Are You Being Rejected or Excluded? Insights from Neuroimaging Studies Using Different Rejection Paradigms

Rejection sensitivity is the heightened tendency to perceive or anxiously expect disengagement from others during social interaction. There has been a recent wave of neuroimaging studies of rejection. The aim of the current review was to determine key brain regions involved in social rejection by selectively reviewing neuroimaging studies that employed one of three paradigms of social rejection, namely social exclusion during a ball-tossing game, evaluating feedback about preference from peers and viewing scenes depicting rejection during social interaction. A cross the different paradigms of social rejection, there was concordance in regions for experiencing rejection, namely dorsal anterior cingulate cortex (ACC), subgenual ACC and ventral ACC. Functional dissociation between the regions for experiencing rejection and those for emotion regulation, namely medial prefrontal cortex, ventrolateral prefrontal cortex (VLPFC) and ventral striatum, was evident in the positive association between social distress and regions for experiencing rejection and the inverse association between social distress and the emotion regulation regions. The paradigms of social exclusion and scenes depicting rejection in social interaction were more adept at evoking rejection-specific neural responses. These responses were varyingly influenced by the amount of social distress during the task, social support received, self-esteem and social competence. Presenting rejection cues as scenes of people in social interaction showed high rejection sensitive or schizotypal individuals to under-activate the dorsal ACC and VLPFC, suggesting that such individuals who perceive rejection cues in others down-regulate their response to the perceived rejection by distancing themselves from the scene

Linking density functional and mode coupling models for supercooled liquids

We compare predictions from two familiar models of the metastable supercooled liquid respectively constructed with thermodynamic and dynamic approach. In the so called density functional theory (DFT) the free energy $F[\rho]$ of the liquid is a functional of the inhomogeneous density $\rho({\bf r})$. The metastable state is identified as a local minimum of $F[\rho]$. The sharp density profile characterizing $\rho({\bf r})$ is identified as a single particle oscillator, whose frequency is obtained from the parameters of the optimum density function. On the other hand, a dynamic approach to supercooled liquids is taken in the mode coupling theory (MCT) which predict a sharp ergodicity-nonergodicity transition at a critical density. The single particle dynamics in the non-ergodic state, treated approximately, represents a propagating mode whose characteristic frequency is computed from the corresponding memory function of the MCT. The mass localization parameters in the above two models (treated in their simplest forms) are obtained respectively in terms of the corresponding natural frequencies depicted and are shown to have comparable magnitudes.Comment: 24 pages, 10 figure

Probabilistic Non-Local Means

In this paper, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. The probabilistic nature of the new weight function also provides a theoretical basis to choose thresholds rejecting dissimilar patches for fast computations. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of peak signal noise ratio (PSNR) and structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the probabilistic weights in tested NLM variants.Comment: 11 pages, 3 figure

Blockwise SURE Shrinkage for Non-Local Means

In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising. In particular, we derive the closed-form of the optimal blockwise shrinkage for NLM that minimizes the Stein's unbiased risk estimator (SURE). We also propose a constant complexity algorithm allowing fast blockwise shrinkage. Simulation results show that the proposed blockwise shrinkage method improves NLM performance in attaining higher peak signal noise ratio (PSNR) and structural similarity index (SSIM), and makes NLM more robust against parameter changes. Similar ideas can be applicable to other patchwise image denoising techniques

How do incorrect results change the processing of arithmetic information? Evidence from a divided visual field experiment

Despite several recent important developments in understanding numerical processing of both isolated numbers and numbers in the context of arithmetic equations, the relative impact of congruency on high, compared to low, level processing remains unclear. The current study investigated hemispheric differences in the processing of arithmetic material, as a function of semantic and perceptual congruency, using a delayed answer verification task and divided visual field paradigm. A total of 37 participants (22 females and 15 males, mean age 30.06, SD 9.78) were presented unilaterally or bilaterally with equation results that were either correct or incorrect and had a consistent or inconsistent numerical notation. Statistical analyses showed no visual field differences in a notation consistency task, whereas when judgements had to be made on mathematical accuracy there was a right visual field advantage for incorrect equations that were notation consistent. These results reveal a clear differential processing of arithmetic information by the two cerebral hemispheres with a special emphasis on erroneous calculations. Faced with incorrect results and with a consistent numerical notation, the left hemisphere outperforms its right counterpart in making mathematical accuracy decisions

Adjunctive quetiapine for serotonin reuptake inhibitor-resistant obsessive-compulsive disorder: A meta-analysis of randomised controlled treatment trials

Small studies have shown positive effects from adding a variety of antipsychotic agents in patients with obsessive–compulsive disorder who are unresponsive to treatment with serotonin reuptake inhibitors. The evidence, however, is contradictory. This paper reports a meta-analysis of existing double-blind randomized placebo-controlled studies looking at the addition of the second-generation antipsychotic quetiapine in such cases. Three studies fulfilled the inclusion criteria. Altogether 102 individuals were subjected to analysis using Review Manager (4.2.7). The results showed evidence of efficacy for adjunctive quetiapine (< 400 mg/day) on the primary efficacy criterion, measured as changes from baseline in total Yale–Brown Obsessive Compulsive Scale scores (P = 0.008), the clinical significance of which was limited by between-study heterogeneity. The mechanism underlying the effect may involve serotonin and/or dopamine neurotransmission

Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as &#x2018;junk&#x2019;. The term &#x2018;junk&#x2019; implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer&#x2019;s disease genes. &#xd;&#xa;Alzheimer&#x2019;s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. &#xd;&#xa;&#xd;&#xa;Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). &#xd;&#xa;&#xd;&#xa;The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer&#x2019;s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer&#x2019;s disease have been achieved.&#xd;&#xa