Effects of Transcranial Direct Current Stimulation on Episodic Memory Related to Emotional Visual Stimuli

The present study investigated emotional memory following bilateral transcranial electrical stimulation (direct current of 1 mA, for 20 minutes) over fronto-temporal cortical areas of healthy participants during the encoding of images that differed in affective arousal and valence. The main result was a significant interaction between the side of anodal stimulation and image emotional valence. Specifically, right anodal/left cathodal stimulation selectively facilitated the recall of pleasant images with respect to both unpleasant and neutral images whereas left anodal/right cathodal stimulation selectively facilitated the recall of unpleasant images with respect to both pleasant and neutral images. From a theoretical perspective, this double dissociation between the side of anodal stimulation and the advantage in the memory performance for a specific type of stimulus depending on its pleasantness supported the specific-valence hypothesis of emotional processes, which assumes a specialization of the right hemisphere in processing unpleasant stimuli and a specialization of the left hemisphere in processing pleasant stimuli. From a methodological point of view, first we found tDCS effects strictly dependent on the stimulus category, and second a pattern of results in line with an interfering and inhibitory account of anodal stimulation on memory performance. These findings need to be carefully considered in applied contexts, such as the rehabilitation of altered emotional processing or eye-witness memory, and deserve to be further investigated in order to understand their underlying mechanisms of action

Transcranial direct current stimulation of the prefrontal cortex modulates working memory performance: combined behavioural and electrophysiological evidence

The present study demonstrates that tDCS can alter WM performance by modulating the underlying neural oscillations. This result can be considered an important step towards a better understanding of the mechanisms involved in tDCS-induced modulations of WM performance, which is of particular importance, given the proposal to use electrical brain stimulation for the therapeutic treatment of memory deficits in clinical settings

The Parkinson's Disease-Linked Protein DJ-1 Associates with Cytoplasmic mRNP Granules During Stress and Neurodegeneration.

Mutations in the gene encoding DJ-1 are associated with autosomal recessive forms of Parkinson's disease (PD). DJ-1 plays a role in protection from oxidative stress, but how it functions as an "upstream" oxidative stress sensor and whether this relates to PD is still unclear. Intriguingly, DJ-1 may act as an RNA binding protein associating with specific mRNA transcripts in the human brain. Moreover, we previously reported that the yeast DJ-1 homolog Hsp31 localizes to stress granules (SGs) after glucose starvation, suggesting a role for DJ-1 in RNA dynamics. Here, we report that DJ-1 interacts with several SG components in mammalian cells and localizes to SGs, as well as P-bodies, upon induction of either osmotic or oxidative stress. By purifying the mRNA associated with DJ-1 in mammalian cells, we detected several transcripts and found that subpopulations of these localize to SGs after stress, suggesting that DJ-1 may target specific mRNAs to mRNP granules. Notably, we find that DJ-1 associates with SGs arising from N-methyl-D-aspartate (NMDA) excitotoxicity in primary neurons and parkinsonism-inducing toxins in dopaminergic cell cultures. Thus, our results indicate that DJ-1 is associated with cytoplasmic RNA granules arising during stress and neurodegeneration, providing a possible link between DJ-1 and RNA dynamics which may be relevant for PD pathogenesis

Vehicle Activity Recognition Using DCNN

Abstract. This paper presents a novel Deep Convolutional Neural Net work (DCNN) method for vehicle activity classification. We extend our previous approach to be able to classify a larger number of vehicle trajec tories in a single network.We also highlight the fl exibility of our approach in integrating further scenarios to our classifier. Firstly, a spatiotempo ral calculus method is used to encode the relative movement between vehicles as a trajectory of QTC states. We then map the encoded trajectory to a 2D matrix using the one-hot vector mapping, this preserves the important positional data and order for each QTC state. To do this we associate the QTC sequences with pixels to form a 2D image tex ture. Afterwards, we adapted trained CNN architecture into our vehicles activity recognition task. Two separate types of driving data sets are used to evaluate our method. We demonstrate that the proposed method out-performs existing techniques. Along with the proposed approach we created a new dataset of vehicles interactions. Although the focus of this paper is on the automated analysis of vehicle interactions, the proposed technique is general and can be applied for pairwise analysis for moving objects

Repeated Transcranial Direct Current Stimulation Prevents Abnormal Behaviors Associated with Abstinence from Chronic Nicotine Consumption

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