Neural activity dissociation between thought-based and perception-based response conflict

Based on the idea that intentions have different penetrability to perception and thought (Fodor, 1983), four Stroop-like tasks, AA, AW, WA, and WW are used, where the A represents an arrow and the CPPR (closest processing prior to response) is perception, and the W represents a word and the CPPR is thought. Event-related brain potentials were recorded as participants completed these tasks, and sLORETA (standardized low resolution brain electromagnetic tomography) was used to localize the sources at specific time points. These results showed that there is an interference effect in the AA and WA tasks, but not in the AW or WW tasks. The activated brain areas related to the interference effect in the AA task were the PFC and ACC, and PFC activation took place prior to ACC activation; but only PFC in WA task. Combined with previous results, a new neural mechanism of cognitive control is proposed

Left hemisphere predominance of pilocarpine-induced rat epileptiform discharges

<p>Abstract</p> <p>Background</p> <p>The left cerebral hemisphere predominance in human focal epilepsy has been observed in a few studies, however, there is no related systematic study in epileptic animal on hemisphere predominance. The main goal of this paper is to observe if the epileptiform discharges (EDs) of Pilocarpine-induced epileptic rats could present difference between left hemisphere and right hemisphere or not.</p> <p>Methods</p> <p>The electrocorticogram (ECoG) and electrohippocampogram (EHG) from Pilocarpine-induced epileptic rats were recorded and analyzed using Synchronization likelihood (SL) in order to determine the synchronization relation between different brain regions, then visual check and cross-correlation analysis were adopted to evaluate if the EDs were originated more frequently from the left hemisphere than the right hemisphere.</p> <p>Results</p> <p>The data show that the synchronization between left-EHG and right-EHG, left-ECoG and left-EHG, right-ECoG and right-EHG, left-ECoG and right-ECoG, are significantly strengthened after the brain functional state transforms from non-epileptiform discharges to continuous-epileptiform discharges(p < 0.05). When the state transforms from continuous EDs to periodic EDs, the synchronization is significantly weakened between left-ECoG and left-EHG, left-EHG and right-EHG (p < 0.05). Visual check and the time delay (τ) based cross-correlation analysis finds that 10 out of 13 EDs have a left predominance (77%) and 3 out of 13 EDs are right predominance (23%).</p> <p>Conclusion</p> <p>The results suggest that the left hemisphere may be more prone to EDs in the Pilocarpine-induced rat epilepsy model and implicate that the left hemisphere might play an important role in epilepsy states transition.</p

Precoding-based blind separation of MIMO FIR mixtures

This paper focuses on the problem of blind separation of sources mixed by multi-input multi-output finite impulse response channels, which is also called convolutive blind source separation (BSS) in short. This problem has been intensively studied in the context that the sources possess certain favourable properties, such as independence and sparsity. However, these properties may not exist in some practical applications. In this paper, we propose a precoding-based convolutive BSS method, which can deal with mutually correlated sources without requiring the sources to be sparse. It is also applicable to mutually independent sources. In the proposed method, the sources are preprocessed in transmitters prior to transmission by order-one precoders. At the receiving side, the second-order statistics of the sources and the Z -domain features of the precoders are exploited to estimate the coded signals, from which the sources are recovered. Simulation results demonstrate the effectiveness of the new convolutive BSS method

Contrastive Clustering

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic selection process of salient regions in biological vision systems. Although mechanisms of visual attention have achieved great success in computer vision, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we here propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing historically accumulated spatial-channel information. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and other two SNN models with degenerated attention modules, but also achieves competitive accuracy with existing state-of-the-art methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability to incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that appropriately incorporating cognitive mechanisms of the brain may provide a promising approach to elevate the capability of SNNs.Comment: 12 pages, 8 figures, 5 tabes; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Hyperbranched PEG-based multi-NHS polymer and bioconjugation with BSA

Star-shaped poly(ethylene glycol)-N-hydroxysuccinimide (star- PEG-NHS) has shown great promise in a variety of biomedical applications owing to its non-toxicity, innate non-immunogenic properties and versatile, multifunctional end groups. However, its complex and sophisticated synthetic methods, as well as high costs, have significantly impeded its wide application. Here, we report the design and synthesis of a hyperbranched PEG-based polymer with multiple NHS functional groups (>12). The hyper- branched PEG-based multi-NHS polymer can react easily with a protein (bovine serum albumin, BSA) to form a PEG-protein hydro- gel that displays great potential for biomedical applications

Altered gene-expression profile in rat plasma and promoted body and brain development by environmental enrichment

Environmental enrichment (EE) refers to the exposure of laboratory animals to physical and social stimulation, which can improve animals’ well-being. The study was aimed to explore how the prenatal EE impacts affect the development, behavior, hormones and gene expression of the offspring. 28 pregnant rats were randomized into an EE group (EEG) housed in cages with EE or a control group (CG) housed in normal cages. Measurements included offspring development parameters (body weight, body length, and tail length) and behavior (open-field test, OFT), hormone levels (cortisol, dopamine, 5-HT, and growth hormone) and gene expression profile. Results showed that the development parameters of EEG offspring were statistically superior to the CG offspring. OFT count of EEG offspring was more than CG. EEG and CG offspring did not differ on cortisol, dopamine, 5-HT or growth factor. Gene expression profile chip test showed that 25 genes were up-regulated and 23 genes down-regulated in the EEG vs CG comparison, among which five GO annotations and four KEGG pathways were annotated. Findings indicate that EE during pregnancy could positively promote the body and nervous system development of offspring, involving the evidence for altered gene expression profile.Keywords: Environmental enrichment, rats, gene expression, behavior, developmentAfrican Journal of Biotechnology Vol. 12(20), pp. 3071-308

Causal relationship between rheumatoid arthritis and hypothyroidism or hyperthyroidism: a bidirectional two-sample univariable and multivariable Mendelian randomization study

ObjectiveThe causal relationship between Rheumatoid arthritis (RA) and hypothyroidism/hyperthyroidism remains controversial due to the limitations of conventional observational research, such as confounding variables and reverse causality. We aimed to examine the potential causal relationship between RA and hypothyroidism/hyperthyroidism using Mendelian randomization (MR).MethodWe conducted a bidirectional two-sample univariable analysis to investigate the potential causal relationship between hypothyroidism/hyperthyroidism and RA. Furthermore, we performed a multivariate analysis to account for the impact of body mass index (BMI), smoking quantity, and alcohol intake frequency.ResultsThe univariable analysis indicated that RA has a causative influence on hypothyroidism (odds ratio [OR]=1.07, 95% confidence interval [CI]=1.01–1.14, P=0.02) and hyperthyroidism (OR=1.32, 95% CI=1.15–1.52, P&lt;0.001). When hypothyroidism/hyperthyroidism was considered as an exposure variable, we only observed a causal relationship between hypothyroidism (OR=1.21, 95% CI=1.05–1.40, P=0.01) and RA, whereas no such connection was found between hyperthyroidism (OR=0.91, 95% CI=0.83–1.01, P=0.07) and RA. In the multivariate MR analyses, after separately and jointly adjusting for the effects of daily smoking quantity, alcohol intake frequency, and BMI, the causal impact of RA on hypothyroidism/hyperthyroidism and hypothyroidism on RA remained robust. However, there is no evidence to suggest a causal effect of hyperthyroidism on the risk of RA (P &gt;0.05).ConclusionUnivariate and multivariate MR analyses have validated the causal association between RA and hypothyroidism/hyperthyroidism. Hypothyroidism confirmed a causal relationship with RA when employed as an exposure variable, whereas no such relationship was found between hyperthyroidism and RA

Local Temporal Correlation Common Spatial Patterns for Single Trial EEG Classification during Motor Imagery

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based braincomputer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application