Variations of Brain Functional Connectivity in Alcohol-Preferring and Non-Preferring Rats with Consecutive Alcohol Training or Acute Alcohol Administration

Alcohol addiction is regarded as a series of dynamic changes to neural circuitries. A comparison of the global network during different stages of alcohol addiction could provide an efficient way to understand the neurobiological basis of addiction. Two animal models (P-rats screened from an alcohol preference family, and NP-rats screened from an alcohol non-preference family) were trained for alcohol preference with a two-bottle free choice method for 4 weeks. To examine the changes in the neural response to alcohol during the development of alcohol preference and acute stimulation, different trials were studied with resting-state fMRI methods during different periods of alcohol preference. The correlation coefficients of 28 regions in the whole brain were calculated, and the results were compared for alcohol preference related to the genetic background/training association. The variety of coherence patterns was highly related to the state and development of alcohol preference. We observed significant special brain connectivity changes during alcohol preference in P-rats. The comparison between the P- and NP-rats highlighted the role of genetic background in alcohol preference. The results of this study support the alterations of the neural network connection during the formation of alcohol preference and confirm that alcohol preference is highly related to the genetic background. This study could provide an effective approach for understanding the neurobiological basis of alcohol addiction.

The impact of the Covid-19 related media coverage upon the five major developing markets

This paper analyses the influence of the Covid-19 coverage by the social media upon the shape of the sovereign yield curves of the five major developing countries, namely Federative Republic of B razil, Russian Federation, Republic of India, People’s Republic of China, and the Republic of South Africa (BRICS). The coherenc e between the level, slope, and the curvature of the sovereign yield term structures and the Covid-19 medi a coverage is found to vary between low and high ranges, depending on the phases of the pandemic. The empirical estimations of the yield-curve factors a re performed by means of the Diebold–Li modified version of the Nelson–Siegel model. The intervals of low coherence reveal the capacity of the two latent factors, level and slope, to be used for creating cross-factor diversification strategies, workable under crisis conditions, as evidenced on the example of the ongoing pandemic. Diverse coherence patterns are reported on a per-country basis, highlighting a promising potential of sovereign debt investments for designing cross-country and cross-factor fixed-income strategies, capable of hedging downside risks.

EEG Patterns and Performance When Facing the Cardinal Directions

1) Background and Objectives: Position in space and passage of time are encoded in the firing of thalamic, hippocampal and entorhinal cortices in rodents. Head direction cells have been reported in freely moving monkeys, and differential brain patterns have been observed in humans while playing a navigation video game and in response to changes in electromagnetic fields. The sensitivity of organisms to environmental and electromagnetic cues could explain recommendations from a traditional system of architecture, Vastu architecture, which recommends aligning homes to the cardinal directions. 2) Hypothesis: Vastu architecture predicts that facing east and north are more advantageous than facing west and south. If facing east and north are more advantageous, then subjects should show distinct EEG patterns and improved performance when facing east and north compared to west or south. 3) Materials and Methods: EEG coherence patterns from 32-channel EEG and time-to-complete jigsaw puzzles were compared while subjects faced the four cardinal directions. 4) Results: When facing east and north, subjects’ frontal beta2 and gamma EEG coherence were significantly higher, and they assembled jigsaw puzzles significantly faster than when facing west or south. 5) Discussion: The brain findings fit the performance data. Better focus, which would reasonably be related with faster performance, is associated with higher levels of beta2 and gamma coherence. 6) Conclusion: These data support the possibility that the human brain may be sensitive to cardinal directions. This highlights how intimately we are connected to the environment and suggests a factor that may be important in orienting work spaces and designing class rooms.

Hype, hyperscanning and embodied social neuroscience

Hyperscanning has been hailed as a game-changing method which will allow us to understand the neuroscience of multi-person social interactions and create ‘second person neuroscience’. Here, I present a critical review of fNIRS hyperscanning studies, examining what they can and cannot tell us about social neuroscience. A key problem is that many current hyperscanning methods cannot distinguish a pure pattern of interpersonal coherence from effects driven by a common input. Limited data on participant behaviour during testing sessions compounds this problem, because it is not clear what behaviours might mediate the brain coherence patterns that are reported. Some studies respond to this problem by retreating from strong cognitive interpretations of hyperscanning data and measuring how overall levels of coherence differ with individual factors (such as age, gender, social relationships between people or clinical diagnosis). Here, I suggest that there is a better way to analyse and interpret hyperscanning studies. By tracking behaviour in detail, and analysing behaviour and brain activity patterns together, it will be possible to define what types of action, perception and mutual prediction arising in the interaction of two or more people can lead to coherent brain signals and why. This approach acknowledges that social brains are embodied and that coherent brain activity arises from the social behaviour of two people in an interaction. By integrating our study of the social brain and social behaviour, we will be able to strengthen the science of both.

Finding biologically significant biclusters: a new function for co-expression evaluation

Analysis of DNA microarray data has been very useful for experimental molecular biology, as it provides unprecedented opportunities to study a wide variety of biological processes. As a part of this analysis, biclustering has been consolidated as one of the first steps in the discovery of new knowledge. Biclustering consists in identifying clusters of genes that present coherent behavior patterns for a subset of experimental conditions. The measure to assess this consistency is a key factor in the quality of discovered biclusters. In this paper, we propose a new function (VF) to evaluate the coherence of biclusters. This function recognizes shifting, and positive and negative scaling patterns, more efficiently than well-known reported functions with a similar purpose. Also, the VF function identifies positive and negative scaling subpatterns, which may be of biological interest and have not previously been discussed in the literature. To assess the performance of the VF function, a biclustering genetic algorithm (BGA_VF) was also designed, and tested on both synthetic and real data. The results show that the BGA_VF algorithm obtains high percentages of significant biclusters and recognizes all the analyzed combinations of coherence patterns.

Finding biologically significant biclusters: a new function for co-expression evaluation

Analysis of DNA microarray data has been very useful for experimental molecular biology, as it provides unprecedented opportunities to study a wide variety of biological processes. As a part of this analysis, biclustering has been consolidated as one of the first steps in the discovery of new knowledge. Biclustering consists in identifying clusters of genes that present coherent behavior patterns for a subset of experimental conditions. The measure to assess this consistency is a key factor in the quality of discovered biclusters. In this paper, we propose a new function (VF) to evaluate the coherence of biclusters. This function recognizes shifting, and positive and negative scaling patterns, more efficiently than well-known reported functions with a similar purpose. Also, the VF function identifies positive and negative scaling subpatterns, which may be of biological interest and have not previously been discussed in the literature. To assess the performance of the VF function, a biclustering genetic algorithm (BGA_VF) was also designed, and tested on both synthetic and real data. The results show that the BGA_VF algorithm obtains high percentages of significant biclusters and recognizes all the analyzed combinations of coherence patterns.

Finding biologically significant biclusters: a new function for co-expression evaluation

Analysis of DNA microarray data has been very useful for experimental molecular biology, as it provides unprecedented opportunities to study a wide variety of biological processes. As a part of this analysis, biclustering has been consolidated as one of the first steps in the discovery of new knowledge. Biclustering consists in identifying clusters of genes that present coherent behavior patterns for a subset of experimental conditions. The measure to assess this consistency is a key factor in the quality of discovered biclusters. In this paper, we propose a new function (VF) to evaluate the coherence of biclusters. This function recognizes shifting, and positive and negative scaling patterns, more efficiently than well-known reported functions with a similar purpose. Also, the VF function identifies positive and negative scaling subpatterns, which may be of biological interest and have not previously been discussed in the literature. To assess the performance of the VF function, a biclustering genetic algorithm (BGA VF) was also designed, and tested on both synthetic and real data. The results show that the BGA VF algorithm obtains high percentages of significant biclusters and recognizes all the analyzed combinations of coherence patterns.