The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions

In this review we evaluate the cognitive and neural effects of positive and negative mood on executive function. Mild manipulations of negative mood appear to have little effect on cognitive control processes, whereas positive mood impairs aspects of updating, planning and switching. These cognitive effects may be linked to neurochemistry: with positive mood effects mediated by dopamine while negative mood effects may be mediated by serotonin levels. Current evidence on the effects of mood on regional brain activity during executive functions, indicates that the prefrontal cortex is a recurrent site of integration between mood and cognition. We conclude that there is a disparity between the importance of this topic and awareness of how mood affects, executive functions in the brain. Most behavioural and neuroimaging studies of executive function in normal samples do not explore the potential role of variations in mood, yet the evidence we outline indicates that even mild fluctuations in mood can have a significant influence on neural activation and cognition. (c) 2006 Elsevier Ltd. All rights reserved.

Positive schizotypy and trauma-related intrusions

The current study extends previous investigation of schizotypy as a vulnerability factor for trauma-related intrusions through the use of a clinical sample. Fifty people seeking psychological interventions after experiencing a distressing or traumatic event completed measures of positive schizotypy, posttraumatic stress disorder symptomatology, peritraumatic dissociation, and mood. Individuals scoring high in positive schizotypy were vulnerable to experiencing more frequent trauma-related intrusions along with wider posttraumatic stress disorder symptomatology, including hypervigilance, avoidance, and low mood. Results are discussed within a theoretical context, suggesting that certain information processing styles associated with high schizotype individuals may account for a vulnerability to trauma-related intrusions.

Picture book exposure elicits positive visual preferences in toddlers

Although the relationship between "mere exposure" and attitude enhancement is well established in the adult domain, there has been little similar work with children. This article examines whether toddlers' visual attention toward pictures of foods can be enhanced by repeated visual exposure to pictures of foods in a parent-administered picture book. We describe three studies that explored the number and nature of exposures required to elicit positive visual preferences for stimuli and the extent to which induced preferences generalize to other similar items. Results show that positive preferences for stimuli are easily and reliably induced in children and, importantly, that this effect of exposure is not restricted to the exposed stimulus per se but also applies to new representations of the exposed item. (C) 2009 Elsevier Inc. All rights reserved.

Stabilizing swarm intelligence search via positive feedback resource allocation

A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.

Communicating neurons: a connectionist spiking neuron implementation of stochastic diffusion search

An information processing paradigm in the brain is proposed, instantiated in an artificial neural network using biologically motivated temporal encoding. The network will locate within the external world stimulus, the target memory, defined by a specific pattern of micro-features. The proposed network is robust and efficient. Akin in operation to the swarm intelligence paradigm, stochastic diffusion search, it will find the best-fit to the memory with linear time complexity. information multiplexing enables neurons to process knowledge as 'tokens' rather than 'types'. The network illustrates possible emergence of cognitive processing from low level interactions such as memory retrieval based on partial matching. (C) 2007 Elsevier B.V. All rights reserved.

Sulforaphane protects cortical neurons against 5-S-cysteinyl-dopamine-induced toxicity through the activation of ERK1/2, Nrf-2 and the upregulation of detoxification enzymes

The degeneration of dopaminergic neurons in the substantia nigra has been linked to the formation of the endogenous neurotoxin 5-S-cysteinyl-dopamine. Sulforaphane (SFN), an isothiocyanate derived from the corresponding precursor glucosinolate found in cruciferous vegetables has been observed to exert a range of biological activities in various cell populations. In this study, we show that SFN protects primary cortical neurons against 5-S-cysteinyl-dopamine induced neuronal injury. Pre-treatment of cortical neurons with SFN (0.01-1 microM) resulted in protection against 5-S-cysteinyl-dopamine-induced neurotoxicity, which peaked at 100 nM. This protection was observed to be mediated by the ability of SFN to modulate the extracellular signal-regulated kinase 1 and 2 and the activation of Kelch-like ECH-associated protein 1/NF-E2-related factor-2 leading to the increased expression and activity of glutathione-S-transferase (M1, M3 and M5), glutathione reductase, thioredoxin reductase and NAD(P)H oxidoreductase 1. These data suggest that SFN stimulates the NF-E2-related factor-2 pathway of antioxidant gene expression in neurons and may protect against neuronal injury relevant to the aetiology of Parkinson's disease.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.