Neuroscience Applications Research Articles

Neural Pathways of Social Cognition: From Systems Neuroscience to Psychosocial Applications

The neural pathways underlying social cognition form the basis of human interaction, encompassing processes such as empathy, theory of mind, and social decision-making. This research explores the intersection of systems neuroscience and psychosocial applications, unraveling the neural circuits that enable individuals to perceive, interpret, and respond to social stimuli. Key brain regions such as the medial prefrontal cortex, temporoparietal junction, and amygdala play pivotal roles in these processes, facilitating complex functions like perspective-taking and emotional resonance. By integrating insights from neuroimaging, behavioral studies, and computational modeling, this study aims to map the mechanisms that govern social cognition and their disruptions in conditions such as autism, schizophrenia, and social anxiety. The research also emphasizes the translation of these findings into psychosocial interventions, including therapeutic strategies and social skills training programs. Understanding the neural underpinnings of social cognition has far-reaching implications for enhancing interpersonal relationships, fostering inclusivity, and addressing the challenges posed by neuropsychiatric disorders.

Analysis methods for large-scale neuronal recordings.

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Subthalamic nucleus deep brain stimulation modulates speech graphs in patients with Parkinson’s disease

Graph theory enables a direct quantification of topological properties of any arbitrary network. Its application in neuroscience has unveiled topological changes of brain networks associated with various neurodegenerative diseases. This study used the graph theory to understand speech deficits in patients with Parkinson’s disease (PD). In particular, this study investigated the effect of subthalamic nucleus deep brain stimulation (STN-DBS) on the topology of speech graphs. Sixty patients with PD completed a standard semantic fluency test with DBS switched ON and OFF. A control group of sixty matched nonsurgical PD patients completed the test once. All verbal responses were recorded, transcripted, and transformed into directed speech graphs. Volumes of tissue activated (VTA) were estimated for three STN subregions, including sensorimotor, associative, and limbic parts. First, the patients with DBS OFF produced smaller and denser speech graphs than nonsurgical patients, showing fewer nodes, higher density, shorter diameter, and shorter average shortest path. Second, DBS partially reversed the effect of surgery, leading to larger and sparser speech graphs with more nodes, lower density, longer diameter, and longer average shortest path (ON versus OFF). Third, however, the left associative VTA negatively correlated with the DBS-induced diameter and average shortest path changes (ON versus OFF), suggesting that the patients with greater left associative STN stimulation tended to produce smaller and denser speech graphs. This study demonstrates that STN-DBS can partially restore the topological structure of speech graphs in PD patients. However, stimulating the left associative STN appears to disrupt speech graphs.

Unveiling the Human Brain on a Chip: An Odyssey to Reconstitute Neuronal Ensembles and Explore Plausible Applications in Neuroscience.

The brain is an incredibly complex structure that consists of millions of neural networks. In developmental and cellular neuroscience, probing the highly complex dynamics of the brain remains a challenge. Furthermore, deciphering how several cues can influence neuronal growth and its interactions with different brain cell types (such as astrocytes and microglia) is also a formidable task. Traditional in vitro macroscopic cell culture techniques offer simple and straightforward methods. However, they often fall short of providing insights into the complex phenomena of neuronal network formation and the relevant microenvironments. To circumvent the drawbacks of conventional cell culture methods, recent advancements in the development of microfluidic device-based microplatforms have emerged as promising alternatives. Microfluidic devices enable precise spatiotemporal control over compartmentalized cell cultures. This feature facilitates researchers in reconstituting the intricacies of the neuronal cytoarchitecture within a regulated environment. Therefore, in this review, we focus primarily on modeling neuronal development in a microfluidic device and the various strategies that researchers have adopted to mimic neurogenesis on a chip. Additionally, we have presented an overview of the application of brain-on-chip models for the recapitulation of the blood-brain barrier and neurodegenerative diseases, followed by subsequent high-throughput drug screening. These lab-on-a-chip technologies have tremendous potential to mimic the brain on a chip, providing valuable insights into fundamental brain processes. The brain-on-chip models will also serve as innovative platforms for developing novel neurotherapeutics to address several neurological disorders.

Comparative Validation of Scintillator Materials for X-Ray-Mediated Neuronal Control in the Deep Brain

When exposed to X-rays, scintillators emit visible luminescence. X-ray-mediated optogenetics employs scintillators for remotely activating light-sensitive proteins in biological tissue through X-ray irradiation. This approach offers advantages over traditional optogenetics, allowing for deeper tissue penetration and wireless control. Here, we assessed the short-term safety and efficacy of candidate scintillator materials for neuronal control. Our analyses revealed that lead-free halide scintillators, such as Cs3Cu2I5, exhibited significant cytotoxicity within 24 h and induced neuroinflammatory effects when injected into the mouse brain. In contrast, cerium-doped gadolinium aluminum gallium garnet (Ce:GAGG) nanoparticles showed no detectable cytotoxicity within the same period, and injection into the mouse brain did not lead to observable neuroinflammation over four weeks. Electrophysiological recordings in the cerebral cortex of awake mice showed that X-ray-induced radioluminescence from Ce:GAGG nanoparticles reliably activated 45% of the neuronal population surrounding the implanted particles, a significantly higher activation rate than europium-doped GAGG (Eu:GAGG) microparticles, which activated only 10% of neurons. Furthermore, we established the cell-type specificity of this technique by using Ce:GAGG nanoparticles to selectively stimulate midbrain dopamine neurons. This technique was applied to freely behaving mice, allowing for wireless modulation of place preference behavior mediated by midbrain dopamine neurons. These findings highlight the unique suitability of Ce:GAGG nanoparticles for X-ray-mediated optogenetics. The deep tissue penetration, short-term safety, wireless neuronal control, and cell-type specificity of this system offer exciting possibilities for diverse neuroscience applications and therapeutic interventions.

Automated EEG-based language detection using directed quantum pattern technique

Electroencephalogram (EEG) signals contain complex useful information about brain activities. These EEG signals are noisy, highly varying and nonstationary in nature. Hence, extracting meaningful information from these signals is challenging. The existing machine learning systems struggle to capture the minute changes from the signals and yield high performance.This study introduces a novel quantum-inspired feature extraction technique called Directed Quantum Pattern (DQP), designed to address these challenges by using a lattice structure to capture directional binary features. These directions (paths) are computed using a maximum function providing a dynamic and adaptive feature representation.This paper presents a novel DQP-LangNet developed using DQP for automated classification of two- languages using EEG signals. We have proposed a hybrid approach, combining DQP, statistical features, and multi-level discrete wavelet transform (MDWT) to extract salient features similar to the deep learning approach. The EEG dataset consisting of 14 channels, produces 7 feature vectors per channel, yielding 98 feature vectors. Neighborhood component analysis and Chi-square (Chi2) feature selection approaches generated 196 feature vectors.In addition to the innovative feature extraction a new classification structure called “t” is proposed k-nearest neighbor (tkNN) and support vector machine (tSVM) classifiers are employed. Using the proposed tkNN and tSVM classifiers, 392 (=196×2) classifier-based outcomes are obtained. To further improve classification performance, we applied the iterative majority voting (IMV) technique to automatically select the best result.Our DQP-based model achieved a classification accuracy of 95.68 %using EEG language dataset with leave-one-subject-out (LOSO) cross-validation strategy. Also, an explainable feature engineering (XFE) structure of DQP-LangNet is employed to obtain channel-specific explainable results. Our proposed DQP-LangNet model can be employed for other applications in neuroscience.

Advancing research on odor-induced sweetness enhancement: A EEG local-global fusion transformer network for sweetness quantification combined with EEG technology

Reducing sugar intake is crucial for health, and odor sweetening enhances food enjoyment and quality perception. Current research relies on subjective manual sensory evaluations, which are poorly reproducible. Traditional methods also fail to capture dynamic neural responses to odor-induced sweetness. We propose an electroencephalogram local-global fusion transformer network (EEG-LGFNet) model to decode this impact objectively. Electroencephalogram data were collected from 16 subjects under different odor and sucrose stimuli. The model captures complex neural signals by integrating local and global feature extraction mechanisms. Its performance was validated across three-time windows, demonstrating efficacy over various temporal ranges. Analysis of the coefficient of determination across brain regions confirmed the importance of the frontal, central, and parietal areas of sweetness perception. The EEG-LGFNet model excelled in quantifying odor-enhanced sweetness, significantly outperforming state-of-the-art models. This research offers new insights into odor sweetening, with applications in food development, personalized nutrition, and neuroscience.

Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings

Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.

Leveraging tardigrade proteins Dsup and CAHS D for enhanced neural protection in neurosurgery and neuroscience.

Tardigrades are microscopic organisms known for their remarkable resilience to extreme environmental conditions, including radiation and desiccation. Two key proteins, Dsup (Damage suppressor protein) and CAHS D (Cytoplasmic Abundant Heat Soluble protein D), play crucial roles in this resilience. Dsup protects DNA from radiation-induced damage, while CAHS D stabilizes cellular structures during desiccation by interacting with, but not retaining, water. These unique mechanisms have significant potential applications in neurosurgery and neuroscience. Dsup could inspire the development of protective agents for neural tissues during radiation-based treatments, minimizing collateral damage and improving patient outcomes. Meanwhile, CAHS D's stabilization properties could lead to new neuroprotective strategies, safeguarding brain cells under stress. Together, these tardigrade proteins offer innovative solutions for enhancing neural protection, opening new avenues for treating neurological conditions and improving the safety and efficacy of neurosurgical procedures.

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingredient for the inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process. Combining recent martingale central limit theorem with the new concentration inequality, we then characterize the convergence rate of the test statistics in a continuous time domain. Finally, to account for potential non-stationarity of the process in practice, we extend our statistical inference procedure to a flexible class of Hawkes processes with time-varying background intensities and unknown transition functions. The finite sample validity of the inferential tools is illustrated via extensive simulations and further applied to a neuron spike train dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Wireless optically pumped magnetometer MEG

The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research.

The Scientific Frontiers of Neuroengineering, the Treatment of Schizophrenia and the Visionary Future

Science, civilization and mankind are today in the path of newer scientific divination as regards application of neuroscience and neuro-engineering in diverse areas of science and technology. Neuroengineering is the agglomeration of neuroscience, experimental neuroscience, computational neuroscience, neurology, electrical engineering and nanotechnology. The scientific frontier of neuro-engineering needs to be envisioned and revamped with the passage of scientific history and time. Scientific provenance, scientific divination and scientific revelation are the key highlights of neuroengineering, neuroscience and electrical engineering science today. Today the science of psychiatry and psychology are merged with neuroengineering and neuroscience. The author is a schizophrenic patient since November,2023. From October 2012, he is diagnosed with glaucoma in his both eyes. In this article, the author deeply and profoundly highlights the lucid and insightful world of neuroengineering and the science of psychiatry. This article is targeted towards a newer world in the field of neuroengineering with a deep interface with the trials and tribulations in his life as regards treatment of schizophrenia.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

Electro-Optically Configurable Synaptic Transistors With Cluster-Induced Photoactive Dielectric Layer for Visual Simulation and Biomotor Stimuli.

The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200°C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

General In Situ Engineering of Carbon-Based Materials on Carbon Fiber for In Vivo Neurochemical Sensing.

Developing real-time, dynamic, and in situ analytical methods with high spatial and temporal resolutions is crucial for exploring biochemical processes in the brain. Although in vivo electrochemical methods based on carbon fiber (CF) microelectrodes are effective in monitoring neurochemical dynamics during physiological and pathological processes, complex post modification hinders large-scale productions and widespread neuroscience applications. Herein, we develop a general strategy for the in situ engineering of carbon-based materials to mass-produce functional CFs by introducing polydopamine to anchor zeolitic imidazolate frameworks as precursors, followed by one-step pyrolysis. This strategy demonstrates exceptional universality and design flexibility, overcoming complex post-modification procedures and avoiding the delamination of the modification layer. This simplifies the fabrication and integration of functional CF-based microelectrodes. Moreover, we design highly stable and selective H+, O2, and ascorbate microsensors and monitor the influence of CO2 exposure on the O2 content of the cerebral tissue during physiological and ischemia-reperfusion pathological processes.

A Bibliometric Analysis of the Trends and Impact of Neuromarketing Research: Peering Into the Consumer Brain.

Neuromarketing is the application of neuroscience and cognitive science to understand and influence consumer behavior and the different underlying decision-making processes. This neuromarketing bibliometric study uses Scopus for bibliographic data and Biblioshiny and Citespace for the overall analysis. Annual scientific production is considered to outline some general lines of critical trends in volume over time. It provides an overview of the most relevant authors and their contributions and creates co-citation networks with the cited authors to identify influential researchers and collaborative networks. Sources that are most relevant to the discussion, together with co-citation patterns, show important articles and journals. Mapping countries' scientific production and collaboration show the manifold contributions of single countries to global research and how some topics can be created out of cooperation. Trend topics were analyzed to detect emerging research themes; factorial analysis helped in the actual clustering of the research topics. It identifies keywords with the most robust citation bursts, meaning times of the most concentrated research activity and emerging areas of interest. The research identifies gaps and produces some practical implications, putting forward a roadmap for future research directions and the necessity of advanced computational techniques in neuromarketing research.

Enhanced Motor Imagery Classification through Channel Selection and Machine Learning Algorithms for BCI Applications

Brain-Computer Interface (BCI) applications utilizing Electroencephalography (EEG) signals have garnered significant attention for their potential to facilitate through communication between the brain and external devices. EEG-based BCIs offer a non-invasive means to interpret neural activity, enabling a range of applications in healthcare, gaming, and cognitive neuroscience. This study explores motor imagery (MI) EEG signals classification, employing a variety of signal processing techniques as well as machine learning algorithms to increase accuracy and reliability. Using data from the BCI Competition IV dataset, the proposed methodology involves EEG band separation via Butterworth bandpass filters, channel selection through a wrapper method using K-nearest neighbors (KNN), and classification of motor imagery tasks. The study demonstrates a high classification accuracy of 98% across different motor imagery tasks, highlighting the effectiveness of the proposed approach. This method not only shows promise for BCI applications aimed at assisting individuals with motor disabilities but also for gaming and potential security applications such as user authentication. Future work will focus on further enhancing the model's accuracy and exploring its integration into diverse practical applications.

Quantitative Total-Body Imaging of Blood Flow with High Temporal Resolution Early Dynamic 18F-Fluorodeoxyglucose PET Kinetic Modeling.

The first two minutes of dynamic PET scans were reconstructed at high temporal resolution (60×1 s, 30×2 s) to resolve the rapid passage of the radiotracer through blood vessels. In contrast to existing methods that use blood-to-tissue transport rate ( ) as a surrogate of blood flow, our method directly estimates blood flow using a distributed kinetic model (adiabatic approximation to the tissue homogeneity model; AATH). To validate our 18F-FDG measurements of blood flow against a flow radiotracer, we analyzed total-body dynamic PET images of six human participants scanned with both 18F-FDG and 11C-butanol. An additional thirty-four total-body dynamic 18F-FDG PET scans of healthy participants were analyzed for comparison against literature blood flow ranges. Regional blood flow was estimated across the body and total-body parametric imaging of blood flow was conducted for visual assessment. AATH and standard compartment model fitting was compared by the Akaike Information Criterion at different temporal resolutions. 18F-FDG blood flow was in quantitative agreement with flow measured from 11C-butanol across same-subject regional measurements (Pearson R=0.955, p<0.001; linear regression y=0.973x-0.012), which was visually corroborated by total-body blood flow parametric imaging. Our method resolved a wide range of blood flow values across the body in broad agreement with literature ranges (e.g., healthy cohort average: 0.51±0.12 ml/min/cm3 in the cerebral cortex and 2.03±0.64 ml/min/cm3 in the lungs, respectively). High temporal resolution (1 to 2 s) was critical to enabling AATH modeling over standard compartment modeling. Total-body blood flow imaging was feasible using early-dynamic 18F-FDG PET with high-temporal resolution kinetic modeling. Combined with standard 18F-FDG PET methods, this method may enable efficient single-tracer flow-metabolism imaging, with numerous research and clinical applications in oncology, cardiovascular disease, pain medicine, and neuroscience.

The role (and limits) of developmental neuroscience in determining adolescents’ autonomy rights: The case for reproductive and voting rights

Neuroscientific evidence documenting continued neural development throughout adolescence has been leveraged in advocacy for more lenient treatment of adolescents in the criminal justice system. In recent years, developmental science, including neuroscience, has progressed and enabled more nuanced interpretations of what continuing neural development in adolescence likely means functionally for adolescents’ capabilities. However, oversimplified interpretations equating continuing neural development to overall “immaturity” are frequently used to make the case that adolescents should have fewer legal rights to make decisions on their own behalf, including regarding reproductive and voting rights. Here we address ongoing debates about adolescents’ autonomy rights and whether such rights should be expanded or restricted. We review extant neuroscientific and developmental research that can inform these debates. We call for: (1) a more nuanced application of developmental neuroscience to specific rights issues in specific contexts; (2) additional research designed to inform our understanding of the developmental benefits or harms of rights-based policies on young people over time; and (3) the grounding of developmental neuroscientific research on adolescents within a human rights framework. We offer suggestions to developmental and neuroscience scholars on how to discuss the science of adolescent development with those seeking guidance in their design of law and policy.

From Infancy to Childhood: A Comprehensive Review of Event- and Task-Related Brain Oscillations.

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.