In human society, which is organized by social hierarchies, resources are usually allocated unequally and based on social status. In this study, we analyze how being endowed with different social statuses in a math competition affects the perception of fairness during asset allocation in a subsequent Ultimatum Game (UG). Behavioral data showed that when participants were in high status, they were more likely to reject unfair UG offers than in low status. This effect of social status correlated with activity in the right anterior insula (rAI) and with the functional connectivity between the rAI and a region in the anterior middle cingulate cortex, indicating that these two brain regions are crucial for integrating contextual factors and social norms during fairness perception. Additionally, there was an interaction between social status and UG offer fairness in the amygdala and thalamus, implicating the role of these regions in the modulation of social status on fairness perception. These results demonstrate the effect of social status on fairness perception and the potential neural underpinnings for this effect.

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Individuals tend to avoid risk in a gain frame, in which options are presented in a positive way, but seek risk in a loss frame, in which the same options are presented negatively. Previous studies suggest that emotional responses play a critical role in this "framing effect." Given that the Met allele of COMT Val158Met polymorphism (rs4680) is associated with the negativity bias during emotional processing, this study investigated whether this polymorphism is associated with individual susceptibility to framing and which brain areas mediate this gene-behavior association. Participants were genotyped, scanned in resting state, and completed a monetary gambling task with options (sure vs risky) presented as potential gains or losses. The Met allele carriers showed a greater framing effect than the Val/Val homozygotes as the former gambled more than the latter in the loss frame. Moreover, the gene-behavior association was mediated by resting-state functional connectivity (RSFC) between orbitofrontal cortex (OFC) and bilateral amygdala. Met allele carriers showed decreased RSFC, thereby demonstrating higher susceptibility to framing than Val allele carriers. These findings demonstrate the involvement of COMT Val158Met polymorphism in the framing effect in decision-making and suggest RSFC between OFC and amygdala as a neural mediator underlying this gene-behavior association. Hum Brain Mapp 37:1880-1892, 2016. © 2016 Wiley Periodicals, Inc.

Focusing attention on a target creates a center-surround inhibition such that distractors located close to the target do not capture attention. Recent research showed that a distractor can break through this surround inhibition when associated with reward. However, the brain basis for this reward-based attention is unclear. In this fMRI study, we presented a distractor associated with high or low reward at different distances from the target. Behaviorally the low-reward distractor did not capture attention and thus did not cause interference, whereas the high-reward distractor captured attention only when located near the target. Neural activity in extrastriate cortex mirrored the behavioral pattern. A comparison between the high-reward and the low-reward distractors presented near the target (i.e., reward-based attention) and a comparison between the high-reward distractors located near and far from the target (i.e., spatial attention) revealed a common frontoparietal network, including inferior frontal gyrus and inferior parietal sulcus as well as the visual cortex. Reward-based attention specifically activated the anterior insula (AI). Dynamic causal modelling showed that reward modulated the connectivity from AI to the frontoparietal network but not the connectivity from the frontoparietal network to the visual cortex. Across participants, the reward-based attentional effect could be predicted both by the activity in AI and by the changes of spontaneous functional connectivity between AI and ventral striatum before and after reward association. These results suggest that AI encodes reward-based salience and projects it to the stimulus-driven attentional network, which enables the reward-associated distractor to break through the surround inhibition in the visual cortex.

Summary Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface (BCI), which enables the recognition of personal intention. So far, numerous methods have been designed to classify EEG signal features for MI task. However, deep neural networks have been seldom applied to analyze EEG signals. In this study, two novel kinds of deep learning schemes based on convolutional neural networks (CNN) and Long Short‐Term Memory (LSTM) were proposed for MI‐classification. The frequency domain representations of EEG signals were obtained using short time Fourier transform (STFT) to train models. Classification results were compared between conventional algorithm, CNN, and LSTM models. Compared with two other methods, CNN algorithms had shown better performance. These conclusions verified that CNN method was promising for MI‐based BCIs.

A log/normal MWD is characterized by two parameters, its mean molecular weight M0 and width σ. It is demonstrated how these parameters can be obtained by model fitting a stretched exponential function (SEF), as characterized by two parameters β and DS, to the PGSTE response curve. Based on simulations, two general empirical equations relating β and DS to M0 and σ are found. The model enables the MWD characteristics to be determined if the scaling law between diffusivity and molecular weight is known. The sensitivity and relative error of σ and M0 are discussed and the applicability of the model is illustrated by analyzing experimental NMR response curve of some PEO samples. The key advantages of this technique are its simplicity, numerical robustness, and reliability. magnified image

Assuming the signal response in a pulsed gradient stimulated echo (PGSTE) experiment (on a polymer) to be described by a simple stretched-exponential function (SEF) and knowing the scaling law between diffusivity and molecular weight, the molecular weight distribution (MWD) characteristics (kurtosis, skewness, moment, and width) are derived and compared to the corresponding distribution characteristics obtained by a log-normal function fi t (to the same MWD). Also, the challenge involved in obtaining a reliable weight average molecular weight from an SEF response function is discussed.This paper was amended because of mistakes in the equations 11, A3, and A6. polymers in solution can be well approximated by a stretched exponential function (SEF). [ 6 ] Because of the inherent nature of the PGSTE NMR experiment, the signal response from a polymer dissolved in a solvent can be represented by the Laplace transform (LT) of a distribution function, namely, the distribution of diffusivities. Or, alternatively, the distribution function can be expressed by the inverse Laplace transform (ILT) of the response function. It is well known, however, that this is an illposed numerical problem of particular complexity. However, it is known that the ILT of an SEF can be expressed explicitly by a series expansion, [ 7 ] which then bypass the numerical problem of performing an ILT of a SEF.We recently presented an alternative approach to overcome the above numerical problem [ 8 ] whereby a numerical Laplace transform of a log-normal distribution (LND) function was performed and the resulting transform was fi tted to an SEF. By applying the well-known relation between the molecular weight and molecular diffusivity, two empirical equations relating the two SEF parameters of the PGSTE NMR response function and the two corresponding parameters of the log-normal molecular weight distribution (LNMWD) were derived. The above method is, by nature, somewhat approximate as it implicitly

In the present study, we focus on the interactions between poly(propylene imine) (PPI) dendrimer and 18 of the 20 common amino acids by several NMR techniques, including NMR titrations and NOESY analysis. Surface ionic interactions and interior encapsulations were observed, and the binding behavior of amino acids with PPI dendrimer depends much on the side-chain properties of the amino acid, such as charge and hydrophobic/hydrophilic properties. The (1)H NMR titration results show that the formation of PPI dendrimer-amino acid complexes are driven mainly by ionic interactions for all the amino acids except tryptophan, which is involved in strong hydrophobic interactions with the interior pockets of PPI. The hydrophobic encapsulation of tryptophan in PPI pockets is confirmed by NOESY analysis. Amino acids with negatively charged residues much more easily saturate the surface charges on PPI than amino acids with uncharged residues, whereas amino acids with positively charged residues are the most difficult to bind with the surface amine groups on the PPI dendrimer. A simultaneous occurrence of interior encapsulation (hydrophobic, hydrogen bond, or ionic interactions) and surface binding (ionic interactions) was observed for tryptophan, phenylalanine, arginine, lysine, histidine, cysteine, and asparagine, and a preferential surface ionic binding on the PPI surface rather than encapsulations in the interior was obtained for the other amino acids.