Neuroscience Research Research Articles (Page 1)

ABSTRACT In this paper we analyze coping during public service delivery and stress among workers in human service organizations whose discretion is protected by the authority of their jobs within the police, healthcare psychiatry, and social work. The data come from a survey sent to 990 frontline workers in Sweden. In line with recent neuroscience and psychology research, our findings suggest that the discretionary worker interacts through two families/forms with ways of coping: 1) approaching clients – through instrumental action, bending/breaking rules, using personal resources, or prioritizing motivated clients and 2) avoiding clients – through rationing, routinizing, or aggression. Statistically significant and systematic differences between policy sectors suggest that coping depends on institutionalized moral practices: prioritization and aggression are most common within the police; instrumental action is most common within healthcare; and bending/breaking rules and routinizing are most common within social work. Lower levels of stress are identified in relation to avoidant coping strategies.

The advancements in neuroscience research and omics technologies generate extensive data for brain-related diseases and disorders that are scattered across various manuscript repositories and databases, potentially hindering global initiatives to advance neuroscience research. This study introduces BrainProt v3.0 (https://www.brainprot.org/), an omics-driven knowledge-base of human brain and associated diseases. BrainProt version 3.0 navigates the Human Brain Disease Atlas (HBDA) and integrates the Brain Disease Marker Curator (BDMC) and Brain Disease Drug Finder (BDDF). It features 20,202 disease-associated genes, 136,557 chemical target interactions, and 2,145 clinical trial details. Additionally, the Brain Disease Transcriptome Map (BDTM) and Brain Disease Proteome Map (BDPM) collate multiomics data from the most significant brain diseases, displaying an expression profile across 52 datasets and 1,868 samples. This platform also facilitates the exploration of biological cross-talk and correlations between transcriptomics and proteomics data to identify critical disease-associated markers. In conclusion, BrainProt is designed to advance the Human Brain Proteome Project (HBPP) by expanding the integration of diseases and datasets, thereby elucidating the complexities of brain diseases and enhancing the research on the interplay between brain tumors and neurodegenerative disorders.

ABSTRACT In the last few decades, BCIs have transitioned from science fiction to reality through the development of neuroimaging, signal processing, and device engineering. BCIs are interfaces that can be invasive acquiring brain signals through electrode implants or non-invasive via EEG, fMRI and fNIRS the interfaces clean, classify and use them with algorithms to achieve applications like rehabilitation, assistive technologies, robotics, gaming or even neuroscience research. The future of the BCI field is to develop direct C5 brain experiences by mitigating the technology challenges of non-linear brain signal dynamics, feature extraction and psycho-neurophysiological fluctuations to create plug-and-use business opportunities for the end-users. BCIs have already changed lives through communication. The more is to be discovered in the field and has a really good potential for the well being of others .It aims to help humans that lacks with the daily skills due to any reason that has disturbed the neurolink between the brain and the body . The electroencephalogram, the most common non-invasive brain-computer interface method, is susceptible to noise and artifacts. It also exhibits variability both within and between individuals across different sessions, devices, and tasks. Consequently, developing a universal pattern recognition model for EEG-based BCI systems that performs optimally for all users and conditions is challenging

Objective To explore and analyze the current research status, hotspots, and development trend of labor and delivery experience, to provide a reference for subsequent related research and clinical practice. Methods We searched the Web of Science database for literature related to labor and delivery experience published between its establishment and December 20, 2024, and conducted bibliometric analysis using CiteSpace software. Results After screening, 1089 papers were included in the analysis, and the number of annual publications showed a growing trend, reaching its highest in 2024. The United States and Sweden dominated the list. The research hotspots focused on maternal mental health, delivery methods, and quality of Intrapartum care. Conclusion The field of birth experiences is currently undergoing rapid development, with leading trends including innovations in delivery methods, prenatal care, research in the cognitive neuroscience of childbirth, and a focus on mothers undergoing induced labor and those in low-income areas to optimize the overall birth experience.

Abstract Researchers administered MDMA (known as ecstasy) to octopuses and observed their behavior. Due, at least in part, to serotonin transport systems nearly identical to human ones, octopuses on ecstasy behave in a prosocial manner. This observation suggests that neurochemicals, like serotonin, which are conserved across taxa, contribute to the realization of some psychological states. Since standard functionalism claims that not only can psychological states be multiply realized, but that psychological states are also autonomous from neural states, then this eccentric experiment reveals an important challenge. Using recent research in neuroscience and a mechanistic account of multiple realization, I argue that certain cases of multiply realized psychological states always include specific neurochemicals (i.e. neurotransmitters), and this undermines the kind of multiple realization that is needed to warrant autonomy. I conclude that the multiple realization of psychological states does not guarantee autonomy from neural states and that the functionalist claims for autonomy are not empirically justified.

Mapping the brain's fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Here, we show that computational scattered light imaging (ComSLI) can map fiber networks in histology independent of sample preparation, also in formalin-fixed paraffin-embedded (FFPE) tissues including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different sample preparations (in paraffin, deparaffinized, various stains, unstained fresh-frozen). We convert microscopic orientations to microstructure-informed fiber orientation distributions (μFODs). Adapting tractography tools from diffusion magnetic resonance imaging (dMRI), we trace axonal trajectories revealing white and gray matter connectivity. These allow us to identify altered microstructure or deficient tracts in demyelinating or neurodegenerating pathology, and to show key advantages over dMRI, polarization microscopy, and structure tensor analysis. Finally, we map fibers in non-brain tissues, including muscle, bone, and blood vessels, unveiling the tissue's function. Our cost-effective, versatile approach enables micron-resolution studies of intricate fiber networks across tissues, species, diseases, and sample preparations, offering new dimensions to neuroscientific and biomedical research.

Rehabilitation, neuroplasticity, and machine learning: Approaching artificial intelligence for equitable health systems.

Estimation of brain activity sources of sympathovagal dynamics.

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety.

Long-term deep phenotyping of behavioral traits in mice using homecage monitoring.

Understanding the genetic foundations of brain development has been made possible by the use of traditional biological models. However, these models frequently fail to capture the complexity of human brain development, particularly the considerable cortical expansion that sets humans apart from other vertebrates and non-human primates. The purpose of this review is to outline the methodology, applications, and potential prospects for using human brain organoids as sophisticated models for researching brain development and illness mechanisms. Organoids, or three-dimensional (3-D) structures, are generated by utilizing adult or embryonic stem cells to mimic the main structural and functional features of the human brain. The present investigation emphasizes the advantages of these organoids over traditional two-dimensional (2-D) monolayer models in relation to cellular variety and the ability to create complex 3-D networks, addressing various methods established by researchers to culture these cells. Organoids precisely mimic numerous features of human brain development, overcoming the limitations of conventional models. They have demonstrated significant utility in investigating the mechanisms that contribute to neurodegenerative diseases like Parkinson's and Alzheimer's, in addition to tumor biology, providing a valuable understanding of both the normal physiological processes and the underlying cause of the human brain. Human brain organoids signify a notable progression in the field of neuroscience research, facilitating enhanced modeling of brain disorders. Future investigations will further enhance these methodologies and examine their applications, leading to innovative therapeutic strategies and broadening the knowledge of human brain disorders.

In this paper, a discrete memristor model is first developed and then utilized to construct a tri-cycle memristive synapse Hopfield neural network, replacing traditional resistive synapses. The equilibrium point stability is analyzed to reveal the Neimark–Sacker bifurcation behaviors, clarifying the intricate dynamics of the proposed network. Dynamical characteristics, including periodic, quasi-periodic, chaotic, and hyperchaotic states, are systematically explored using phase diagrams, iterative series, Lyapunov exponent spectra, and bifurcation diagrams. Furthermore, symmetry-induced dynamical phenomena under symmetric initial conditions are observed, demonstrating the coexistence of stable points and chaotic attractors. Compared with traditional tri-cycle resistive synapse HNN models, the proposed TCMS-HNN significantly enhances dynamic complexity and computational efficiency. Finally, the DSP implementation is completed, realizing the digital circuit of the TCMS-HNN model. These results provide important theoretical insights for neuroscience research and complex dynamical systems, also highlight its substantial potential in neuromorphic computing and secure information encryption.

The golden age of online readout: EEG-informed TMS from manual probing to closed-loop neuromodulation.

Remote modulation of specific neurons is critical for neural circuit dissection and neurological disease therapy. Traditional neuromodulation strategies applying electrodes and external stimuli may suffer from invasive injuries and insufficient spatiotemporal resolution, respectively. Herein, transcranial optical neuromodulation with precise spatiotemporal control was realized by establishing a nanophotonic neural interface, where the polyethylene glycol-modified two-dimensional Ti3C2Tx (MXene) served as near-infrared (NIR) photoactive nanosheets (PANS). Polymer modification of the nanosheets contributed to a robust PANS-neuron interface, which allowed efficient modulation of non-transgenic neurons with subcellular resolution and millisecond precision. The PANS-enabled transcranial optical neuromodulation not only evoked neuronal action potentials in brain slices with a high synchronization rate of 98.33%, but also facilitated neuronal firing in vivo. With PANS as stimulus targets, the transcranial NIR light can excite neurons in desired brain regions and activate defined neural circuits, thus regulating mouse behaviors with minimal invasiveness. Such nanosheet-enabled transcranial and precise optical neuromodulation, eliminating invasive implants and genetic manipulation, may open opportunities for neurophotonics and neuroscience research. Transcranial optical neuromodulation with precise spatiotemporal control was realized via a nano-enabled optical neural interface, where the polyethylene glycol-modified two-dimensional Ti3C2Tx serves as near-infrared photoactive nanosheets. Such efficient neuromodulation of non-transgenic animals with subcellular resolution and millisecond precision, eliminating invasive implants and genetic manipulation, holds potential for neuroscience research.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s12951-025-03788-y.

Flexible electronic sensors, due to their mechanical compliance with soft tissues and excellent biocompatibility, have demonstrated unique advantages in neuroscience research and clinical applications in recent years. Compared with traditional rigid electrodes, flexible sensors show greater potential in long-term stability, large-area coverage, and multimodal integration, thereby meeting the diverse needs of neural signal acquisition and functional monitoring. This review summarizes the latest advances in the application of flexible electronics for brain monitoring, covering electrophysiological signal acquisition, neurotransmitter detection, multimodal and region-synchronized recording, and clinical rehabilitation applications. It further discusses critical aspects influencing performance and applications, including material selection, device design, circuit integration, energy supply, and mechanical modeling. On this basis, the challenges and prospects regarding long-term stability, data processing, multimodal integration, and clinical translation are analyzed. Overall, flexible brain sensors are gradually progressing from laboratory validation to systematic applications, and their development is expected to exert profound impacts on neuroscience research, disease diagnosis and treatment, and intelligent rehabilitation in the future.

The rapid advance of genetically encoded fluorescent functional indicators has transformed neuroscience research. Fluorescence-based optical neural recording offers excellent sensitivity and spatiotemporal resolutions. A major limitation of optical measurement is the superficial access depth due to the random light scattering in the mammalian brain. Currently, implanting miniature gradient-index (GRIN) lenses has become the preferred method for deep brain optical imaging. However, the image quality and throughput are majorly impacted by the severe optical aberration of GRIN lenses. In this work, we present an easy-to-adopt solution to overcome these challenges and improve the image quality, volume, and throughput. Specifically, we develop a correction objective lens that corrects the aberration of a GRIN lens to enable high-throughput volumetric functional imaging with a ~ 400% larger field-of-view (FOV). We demonstrate the capabilities of in vivo large-FOV 3D volumetric calcium imaging by recording over 1000 neurons in deep brain regions through a 0.5 mm diameter GRIN lens. The simplicity and robust performance of the method promise broad applications in neuroscience research.

Although in vitro neuronal models are accessible and versatile systems for functional electrophysiological studies, the spontaneous and random formation of neural circuits often compromises the structural control and reproducibility. Here, we introduce a robust method for engineering human neuronal networks in vitro with single-cell precision and reproducibility. Our integrated platform combines direct laser-written microstructure templates and soft lithography-based fabrication of microscaffolds with functional multielectrode array recordings. This system enables high-throughput production of diverse circuit designs and allows for the exact placement of neurons within confined microenvironments. The system enables precise recording of spontaneous neuronal activity, as well as electrical and optogenetic stimulations. Using this approach, we constructed reproducible, bottom-up neuronal circuits composed of a defined number of human neurons. As a proof of principle, we employed these circuits to investigate ephaptic coupling, which refers to the modulation of neuronal activity by endogenous electric fields. Although it is believed to play a role in neural computations and cardiac conduction and is associated with epilepsy and arrhythmia, its mechanisms are unclear due to limitations in experimental models, both in vivo and in vitro. By controlling axonal proximity within microchannels and the number of neurons in the engineered circuits, we can quantify ephaptic coupling at different strengths, which validates theoretical predictions, including reduced action potential velocity, increased activity synchronization, and lower stimulation thresholds. Furthermore, the platform has broad potential for studying synaptic and nonsynaptic interactions, myelination processes, advancing disease modeling, and fundamental neuroscience research.

Abstract: Yoga has transcended its traditional Indian roots to become a global therapeutic practice with measurable neurobiological effects. Emerging neuroscientific research demonstrates that yogic practices that comprising asana (postures), pranayama (breath regulation), and dhyana (meditation) induce structural and functional brain changes associated with improved mental health, emotional stability, and cognitive performance. Mental health disorders such as depression, anxiety, and chronic stress have emerged as global health challenges, significantly affecting productivity, emotional stability, and overall quality of life. Despite advances in modern medicine, the neurobiological understanding of mind–body interventions such as yoga remains underexplored in mainstream neuroscience. Yoga, an ancient Indian system integrating postures (asanas), breathing techniques (pranayama), and meditation (dhyana), acts as a holistic regulator of both the central and autonomic nervous systems. By analysing recent neuroimaging and biochemical studies, this research identifies significant correlations between yoga practice and enhanced gamma-aminobutyric acid (GABA) activity, increased serotonin and dopamine levels and reduced cortisol secretion. These neurochemical modulations correspond to decreased anxiety, improved mood regulation, and enhanced cognitive performance. Functional MRI and EEG evidence further indicate that yoga strengthens the prefrontal cortex, hippocampus, and insular connectivity while reducing amygdala overactivation — mechanisms central to emotional regulation and stress resilience. Physiological parameters such as heart rate variability and blood pressure also show measurable improvements, reflecting balanced sympathetic–parasympathetic interactions. This paper concludes that yoga operates as a neurobehavioral modulator, inducing adaptive neuroplastic changes and restoring psychophysiological harmony. The integration of yoga into public health and mental wellness programs could provide a sustainable, evidence-based model for enhancing emotional stability, cognitive clarity, and resilience in an increasingly stressful world.

Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strategy (FAST), a deep-learning framework designed for high-speed fluorescence neural imaging, including in vivo calcium, voltage, and volumetric time-lapse imaging. FAST balances spatial and temporal redundancy across neighboring pixels, preserving structural fidelity while preventing over-smoothing of rapidly evolving fluorescence signals. Utilizing an ultra-light convolutional neural network, FAST enables real-time processing at speeds exceeding 1000 frames per second, substantially surpassing the acquisition rates of most high-speed imaging systems. We also introduce an intuitive graphical user interface that integrates FAST into standard imaging workflows, providing a real-time denoising tool for recorded neural activity and enabling downstream analysis in neuroscience research that requires millisecond-scale temporal precision, particularly in closed-loop studies.

Studying locomotor activity in animal models is crucial for understanding physiological, behavioral, and pathological processes. This study aimed to develop an artificial intelligence-based tracking system called Goblotrop, designed to localize rodents within their laboratory environment. The Goblotrop system uses two infrared cameras to record videos of rodents in their home cages. A neural network analyzes these videos to determine the rodent's position at each time point. By tracking changes in position over time, the system provides detailed insights into rodent behavior, including speed, mobility, and climbing activity. To evaluate the system's reliability, we utilized a starvation-induced hyperactivity model, employed as a female mouse model for anorexia nervosa. This model is characterized by pronounced hyperactivity, typically assessed using electronically monitored running wheels. Both the Goblotrop system and running wheel measurements demonstrated that starvation increases food-anticipatory activity (up to 4 h before food availability) while reducing nocturnal activity. The results from the Goblotrop system and running wheel measurements exhibited remarkable consistency. Thus, the Goblotrop system proves to be a valuable tool for studying locomotor activity and circadian rhythms in different cage areas in animal models. This tool provides potential for various scientific fields, including neuroscience, pharmacology, toxicology, and behavioral research.