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Abstract Background Immune dysregulation is a hallmark of autoimmune diseases of the central nervous system (CNS), characterized by an excessive immune response, and primary CNS tumors (pCNS-tumors) showing a highly immunosuppressive parenchymal microenvironment. Methods Aiming to provide novel insights into the pathogenesis of CNS autoimmunity and cerebral tumor immunity, we analyzed the peripheral blood (PB) and cerebrospinal fluid (CSF) of 81 autoimmune limbic encephalitis (ALE), 148 relapsing–remitting multiple sclerosis (RRMS), 33 IDH-wildtype glioma, 9 primary diffuse large B cell lymphoma of the CNS (CNS-DLBCL), and 110 controls by flow cytometry (FC). Additionally, an in-depth immunophenotyping of the PB from an independent cohort of 20 RRMS and 18 IDH-wildtype glioblastoma patients compared to 19 controls was performed by FC combined with unsupervised computational approaches. Results We identified alterations in peripheral and intrathecal adaptive immunity, mainly affecting the T cell (Tc) but also the B cell (Bc) compartment in ALE, RRMS, and pCNS-tumors compared to controls. ALE, RRMS, and pCNS-tumors featured higher expression of the T cell activation marker HLA-DR, which was even more pronounced in pCNS-tumors than in ALE or RRMS. Glioblastoma patients showed signs of T cell exhaustion that were not visible in RRMS patients. In-depth characterization of the PB revealed differences mainly in the T effector and memory compartment between RRMS and glioblastoma patients and similar alterations in the Bc compartment, including atypical Bc, CD19+CD20− double negative Bc, and plasma cells. PB and CSF mFC together with CSF routine parameters could reliably differentiate ALE and RRMS from pCNS-tumors facilitating early diagnosis and treatment. Conclusions ALE, RRMS, and pCNS-tumors show distinct but partially overlapping changes mainly in HLA-DR+ Tc, memory Tc, exhausted Tc, and Bc subsets providing insights into disease pathogenesis. Moreover, mFC shows diagnostic potential facilitating early diagnosis and treatment. Graphical Abstract

Abstract Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets. A data management plan was introduced to ensure data security in addition to a homogeneous file and folder structure. Changes to the data were automatically tracked using DataLad and all data was shared on the research data platform GIN. This simple and cost-effective workflow facilitates the adoption of FAIR data logistics and processing workflows by making the raw and processed data available and providing the technical infrastructure to independently reproduce the data processing steps. It enables the community to collect heterogeneously acquired and stored datasets not limited to a specific category of data and serves as a technical infrastructure blueprint with rich potential to improve data handling at other sites and extend to other research areas.

Abstract We analyze drivers of the EUR/CHF exchange rate in different regimes between 2000 and 2020. Structural breaks between these subperiods are estimated in an integrated way together with the drivers that are relevant during these subperiods. Overall, the main drivers of the exchange rate include European equity and volatility indices, interest rate and term structure slope differentials, as well as monetary policy interventions. For the “peg period” September 2011–January 2015, in addition to the observed exchange rate we also analyze the drivers of the latent exchange rate that could have been observed in the absence of the peg. Interestingly, the SNB’s foreign currency investments became a significant driver of the EUR/CHF exchange rate only after the end of the peg period when there was no longer an officially communicated target rate.

Abstract The “parahippocampal place area” (PPA) in the human ventral visual stream exhibits increased hemodynamic activity correlated with the perception of landscape photos compared to faces or objects. Here, we investigate the perception of scene-related, spatial information embedded in two naturalistic stimuli. The same 14 participants were watching a Hollywood movie and listening to its audio-description as part of the open-data resource studyforrest.org. We model hemodynamic activity based on annotations of selected stimulus features, and compare results to a block-design visual localizer. On a group level, increased activation correlating with visual spatial information occurring in the movie is overlapping with a traditionally localized PPA. Activation correlating with semantic spatial information occurring in the audio-description is more restricted to the anterior PPA. On an individual level, we find significant bilateral activity in the PPA of nine individuals and unilateral activity in one individual. Results suggest that activation in the PPA generalizes to spatial information embedded in a movie and an auditory narrative, and may call for considering a functional subdivision of the PPA.

Abstract Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions’ connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

We systematically investigate structural parameters, such as shape, size, elastic strain, and relaxations, of metal-assisted etched vertically modulated Si/SiGe superlattice nanowires by using electron microscopy, synchrotron-based x-ray diffraction, and numerical linear elasticity theory. A vertical Si/Ge superlattice with atomically flat interfaces is grown by using molecular beam epitaxy on Si-buffered Si(001) substrates. The lattice constants for Si and Ge are 5.43 and 5.66 Å, respectively, which indicate a lattice mismatch of 4.2%. This results in a strained layer in the boundary between Si and Ge leading to dislocations. These substrates serve as the starting material for nanostructuring the surface by using metal-assisted etching. It is shown that the high quality crystalline structure is preserved in the fabrication process, while the lattice mismatch is partially relieved by dislocation formation. Despite this highly effective relaxation path, dislocations present in the parent superlattice do not vanish upon nanostructuring for wires with diameters of down to at least 80 nm. We relate these observations to the applicability of silicon-based nanowires for high-performance thermoelectric generators.

The fusiform face area and parahippocampal place area respond to face and scene stimuli respectively. Here, the authors show using fMRI that these brain areas are also preferentially activated by eye movements associated with looking at faces and scenes even when no images are shown.

Here we present an annotation of speech in the audio-visual movie “Forrest Gump” and its audio-description for a visually impaired audience, as an addition to a large public functional brain imaging dataset (studyforrest.org). The annotation provides information about the exact timing of each of the more than 2500 spoken sentences, 16,000 words (including 202 non-speech vocalizations), 66,000 phonemes, and their corresponding speaker. Additionally, for every word, we provide lemmatization, a simple part-of-speech-tagging (15 grammatical categories), a detailed part-of-speech tagging (43 grammatical categories), syntactic dependencies, and a semantic analysis based on word embedding which represents each word in a 300-dimensional semantic space. To validate the dataset’s quality, we build a model of hemodynamic brain activity based on information drawn from the annotation. Results suggest that the annotation’s content and quality enable independent researchers to create models of brain activity correlating with a variety of linguistic aspects under conditions of near-real-life complexity.

Naturalistic stimuli show significant potential to inform behavioral, cognitive, and clinical neuroscience. To date, this impact is still limited by the relative inaccessibility of both generated neuroimaging data as well as the supporting naturalistic stimuli. In this perspective, we highlight currently available naturalistic datasets and technical solutions such as DataLad that continue to advance our ability to share this data. We also review scientific and sociological challenges in selecting naturalistic stimuli for reproducible research. Overall, we encourage researchers to share their naturalistic datasets to the full extent possible under local copyright law.

Abstract Using a data set of German stocks that includes the financial crisis, this paper identifies market liquidity as the main driver of return seasonality. In comparison, the economic significance of order flow imbalance is markedly weaker. Applying panel regressions and controlling for unobserved effects, we investigate the effects of both variables simultaneously, together with dummies for calendar effects. US macroeconomic news announcements, which have been identified as one driver of return seasonality in previous studies using non-US data, are of little importance for our data set of German stocks.

Multivariate pattern classification methods have been successfully applied to decode orientation of visual grating stimuli from BOLD fMRI activity recorded in human visual cortex (Kamitani and Tong, 2005; Haynes and Rees, 2005) [12,10]. Though there has been extensive research investigating the true spatial scale of the orientation specific signals (Op de Beeck, 2010; Swisher et al., 2010; Alink et al., 2013; Freeman et al., 2011, 2013) [2,15,1,4,5], it remained inconclusive what spatial acquisition resolution is required, or is optimal, for decoding analyses. The research article entitled “The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7 T” Sengupta et al. (2017) [14] studied the effect of spatial acquisition resolution and also analyzed the strength and spatial scale of orientation discriminating signals. In this article, for the first time, we present empirical ultra high-field fMRI data, obtained as a part of the aforementioned study, which were recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) for orientation decoding in visual cortex. The dataset is compliant with the BIDS (Brain Imaging Data Structure) format, and freely available from the OpenfMRI portal (dataset accession number: http://openfmri.org/dataset/ds000113c ds000113c).

Abstract Little is known about the cortical regions involved in representing task-related content in preparation for visual task performance. Here we used representational similarity analysis (RSA) to investigate the BOLD response pattern similarity between task relevant and task irrelevant feature dimensions during conjunction viewing and target template maintenance prior to visual search. Subjects were cued to search for a spatial frequency (SF) or orientation of a Gabor grating and we measured BOLD signal during cue and delay periods before the onset of a search display. RSA of delay period activity revealed that widespread regions in frontal, posterior parietal, and occipitotemporal cortices showed general representational differences between task relevant and task irrelevant dimensions (e.g., orientation vs. SF). In contrast, RSA of cue period activity revealed sensory-related representational differences between cue images (regardless of task) at the occipital pole and additionally in the frontal pole. Our data show that task and sensory information are represented differently during viewing and during target template maintenance, and that task relevance modulates the representation of visual information across the cortex.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the “last mile” implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation – primarily in the visual cortex. Previous research indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we applied an analysis strategy from a previous study on decoding visual orientation from V1 to publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex. The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

The application of lattice strain through epitaxial growth of oxide films on lattice mismatched perovskite-like substrates strongly influences the structural properties of ferroelectric domains and their corresponding piezoelectric behavior. The formation of different ferroelectric phases can be understood by a strain-phase diagram, which is calculated within the framework of the Landau–Ginzburg–Devonshire theory. In this paper, we illustrate the opportunity of ferroelectric domain engineering in the KxNa1−xNbO3 lead-free material system. In particular, the following examples are discussed in detail: (i) Different substrates (NdGaO3, SrTiO3, DyScO3, TbScO3, and GdScO3) are used to systematically tune the incorporated epitaxial strain from compressive to tensile. This can be exploited to adjust the NaNbO3 thin film surface orientation and, concomitantly, the vector of electrical polarization, which rotates from mainly vertical to exclusive in-plane orientation. (ii) In ferroelectric NaNbO3, thin films grown on rare-earth scandate substrates, highly regular stripe domain patterns are observed. By using different film thicknesses, these can be tailored with regard to domain periodicity and vertical polarization component. (iii) A featured potassium concentration of x = 0.9 of KxNa1−xNbO3 thin films grown on (110) NdScO3 substrates favors the coexistence of two equivalent, monoclinic, but differently oriented ferroelectric phases. A complicated herringbone domain pattern is experimentally observed which consists of alternating MC and a1a2 domains. The coexistence of different types of ferroelectric domains leads to polarization discontinuities at the domain walls, potentially enabling high piezoelectric responses. In each of these examples, the experimental results are in excellent agreement with predictions based on the linear elasticity theory.

Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. One of them is that the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components of a multiscale test system. We introduce an efficient coupling method based on the second-order backward differentiation formula numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

Here we present an annotation of locations and temporal progression depicted in the movie “Forrest Gump”, as an addition to a large public functional brain imaging dataset (http://studyforrest.org). The annotation provides information about the exact timing of each of the 870 shots, and the depicted location after every cut with a high, medium, and low level of abstraction. Additionally, four classes are used to distinguish the differences of the depicted time between shots. Each shot is also annotated regarding the type of location (interior/exterior) and time of day. This annotation enables further studies of visual perception, memory of locations, and the perception of time under conditions of real-life complexity using the studyforrest dataset.

Here we extend the information on the structure of the core stimulus of the studyforrest project (http://studyforrest.org) with a description of semantic conflict in the “Forrest Gump” movie. Three observers annotated the movie independently regarding episodes with portrayal of lies, irony or sarcasm. We present frequency statistics, and inter-observer reliability measures that qualify and quantify semantic conflict in the stimulus. While the number of identified events is limited, this annotation nevertheless enriches the knowledge about the complex high-level structure of this stimulus, and can help to evaluate its utility for future studies, and the usability of the existing brain imaging data regarding this aspect of cognition.

Este artigo aborda aspectos pontuais da escalada de abstração das mídias apresentada na obra de Vilém Flusser a partir da imagem técnica, conceito que engloba os diferentes tipos de imagens que são marcas de culturas midiatizadas. Para isso, traçamos uma linha evolutiva da xilogravura como imagem técnica dentro do contexto midiático brasileiro, analisando sua produção imagética desde a concepção artesanal, passando pela perda dos suportes tradicionais, até a imersão de suas imagens em meios eletrônicos e digitais.

Here we present a dataset with a description of portrayed emotions in the movie ”Forrest Gump”. A total of 12 observers independently annotated emotional episodes regarding their temporal location and duration. The nature of an emotion was characterized with basic attributes, such as arousal and valence, as well as explicit emotion category labels. In addition, annotations include a record of the perceptual evidence for the presence of an emotion. Two variants of the movie were annotated separately: 1) an audio-movie version of Forrest Gump that has been used as a stimulus for the acquisition of a large public functional brain imaging dataset, and 2) the original audio-visual movie. We present reliability and consistency estimates that suggest that both stimuli can be used to study visual and auditory emotion cue processing in real-life like situations. Raw annotations from all observers are publicly released in full in order to maximize their utility for a wide range of applications and possible future extensions. In addition, aggregate time series of inter-observer agreement with respect to particular attributes of portrayed emotions are provided to facilitate adoption of these data.

We can identify objects by sight or by sound, yet far less is known about auditory object recognition than about visual recognition. Any exemplar of a dog (eg, a picture) can be recognized on multiple categorical levels (eg, animal, dog, poodle). Using fMRI combined with machine-learning techniques, we studied these levels of categorization with sounds rather than images. Subjects heard sounds of various animate and inanimate objects, and unrecognizable control sounds. We report four primary findings: (1) some distinct brain regions selectively coded for basic (“dog”) versus superordinate (“animal”) categorization; (2) classification at the basic level entailed more extended cortical networks than those for superordinate categorization; (3) human voices were recognized far better by multiple brain regions than were any other sound categories; (4) regions beyond temporal lobe auditory areas were able to distinguish and categorize auditory objects. We conclude that multiple representations of an object exist at different categorical levels. This neural instantiation of object categories is distributed across multiple brain regions, including so-called “visual association areas,” indicating that these regions support object knowledge even when the input is auditory. Moreover, our findings appear to conflict with prior well-established theories of category-specific modules in the brain.

A general description of effects of toxic compounds in mammalian cells is facing several problems. Firstly, most toxic compounds are hydrophobic and partition phenomena strongly influence their behaviour. Secondly, cells display considerable heterogeneity regarding the presence, activity and distribution of enzymes participating in the metabolism of foreign compounds i.e. bioactivation/biotransformation. Thirdly, cellular architecture varies greatly. Taken together, complexity at several levels has to be addressed to arrive at efficient in silico modelling based on physicochemical properties, metabolic preferences and cell characteristics. In order to understand the cellular behaviour of toxic foreign compounds we have developed a mathematical model that addresses these issues. In order to make the system numerically treatable, methods motivated by homogenization techniques have been applied. These tools reduce the complexity of mathematical models of cell dynamics considerably thus allowing to solve efficiently the partial differential equations in the model numerically on a personal computer. Compared to a compartment model with well-stirred compartments, our model affords a more realistic representation. Numerical results concerning metabolism and chemical solvolysis of a polycyclic aromatic hydrocarbon carcinogen show good agreement with results from measurements in V79 cell culture. The model can easily be extended and refined to include more reactants, and/or more complex reaction chains, enzyme distribution etc, and is therefore suitable for modelling cellular metabolism involving membrane partitioning also at higher levels of complexity.

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires ``neuroscience-aware'' technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here we review its features and applicability to various neural data modalities.

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied machine learning methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine-learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine-learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.

Objects in our visual environment are arranged in depth and hence there is a considerable amount of overlap and occlusion in the image they generate on the retina. In order to properly segment the image into fi gure and background, boundary interpolation is required even across large distances. Here we study the cortical mechanisms involved in collinear contour interpolation using fMRI. Human observers were asked to discriminate the curvature of interpolated boundaries in Kanizsa fi gures and in control confi gurations, which contained identical physical information but did not generated subjective shapes. We measured a spatially precise spin-echo BOLD signal and found stronger responses to subjective shapes than non-shapes at the subjective boundary locations, but not at the inducer locations. The responses to subjective contours within primary visual cortex were retinotopically specifi c and analogous to that to real contours, which is intriguing given that subjective and luminance-defi ned contours are physically fundamentally different. We suggest that in the absence of retinal stimulation, the observed activation changes in primary visual cortex are driven by intracortical interactions and feedback, which are revealed in the absence of a physical stimulus.

Este artigo trata da “comunicologia”, ciência da mí­dia e da comunicação, de Vilém Flusser, um dos pioneiros dessa área no Brasil, criador do primeiro curso da comunicação em São Paulo na década de 60. Apresenta a sua história biográfica e institucional, as linhas de influência intelectual assim como um panorama teórico e caracterí­sticas da sua obra, sendo estas cunhadas pela semiótica, fenomenologia e cibernética. Palavras-chave teoria da comunicação, comunicologia, Vilém Flusser Abstract This article is about the so-called “communicology”, science of media and communication, as developed by Vilém Flusser, one of the pioneers of this area in Brazil, who also founded the first course in communications in São Paulo in the 1960s. After presenting his biographical and institutional history, the lines of intellectual influence are sketched and an overview of his theory as well as characteristics of his work are presented, these being coined by semiotics, phenomenology and cybernetics. Key words communication theory, communicology, Vilém Flusser

Esta contribuição tem o objetivo de apresentar e discutir o conceito da materialidade da comunicação a partir do obra homônima publicado em alemão em 1988, e traduzido parcialmente para o inglês. "Materialidade da Comunicação" é um programa de pesquisa que pretende indagar sobre as condições, o lugar, o suporte e as modalidades de produção de sentido, que, por si, são isentos de sentido. Foi desenvolvido no centro de estudos avançados em pós-graduação de Siegen, Alemanha, com a área de concentração denominada "Formas de comunicação e formas de vida". Participaram deste projeto interdisciplinar pesquisadores de várias disciplinas, tais como letras e ciências sociais; e foi a busca para um consenso mínimo sobre o conteúdo específico dessa área que gerou a ideia de que qualquer comunicação precisa uma componente de materialidade, sendo este o fundamento básico do conceito.

Através da análise da relação entre o indivíduo e a sociedade, Alfred Schütz, em sua abordagem socio-fenomenológica, percebeu a importância da relação entre comunicação e processos semióticos desenvolvendo uma teoria semiótica. Representação, essencial para a noção de signos em Schütz, assim entendida como uma relação entre significante e significado, é por sua vez introduzida pelo usuário em um ato de interpretação construtiva. O conceito de "apresentação" desenvolvido subseqüentemente engloba o sentido objetivo no nível de: a) um sistema de signos; b) interpretação subjetiva; c) situaç'ão ou contexto; e, levando em consideração a função comunicativa do processo de signos, d) um produtor de signos e seu complemento e e) um receptor de signos.

Esta contribuição discute as narrativas orais como uma espécie de comunicação cotidiana, abordadas sob duas perspectivas complementares: as formas e as funções. A análise formal trata a estruturação da sintaxe narrativa, segundo a tradição inaugurada pela análise estrutural de Labov/Waletzky. O aspecto funcional trata também a função argumentativa (St. Toulmin), estabelecida entre a própria narrativa e a inserção na conversação. O recorte empírico é composto por narrativas orais de sonhos, já tratado em outros textos, na perspectiva da análise do discurso.

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation -- primarily in the visual cortex. Reported evidence indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we performed an analysis of publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex that matches a previously conducted study on decoding visual orientation from V1. The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

Here we present an annotation of locations and temporal progression depicted in the movie “Forrest Gump”, as an addition to a large public functional brain imaging dataset ( http://studyforrest.org). The annotation provides information about the exact timing of each of the 870 shots, and the depicted location after every cut with a high, medium, and low level of abstraction. Additionally, four classes are used to distinguish the differences of the depicted time between shots. Each shot is also annotated regarding the type of location (interior/exterior) and time of day. This annotation enables further studies of visual perception, memory of locations, and the perception of time under conditions of real-life complexity using the studyforrest dataset.

Here we present an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity ( http://studyforrest.org). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (fMRI) data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. The data release includes raw fMRI data, as well as precomputed structural alignments for within-subject and group analysis. In addition to fMRI, simultaneously recorded cardiac and respiratory traces, as well the complete implementation of the stimulation paradigm, including stimuli, are provided. An initial quality control analysis reveals distinguishable patterns of response to individual genres throughout a large expanse of areas known to be involved in auditory and speech processing. The present data can be used to, for example, generate encoding models for music perception that can be validated against the previously released fMRI data from stimulation with the “Forrest Gump” audio-movie and its rich musical content. In order to facilitate replicative and derived works, only free and open-source software was utilized.

Here we present a dataset with a description of portrayed emotions in the movie ”Forrest Gump”. A total of 12 observers independently annotated emotional episodes regarding their temporal location and duration. The nature of an emotion was characterized with basic attributes, such as arousal and valence, as well as explicit emotion category labels. In addition, annotations include a record of the perceptual evidence for the presence of an emotion. Two variants of the movie were annotated separately: 1) an audio-movie version of Forrest Gump that has been used as a stimulus for the acquisition of a large public functional brain imaging dataset, and 2) the original audio-visual movie. We present reliability and consistency estimates that suggest that both stimuli can be used to study visual and auditory emotion cue processing in real-life like situations. Raw annotations from all observers are publicly released in full in order to maximize their utility for a wide range of applications and possible future extensions. In addition, aggregate time series of inter-observer agreement with respect to particular attributes of portrayed emotions are provided to facilitate adoption of these data.

Studying the brain’s behavior in situations of real-life complexity is crucial for an understanding of brain function as a whole. However, methodological difficulties and a general lack of public resources are hindering scientific progress in this domain. This channel will serve as a communication hub to collect relevant resources and curate knowledge about working paradigms, available resources, and analysis techniques.