Field Of Neuroscience Research Articles

DTP-Net: Learning to Reconstruct EEG Signals in Time-Frequency Domain by Multi-Scale Feature Reuse.

Electroencephalography (EEG) signals are prone to contamination by noise, such as ocular and muscle artifacts. Minimizing these artifacts is crucial for EEG-based downstream applications like disease diagnosis and brain-computer interface (BCI). This paper presents a new EEG denoising model, DTP-Net. It is a fully convolutional neural network comprising Densely-connected Temporal Pyramids (DTPs) placed between two learnable time-frequency transformations. In the time-frequency domain, DTPs facilitate efficient propagation of multi-scale features extracted from EEG signals of any length, leading to effective noise reduction. Comprehensive experiments on two public semi-simulated datasets demonstrate that the proposed DTP-Net consistently outperforms existing state-of-the-art methods on metrics including relative root mean square error (RRMSE) and signal-to-noise ratio improvement ( ∆SNR). Moreover, the proposed DTP-Net is applied to a BCI classification task, yielding an improvement of up to 5.55% in accuracy. This confirms the potential of DTP-Net for applications in the fields of EEG-based neuroscience and neuro-engineering. An in-depth analysis further illustrates the representation learning behavior of each module in DTP-Net, demonstrating its robustness and reliability.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification—further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

Motor Behavior Regulation of Rat Robots Using Integrated Electrodes Stimulated by Micro-Nervous System.

As a cutting-edge technology, animal robots based on living organisms are being extensively studied, with potential for diverse applications in the fields of neuroscience, national security, and civil rescue. However, it remains a significant challenge to reliably control the animal robots with the objective of protecting their long-term survival, and this has seriously hindered their practical implementation. To address this issue, this work explored the use of a bio-friendly neurostimulation system that includes integrated stimulation electrodes together with a remote wireless stimulation circuit to control the moving behavior of rat robots. The integrated electrodes were implanted simultaneously in four stimulation sites, including the medial forebrain bundle (MFB) and primary somatosensory cortex, barrel field (S1BF). The control system was able to provide flexibility in adjusting the following four stimulation parameters: waveform, amplitude, frequency, and duration time. The optimized parameters facilitated the successful control of the rat's locomotion, including forward movement and left and right turns. After training for a few cycles, the rat robots could be guided along a designated route to complete the given mission in a maze. Moreover, it was found that the rat robots could survive for more than 20 days with the control system implanted. These findings will ensure the sustained and reliable operation of the rat robots, laying a robust foundation for advances in animal robot regulation technology.

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

AbstractThis comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

The transmission of information between neurons is accomplished in living organisms through synapses. The memristor is an electronic component that simulates the tunability of the strength of biological synaptic connections in artificial neural networks. This article constructs a novel type of locally active memristor and verifies by nonlinear theoretical analysis, locally active analysis and circuit simulation. The designed memristor is simulated as a biological autapse of Hindmarsh-Rose(HR) neuron to obtain the improved HR neuron model of memristive autapse, and the Hamilton energy is obtained according to Helmholtz theorem. By varying the external forcing current and the memristive autapse strength, this article analyses the changes of the Hamilton energy and explores its self-excited and hidden firing behavior. The analog circuit simulation and digital circuit implementation of the HR model confirm the consistency between the mathematical model and the actual behavior, which can advance the field of neuroscience and artificial intelligence.

Bibliometric analysis of publications on neuroscience and noncommunicable diseases in the Scopus database

Introduction: The present study aimed to perform a bibliometric analysis of neuroscience and noncommunicable diseases in the Scopus database between 2003 and 2023. Bibliometric analysis served as the main tool to analyze academic production.Methods: 867 papers were selected for the study based on English keywords ("neuroscience," "noncommunicable," and "diseases"). Results: The period from 2015 to 2023 accounted for 58% of the total publications, with 503 academic publications, which had the most significant influence on scientific production in terms of percentage increase. The United States accounted for 35.9% of the production. The most relevant publication sources, with n=10 each, were Neuromethods and Neuroscientist. Farooqui, A.A. obtained the most citations (105) in his four papers. Of the total number of papers, 21% were scientific articles, of which 32% pertained to medicine and 20% to neuroscience. Neuroscience and noncommunicable diseases have advanced significantly in terms of thematic variety, authorship, sources, and accessible resources.Conclusions: This bibliometric study provides a solid foundation for future research in the field of neuroscience and noncommunicable diseases, highlighting the importance of this area and its growth in the academic realm..

Artificial Intelligence Computer Weapon Based True Research on Corona Virus [Covid-19

Electronic torture, Electromagnetic radiation torture, or Psychotronic torture are terms used by individuals who are targeted by this wireless Synthetic telepathic technology or Direct Energy Weapon [Electromagnetic Radiation weapon]. Wireless Synthetic telepathy technology or Super artificial intelligence computers Criminal operators, often actors or often government employees, agents or crime syndicates, use transmitted electromagnetic radiation (Such as microwave listening effects), satellite technology and surveillance techniques. Wireless Remote Neural Monitoring: Bridging the gap between brain and technology In the field of neuroscience and technological advancement, the concept of deep tech satellite wireless neural monitoring technology has emerged as an unprecedented innovation weapon. Which has been developed to read and understand the human brain and to interact with the human brain and understand the human body, it is also a wireless direct energy weapon. These deep tech satellite surveillance criminal operators are using this Neurotechnology and wireless Direct Energy Weapons on a large scale against civilians. This true research has been done to protect citizens from illegal deep tech satellite surveillance weapon technology and cyber terrorism. The researcher has presented his research to the whole world in very simple words and in an easy manner. So that the world can easily understand this research and the governments of the countries of the world can protect their citizens from future man-made diseases and viruses.

Fasting duration impacts ribosome protein 6 phosphorylation in zebrafish brain: New insights in aquatic organisms’ welfare

BackgroundShort- or mid-term fasting, full or partial, triggers metabolic response known to have in turn health effects in an organism. At central level, the metabolic stimulus triggered by fasting is known to be perceived firstly by hypothalamic neurons.In the field of neuroscience, ribosomal protein S6 (S6) phosphorylation is commonly used as a readout of the mammalian target of rapamycin complex 1 signalling activation or as a marker for neuronal activity. The aim of this study is addressed to evaluate whether the phosphorylation of S6 occurs in the central neurons of zebrafish exposed to four (short-term) and seven (mid-term) days of complete fasting. MethodsGroup-housed adult zebrafish were exposed to four and seven days of complete food withdrawal. At the end of the experimental period, Western blotting analyses were carried out to measure the expression levels of the phosphorylated S6 (pS6) by comparing the two experimental conditions versus the control group. The same antibody was then used to identify the distribution pattern of pS6 immunoreactive neurons in the whole brain and in the taste buds. ResultsWe did not observe increased pS6 levels expression in the brain of animals exposed to short-term fasting compared to the control, whereas the expression increased in brain homogenates of animals exposed to mid-term fasting. pS6 immunoreactivity was reported in some hypothalamic neurons, as well as in the dorsal area of telencephalon and preoptic area, a neurosecretory region homolog to the mammalian paraventricular nucleus. Remarkably, we observed pS6 immunostaining in the sensory cells of taste buds lining the oral epithelium. ConclusionsTaken together, our data show that in zebrafish, differently from other fish species, seven days of fasting triggers neuronal activity. Furthermore, the immunostaining on sensory cells of taste buds suggests that metabolic changes may modulate also peripheral sensory cells. This event may have valuable implications when using zebrafish to design metabolic studies involving fasting as well as practical consequences on the animal welfare, in particularly stressful conditions, such as transportation.

Diagnosis of brain disease based on the deep learning algorithms of neural imaging techniques

Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%).

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

CSF neurofilament light chain profiling and quantitation in neurological diseases.

Neurofilament light chain is an established marker of neuroaxonal injury that is elevated in CSF and blood across various neurological diseases. It is increasingly used in clinical practice to aid diagnosis and monitor progression and as an outcome measure to assess safety and efficacy of disease-modifying therapies across the clinical translational neuroscience field. Quantitative methods for neurofilament light chain in human biofluids have relied on immunoassays, which have limited capacity to describe the structure of the protein in CSF and how this might vary in different neurodegenerative diseases. In this study, we characterized and quantified neurofilament light chain species in CSF across neurodegenerative and neuroinflammatory diseases and healthy controls using targeted mass spectrometry. We show that the quantitative immunoprecipitation-tandem mass spectrometry method developed in this study strongly correlates to single-molecule array measurements in CSF across the broad spectrum of neurodegenerative diseases and was replicable across mass spectrometry methods and centres. In summary, we have created an accurate and cost-effective assay for measuring a key biomarker in translational neuroscience research and clinical practice, which can be easily multiplexed and translated into clinical laboratories for the screening and monitoring of neurodegenerative disease or acute brain injury.

Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?

In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs)are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

Tissue clearing method in visualization of cancer progression and metastasis.

Since various imaging modalities have been developed, cancer metastasis can be detected from an early stage. However, limitations still exist, especially in terms of spatial resolution. Tissue-clearing technology has emerged as a new imaging modality in cancer research, which has been developed and utilized for a long time mainly in neuroscience field. This method enables us to detect cancer metastatic foci with single-cell resolution at whole mouse body/organ level. On top of that, 3D images of cancer metastasis of whole mouse organs make it easy to understand their characteristics. Recently, further applications of tissue clearing methods were reported in combination with reporter systems, labeling, and machine learning. In this review, we would like to provide an overview of this technique and current applications in cancer research and discuss their potentials and limitations.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

HAT and HDAC: Enzyme with Contradictory Action in Neurodegenerative Diseases.

In view of the increasing risk of neurodegenerative diseases, epigenetics plays a fundamental role in the field of neuroscience. Several modifications have been studied including DNA methylation, histone acetylation, histone phosphorylation, etc. Histone acetylation and deacetylation regulate gene expression, and the regular activity of histone acetyltransferases (HATs) and histone deacetylases (HDACs) provides regulatory stages for gene expression and cell cycle. Imbalanced homeostasis in these enzymes causes a detrimental effect on neurophysiological function. Intriguingly, epigenetic remodelling via histone acetylation in certain brain areas has been found to play a key role in the neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. It has been demonstrated that a number of HATs have a role in crucial brain processes such regulating neuronal plasticity and memory formation. The most recent therapeutic methods involve the use of small molecules known as histone deacetylase (HDAC) inhibitors that antagonize HDAC activity thereby increase acetylation levels in order to prevent the loss of HAT function in neurodegenerative disorders. The target specificity of the HDAC inhibitors now in use raises concerns about their applicability, despite the fact that this strategy has demonstrated promising therapeutic outcomes. The aim of this review is to summarize the cross-linking between histone modification and its regulation in the pathogenesis of neurological disorders. Furthermore, these findings also support the notion of new pharmacotherapies that target particular areas of the brain using histone deacetylase inhibitors.

Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.

In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.

Guidance To Build Mental Well-Being: The Role Of Guidance And Counseling

Guidance and counseling plays a crucial role in helping individuals overcome various psychological and emotional problems in contemporary society. The need for these services is increasingly urgent, not only as a response to psychological crises, but also as an effort to prevent and build sustainable mental well-being. Research shows that guidance and counseling is effective in helping individuals overcome stress, anxiety, depression, as well as develop adaptive and problem-solving skills necessary in facing life's challenges. Moreover, the importance of this service lies not only in its individual dimension, but also in its contribution to overall public health. Through holistic and coordinated services, guidance and counseling can help reduce the burden of mental illness, increase productivity, and strengthen social and support networks. However, guidance and counseling practitioners are faced with new challenges and rapidly evolving opportunities, such as the application of technology in online counseling practices. In addition, social and cultural contexts play an important role in guidance and counseling practice, making the integration of cross-cultural perspectives an urgent necessity. In response to the evolving challenges and opportunities, guidance and counseling practitioners need to strengthen their competencies through continuous education, training, and professional development. New research and innovations in the fields of neuroscience, positive psychology, and technology-based therapies also have a significant impact on guidance and counseling, so practitioners must continue to adapt to the latest scientific developments. In an era where stress and life pressure are on the rise, a proactive approach in promoting mental wellbeing is becoming increasingly urgent. Therefore, guidance and counseling should not only focus on addressing problems, but also on prevention and building adaptive skills that can improve the overall quality of life. All in all, it can be concluded that guidance and counseling has a very important role in supporting the mental and social well-being of individuals as well as society at large.

Advantages of Red Light-Operated Optogenetic Tools in Neuroscience

Optogenetics is a technology that combines genetic engineering and light stimuli to manipulate specific groups or types of cells. It allows researchers to have spatial and temporal control, therefore showing great promise in the field of neuroscience [1]. In regards to neuroscience, the technology allows researchers to conduct advanced experiments to uncover the functions of specific brain areas. Optogenetics can also be used in the therapeutic side for neurological diseases by manipulating dysfunctional cells to restore function and alleviate disease symptoms [1].

The Elements of the Life Script and Neurobiology

This article is based on the speech ”The Seven Elements of the Life Script” at the MIR-TA (International Institute of Developmental Transactional Analysis, IIDTA) Student Conference, which was held in July 2023. In this paper, the author relies on Tony White’s article “The Life Script Heptagon” (2022), as well as works of Adriana Montheiro and Louis Kozolino on the topic of neuroscience. Advances in neuroscience may now provide new insights into the concept of the life script proposed by Eric Bern. The motivation of the client to change and study his life script is in the locus of control of a psychologist working in the modality of transactional analysis. The purpose of the author is to analyse a new view on the constituent components of a life script and to raise the question: what is the main target in the work of a psychologist in different methods of psychotherapy, as well as studying the connection between the script process and new research in the field of neuroscience, which will expand the understanding of the life script for more productive work.