25th Annual Computational Neuroscience Meeting: CNS-2016

Establishment of animal models and behavioral studies for autism spectrum disorders

In recent years, the incidence of autism spectrum disorder (ASD) has increased, but the etiology and pathogenesis remain unclear. In this narrative review, we review and systematically summarize the methods used to construct animal models to study ASD and the related behavioral studies based on recent literature. Utilization of various ASD animal models can complement research on the etiology, pathogenesis, and core behaviors of ASD, providing information and a foundation for further basic research and clinical treatment of ASD.

Fabrication strategies for chiral self-assembly surface

Chiral self-assembly is the spontaneous organization of individual building blocks from chiral (bio)molecules to macroscopic objects into ordered superstructures. Chiral self-assembly is ubiquitous in nature, such as DNA and proteins, which formed the foundation of biological structures. In addition to chiral (bio) molecules, chiral ordered superstructures constructed by self-assembly have also attracted much attention. Chiral self-assembly usually refers to the process of forming chiral aggregates in an ordered arrangement under various non-covalent bonding such as H-bond, π-π interactions, van der Waals forces (dipole-dipole, electrostatic effects, etc.), and hydrophobic interactions. Chiral assembly involves the spontaneous process, which followed the minimum energy rule. It is essentially an intermolecular interaction force. Self-assembled chiral materials based on chiral recognition in electrochemistry, chiral catalysis, optical sensing, chiral separation, etc. have a broad application potential with the research development of chiral materials in recent years.

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Epilepsy, a prevalent neurological disorder characterized by high morbidity, frequent recurrence, and potential drug resistance, profoundly affects millions of people globally. Understanding the microscopic mechanisms underlying seizures is crucial for effective epilepsy treatment, and a thorough understanding of the intricate neural circuits underlying epilepsy is vital for the development of targeted therapies and the enhancement of clinical outcomes. This review begins with an exploration of the historical evolution of techniques used in studying neural circuits related to epilepsy. It then provides an extensive overview of diverse techniques employed in this domain, discussing their fundamental principles, strengths, limitations, as well as their application. Additionally, the synthesis of multiple techniques to unveil the complexity of neural circuits is summarized. Finally, this review also presents targeted drug therapies associated with epileptic neural circuits. By providing a critical assessment of methodologies used in the study of epileptic neural circuits, this review seeks to enhance the understanding of these techniques, stimulate innovative approaches for unraveling epilepsy's complexities, and ultimately facilitate improved treatment and clinical translation for epilepsy.

Bridging Retinal and Cerebral Neurodegeneration: A Focus on Crosslinks between Alzheimer-Perusini's Disease and Retinal Dystrophies

In the early stages of Alzheimer-Perusini's disease (AD), individuals often experience vision-related issues such as color vision impairment, reduced contrast sensitivity, and visual acuity problems. As the disease progresses, there is a connection with glaucoma and age-related macular degeneration (AMD) leading to retinal cell death. The retina's involvement suggests a link with the hippocampus, where most AD forms start. A thinning of the retinal nerve fiber layer (RNFL) due to the loss of retinal ganglion cells (RGCs) is seen as a potential AD diagnostic marker using electroretinography (ERG) and optical coherence tomography (OCT). Amyloid beta fragments (Aβ), found in the eye's vitreous and aqueous humor, are also present in the cerebrospinal fluid (CSF) and accumulate in the retina. Aβ is known to cause tau hyperphosphorylation, leading to its buildup in various retinal layers. However, diseases like AD are now seen as mixed proteinopathies, with deposits of the prion protein (PrP) and α-synuclein found in affected brains and retinas. Glial cells, especially microglial cells, play a crucial role in these diseases, maintaining immunoproteostasis. Studies have shown similarities between retinal and brain microglia in terms of transcription factor expression and morphotypes. All these findings constitute a good start to achieving better comprehension of neurodegeneration in both the eye and the brain. New insights will be able to bring the scientific community closer to specific disease-modifying therapies.

What multiplexing means for the interpretation of functional MRI data

Despite technology advances that have enabled routine acquisition of functional MRI data with sub-millimeter resolution, the inferences that cognitive neuroscientists must make to link fMRI data to behavior are complicated. Thus, a single dataset subjected to different analyses can be interpreted in different ways. This article presents two optical analogies that can be useful for framing fMRI analyses in a way that allows for multiple interpretations of fMRI data to be valid simultaneously without undermining each other. The first is reflection: when an object is reflected in a mirrored surface, it appears as if the reflected object is sharing space with the mirrored object, but of course it is not. This analogy can be a good guide for interpreting the fMRI signal, since even at sub-millimeter resolutions the signal is determined by a mixture of local and long-range neural computations. The second is refraction. If we view an object through a multi-faceted prism or gemstone, our view will change-sometimes dramatically-depending on our viewing angle. In the same way, interpretation of fMRI data (inference of underlying neuronal activity) can and should be different depending on the analysis approach. Rather than representing a weakness of the methodology, or the superiority of one approach over the other (for example, simple regression analysis versus multi-voxel pattern analysis), this is an expected consequence of how information is multiplexed in the neural networks of the brain: multiple streams of information are simultaneously present in each location. The fact that any one analysis typically shows only one view of the data also puts some parentheses around fMRI practitioners' constant search for ground truth against which to compare their data. By holding our interpretations lightly and understanding that many interpretations of the data can all be true at the same time, we do a better job of preparing ourselves to appreciate, and eventually understand, the complexity of the brain and the behavior it produces.

Understanding mental health through computers: An introduction to computational psychiatry

Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals

Introduction

Can we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain functional connectivity patterns. Attempts to unveil the common neural patterns emerged in ASD are the essence of ASD classification. We claim that graph representation learning methods can appropriately extract the connectivity patterns of the brain, in such a way that the method can be generalized to every recording condition, and phenotypical information of subjects. These methods can capture the whole structure of the brain, both local and global properties.

Methods

The investigation is done for the worldwide brain imaging multi-site database known as ABIDE I and II (Autism Brain Imaging Data Exchange). Among different graph representation techniques, we used AWE, Node2vec, Struct2vec, multi node2vec, and Graph2Img. The best approach was Graph2Img, in which after extracting the feature vectors representative of the brain nodes, the PCA algorithm is applied to the matrix of feature vectors. The classifier adapted to the features embedded in graphs is an LeNet deep neural network.

Results and discussion

Although we could not outperform the previous accuracy of 10-fold cross-validation in the identification of ASD versus control patients in this dataset, for leave-one-site-out cross-validation, we could obtain better results (our accuracy: 80%). The result is that graph embedding methods can prepare the connectivity matrix more suitable for applying to a deep network.

Detection of epileptic seizures through EEG signals using entropy features and ensemble learning

Introduction

Epilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pathological information of brain and they are a prominent medical tool for detecting epileptic seizures, visual interpretation of such tools is time-consuming. Since early diagnosis of epilepsy is essential to control seizures, we present a new method using data mining and machine learning techniques to diagnose epileptic seizures automatically.

Methods

The proposed detection system consists of three main steps: In the first step, the input signals are pre-processed by discrete wavelet transform (DWT) and sub-bands containing useful information are extracted. In the second step, the features of each sub-band are extracted by approximate entropy (ApEn) and sample entropy (SampEn) and then these features are ranked by ANOVA test. Finally, feature selection is done by the FSFS technique. In the third step, three algorithms are used to classify seizures: Least squared support vector machine (LS-SVM), K nearest neighbors (KNN) and Naive Bayes model (NB).

Results and discussion

The average accuracy for both LS-SVM and NB was 98% and it was 94.5% for KNN, while the results show that the proposed method can detect epileptic seizures with an average accuracy of 99.5%, 99.01% of sensitivity and 100% of specificity which show an improvement over most similar methods and can be used as an effective tool in diagnosing this complication.

Manual acupuncture for neuromusculoskeletal disorders: The selection of stimulation parameters and corresponding effects

As a minimally invasive method of physical stimulation, manual acupuncture (MA) is used globally as a sort of therapy for neuromusculoskeletal disorders. In addition to selecting appropriate acupoints, acupuncturists should also determine the stimulation parameters of needling, such as the manipulation (lifting-thrusting or twirling), needling amplitude, velocity, and stimulation time. At present, most studies focus on acupoint combination and mechanism of MA, the relationship between stimulation parameters and their therapeutic effects, as well as the influence on mechanism of action are relatively scattered, and lack of systematic summary and analysis. This paper reviewed the three types of stimulation parameters of MA, their common options and values, corresponding effects and potential mechanisms of action. The purpose of such efforts is to provide a useful reference for the dose-effect relationship of MA and the quantification and standardization of its clinical treatment of neuromusculoskeletal disorders to further promote the application of acupuncture in the world.

A spiking network model for clustering report in a visual working memory task

Introduction

Working memory (WM) plays a key role in many cognitive processes, and great interest has been attracted by WM for many decades. Recently, it has been observed that the reports of the memorized color sampled from a uniform distribution are clustered, and the report error for the stimulus follows a Gaussian distribution.

Methods

Based on the well-established ring model for visuospatial WM, we constructed a spiking network model with heterogeneous connectivity and embedded short-term plasticity (STP) to investigate the neurodynamic mechanisms behind this interesting phenomenon.

Results

As a result, our model reproduced the clustering report given stimuli sampled from a uniform distribution and the error of the report following a Gaussian distribution. Perturbation studies showed that the heterogeneity of connectivity and STP are necessary to explain experimental observations.

Conclusion

Our model provides a new perspective on the phenomenon of visual WM in experiments.

A pathway towards the development and evolution of consumer behavior: Policy directions for sustainable development and improvement of nutrition

Introduction

The virtuality, concealment, uncertainty and complexity of online trading make the online food trading market have security risks, while the lack of information, information asymmetry and imperfect market system make the "lemon problem" in the market increasingly obvious.

Methods

In order to clearly understand and manage the "lemon problem" in the online food trading market, we built an evolutionary game model involving the seller, buyers and online food trading platform, deeply analyzed the formation process of the "lemon problem" in the online food trading market, and revealed the influencing factors and effects of each subject's strategy choice from the perspectives of subsidy, punishment, cost, and benefit.

Results

Findings of this study reveal that: (1) In the online food trading market, the strategy of the seller, buyer and platform will be stable in six situations, and the "lemon problem" will emerge with the development and evolution of the online food trading market. (2) The strategy of each subject in the online food trading market will be affected by variables like cost difference between positive performance and negative performance of the seller, punishment from the buyer with positive participation to the seller with negative performance, subsidy from the platform with positive regulation to the seller with positive performance, etc., and different factors have different influence directions and degrees on the subject strategy. (3) In the online food trading market, cost, punishment, subsidy and benefit have different effects on the subject's strategy. Among them, cost and cost difference have the most significant impact on the subject's strategy, followed by punishment and benefit difference, and subsidy and additional benefit have less impact on the subject's strategy.

Discussion

Based on our study findings, it is proposed that by constructing a complete and standardized system of online food trading market from the aspects of examination and verification institution, reward and punishment institution, and supervision institution, it will be able to provide reference for managing the "lemon problem" in the online food trading market, promoting the sustainable development of the market, and ensuring the safety of online food.

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Interpersonal sensorimotor communication shapes intrapersonal coordination in a musical ensemble

Social behaviors rely on the coordination of multiple effectors within one's own body as well as between the interacting bodies. However, little is known about how coupling at the interpersonal level impacts coordination among body parts at the intrapersonal level, especially in ecological, complex, situations. Here, we perturbed interpersonal sensorimotor communication in violin players of an orchestra and investigated how this impacted musicians' intrapersonal movements coordination. More precisely, first section violinists were asked to turn their back to the conductor and to face the second section of violinists, who still faced the conductor. Motion capture of head and bow kinematics showed that altering the usual interpersonal coupling scheme increased intrapersonal coordination. Our perturbation also induced smaller yet more complex head movements, which spanned multiple, faster timescales that closely matched the metrical levels of the musical score. Importantly, perturbation differentially increased intrapersonal coordination across these timescales. We interpret this behavioral shift as a sensorimotor strategy that exploits periodical movements to effectively tune sensory processing in time and allows coping with the disruption in the interpersonal coupling scheme. As such, head movements, which are usually deemed to fulfill communicative functions, may possibly be adapted to help regulate own performance in time.

An ounce of prevention or a pound of cure? Multi-level modelling on the antecedents of mobile-wallet adoption and the moderating role of e-WoM during COVID-19

During the COVID-19 epidemic, personal safety has received increasing attention, leading to behavioral changes. Mobile-wallet (m-wallet) makes it easier for people to keep social distance, which helps stop the spread of the COVID-19 virus. Evolving Internet technology has brought about changes in consumer lifestyle. The current situation of COVID-19 has created a business environment to shift from traditional ways and adopt e-commerce solutions worldwide. Grounded in technology acceptance model (TAM) theory, this study’s objective is two-fold: First, this study intends to examine perceived susceptibility to COVID-19, perceived severity of COVID-19, insecurity and discomfort as the predictors of perceived usefulness (PU) and perceived ease of use (PEOU). Second, the current research intends to test the moderating effect of electronic words-of-mouth (eWOM) on the relationship between attitude and usage intention. Using survey methods, 226 usable responses were collected through a mall intercept survey in Pakistan. Data were analyzed using partial least square (PLS). The results revealed that PEOU and PU positively influence attitude toward M-wallet. This study has found that attitude positively influences the usage intention in adopting M-wallet. The results also support the moderating role of eWOM. These findings contribute to the marketing literature in several ways, particularly in Pakistan. This is the first study to use eWOM as a moderating variable in the TAM theory. In addition, this study adds to the current body of knowledge by considering eWOM as a multi-dimensional construct novel in m-wallet literature.

Customer's decision and affective assessment of online product recommendation: A recommendation-product congruity proposition

Online product recommendation (OPR) systems have gained prominence in the context of e-commerce over the past years. Despite the increased research on OPR use, less attention has been paid to examining how decision and affective assessment of the OPR are contingent upon the product type. This study proposes and examines a recommendation-product congruity proposition based on cognitive fit and schema congruity theories. The proposition states that when the content (i.e., a stimulus-based schema) of the OPR [either system-generated recommendation (SGR) or a consumer-generated recommendation (CGR)] matches the brain-stored schema initiated by a particular product (either a search product or an experienced product), then a consumer would use a schema-based information assessment strategy and experience favorable decision and affective assessment of the OPR. This then affects consumers' intentions to purchase and reuse OPR. The proposition is tested via a 2 × 2 between-respondents factorial design of a cross-sectional survey with 482 Amazon customers. The results support the following two matching conditions of the proposition: (1) SGR describing a search product and (2) CGR explaining an experienced product, which might lead customers to perceive lower decision effort, greater decision quality, and higher enjoyment with the OPR that subsequently have a significant impact on their intentions to purchase and reuse OPR. This study expands our understanding of how recommendation-product congruence influences the consumer's decision and affective assessment behavior and provides practical implications for the identification and presentation of the recommendation type and product type for a better customer decision.

The correlations between kinematic profiles and cerebral hemodynamics suggest changes of motor coordination in single and bilateral finger movement

Objective

The correlation between the performance of coordination movement and brain activity is still not fully understood. The current study aimed to identify activated brain regions and brain network connectivity changes for several coordinated finger movements with different difficulty levels and to correlate the brain hemodynamics and connectivity with kinematic performance.

Methods

Twenty-one right-dominant-handed subjects were recruited and asked to complete circular motions of single and bilateral fingers in the same direction (in-phase, IP) and in opposite directions (anti-phase, AP) on a plane. Kinematic data including radius and angular velocity at each task and synchronized blood oxygen concentration data using functional near-infrared spectroscopy (fNIRS) were recorded covering six brain regions including the prefrontal cortex, motor cortex, and occipital lobes. A general linear model was used to locate activated brain regions, and changes compared with baseline in blood oxygen concentration were used to evaluate the degree of brain region activation. Small-world properties, clustering coefficients, and efficiency were used to measure information interaction in brain activity during the movement.

Result

It was found that the radius error of the dominant hand was significantly lower than that of the non-dominant hand (p < 0.001) in both clockwise and counterclockwise movements. The fNIRS results confirmed that the contralateral brain region was activated during single finger movement and the dominant motor area was activated in IP movement, while both motor areas were activated simultaneously in AP movement. The Δhbo were weakly correlated with radius errors (p = 0.002). Brain information interaction in IP movement was significantly larger than that from AP movement in the brain network (p < 0.02) in the right prefrontal cortex. Brain activity in the right motor cortex reduces motor performance (p < 0.001), while the right prefrontal cortex region promotes it (p < 0.05).

Conclusion

Our results suggest there was a significant correlation between motion performance and brain activation level, as well as between motion deviation and brain functional connectivity. The findings may provide a basis for further exploration of the operation of complex brain networks.

Innovative application of new media in visual communication design and resistance to innovation

It has become essential to create and apply new media in visual communication design due to social media existence. This study aims to investigate the role of innovative applications of new media in visual communication design in educational institutions. Traditional media design in visual communication lacks to disseminate information more effectively, which requires innovative change. Therefore, this study attempts to highlight the role of innovative application of new media in visual communication by considering visual expression design with information technology (IT), flexible layout, diversified modes of transmission, and interactivity of integration. For this purpose, this study adopted a quantitative research approach in which a cross-sectional research design is followed. A questionnaire survey is carried out to collect data from educational institutions in China. Partial Least Square-Structural Equation Modeling (PLS-SEM) is used for data analysis. Results of the study indicated that innovative applications of new media have central importance in visual communication. However, resistance to innovative change has a negative role in the relationship between innovative applications of new media and visual communication design. Results of the study highlighted that visual expression design with IT, flexible layout, diversified modes of transmission, and interactivity of integration have a positive effect on visual communication design. Therefore, among the educational intuitions of China, implementing innovative applications related to the new media can lead to visual communication design. The results of this study provided several insights for the practitioners to promote communication methods among educational institutions.

The Effects of Sustained Literacy Engagement on Cognition and Sentence Processing Among Older Adults

Considerable evidence suggests that language processing depends on memory processes, which are vulnerable to declines with aging. Yet little is known about the effects of language processing in the form of sustained literacy engagement on memory and other aspects of cognition. In the current study, adults (60-79 years of age) were randomly assigned to an 8-week program of leisure reading (n = 38) or to an active puzzle control (n = 38). Relative to the control, the experimental group showed differential improvement in verbal working memory and episodic memory. The experimental group also showed evidence of enhanced conceptual integration in sentence processing. These effects did not vary as a function of personality characteristics (e.g., openness) hypothesized to be compatible with literacy engagement. These findings support the idea that the exercise of cognitive capacities in the context of everyday life may offset age-related impairment in areas of cognition engaged by the activity, regardless of dispositional fit.

Evaluating Users' Emotional Experience in Mobile Libraries: An Emotional Model Based on the Pleasure-Arousal-Dominance Emotion Model and the Five Factor Model

As a part of user experience, user emotion has rarely been studied in mobile libraries. Specifically, with the proposed emotional model in combination with the Pleasure-Arousal-Dominance (PAD) Emotion Model and the Five Factor Model (FFM), we evaluate user emotions on the mobile library's three IS features (i.e., user interface, interaction quality, and service environment). An experience procedure with three tasks has been designed to collect data. 50 participants were enrolled, and they were asked to fill in questionnaires right after the experience. The correlations among the PAD emotions were examined. Specifically, users have a low perception of pleasure (P), high perception of arousal (A), and low perception of dominance (D). However, these three emotional states were not always significantly correlated with each other. This study extends mobile library research by focusing on users' emotional experience. Specifically, the detailed PAD emotions have been examined. This study provides a new approach for application developers and managers to evaluate the user experience of an application.

Characterizing Sleep Differences in Children With and Without Sensory Sensitivities

Objectives

Individuals register and react to daily sensory stimuli differently, which influences participation in occupations. Sleep is a foundational nightly occupation that impacts overall health and development in children. Emerging research suggests that certain sensory processing patterns, specifically sensory sensitivities, may have a negative impact on sleep health in children. In this study, we aimed to (i) characterize sleep in children with and without sensory sensitivities and (ii) examine the relationship between sensory processing patterns (using the Sensory Profile-2) and sleep using validated parent- and child-reported questionnaires. We hypothesized that children with sensory sensitivities will exhibit more difficulties with sleep.

Methods

We recruited 22 children (ages 6-10) with sensory sensitivities (SS) and 33 children without sensory sensitivities (NSS) to complete validated sleep and sensory processing questionnaires: the Children's Sleep Habits Questionnaire (CSHQ), Sleep Self-Report (SSR), and Sensory Profile-2.

Results

Children with SS had significantly more sleep behaviors reported by both parents (p < 0.001, g = 1.11) and children (p < 0.001, g = 1.17) compared to children with NSS. Specifically, children with SS had higher frequencies of sleep anxiety (p = 0.004, g = 0.79), bedtime resistance (p = 0.001, g = 0.83), and sleep onset delay (p = 0.003, g = 0.95). Spearman's ρ correlations indicated significant positive correlations between parent- and child-reported sleep. Children with SS showed a larger association and greater variability between sleep and sensory processing compared to their peers. Significant positive correlations between parent-reported sleep behaviors and sensory sensitive and avoiding patterns were identified for both children with SS and NSS. Child-reported sleep behaviors were most strongly associated with sensitive and avoiding patterns for children with NSS and seeking patterns for children with SS.

Conclusion

We present evidence that sleep is impacted for children with SS to a greater extent than children with NSS. We also identified that a child's sensory processing pattern may be an important contributor to sleep problems in children with and without sensory sensitivities. Sleep concerns should be addressed within routine care for children with sensory sensitivities. Future studies will inform specific sleep intervention targets most salient for children with SS and other sensory processing patterns.

Socially Extended Scientific Knowledge

A three-tiered account of social cognition is set out-along with the corresponding variety of social knowledge that results from this social cognition-and applied to the special case of scientific collaboration. The first tier is socially-facilitated cognition, which results in socially-facilitated knowledge. This is a form of cognition which, while genuinely social (in that social factors play an important explanatory role in producing the target cognitive success), falls short of socially extended cognition. The second tier is socially extended cognition, which generates socially extended knowledge. This form of cognition is social in the specific sense of the information-processing of other agents forms part of the socially extended cognitive process at issue. It is argued, however, that the core notion of socially extended cognition is individual in nature, in that the target cognitive success is significantly creditable to the socially extended cognitive agency of the individual. Socially extended cognition, in its core sense, thus generates individual knowledge. Finally, there is distributed cognition, which generates distributed knowledge. This is where the cognitive successes produced by a research team are attributable to a group agent rather than to individuals within the team. Accordingly, where this form of social cognition generates knowledge (distributed knowledge), the knowledge is irreducibly group knowledge. It is argued that by making clear this three-tiered structure of social scientific knowledge a prima facie challenge is posed for defenders of distributed scientific cognition and knowledge to explain why this form of social knowledge is being exhibited and not one of the two weaker (and metaphysically less demanding) forms of social knowledge.

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.

Investigating the Role of Perceived Information Overload on COVID-19 Fear: A Moderation Role of Fake News Related to COVID-19

During crises and uncertain situations such as the coronavirus disease 2019 (COVID-19) pandemic, social media plays a key function because it allows people to seek and share news, as well as personal views and ideas with each other in real time globally. Past research has highlighted the implications of social media during disease outbreaks; nevertheless, this study refers to the possible negative effects of social media usage by individuals in the developing country during the COVID-19 epidemic lockdown. Specifically, this study investigates the COVID-19 fear using the survey data collected from a developing country. In total, 880 entries were used to analyze the COVID-19 fear using the AMOS software. Findings indicated that information-seeking and sharing behavior of individuals on social media has a significant impact on perceived COVID-19 information overload. Perceived COVID-19 information overload has a positive impact on COVID-19 fear. In addition, fake news related to COVID-19 strengthens the relationship between perceived COVID-19 information overload and COVID-19 fear. The implication and limitations of the study are also discussed in the final section of the study.

Time Is of the Essence: Neural Codes, Synchronies, Oscillations, Architectures

Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Toward reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in candidate neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.

Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning.

Graph Theoretical Analysis of Structural Covariance Reveals the Relevance of Visuospatial and Attentional Areas in Essential Tremor Recovery After Stereotactic Radiosurgical Thalamotomy

Essential tremor (ET) is the most common movement disorder. Its pathophysiology is only partially understood. Here, we leveraged graph theoretical analysis on structural covariance patterns quantified from morphometric estimates for cortical thickness, surface area, and mean curvature in patients with ET before and one year after (to account for delayed clinical effect) ventro-intermediate nucleus (Vim) stereotactic radiosurgical thalamotomy. We further contrasted the observed patterns with those from matched healthy controls (HCs). Significant group differences at the level of individual morphometric properties were specific to mean curvature and the post-/pre-thalamotomy contrast, evidencing brain plasticity at the level of the targeted left thalamus, and of low-level visual, high-level visuospatial and attentional areas implicated in the dorsal visual stream. The introduction of cross-correlational analysis across pairs of morphometric properties strengthened the presence of dorsal visual stream readjustments following thalamotomy, as cortical thickness in the right lingual gyrus, bilateral rostral middle frontal gyrus, and left pre-central gyrus was interrelated with mean curvature in the rest of the brain. Overall, our results position mean curvature as the most relevant morphometric feature to understand brain plasticity in drug-resistant ET patients following Vim thalamotomy. They also highlight the importance of examining not only individual features, but also their interactions, to gain insight into the routes of recovery following intervention.

Understanding the Impact of Competitive Advantage and Core Competency on Regional Tourism Revitalization: Empirical Evidence in Taiwan

Competitive advantage and core competency are the unique capabilities and assets of an organization to provide valuable products or services to customers, thus giving the organization a better competitive position in the market than its competitors. In addition, how to create a competitive advantage is also one of the main objectives of business strategy. Therefore, this study focuses on understanding the decisive factors in regional revitalization and the relationship between business strategy, strategic alliance, and alliance performance through small and medium enterprises (SMEs) in Taiwan. This study selected a sample of 220 SMEs in Taiwan that had participated in the SME regional revitalization counseling program. The results showed that competitive advantage, core competency and strategic alliance partner selection had significant effects on alliance performance. In addition, core competency had an indirect effect on alliance performance through strategic alliance partner selection. However, competitive advantage did not have a significant effect on strategic alliance partner selection. Finally, this study proposes management implications and practical suggestions for SMEs' competitive advantage, core competency, and alliance performance.

Sleep, Sensory Integration/Processing, and Autism: A Scoping Review

The prevalence of sleep dysfunction is considerably higher in the autistic population than in the non-autistic. Similarly, the incidence of sensory reactivity differences in autism exceeds that in the neurotypical population. The basis of sleep disorders in autism is multifactorial, but sensory integration/processing concerns may play a role. Research that investigates this interplay for autistic individuals is limited but vital. In this scoping review, we examined literature addressing the following research question: What is the relationship between sleep and sensory integration/processing in autism? We included articles if they were peer-reviewed, English or Spanish, purposefully addressed sensory integration/processing differences, were sleep focused and included autism as the primary diagnosis or population. Articles were excluded if the language was not English or Spanish, research was conducted with animals, they were non-peer-reviewed, the primary population was not autistic, the sensory focus reflected a specific sensorineural loss (e.g., blindness, or deafness), there was not a clear inclusion of sensory integration/processing or sleep. We searched six databases and included all citations from the inception of each database through June 2021. The search strategy identified 397 documents that were reduced to 24 included articles after exclusion criteria were applied. The majority of studies we identified characterized the relation between sleep and sensory integration/processing differences in autism. Investigators found multiple sleep concerns such as bedtime resistance, sleep anxiety, delayed sleep onset, night awaking, and short sleep duration in autistic individuals. Identified sensory concerns focused on reactivity, finding hyper- and hypo-reactivity as well as sensory seeking across sensory domains. Co-existence of sleep concerns and sensory integration/processing differences was frequently reported. Few intervention studies showed a clear sensory focus; those that did emphasized pressure, movement, touch, and individual sensory preferences/needs. Swimming programs and massage showed promising results. No studies were of high quality. At a minimum, there is a co-existence of sensory reactivity differences and sleep concerns in autistic children, and possibly autistic adults. The relationship between poor sleep and sensory integration/processing differences is complex and multi-faceted, requiring additional research. Interventions that purposefully include a central sensory component have not been well studied in autistic children or adults. Overall studies with greater rigor and purposeful use of sensation and sensorimotor supports as a component of intervention are needed. This study was not funded.

Interpreting resting heart rate variability in complex populations: the role of autonomic reflexes and comorbidities

PURPOSE

Resting heart rate variability (HRV) is an important biomarker linking mental health to cardiovascular outcomes. However, resting HRV is also impaired in autonomic neuropathy, a common and underdiagnosed complication of common medical conditions which is detected by testing autonomic reflexes. We sought to describe the relationship between autonomic reflex abnormalities and resting HRV, taking into consideration medical comorbidities and demographic variables.

METHODS

Participants (n = 209) underwent a standardized autonomic reflex screen which was summarized as the Composite Autonomic Severity Score (CASS) and included measures of reflexive HRV, e.g., heart rate with deep breathing (HRDB). Resting HRV measures were: pNN50 (percentage of NN intervals that differ by > 50 ms) and cvRMSSD (adjusted root mean square of successive differences).

RESULTS

In univariate analyses, lower resting HRV was associated with: older age, higher CASS, neuropathy on examination, hypertension, diabetes, chronic obstructive pulmonary disease, chronic kidney disease, and psychiatric disease. Adaptive regression spline analysis revealed that HRDB explained 27% of the variability in resting HRV for participants with values of HRDB in the normal range. Outside this range, there was no linear relationship because: (1) when HRDB was low (indicating autonomic neuropathy), resting HRV was also low with low variance; and (2) when HRDB was high, the variance in resting HRV was high. In multivariate models, only HRDB was significantly independently associated with cvRMSSD and pNN50.

CONCLUSION

Subclinical autonomic neuropathy, as evidenced by low HRDB and other autonomic reflexes, should be considered as a potential confounder of resting HRV in research involving medically and demographically diverse populations.

A Moderated Mediation Model Linking Excessive Enterprise Social Media Usage With Job Performance

Despite the larger interest of information systems scholars in excessive ESM usage, little is known about how excessive ESM usage is related to employee performance. This study focused on excessive ESM usage and investigated its impact on employee performance. Based on the status quo perspective with the integration of social cognitive theory, this study first proposed that excessive ESM usage has a positive and negative relationship with employee performance through ESM usage regret and ESM usage inertia. Furthermore, COVID-19 threat moderates the direct relationship between excessive ESM usage and ESM usage regret, and ESM usage inertia. Time-lagged, multi-source data collected in China support most of our hypothesis. Results reveal that excessive ESM has a positive and negative indirect effect on employee performance via ESM usage regret and ESM usage inertia. Furthermore, the COVID-19 threat moderates the positive direct effect of excessive ESM usage on ESM usage inertia. In the later section, theoretical contributions and practical implications are discussed.

The Construction of Intelligent Emotional Analysis and Marketing Model of B&B Tourism Consumption Under the Perspective of Behavioral Psychology

This manuscript constructs an intelligent sentiment analysis and marketing model for bed and breakfast (B&B) consumption based on a behavioral psychology perspective. Based on the LDA theme model, the theme features and keywords of the reviews covering user feedback are explored from the text data, and the theme framework of user sentiment perception is constructed by combining previous literature on user perception in the B&B market, and the themes of user online reviews are summarized in four dimensions: practical, sensory, cognitive, and emotional components of user experience. In this manuscript, GooSeeker software was selected for data crawling and ROST CM (ROST content mining) developed by Wuhan University was used for text processing. To improve the accuracy of text classification and improve the missing data, the online comment text is divided into sentences by symbols, and the text is divided into words based on sentences, and the spatial vector model and the text feature word weighting method of TF-IDF are used for vector representation, and the polynomial Bayesian classifier is called to identify the topics of sentences. The classical Theory of Planned Behavior (TPB) was used to analyze the influencing factors of the willingness to consume experiential B&B tourism, and countermeasure suggestions for the development of B&B tourism were proposed based on the research findings In the empirical testing stage, a questionnaire on the willingness to consume experiential B&B tourism was designed, and web research was chosen to collect the data. SPSS20.0 was used to conduct reliability analysis, factor analysis, correlation analysis, and regression analysis on the data, and AMOS statistics were used to establish a structural equation model to verify the influence path of willingness to consume experiential B&B tourism. Finally, the moderating path of willingness to consume experiential B&B tourism was verified by using multi-group analysis.

Prediction of Human-Computer Interaction Intention Based on Eye Movement and Electroencephalograph Characteristics

In order to solve the problem of unsmooth and inefficient human-computer interaction process in the information age, a method for human-computer interaction intention prediction based on electroencephalograph (EEG) signals and eye movement signals is proposed. This approach is different from previous methods where researchers predict using data from human-computer interaction and a single physiological signal. This method uses the eye movements and EEG signals that clearly characterized the interaction intention as the prediction basis. In addition, this approach is not only tested with multiple human-computer interaction intentions, but also takes into account the operator in different cognitive states. The experimental results show that this method has some advantages over the methods proposed by other researchers. In Experiment 1, using the eye movement signal fixation point abscissa Position X (PX), fixation point ordinate Position Y (PY), and saccade amplitude (SA) to judge the interaction intention, the accuracy reached 92%, In experiment 2, only relying on the pupil diameter, pupil size (PS) and fixed time, fixed time (FD) of eye movement signals can not achieve higher accuracy of the operator’s cognitive state, so EEG signals are added. The cognitive state was identified separately by combining the screened EEG parameters Rα/β with the eye movement signal pupil diameter and fixation time, with an accuracy of 91.67%. The experimental combination of eye movement and EEG signal features can be used to predict the operator’s interaction intention and cognitive state.

Numerosity Categorization by Parity in an Insect and Simple Neural Network

A frequent question as technology improves and becomes increasingly complex, is how we enable technological solutions and models inspired by biological systems. Creating technology based on humans is challenging and costly as human brains and cognition are complex. The honeybee has emerged as a valuable comparative model which exhibits some cognitive-like behaviors. The relative simplicity of the bee brain compared to large mammalian brains enables learning tasks, such as categorization, that can be mimicked by simple neural networks. Categorization of abstract concepts can be essential to how we understand complex information. Odd and even numerical processing is known as a parity task in human mathematical representations, but there appears to be a complete absence of research exploring parity processing in non-human animals. We show that free-flying honeybees can visually acquire the capacity to differentiate between odd and even quantities of 1–10 geometric elements and extrapolate this categorization to the novel numerosities of 11 and 12, revealing that such categorization is accessible to a comparatively simple system. We use this information to construct a neural network consisting of five neurons that can reliably categorize odd and even numerosities up to 40 elements. While the simple neural network is not directly based on the biology of the honeybee brain, it was created to determine if simple systems can replicate the parity categorization results we observed in honeybees. This study thus demonstrates that a task, previously only shown in humans, is accessible to a brain with a comparatively small numbers of neurons. We discuss the possible mechanisms or learning processes allowing bees to perform this categorization task, which range from numeric explanations, such as counting, to pairing elements and memorization of stimuli or patterns. The findings should encourage further testing of parity processing in a wider variety of animals to inform on its potential biological roots, evolutionary drivers, and potential technology innovations for concept processing.

Effect of Frugality and Cognition on Forest Health Tourism Intention-A Mediating Effect Analysis Based on Multigroup Comparison

At present, the market demand for forest health tourism is weak. The main purpose of this study is to investigate whether frugality inhibits the intention of forest health tourism and whether the positive effect of cognition on the intention of forest health tourism can compensate for the inhibition of frugality. Based on mental account theory and planned behavior theory, this study constructs a structural equation model with intermediary variables-health consumption mental account and forest health consumption attitude. According to the results of the path analysis of the data, which was collected through the questionnaire survey of urban residents, the positive influence of cognition can compensate for the inhibitory effect of frugality. On this basis, mediating effect analysis based on multigroup comparison is further carried out. This study verifies for the first time the inhibitory effect of frugality on the intention for forest health tourism, enriches the theoretical system of tourism consumer behavior, and provides a scientific basis for the market positioning of forest health and the formulation of marketing strategies.

The Role of Rare Avian Species for Spatial Resilience of Shifting Biomes in the Great Plains of North America

Human activity causes biome shifts that alter biodiversity and spatial resilience patterns. Rare species, often considered vulnerable to change and endangered, can be a critical element of resilience by providing adaptive capacity in response to disturbances. However, little is known about changes in rarity patterns of communities once a biome transitions into a novel spatial regime. We used time series modeling to identify rare avian species in an expanding terrestrial (southern) spatial regime in the North American Great Plains and another (northern) regime that will become encroached by the southern regime in the near future. In this time-explicit approach, presumably rare species show stochastic dynamics in relative abundance – this is because they occur only rarely throughout the study period, may largely be absent but show occasional abundance peaks or show a combination of these patterns. We specifically assessed how stochastic/rare species of the northern spatial regime influence aspects of ecological resilience once it has been encroached by the southern regime. Using 47 years (1968–2014) of breeding bird survey data and a space-for-time substitution, we found that the overall contribution of stochastic/rare species to the avian community of the southern regime was low. Also, none of these species were of conservation concern, suggesting limited need for revised species conservation action in the novel spatial regime. From a systemic perspective, our results preliminarily suggest that stochastic/rare species only marginally contribute to resilience in a new spatial regime after fundamental ecological changes have occurred.

Toward Understanding the Brain Dynamics of Music: Learning and Conscious Performance of Lyrics and Melodies With Variable Rhythms and Beats

A neural network architecture models how humans learn and consciously perform musical lyrics and melodies with variable rhythms and beats, using brain design principles and mechanisms that evolved earlier than human musical capabilities, and that have explained and predicted many kinds of psychological and neurobiological data. One principle is called factorization of order and rhythm: Working memories store sequential information in a rate-invariant and speaker-invariant way to avoid using excessive memory and to support learning of language, spatial, and motor skills. Stored invariant representations can be flexibly performed in a rate-dependent and speaker-dependent way under volitional control. A canonical working memory design stores linguistic, spatial, motoric, and musical sequences, including sequences with repeated words in lyrics, or repeated pitches in songs. Stored sequences of individual word chunks and pitch chunks are categorized through learning into lyrics chunks and pitches chunks. Pitches chunks respond selectively to stored sequences of individual pitch chunks that categorize harmonics of each pitch, thereby supporting tonal music. Bottom-up and top-down learning between working memory and chunking networks dynamically stabilizes the memory of learned music. Songs are learned by associatively linking sequences of lyrics and pitches chunks. Performance begins when list chunks read word chunk and pitch chunk sequences into working memory. Learning and performance of regular rhythms exploits cortical modulation of beats that are generated in the basal ganglia. Arbitrary performance rhythms are learned by adaptive timing circuits in the cerebellum interacting with prefrontal cortex and basal ganglia. The same network design that controls walking, running, and finger tapping also generates beats and the urge to move with a beat.

Research on the Impact of Challenge-Hindrance Stress on Employees' Innovation Performance: A Chain Mediation Model

Based on the transaction theory of stress and the theory of resource conservation, which introduces knowledge acquisition and intrinsic motivation as mediating variables, a chain mediating model for the influence of challenge-hindrance stress on innovation performance is constructed. Data of 295 samples collected in three stages were used to testify hypothesis. The results confirmed a positive relationship between challenge stress and innovation performance, and a negative relationship between hindrance stress and innovation performance. Intrinsic motivation and knowledge acquisition play a parallel and chain mediating role in the relationship between challenge-hindrance stress and innovation performance. These findings contribute to a deeper understanding of how challenge -hindrance stress affects innovation performance and provide important practical guidance for improving innovation performance.

Artificial Intelligence Inheriting the Historical Crisis in Psychology: An Epistemological and Methodological Investigation of Challenges and Alternatives

By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body rupture that has governed the production of scientific thought and the proliferation of approaches. In addition, by highlighting the sociohistorical conditions of AI, this paper investigates the historical characteristics of the shift of the crisis from psychology to AI. Additionally, we examine the epistemological and methodological roots of the main challenges encountered in AI research by noting that empiricism is the dominant tendency in the field. Empiricism gives rise to methodological and practical challenges, including challenges related to the emergence of meaning, abstraction, generalization, the emergence of symbols, concept formation, functional reflection of reality, and the emergence of higher psychological functions. Furthermore, through discussing attempts to formalize dialectical logic, the paper, based on contradiction formation, proposes a qualitative epistemological, methodological, and formal alternative by using a preliminary algorithmic model that grasps the formation of meaning as an essential ability for the qualitative reflection of reality and the emergence of other mental functions.

Physiological Electric Field: A Potential Construction Regulator of Human Brain Organoids

Brain organoids can reproduce the regional three-dimensional (3D) tissue structure of human brains, following the in vivo developmental trajectory at the cellular level; therefore, they are considered to present one of the best brain simulation model systems. By briefly summarizing the latest research concerning brain organoid construction methods, the basic principles, and challenges, this review intends to identify the potential role of the physiological electric field (EF) in the construction of brain organoids because of its important regulatory function in neurogenesis. EFs could initiate neural tissue formation, inducing the neuronal differentiation of NSCs, both of which capabilities make it an important element of the in vitro construction of brain organoids. More importantly, by adjusting the stimulation protocol and special/temporal distributions of EFs, neural organoids might be created following a predesigned 3D framework, particularly a specific neural network, because this promotes the orderly growth of neural processes, coordinate neuronal migration and maturation, and stimulate synapse and myelin sheath formation. Thus, the application of EF for constructing brain organoids in a3D matrix could be a promising future direction in neural tissue engineering.

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.

Maximal Dependence Capturing as a Principle of Sensory Processing

Sensory inputs conveying information about the environment are often noisy and incomplete, yet the brain can achieve remarkable consistency in recognizing objects. Presumably, transforming the varying input patterns into invariant object representations is pivotal for this cognitive robustness. In the classic hierarchical representation framework, early stages of sensory processing utilize independent components of environmental stimuli to ensure efficient information transmission. Representations in subsequent stages are based on increasingly complex receptive fields along a hierarchical network. This framework accurately captures the input structures; however, it is challenging to achieve invariance in representing different appearances of objects. Here we assess theoretical and experimental inconsistencies of the current framework. In its place, we propose that individual neurons encode objects by following the principle of maximal dependence capturing (MDC), which compels each neuron to capture the structural components that contain maximal information about specific objects. We implement the proposition in a computational framework incorporating dimension expansion and sparse coding, which achieves consistent representations of object identities under occlusion, corruption, or high noise conditions. The framework neither requires learning the corrupted forms nor comprises deep network layers. Moreover, it explains various receptive field properties of neurons. Thus, MDC provides a unifying principle for sensory processing.

Does Amount of Information Support Aesthetic Values?

Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli. Hence, given its importance, the amount of available information may underlie aesthetic values. Such information-based aesthetic values would be significant because they would compete with others to drive decision-making. In this article, we ask, "What is the evidence that amount of information support aesthetic values?" An important concept in the measurement of informational volume is entropy. Research on aesthetic values has thus used Shannon entropy to evaluate the contribution of quantity of information. We review here the concepts of information and aesthetic values, and research on the visual and auditory systems to probe whether the brain uses entropy or other relevant measures, specially, Fisher information, in aesthetic decisions. We conclude that information measures contribute to these decisions in two ways: first, the absolute quantity of information can modulate aesthetic preferences for certain sensory patterns. However, the preference for volume of information is highly individualized, with information-measures competing with organizing principles, such as rhythm and symmetry. In addition, people tend to be resistant to too much entropy, but not necessarily, high amounts of Fisher information. We show that this resistance may stem in part from the distribution of amount of information in natural sensory stimuli. Second, the measurement of entropic-like quantities over time reveal that they can modulate aesthetic decisions by varying degrees of surprise given temporally integrated expectations. We propose that amount of information underpins complex aesthetic values, possibly informing the brain on the allocation of resources or the situational appropriateness of some cognitive models.

Tell Me Where You Live… How the Perceived Entitativity of Neighborhoods Determines the Formation of Impressions About Their Residents

The studies presented here apply the concept of entitativity in order to understand how belonging to a particular geographical area - neighborhood - can determine the way others organize information and form impressions about area's residents. In order to achieve this objective, three studies were carried out. The first study aims to verify if a neighborhood varies in terms of perceived entitativity, and identify the physical and social characteristics of the neighborhoods that are more strongly associated with the perception of entitativity. The Study 2 and 3 used an experimental paradigm to explore how people's perceptions of neighborhoods' entitativity influenced their impressions of residents. To activate stereotypes, Study 2 used the name of real neighborhoods, and Study 3 employed only a set of pictures of unknown neighborhoods. The results show that the neighborhoods vary significantly with the regard to the perception of entitativity, and a set of physical attributes of place were strongly related with entitativity. The results showed that, independent of stimuli, the neighborhoods perceived as highly entitative, the supposed residents were subject to more extreme and quicker trait judgments, supported by greater confidence on the part of perceivers. Study 3 also reported that in highly entitative neighborhoods, the perceivers transferred more traits from the group to individual members. These results provide strong evidence that physical structure of neighborhoods imply different entitatity judgments that influences the way in which residents are perceived.

Neural Correlates of Listening to Varying Synchrony Between Beats in Samba Percussion and Relations to Feeling the Groove

Listening to samba percussion often elicits feelings of pleasure and the desire to move with the beat-an experience sometimes referred to as "feeling the groove"- as well as social connectedness. Here we investigated the effects of performance timing in a Brazilian samba percussion ensemble on listeners' experienced pleasantness and the desire to move/dance in a behavioral experiment, as well as on neural processing as assessed via functional magnetic resonance imaging (fMRI). Participants listened to different excerpts of samba percussion produced by multiple instruments that either were "in sync", with no additional asynchrony between instrumental parts other than what is usual in naturalistic recordings, or were presented "out of sync" by delaying the snare drums (by 28, 55, or 83 ms). Results of the behavioral experiment showed increasing pleasantness and desire to move/dance with increasing synchrony between instruments. Analysis of hemodynamic responses revealed stronger bilateral brain activity in the supplementary motor area, the left premotor area, and the left middle frontal gyrus with increasing synchrony between instruments. Listening to "in sync" percussion thus strengthens audio-motor interactions by recruiting motor-related brain areas involved in rhythm processing and beat perception to a higher degree. Such motor related activity may form the basis for "feeling the groove" and the associated desire to move to music. Furthermore, in an exploratory analysis we found that participants who reported stronger emotional responses to samba percussion in everyday life showed higher activity in the subgenual cingulate cortex, an area involved in prosocial emotions, social group identification and social bonding.

Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks

Background

A multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure–function network dynamics involved in complex neurodegenerative network disorders such as Parkinson’s disease (PD). Deep learning-based graph neural network models generate higher-level embeddings that could capture intricate structural and functional regional interactions related to PD.

Objective

This study aimed at investigating the role of structure–function connections in predicting PD, by employing an end-to-end graph attention network (GAT) on multimodal brain connectomes along with an interpretability framework.

Methods

The proposed GAT model was implemented to generate node embeddings from the structural connectivity matrix and multimodal feature set containing morphological features and structural and functional network features of PD patients and healthy controls. Graph classification was performed by extracting topmost node embeddings, and the interpretability framework was implemented using saliency analysis and attention maps. Moreover, we also compared our model with unimodal models as well as other state-of-the-art models.

Results

Our proposed GAT model with a multimodal feature set demonstrated superior classification performance over a unimodal feature set. Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and an F1 score of 86% and a moderate test accuracy of 73%. The interpretability framework highlighted the structural and functional topological influence of motor network and cortico-subcortical brain regions, among which structural features were correlated with onset of PD. The attention maps showed dependency between large-scale brain regions based on their structural and functional characteristics.

Conclusion

Multimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and provide an attention mechanism-based interpretability framework that can highlight the pathology-specific relation between brain regions.

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic energy features. In this method, first, to reduce the noise superposition caused by feature analysis and extraction, the concept of an energy sequence is proposed. Second, to obtain the feature set reflecting the time persistence and multicomponent complexity of EEG signals, the construction method of the dynamic energy feature set is given. Finally, to make the network model suitable for small datasets, we used fully connected layers and bidirectional long short-term memory (Bi-LSTM) networks. To verify the effectiveness of the proposed method, we used leave one subject out (LOSO) and 10-fold cross validation (CV) strategies to carry out experiments on the SEED and DEAP datasets. The experimental results show that the accuracy of the proposed method can reach 89.42% (SEED) and 77.34% (DEAP).

Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction

Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.

Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis

The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality (EEQ) is essential. In particular, fault tree analysis (FTA), a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning (DL) method to analyze the factors of IEE quality; consequently, based on the traditional FTA, fuzzy fault tree analysis (FFTA) is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey (QS), based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students' cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of "teaching failure" in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality.