Altered adaptive but not veridical decision-making in substance dependent individuals.

Drug addiction is associated with impaired judgment in unstructured situations in which success depends on self-regulation of behavior according to internal goals (adaptive decision-making). However most executive measures are aimed at assessing decision-making in structured scenarios, in which success is determined by external criteria inherent to the situation (veridical decision-making). The aim of this study was to examine the performance of Substance Abusers (SA, n = 97) and Healthy Comparison participants (HC, n = 81) in two behavioral tasks that mimic the uncertainty inherent in real-life decision-making: the Cognitive Bias Task (CB) and the Iowa Gambling Task (IGT) (administered only to SA). A related goal was to study the interdependence between performances on both tasks. We conducted univariate analyses of variance (ANOVAs) to contrast the decision-making performance of both groups; and used correlation analyses to study the relationship between both tasks. SA showed a marked context-independent decision-making strategy on the CB's adaptive condition, but no differences were found on the veridical conditions in a subsample of SA (n = 34) and HC (n = 22). A high percentage of SA (75%) also showed impaired performance on the IGT. Both tasks were only correlated when no impaired participants were selected. Results indicate that SA show abnormal decision-making performance in unstructured situations, but not in veridical situations.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

A guide to making an advance health care directive

This guide helps people residing in Andalucía to fill in their Advance Health Care Directives, also known as a Living Will. This document allows you to make known in advance and in writing your wishes and preferences about the health care that you wish to receive when you are unable to communicate because of illness. This guide gives simple and rapid information about how to fill in the document and how to express your wishes. It also contains additional information on Advance Health Care Directives and information onthe Andalusian Registry of Advance Health Care Directives.