Contextual, but not auditory, fear conditioning is disrupted by neurotoxic selective lesion of the basal nucleus of amygdala in rats

The basolateral amygdala complex (BLA) is involved in acquisition of contextual and auditory fear conditioning. However, the BLA is not a single structure but comprises a group of nuclei, including the lateral (LA), basal (BA) and accessory basal (AB) nuclei. While it is consensual that the LA is critical for auditory fear conditioning, there is controversy on the participation of the BA in fear conditioning. Hodological and neurophysiological findings suggest that each of these nuclei processes distinct information in parallel; the BA would deal with polymodal or contextual representations, and the LA would process unimodal or elemental representations. Thus, it seems plausible to hypothesize that the BA is required for contextual, but not auditory, fear conditioning. This hypothesis was evaluated in Wistar rats submitted to multiple-site ibotenate-induced damage restricted to the BA and then exposed to a concurrent contextual and auditory fear conditioning training followed by separated contextual and auditory conditioning testing. Differing from electrolytic lesion and lidocaine inactivation, this surgical approach does not disturb fibers of passage originating in other brain areas, restricting damage to the aimed nucleus. Relative to the sham-operated controls, rats with selective damage to the BA exhibited disruption of performance in the contextual, but not the auditory, component of the task. Thus, while the BA seems required for contextual fear conditioning, it is not critical for both an auditory-US association, nor for the expression of the freezing response. (C) 2009 Elsevier Inc. All rights reserved.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.