53 resultados para Processing and sinterization
Resumo:
Purpose of the study: Reduced subjective experience of reward (anhedonia) is a key symptom of major depression. We have developed a human model of reward processing to investigate the neural correlates of anhedonia. Methods: We report the data from studies that examined reward processing using functional magnetic resonance imaging (fMRI) in those vulnerable to depression. We also report the effects of antidepressant medications on our neural model of reward processing and on the resting state in healthy volunteers. Results: Our results thus far indicate that deficits in reward processing are apparent in those vulnerable to depression, and also that antidepressant medication modulates reward processing and resting state functional connectivity in parts of the brain consistent with serotonin and catecholamine transmitter pathways in healthy volunteers. Conclusions: We conclude that this type of human model of reward processing might be useful in detecting biomarkers for depression and also in illuminating why antidepressant medications may not be very effective in treating anhedonia.
Resumo:
A wealth of literature suggests that emotional faces are given special status as visual objects: Cognitive models suggest that emotional stimuli, particularly threat-relevant facial expressions such as fear and anger, are prioritized in visual processing and may be identified by a subcortical “quick and dirty” pathway in the absence of awareness (Tamietto & de Gelder, 2010). Both neuroimaging studies (Williams, Morris, McGlone, Abbott, & Mattingley, 2004) and backward masking studies (Whalen, Rauch, Etcoff, McInerney, & Lee, 1998) have supported the notion of emotion processing without awareness. Recently, our own group (Adams, Gray, Garner, & Graf, 2010) showed adaptation to emotional faces that were rendered invisible using a variant of binocular rivalry: continual flash suppression (CFS, Tsuchiya & Koch, 2005). Here we (i) respond to Yang, Hong, and Blake's (2010) criticisms of our adaptation paper and (ii) provide a unified account of adaptation to facial expression, identity, and gender, under conditions of unawareness
Resumo:
Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.
Resumo:
Compared to skilled adult readers, children typically make more fixations that are longer in duration, shorter saccades, and more regressions, thus reading more slowly (Blythe & Joseph, 2011). Recent attempts to understand the reasons for these differences have discovered some similarities (e.g., children and adults target their saccades similarly; Joseph, Liversedge, Blythe, White, & Rayner, 2009) and some differences (e.g., children’s fixation durations are more affected by lexical variables; Blythe, Liversedge, Joseph, White, & Rayner, 2009) that have yet to be explained. In this article, the E-Z Reader model of eye-movement control in reading (Reichle, 2011; Reichle, Pollatsek, Fisher, & Rayner, 1998) is used to simulate various eye-movement phenomena in adults versus children in order to evaluate hypotheses about the concurrent development of reading skill and eye-movement behavior. These simulations suggest that the primary difference between children and adults is their rate of lexical processing, and that different rates of (post-lexical) language processing may also contribute to some phenomena (e.g., children’s slower detection of semantic anomalies; Joseph et al., 2008). The theoretical implications of this hypothesis are discussed, including possible alternative accounts of these developmental changes, how reading skill and eye movements change across the entire lifespan (e.g., college-aged vs. elderly readers), and individual differences in reading ability.
Resumo:
Prior literature showed that Felder and Silverman learning styles model (FSLSM) was widely adopted to cater to individual styles of learners whether in traditional or Technology Enhanced Learning (TEL). In order to infer this model, the Index of Learning Styles (ILS) instrument was proposed. This research aims to analyse the soundness of this instrument in an Arabic sample. Data were integrated from different courses and years. A total of 259 engineering students participated voluntarily in the study. The reliability was analysed by applying internal construct reliability, inter-scale correlation, and total item correlation. The construct validity was also considered by running factor analysis. The overall results indicated that the reliability and validity of perception and input dimensions were moderately supported, whereas processing and understanding dimensions showed low internal-construct consistency and their items were weakly loaded in the associated constructs. Generally, the instrument needs further effort to improve its soundness. However, considering the consistency of the produced results of engineering students irrespective of cross-cultural differences, it can be adopted to diagnose learning styles.
Resumo:
Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.
Resumo:
Public health policies recommend a population wide decrease in the consumption of saturated fatty acids (SFA) to lower the incidence of cardiovascular and metabolic diseases. In most developed countries, milk and dairy products are the major source of SFA in the human diet. Altering milk fat composition offers the opportunity to lower the consumption of SFA without requiring a change in eating habits. Supplementing the diet of lactating cows with oilseeds, plant oils and marine lipids can be used to replace the SFA in milk fat with monounsaturated fatty acids (MUFA), and to a lesser extent, polyunsaturated fatty acids (PUFA). Due to ruminal metabolism, the decreases in milk SFA are also accompanied by increases in trans fatty acids (TFA), including conjugated isomers. The potential to lower SFA, enrich cis MUFA and PUFA, and alter the abundance and distribution of individual TFA in milk differs according to oil source, form of lipid supplement and degree of oilseed processing, and the influence of other components in the diet. The present review summarises recent evidence on changes in milk fat composition that can be achieved using dietary lipid supplements and highlights the challenges to commercial production of modified milk and dairy products. A meta-analysis on the effects of oilseeds on milk fatty acid composition is also presented.
Resumo:
Excavation west of Wivelsfield, East Sussex, revealed part of an early Romano-British settlement. One of the round-houses may have had a non-domestic, possibly ritual, function. The settlement appears to have been subsequently incorporated within a rectilinear arrangement of field/enclosure ditches. Along the edge of one of these ditches were built a series of features interpreted as ovens, of varying form and likely use, from which charred waste from cereal processing and charcoal from coppiced woodland were recovered.