments made within the resultant semantic classes in the second phase. The methodology uses a spatial multi-arrangement approach proposed in the field of cognitive neuroscience for capturing multi-way similarity judgments of visual stimuli. We have adapted this method to handle polysemous linguistic stimuli and much larger samples than previous work. We specifically target verbs, but the method can equally be applied to other parts of speech. We perform cluster analysis on the data from the first phase and demonstrate how this might be useful in the construction of a comprehensive verb resource. We also analyze the semantic information captured by the second phase and discuss the potential of the spatially induced similarity judgments to better reflect human notions of word similarity. We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity. In particular, we find that stronger static word embedding methods still outperform lexical representations emerging from more recent pre-training methods, both on word-level similarity and clustering. Moreover, thanks to the data set’s vast coverage, we are able to compare the benefits of specializing vector representations for a particular type of external knowledge by evaluating FrameNet- and VerbNet-retrofitted models on specific semantic domains such as “Heat” or “Motion.”</jats:p

Comparison of Different Phenotypic Approaches to Screen and Detect mecC-Harboring Methicillin-Resistant Staphylococcus aureus

Similar to mecA, mecC confers resistance against beta-lactams, leading to the phenotype of methicillin-resistant Staphylococcus aureus (MRSA). However, mecC-harboring MRSA strains pose special difficulties in their detection. The aim of this study was to assess and compare different phenotypic systems for screening, identification, and susceptibility testing of mecC-positive MRSA isolates. A well-characterized collection of mecC-positive S. aureus isolates (n 111) was used for evaluation. Routinely used approaches were studied to determine their suitability to correctly identify mecC-harboring MRSA, including three (semi)automated antimicrobial susceptibility testing (AST) systems and five selective chromogenic agar plates. Additionally, a cefoxitin disk diffusion test and an oxacillin broth microdilution assay were examined. All mecC-harboring MRSA isolates were able to grow on all chromogenic MRSA screening plates tested. Detection of these isolates in AST systems based on cefoxitin and/or oxacillin testing yielded overall positive agreements with the mecC genotype of 97.3% (MicroScan WalkAway; Siemens), 91.9% (Vitek 2; bioMérieux), and 64.9% (Phoenix, BD). The phenotypic resistance pattern most frequently observed by AST devices was “cefoxitin resistance/oxacillin susceptibility,” ranging from 54.1% (Phoenix) and 83.8% (Vitek 2) to 92.8% (WalkAway). The cefoxitin disk diffusion and oxacillin broth microdilution assays categorized 100% and 61.3% of isolates to be MRSA, respectively. The chromogenic media tested confirmed their suitability to reliably screen for mecC-harboring MRSA. The AST systems showed false-negative results with varying numbers, misidentifying mecC-harboring MRSA as methicillin-susceptible S. aureus. This study underlines cefoxitin’s status as the superior surrogate mecC-positive MRSA marker.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Staphylococcus aureus small colony variants show common metabolic features in central metabolism irrespective of the underlying auxotrophism

In addition to the classical phenotype, Staphylococcus aureus may exhibit the small colony-variant (SCV) phenotype, which has been associated with chronic, persistent and/or relapsing infections. SCVs are characterized by common phenotypic features such as slow growth, altered susceptibility to antibiotic agents and pathogenic traits based on increased internalization and intracellular persistence. They show frequently auxotrophiesms mainly based on two different mechanisms: (i) deficiencies in electron transport as shown for menadione- and/or hemin-auxotrophs and (ii) thymidylate biosynthetic-defective SCVs. To get a comprehensive overview of the metabolic differences between both phenotypes, we compared sets of clinically derived menadione-, hemin- and thymidine-auxotrophic SCVs and stable site directed mutants exhibiting the SCV phenotype with their corresponding isogenic parental strains displaying the normal phenotype. Isotopologue profiling and transcriptional analysis of central genes involved in carbon metabolism, revealed large differences between both phenotypes. Labeling experiments with [U-13C6]glucose showed reduced 13C incorporation into aspartate and glutamate from all SCVs irrespective of the underlying auxotrophism. More specifically, these SCVs showed decreased fractions of 13C2-aspartate and glutamate; 13C3-glutamate was not detected at all in the SCVs. In comparison to the patterns in the corresponding experiment with the classical S. aureus phenotype, this indicated a reduced carbon flux via the citric acid cycle in all SCV phenotypes. Indeed, the aconitase-encoding gene (acnA) was found down-regulated in all SCV phenotypes under study. In conclusion, all SCV phenotypes including clinical isolates and site-directed mutants displaying the SCV phenotype were characterized by down-regulation of citric acid cycle activity. The common metabolic features in central carbon metabolism found in all SCVs may explain similar characteristics of the S. aure

FFA and OFA encode distinct types of face identity information

Faces of different people elicit distinct functional MRI (fMRI) patterns in several face-selective brain regions. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). We used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. Models included low-level to high-level image-computable properties and complex human-rated properties. We found that the FFA representation reflected perceived face similarity, social traits, and gender, and was well accounted for by the OpenFace model (deep neural network, trained to cluster faces by identity). The OFA encoded low-level image-based properties (pixel-wise and Gabor-jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information

Retrospective model-based inference guides model-free credit assignment

An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants’ momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions