Selective binding of facial features reveals dynamic expression fragments

The temporal correspondence between two arbitrarily chosen pairs of alternating features can generally be reported for rates up to 3–4 Hz. This limit is however surpassed for specialised visual mechanisms that encode conjunctions of features. Here we show that this 3–4 Hz limit is exceeded for eye gaze and eyebrow pairing, but not for eye gaze and mouth pairing, suggesting combined eye and eyebrow motion constitutes a dynamic expression fragment; a building block of superordinate facial actions

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction

Neural Representations of Personally Familiar and Unfamiliar Faces in the Anterior Inferior Temporal Cortex of Monkeys

To investigate the neural representations of faces in primates, particularly in relation to their personal familiarity or unfamiliarity, neuronal activities were chronically recorded from the ventral portion of the anterior inferior temporal cortex (AITv) of macaque monkeys during the performance of a facial identification task using either personally familiar or unfamiliar faces as stimuli. By calculating the correlation coefficients between neuronal responses to the faces for all possible pairs of faces given in the task and then using the coefficients as neuronal population-based similarity measures between the faces in pairs, we analyzed the similarity/dissimilarity relationship between the faces, which were potentially represented by the activities of a population of the face-responsive neurons recorded in the area AITv. The results showed that, for personally familiar faces, different identities were represented by different patterns of activities of the population of AITv neurons irrespective of the view (e.g., front, 90° left, etc.), while different views were not represented independently of their facial identities, which was consistent with our previous report. In the case of personally unfamiliar faces, the faces possessing different identities but presented in the same frontal view were represented as similar, which contrasts with the results for personally familiar faces. These results, taken together, outline the neuronal representations of personally familiar and unfamiliar faces in the AITv neuronal population

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time

Rhodolith Beds Are Major CaCO3 Bio-Factories in the Tropical South West Atlantic

Rhodoliths are nodules of non-geniculate coralline algae that occur in shallow waters (<150 m depth) subjected to episodic disturbance. Rhodolith beds stand with kelp beds, seagrass meadows, and coralline algal reefs as one of the world's four largest macrophyte-dominated benthic communities. Geographic distribution of rhodolith beds is discontinuous, with large concentrations off Japan, Australia and the Gulf of California, as well as in the Mediterranean, North Atlantic, eastern Caribbean and Brazil. Although there are major gaps in terms of seabed habitat mapping, the largest rhodolith beds are purported to occur off Brazil, where these communities are recorded across a wide latitudinal range (2°N - 27°S). To quantify their extent, we carried out an inter-reefal seabed habitat survey on the Abrolhos Shelf (16°50′ - 19°45′S) off eastern Brazil, and confirmed the most expansive and contiguous rhodolith bed in the world, covering about 20,900 km2. Distribution, extent, composition and structure of this bed were assessed with side scan sonar, remotely operated vehicles, and SCUBA. The mean rate of CaCO3 production was estimated from in situ growth assays at 1.07 kg m−2 yr−1, with a total production rate of 0.025 Gt yr−1, comparable to those of the world's largest biogenic CaCO3 deposits. These gigantic rhodolith beds, of areal extent equivalent to the Great Barrier Reef, Australia, are a critical, yet poorly understood component of the tropical South Atlantic Ocean. Based on the relatively high vulnerability of coralline algae to ocean acidification, these beds are likely to experience a profound restructuring in the coming decades

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system’s optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.National Science Foundation (U.S.). Science and Technology Center (Award CCF-1231216)National Science Foundation (U.S.) (Grant NSF-0640097)National Science Foundation (U.S.) (Grant NSF-0827427)United States. Air Force Office of Scientific Research (Grant FA8650-05-C-7262)Eugene McDermott Foundatio

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership

A network linking scene perception and spatial memory systems in posterior cerebral cortex

The neural systems supporting scene-perception and spatial-memory systems of the human brain are well-described. But how do these neural systems interact? Here, using fine-grained individual-subject fMRI, we report three cortical areas of the human brain, each lying immediately anterior to a region of the scene perception network in posterior cerebral cortex, that selectively activate when recalling familiar real-world locations. Despite their close proximity to the scene-perception areas, network analyses show that these regions constitute a distinct functional network that interfaces with spatial memory systems during naturalistic scene understanding. These “place-memory areas” offer a new framework for understanding how the brain implements memory-guided visual behaviors, including navigation

A Comparison of Accuracy between A New Commercial ELISA Test, GenediaTM Test and Other Commercial ELISA Tests for Serological Diagnosis of Helicobacter pylori Infection in Korea

Background/Aims : A new commercial enzyme linked immunosorbent assay (ELISA) test using Korean Helicobacter pylori (H. pylori) as an antigen, GenediaTM test, was compared to other serologic tests for H. pylori infection. Methods: Among two hundred seventy three subjects, H. pylori-positive group was consisted of 132 patients (50 peptic ulcer diseases, 52 chronic gastritis, and 30 gastric cancers) and H. pylori-negative group was consisted of 141 patients (121 adults and 20 pediatric patients). Endoscopic antral biopsy specimens were obtained for microscopy and rapid urease test (CLOTM test). We also performed GenediaTM IgG, IgA ELISA, G.A.P IgG, IgA ELISA, and Cobas-core IgG EIA. H. pylori infection was defermined when H. pylori was detected histologically or the results of CLOTM tests were positive. Results : The sensitivities and specificities of the serologic tests were 96.2% and 46.1% in GenediaTM IgG, 91.7% and 52.5% in GenediaTM IgA, 81.8% and 46.8% in G.A.P IgG, 25.0% and 85.1% in G.A.P IgA, 96.9% and 38.6% in Cobas-core test, respectively. In H. pylori-negative pediatric patients, the specificity of the tests was 80% in GenediaTM IgG, 95% in GenediaTM IgA, 60% in G.A.P. IgG, 100% in G.A.P IgA, and 75% in Cobas-core test. Conclusions: In Korea, GenediaTM test was comparable or superior to general serologic tests used for diagnosing H. pylori infection. However, it is necessary to improve the specificity of the GenediaTM test. (Kor J Gastroenterol 2000;36:20 - 28)ope