781,031 research outputs found

    The impact of ageing reveals distinct roles for human dentate gyrus and CA3 in pattern separation and object recognition memory

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    © 2017 The Author(s). Both recognition of familiar objects and pattern separation, a process that orthogonalises overlapping events, are critical for effective memory. Evidence is emerging that human pattern separation requires dentate gyrus. Dentate gyrus is intimately connected to CA3 where, in animals, an autoassociative network enables recall of complete memories to underpin object/event recognition. Despite huge motivation to treat age-related human memory disorders, interaction between human CA3 and dentate subfields is difficult to investigate due to small size and proximity. We tested the hypothesis that human dentate gyrus is critical for pattern separation, whereas, CA3 underpins identical object recognition. Using 3 T MR hippocampal subfield volumetry combined with a behavioural pattern separation task, we demonstrate that dentate gyrus volume predicts accuracy and response time during behavioural pattern separation whereas CA3 predicts performance in object recognition memory. Critically, human dentate gyrus volume decreases with age whereas CA3 volume is age-independent. Further, decreased dentate gyrus volume, and no other subfield volume, mediates adverse effects of aging on memory. Thus, we demonstrate distinct roles for CA3 and dentate gyrus in human memory and uncover the variegated effects of human ageing across hippocampal regions. Accurate pinpointing of focal memory-related deficits will allow future targeted treatment for memory loss

    Adaptive Smoothing in fMRI Data Processing Neural Networks

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    Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks.Comment: 4 pages, 3 figures, 1 table, IEEE 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI

    The ALICE TPC, a large 3-dimensional tracking device with fast readout for ultra-high multiplicity events

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    The design, construction, and commissioning of the ALICE Time-Projection Chamber (TPC) is described. It is the main device for pattern recognition, tracking, and identification of charged particles in the ALICE experiment at the CERN LHC. The TPC is cylindrical in shape with a volume close to 90 m^3 and is operated in a 0.5 T solenoidal magnetic field parallel to its axis. In this paper we describe in detail the design considerations for this detector for operation in the extreme multiplicity environment of central Pb--Pb collisions at LHC energy. The implementation of the resulting requirements into hardware (field cage, read-out chambers, electronics), infrastructure (gas and cooling system, laser-calibration system), and software led to many technical innovations which are described along with a presentation of all the major components of the detector, as currently realized. We also report on the performance achieved after completion of the first round of stand-alone calibration runs and demonstrate results close to those specified in the TPC Technical Design Report.Comment: 55 pages, 82 figure

    RESPIRATORY RESPONSES TO ACUTE INTERMITTENT HYPOXIA AND HYPERCAPNIA IN AWAKE RATS

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    This article deals with the recognition of early changes in the breathing pattern, in response to acute intermittent stimuli in awake rats. Two different types of stimuli were given: 9% hypoxia in N 2 and 10% hypercapnia in O 2 . Animals were exposed to 3 consecutive cycles consisting of 3-min stimulus period separated by 8-min normoxic recovery intervals. Features of the breathing pattern, such as respiratory frequency, tidal volume, minute ventilation, inspiration and expiration times, peak inspiratory and expiratory flows, were measured by whole body plethysmography. The data were analyzed with the use of pattern recognition methods. We conclude that the overall respiratory changes were rather slight. However, computerized analysis using a k-nearest neighbor decision rule (k-NN) allowed for a good recognition of the respiratory responses to the stimuli. The misclassification rate (E r ) varied from 5 to 10%. After feature selection, E r decreased below 1%. The k-NN classifier differentiated correctly also the type of intermittent stimulus. Our experimental results demonstrate usefulness of pattern recognition algorithms in studying respiratory effects in biological models

    Partial surface matching by using directed footprints

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    AbstractIn this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem, we are given two objects in 3-space, each represented as a set of points, scattered uniformly along its boundary or inside its volume. The goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our algorithm is based on assigning a directed footprint to every point of the two sets, and locating all the pairs of points (one of each set) whose undirected components of the footprints are sufficiently similar. The algorithm then computes for each such pair of points all the rigid transformations that map the first point to the second, while making the respective direction components of their footprints coincide. A voting scheme is employed for computing transformations which map significantly large number of points of the first set to points of the second set. Experimental results on various examples are presented and show the accurate and robust performance of our algorithm

    Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

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    IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients
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