Human Brain Function Research Articles

Analysis of Cortico-Muscular Coupling and Functional Brain Network under Different Standing Balance Paradigms.

Maintaining standing balance is essential for people to engage in productive activities in daily life. However, the process of interaction between the cortex and the muscles during balance regulation is understudied. Four balance paradigms of different difficulty were designed by closing eyes and laying sponge pad under feet. Ten healthy subjects were recruited to stand for ten 15 s trials in each paradigm. This study used simultaneously acquired electroencephalography (EEG) and electromyography (EMG) to investigate changes in the human cortico-muscular coupling relationship and functional brain network characteristics during balance control. The coherence and causality of EEG and EMG signals were calculated by magnitude-squared coherence (MSC) and transfer entropy (TE). It was found that changes in balance strategies may lead to a shift in cortico-muscular coherence (CMC) from the beta band to the gamma band when the difficulty of balance increased. As subjects performed the four standing balance paradigms, the causality of the beta band and the gamma band was stronger in the descending neural pathway than that in the ascending neural pathway. A multi-rhythmic functional brain network with 19 EEG channels was constructed and analyzed based on graph theory, showing that its topology also changed with changes in balance difficulty. These results show an active adjustment of the sensorimotor system under different balance paradigms and provide new insights into the endogenous physiological mechanisms underlying the control of standing balance.

Lateral Migration‐based Flash‐like Synaptic Device for Hybrid Off‐chip/On‐chip Training

AbstractAn increase in the demand for artificial intelligence is leading to advanced research in the field of neuromorphic systems, which imitate human brain functions with the hope of increasing computational speed and lowering power consumption. Especially, the development of energy‐efficient and reliable synaptic devices is critical as synapses are fundamental building blocks of neuromorphic systems. In this study, by adjusting the charge injection pathway of conventional flash memory devices, a lateral migration‐based synaptic device is proposed. Using the efficient program/erase method, the proposed device is operable at a significantly low voltage while maintaining formidable retention and endurance characteristics. Furthermore, an efficient hybrid off‐chip/on‐chip training method using the proposed device is presented. The results demonstrate a variation‐robust neuromorphic system, indicating the superiority of the proposed device.

Towards ubiquitous and nonintrusive measurements of brain function in the real world: assessing blink-related oscillations during simulated flight using portable low-cost EEG.

Blink-related oscillations (BRO) are newly discovered neurophysiological phenomena associated with spontaneous blinking and represent cascading neural mechanisms including visual sensory, episodic memory, and information processing responses. These phenomena have been shown to be present at rest and during tasks and are modulated by cognitive load, creating the possibility for brain function assessments that can be integrated seamlessly into real-world settings. Prior works have largely examined the BRO phenomenon within controlled laboratory environments using magnetoencephalography and high-density electroencephalography (EEG) that are ill-suited for real-world deployment. Investigating BROs using low-density EEG within complex environments reflective of the real-world would further our understanding of how BRO responses can be utilized in real-world settings. We evaluated whether the BRO response could be captured in a high-fidelity flight simulation environment using a portable, low-density wireless EEG system. The effects of age and task demands on BRO responses were also examined. EEG data from 30 licensed pilots (age 43.37 +/- 17.86, 2 females) were collected during simulated flights at two cognitive workload levels. Comparisons of signal amplitudes were undertaken to confirm the presence of BRO responses and mixed model ANOVAs quantified the effects of workload and age group on BRO amplitudes. Significant increases in neural activity were observed post-blink compared to the baseline period (p < 0.05), confirming the presence of BRO responses. In line with prior studies, results showed BRO time-domain responses from the delta band (0.5-4 Hz) consisting of an early negative peak followed by a positive peak post-blink in temporal and parietal electrodes. Additionally, task workload and age-related effects were also found, with observations of the enhancement of BRO amplitudes with older age and attenuation of BRO responses in high workloads (p < 0.05). These findings demonstrate that it is possible to capture BRO responses within simulated flight environments using portable, low-cost, easy-to-use EEG systems. Furthermore, biological and task salience were reflected in these BRO responses. The successful detection and demonstration of both task-and age-related modulation of BRO responses in this study open the possibility of assessing human brain function across the lifespan with BRO responses in complex and realistic environments.

Genetic overlap between multivariate measures of human functional brain connectivity and psychiatric disorders

Genetic overlap between multivariate measures of human functional brain connectivity and psychiatric disorders

Recent Developments and Resources in Neuroinformatics and Computational Neuroscience

In comparison to other natural systems, the temporal dynamics of the human brain's growth, structure, and function are notably intricate. The human brain is comprised of an estimated 86.1 to 8.0 billion neurons and a comparable non-neural glial cells number. Additionally, the brain contains neuronal systems with over 100 trillion connections. The modeling, analysis, and comprehension of these complex structures require the use of code and automation. Neuroinformatics methodologies are employed to manage, retrieve, and integrate the copious quantities of data produced through clinical documentation, scientific literature, and specialized databases. Conversely, computational neuroscience, which draws heavily upon the fields of biology, physics, mathematics, and computation, tackles these issues. Neuroinformatics is an interdisciplinary field that integrates computational neuroscience and neuroscientific experimentation. This paper functions as an introductory guide for individuals who lack familiarity with the domains of neuroinformatics and computational neuroscience, along with their consistent sophisticated software, resources, and tools.

Techniques, Advantages and Limitations of Neuroimaging: A Systematic Review

Neuroimaging was developed as a non-invasive and objective technique for studying the function and structure of the normal human brain, utilizing quantitative computational methods. The utilization of quantitative research in the study of neurological and mental health disorders is experiencing a notable increase. While there are post-residency programs offered in the field of Neuro-Radiology, it is important to note that neuroimaging is not recognized as an independent medical specialty. An increasing number of studies are placing emphasis on matters pertaining to rehabilitation. Hence, possessing a fundamental understanding of the prevailing neuroimaging approaches is crucial for comprehending and analyzing this rapidly evolving research area. This article provides an overview of the signal source, reviews the advantages and limitations associated with the techniques, and presents a comprehensive evaluation of the field. Functional magnetic resonance imaging (fMRI) has materialized as a prominent tool within the realm of rehabilitation science due to its widespread utilization and significance.

Brain organoids and organoid intelligence from ethical, legal, and social points of view.

Human brain organoids, aka cerebral organoids or earlier "mini-brains", are 3D cellular models that recapitulate aspects of the developing human brain. They show tremendous promise for advancing our understanding of neurodevelopment and neurological disorders. However, the unprecedented ability to model human brain development and function in vitro also raises complex ethical, legal, and social challenges. Organoid Intelligence (OI) describes the ongoing movement to combine such organoids with Artificial Intelligence to establish basic forms of memory and learning. This article discusses key issues regarding the scientific status and prospects of brain organoids and OI, conceptualizations of consciousness and the mind-brain relationship, ethical and legal dimensions, including moral status, human-animal chimeras, informed consent, and governance matters, such as oversight and regulation. A balanced framework is needed to allow vital research while addressing public perceptions and ethical concerns. Interdisciplinary perspectives and proactive engagement among scientists, ethicists, policymakers, and the public can enable responsible translational pathways for organoid technology. A thoughtful, proactive governance framework might be needed to ensure ethically responsible progress in this promising field.

Deciphering the Role of Rapidly Evolving Conserved Elements in Primate Brain Development and Exploring Their Potential Involvement in Alzheimer's Disease.

Although previous studies have identified human-specific accelerated regions as playing a key role in the recent evolution of the human brain, the characteristics and cellular functions of rapidly evolving conserved elements (RECEs) in ancestral primate lineages remain largely unexplored. Here, based on large-scale primate genome assemblies, we identify 888 RECEs that have been highly conserved in primates that exhibit significantly accelerated substitution rates in the ancestor of the Simiiformes. This primate lineage exhibits remarkable morphological innovations, including an expanded brain mass. Integrative multiomic analyses reveal that RECEs harbor sequences with potential cis-regulatory functions that are activated in the adult human brain. Importantly, genes linked to RECEs exhibit pronounced expression trajectories in the adult brain relative to the fetal stage. Furthermore, we observed an increase in the chromatin accessibility of RECEs in oligodendrocytes from individuals with Alzheimer's disease (AD) compared to that of a control group, indicating that these RECEs may contribute to brain aging and AD. Our findings serve to expand our knowledge of the genetic underpinnings of brain function during primate evolution.

CAT: a computational anatomy toolbox for the analysis of structural MRI data.

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Population Neuroscience: Principles and Advances.

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

Local variation in brain temperature explains gender-specificity of working memory performance.

Exploring gender differences in cognitive abilities offers vital insights into human brain functioning. Our study utilized advanced techniques like magnetic resonance thermometry, standard working memory n-back tasks, and functional MRI to investigate if gender-based variations in brain temperature correlate with distinct neuronal responses and working memory capabilities. We observed a significant decrease in average brain temperature in males during working memory tasks, a phenomenon not seen in females. Although changes in female brain temperature were significantly lower than in males, we found an inverse relationship between the absolute temperature change (ATC) and cognitive performance, alongside a correlation with blood oxygen level dependent (BOLD) signal change induced by neural activity. This suggests that in females, ATC is a crucial determinant for the link between cognitive performance and BOLD responses, a linkage not evident in males. However, we also observed additional female specific BOLD responses aligned with comparable task performance to that of males. Our results suggest that females compensate for their brain's heightened temperature sensitivity by activating additional neuronal networks to support working memory. This study not only underscores the complexity of gender differences in cognitive processing but also opens new avenues for understanding how temperature fluctuations influence brain functionality.

Role of cognitive computing in enhancing innovative healthcare solutions

Cognitive computing represents a groundbreaking development in healthcare, encompassing technological platforms that emulate the human brain's functionality. While cloud computing offers on-demand internet access to computing resources and services, cognitive computing focuses on modelling human mental processes to tackle complicated issues. Cognitive computing enhances human decision-making by integrating reasoning, machine learning, speech, natural language processing (NLP), and human-computer interaction. In the healthcare sector, it facilitates the analysis of clinical and genetic data to forecast diseases, tailor therapies, and elevate drug development. Additionally, it combines data analysis with adaptive page displays to tailor content based on the audience. Relevant papers in cognitive computing for healthcare were identified and studied. This paper aims to undertake an extensive scopic review of the pertinent literature from various sources, including articles and documents from numerous journals and conference proceedings. It delves into the need for cognitive computing in healthcare, elucidates supportive technologies, and expounds on its features within the healthcare domain. Furthermore, it identifies and discusses the substantial applications of cognitive computing in healthcare. These systems utilise computer models to replicate human cognitive processes, streamlining administrative tasks through artificial intelligence and cognitive computing. As a result, healthcare administrators can allocate more of their valuable time to patient care. Cognitive computing enhances outcomes and practitioner productivity and improves treatment decisions. The self-learning system of cognitive computing relies on real-time patient data, medical transcripts, and other pertinent information. These technologies can reduce the administrative burden on healthcare workers by automating tasks such as invoicing, claims processing, and appointment scheduling. This technology is poised to become increasingly indispensable in precision medicine.

Development of a neural network­based transcription method

In today's ever-evolving digital era, the utilization of audio and video materials has become increasingly prevalent in various spheres, including educational sectors, research initiatives, technological developments, and daily communication. This trend is driven by the convenience and efficiency of these mediums, offering quick access to information without the distractions often associated with more traditional methods like manual writing or typing, especially in scenarios where such activities are impractical, such as when traveling in a vehicle. However, this shift towards audio-visual content has introduced the challenge of converting these dynamic formats into text for a range of purposes. This need arises for several reasons. Firstly, editing and refining spoken content is often easier when it's transformed into a written format, allowing for a more thorough review and adjustment process. Secondly, in situations where individuals prefer or require written documentation — for instance, in academic or professional settings — transcription becomes a critical tool. Additionally, the clarity of audio and video content can vary greatly, with factors like diction, accent, background noise, and recording quality affecting the comprehensibility of the material. To address these challenges, transcription — the process of translating audio and video content into text — has become a valuable solution. It involves a meticulous process of listening, interpreting, and typing out the content, ensuring that the essence and nuances of the original material are accurately captured. This task demands not only attention to detail but also a deep understanding of the context and subject matter to ensure precision and reliability. In the realm of technology, advancements such as neural networks have revolutionized the transcription process. Neural networks, complex algorithmic structures that emulate human brain functioning, can learn, adapt, and process diverse types of data. In transcription, they are employed to recognize and interpret various speech patterns, accents, and languages, significantly enhancing the accuracy and speed of converting spoken words into written text. This integration of advanced technology in transcription not only streamlines the process but also opens new possibilities for accessibility, research, and data analysis, making it an indispensable tool in the modern world.

Decoding human brain functions: Multi-modal, multi-scale insights

Decoding human brain functions: Multi-modal, multi-scale insights

Transformer3: A Pure Transformer Framework for fMRI-Based Representations of Human Brain Function.

Effective representation learning is essential for neuroimage-based individualized predictions. Numerous studies have been performed on fMRI-based individualized predictions, leveraging sample-wise, spatial, and temporal interdependencies hidden in fMRI data. However, these studies failed to fully utilize the effective information hidden in fMRI data, as only one or two types of the interdependencies were analyzed. To effectively extract representations of human brain function through fully leveraging the three types of the interdependencies, we establish a pure transformer-based framework, Transformer3, leveraging transformer's strong ability to capture interdependencies within the input data. Transformer3 consists mainly of three transformer modules, with the Batch Transformer module used for addressing sample-wise similarities and differences, the Region Transformer module used for handling complex spatial interdependencies among brain regions, and the Time Transformer module used for capturing temporal interdependencies across time points. Experiments on age, IQ, and sex predictions based on two public datasets demonstrate the effectiveness of the proposed Transformer3. As the only hypothesis is that sample-wise, spatial, and temporal interdependencies extensively exist within the input data, the proposed Transformer3 can be widely used for representation learning based on multivariate time-series. Furthermore, the pure transformer framework makes it quite convenient for understanding the driving factors underlying the predictive models based on Transformer3.

Brain Liberation - An Anthropological Reflection on the Threat of Artificial Intelligence

Abstract This study examines the threat of AI to the emancipation of the human brain, with a particular focus on its impact on human logical thinking and creativity. It reveals the centrality of AI in knowledge engineering by analyzing expert systems constructed by the Symbolist school, which emphasize the role of knowledge bases and reasoning machines by simulating human logical thinking. The study also examines how AI affects modern human subjectivity, utilizing the “Valley of Terror” curve theory to explain the changes in human attitudes toward AI Further, the study introduces human brain-based AI models, such as the application of back-propagation algorithms in neural networks, as well as a cognitive-based model of knowledge operations in the human brain. The results show that the development of AI poses a threat to human brain power, especially in information processing and decision making. AI can replace human brain functions, leading to human logic and creativity degradation. For example, the application of ChatGPT in education can significantly improve students’ test rankings and reduce the reliance on individual thinking skills. In summary, AI may lead to the degradation of human logical thinking and creative ability while improving efficiency and convenience, affecting the overall development of human beings. Therefore, the focus should be on balancing the relationship between the utilization of AI and the development of human brain capacity, significantly to enhance the cultivation of human creativity and imagination in education and innovation.

The Effects of Digital Addiction on Brain Function and Structure of Children and Adolescents: A Scoping Review.

The escalating prevalence of studies investigating digital addiction (DA) and its detrimental impact on the human brain's structure and functionality has been noticeable in recent years. Yet, an overwhelming majority of these reviews have been predominantly geared towards samples comprising college students or adults and have only inspected a single variant of DA, such as internet gaming disorder, internet addiction disorder, problematic smartphone use, tablet overuse, and so forth. Reviews focusing on young children and adolescents (ages 0-18), or those which amalgamate various types of DA, are decidedly scarce. Given this context, summarizing the effects of DA on brain structure and functionality during the vital developmental stage (0-18 years) is of immense significance. A scoping review, complying with the PRISMA extension for such reviews, was conducted to amalgamate findings from 28 studies spanning a decade (2013-2023) and to examine the influence of assorted forms of DA on the brains of children and adolescents (0-18 years). The synthesized evidence indicated two primary results: (1) DA exerts harmful effects on the structure and functionality of the brains of children and adolescents, and (2) the prefrontal lobe is the region most consistently reported as impacted across all research. Furthermore, this review discerned a notable void of studies investigating the neural indices of digital addiction, along with a shortage of studies focusing on young children (0-6 years old) and longitudinal evidence. This research could provide the necessary theoretical basis for the thwarting and intervention of digital addiction, a measure indispensable for ensuring healthy brain development in children and adolescents.

Analysis of the Function and Role of Brain Neurotransmitters in Individual Activities

Studying the function and role of brain neurotransmitters in individual behaviors is an intricate investigation in the fields of neuroscience and psychology. Neurotransmitters are chemical substances that facilitate the transmission of signals between neurons in the brain. Their function is crucial in overseeing diverse facets of the human brain and bodily functions. Intricate connections among neurotransmitters, brain circuitry, and environmental influences influence individual activity. An optimal equilibrium of neurotransmitters is crucial. Imbalances, either in insufficient or excessive amounts, can be associated with a range of mental and neurological diseases. This review aims to describe the function and role of brain neurotransmitters in person activities.

Artificial intelligence changing the future of healthcare diagnostics

Artificial intelligence (AI) is a computer’s capacity to carry out operations like speech and image recognition and decision-making that ordinarily require human intelligence. Healthcare is using AI to automate tasks such as medical image analysis and diagnosis that require high precision and accuracy. The healthcare industry is significantly impacted by the rapid development of machine learning algorithms, which are frequently implemented using deep learning, as well as the growth of digital data and computing power supported by improvements in hardware technologies. Significant progress has been made in the field of artificial intelligence in recent years and is now widely used in healthcare to automate a variety of tasks, which require a high degree of accuracy and precision. The creation of machine learning algorithms, which can learn from data and make predictions based on that learning, has made it possible to use AI in healthcare. Neural networks are used in deep learning, a subfield of machine learning, to simulate how the human brain functions. Crucial advances have been made in clinical decision support, drug discovery, and medical imaging. Furthermore, the rapid development of hardware technologies, such as graphics processing units, has allowed AI systems to process enormous amounts of data quickly and accurately. Due to this, AI-based tools and platforms can help healthcare professionals with tasks such as patient monitoring, disease diagnosis, and treatment planning.

A Novel Approach to Increase Glial Cell Populations in Brain Microphysiological Systems.

Brain microphysiological systems (bMPS) recapitulate human brain cellular architecture and functionality more closely than traditional monolayer cultures and have become increasingly relevant for the study of neurological function in health and disease. Existing 3D brain models vary in reflecting the relative populations of different cell types present in the human brain. Most models consist mainly of neurons, while glial cells represent a smaller portion of the cell populations. Here, by means of a chemically defined glial-enriched medium (GEM), an improved method to expand the population of astrocytes and oligodendrocytes without compromising neuronal differentiation in bMPS, is presented. An important finding is that astrocytes also change in morphology when cultured in GEM, more closely recapitulating primary culture astrocytes. GEM bMPS are electro-chemically active and show different patterns of calcium staining and flux. Synaptic vesicles and terminals observed by electron microscopy are also present. No significant changes in neuronal differentiation are observed by gene expression, however, GEM enhanced neurite outgrowth and cell migration, and differentially modulated neuronal maturation in two different cell lines. These results have the potential to significantly improve functionality of bMPS for the study of neurological diseases and drug discovery, contributing to the unmet need for safe human models.