Field Of Brain Science Research Articles

Implantable Neural Microelectrodes: How to Reduce Immune Response.

Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.

Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.

The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep learning-based approach to deidentifying facial features in brain images using a generative adversarial network to synthesize new facial features and contours. We employed the precision of the three-dimensional U-Net model to detect specific features such as the ears, nose, mouth, and eyes. Results: Our method diverges from prior studies by highlighting partial regions of the head image rather than comprehensive full-head images. We trained and tested our model on a dataset comprising 490 cases from a publicly available head computed tomography image dataset and an additional 70 cases with head MR images. Integrated data proved advantageous, with promising results. The nose, mouth, and eye detection achieved 100% accuracy, while ear detection reached 85.03% in the training dataset. In the testing dataset, ear detection accuracy was 65.98%, and the validation dataset ear detection attained 100%. Analysis of pixel value histograms demonstrated varying degrees of similarity, as measured by the Structural Similarity Index (SSIM), between raw and generated features across different facial features. The proposed methodology, tailored for partial head image processing, is well suited for real-world imaging examination scenarios and holds potential for future clinical applications contributing to the advancement of research in de-identification technologies, thus fortifying privacy safeguards.

Multifrequency transcranial focusing based on acoustic lensing

Transcranial focused ultrasound (tFUS) possesses significant advantages such as non-invasiveness and high tissue penetration depth, making it a promising tool in the field of brain science. Acoustic holographic lenses enable the manipulation of sound fields through phase modulation, providing a low-cost and convenient approach to realize transcranial focusing. Acoustic holographic lenses have been successfully utilized for precise transcranial focusing in live mice to open the blood-brain barrier and for neural modulation, which shows considerable application potential. However, existing transcranial acoustic holographic lenses can only be driven by specific ultrasound frequencies and focus on predetermined locations, which restricts the flexibility in complex applications. To address this issue, this study establishes a multi-frequency transcranial focusing method based on acoustic holographic lenses to enhance its adaptability within the field of tFUS. By integrating acoustic holographic lenses designed for different focal positions at various frequencies, we generate multi-frequency acoustic holographic lenses suitable for transcranial focusing and conduct experiments to evaluate their performance. In simulations, for single-focus tasks, the PSNR of the proposed method achieves 32.16 dB and 40.18 dB under 1 MHz and 2 MHz ultrasound excitation; for multi-focus tasks, the PSNR values are 29.39 dB and 39.89 dB, respectively. In experiments, for single-focus tasks, the PSNR values of the proposed method are 27.48 dB and 32.33 dB under 1 MHz and 2 MHz ultrasound excitation; for multi-focus tasks, the PSNR values are 23.30 dB and 32.17 dB, respectively. These results demonstrate that the multi-frequency transcranial acoustic holographic lens can effectively modulate the sound field under varying ultrasound frequencies and create high-quality focal points at different locations behind the skull, which significantly enhances the application flexibility of transcranial acoustic holographic lenses.

Diagnosis of motor function injury based on near-infrared spectroscopy brain imaging (fNIRS) technology

Most clinical stroke patients may have difficulty moving, affecting their self-care ability and quality of life, and causing serious interference with the normal life and work of other family members. At present, in clinical literature, researchers provide functional training for patients with motor disorders through repeated and effective training, which can ultimately effectively promote the recovery of limb function. Therefore, the near-infrared spectroscopy imaging technology (fNIRS) used in this study combines the diagnosis of sports injury with the mechanism of brain function. FNIRS technology has many advantages, such as fast, and non-invasive, and has shown great value in detecting brain activity. Therefore, it has become a promising method in the biomedical field, especially in the field of brain science. Based on the clinical effects of sports injury treatment, fNIRS technology is used to detect the hemodynamic changes of hemoglobin circulation in the patient's brain tissue during training, and to detect the brain activity mechanism in the exercise mechanism, providing a basis for the clinical application of this method.

Brain works principle followed by neural information processing: a review of novel brain theory

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays.

Recent years have witnessed a spurt of progress in the application of the encoding and decoding of neural activities to drug screening, diseases diagnosis, and brain-computer interactions. To overcome the constraints of the complexity of the brain and the ethical considerations of in vivo research, neural chip platforms integrating microfluidic devices and microelectrode arrays have been raised, which can not only customize growth paths for neurons in vitro but also monitor and modulate the specialized neural networks grown on chips. Therefore, this article reviews the developmental history of chip platforms integrating microfluidic devices and microelectrode arrays. First, we review the design and application of advanced microelectrode arrays and microfluidic devices. After, we introduce the fabrication process of neural chip platforms. Finally, we highlight the recent progress on this type of chip platform as a research tool in the field of brain science and neuroscience, focusing on neuropharmacology, neurological diseases, and simplified brain models. This is a detailed and comprehensive review of neural chip platforms. This work aims to fulfill the following three goals: (1) summarize the latest design patterns and fabrication schemes of such platforms, providing a reference for the development of other new platforms; (2) generalize several important applications of chip platforms in the field of neurology, which will attract the attention of scientists in the field; and (3) propose the developmental direction of neural chip platforms integrating microfluidic devices and microelectrode arrays.

Robust Brain Age Estimation Based on sMRI via Nonlinear Age-Adaptive Ensemble Learning.

Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.

Wearable, Integrated EEG-fNIRS Technologies: A Review.

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control(NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Mechanical thoughts and applications in cognitive neuroscience

This review article systematically summarizes the neural energy theory and methods proposed by our team in the field of brain science, and the internal relationship between mechanics and neural energy theory. This paper introduces how to construct an equivalent W-Z neuron model with the H-H model using the idea of analytic dynamics. Based on this, a large-scale neural model with neural energy as the core and a theoretical framework of global neural coding are proposed in the field of neuroscience. The unique functions and advantages of this novel neuron model are confirmed in the aspects of information processing, including visual perception, brain intelligence exploration, prediction of new working mechanisms of neurons and explanation of experimental phenomena challenging to explain in neuroscience. Because plasticity is the core of cognitive neuroscience and intelligent behavior, through the classical mechanical analysis of protein molecular machines, it is further clarified that the plasticity and neurodevelopment of neurons are not only biochemical reaction processes but also the role and contribution of mechanics are indispensable and important factors. It shows that the research thought of mechanics science in neuroscience and life science and its profound influence on internal logic. These studies will promote the integration of experimental neuroscience and theoretical neuroscience in the future, abandon the shortcomings in the research methods of reductionism and holism in the field of neuroscience, and integrate their respective advantages effectively. It is extremely important to promote the penetration of theories and methods of mechanical science.

Document recommendation based on interests of co-authors for brain science.

Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators' interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.

A review of researches on electroencephalogram decoding algorithms in brain-computer interface

Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.

Brain and Artificial Intelligence: From the Viewpoint of Spontaneous and Task-Evoked Brain Dynamics

Recently, the field of brain science often yields ‘big’ data and utilizes machine learning, which is central for the present artificial intelligence (AI) field and starts usually from extracting the hidden features. However, the data recorded from the brain are dynamic where the property of the data changes with time, different from photos that are static over the time. Then, the following question emerges: Are brain’s dynamic data really suitable for the present AI techniques? More specifically, can we extract exact features from brain’s dynamic data and what kind of dynamics makes this feature extraction more reliable? To answer these questions, in this study, we generated two kinds of the brain dynamics computationally, i.e., spontaneous and task-evoked brain dynamics, and both dynamics were applied to a fundamental technique for most feature extraction methods, that is, the principal component analysis (PCA). We suggest that the task-evoked brain dynamics can give rise to a feature space where different features, possibly related to personality traits, are classified more robustly and may lead to a better brain-AI system

Adaptation effects of medial forebrain bundle micro-electrical stimulation

ABSTRACT Brain micro-electrical stimulation and its applications are among the most important issues in the field of brain science and neurophysiology. Deep brain stimulation techniques have been used in different theraputic or alternative medicine applications including chronic pain control, tremor control, Parkinson’s disease control and depression control. Recently, brain electrical stimulation has been used for tele-control and navigation of small animals such as rodents and birds. Electrical stimulation of the medial forebrain bundle (MFB) area has been reported to induce a pleasure sensation in rat which can be used as a virtual reward for rat navigation. In all cases of electrical stimulation, the temporal adaptation may deteriorate the instantaneous effects of the stimulation. Here, we study the adaptation effects of the MFB electrical stimulation in rats. The animals are taught to press a key in an operant conditioning chamber to self-stimulate the MFB region and receive a virtual reward for each key press. Based on the number of key presses, and statistical analyses the effects of adaptation on MFB stimulation is evaluated. The stimulation frequency were changed from 100 to 400 Hz, the amplitude were changed from 50 to 170 µA and the pulse-width were changed from 180 to 2000 µs. In the frequency of 250 Hz the adaptation effect were observed. The amplitude did not show a significant effect on MFB adaptation. For all values of pulse-widths, the adaptation occurred over two consecutive days, meaning that the number of key presses on the second day was less than the first day.

Cooperative Behavior analysis of Owners and Contractors Based on Brain Neurobehavioral Mechanism

According to the analysis of the bidding and tendering for Chinese railway engineering construction projects in the past 10 years, the strong reciprocity phenomenon between the owners and contractors that accepted and maintained the specific cooperation is discovered in this study. Based on the existing supporting theory of cooperation behavior in the field of brain science, the theory of strong reciprocity is proposed to analyze the game and cooperation mechanism between the owner and the contractor under the bounded rationality. Moreover, the evolutionary game model for the relationship between the owners and contractors of railway engineering projects is established in this research. Simultaneously, the numerical simulation analysis for the influence of different parameter variables on the evolution of the model is conducted. Finally, the results of the study show that when they anticipating the cooperation of the counterparty, the owner and the contractor will respond positively to cooperation. On the contrary, the owners and contractors or third-party regulatory agencies will punish uncooperative behaviors that disrupt cooperation rules. The research results about the cooperative behavior between the owner and the contractor coincides with the interpretation of cooperation in the field of brain science.

Brain Science and Music-Research on Pitch Perception Based on Brain Evoked Potential

At the end of the last century, humans’ research in the field of brain science advanced rapidly. Brain science is becoming one of the key science fields researched by different countries. The analysis on neural network of brain is used to research the work mechanism of brain in different tones. In analysis result, the change of brain evoked potential can really reflect the perception of brain to different pitch levels. At different pitch levels, especially for before and after the change, the power spectrum energy density of brain evoked potential signal at different frequency band and the clustering and information absorption of brain’s functional network are obviously different. They are very significant methods of researching music perception at different pitch levels. The two methods complement each other.

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

Brain–computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human–machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical ‘Oddball’ paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Design and Implementation of Real-time Brain-computer Interface System Based on LabVIEW

Brain-computer interface (BCI) is a newly developed man-machine interface method that is applied in the field of brain science, rehabilitation engineering, biomedical engineering and human-machine automatic control. Under the LabVIEW environment of virtual instrument graphical programming language, this paper improves the program of Active One, a physiological signal acquisition system. The off-line processing system of electroencephalogram(EEG) is improved to a real-time processing system and a real-time feedback BCI experiment system is established based on visual evoked potential (VEP). The cumulative average method combined with the FIR filter method was used to extract the VEP signal and form a closed-loop BCI. Real-time feedback of the BCI system was realized and the VEP signal was automatically identified. Using the proposed method of this paper to implement BCI can achieve a higher accuracy with certain practicality and feasibility.

A novel individual-level morphological brain networks constructing method and its evaluation in PET and MR images

Mapping the human brain is one of the great scientific challenges of the 21st century. Brain network analysis is an effective technique based on graph theory that is widely used to investigate network patterns in the human brain. Currently, mapping an individual brain network using a single image has been a hotspot in the field of brain science; techniques, such as the Kullback-Leibler (KL) method, have applications in structural Magnetic Resonance (MR) imaging. However, maintaining an image’s intensity, shape, texture and gradient information during feature extraction is very challenging. In this study, we propose a novel method for individual-level network construction based on the high-resolution Brainnetome Atlas, which shows 246 brain regions. Principal components (PCs) were obtained for each brain region using principal component analysis (PCA) for feature extraction. Individual brain networks were followed and used to construct the PC similarity measurement based on the mutual information (MI) method. To evaluate the robustness of the proposed method, three independent experiments were carried out. In the first, 34 healthy subjects underwent two Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) scans; in the second, 32 healthy subjects underwent two structural MRI scans; and in the last, 10 Alzheimer's disease (AD) subjects and 10Healthy Control (HC) subjects underwent 11C-PiB PET scans. For each subject, network metrics including clustering coefficient, path length, small-world coefficient, efficiency and node betweenness centrality were calculated. The results suggested that both the individual PET and structural MRI networks exhibited a good small-word property, and the variances within subjects was also quite small in all metrics, The average value of Coefficient of variation (CV) map was 0.33 and 0.32 for PiB PET and MR images respectively, and intra-class correlation coefficients (ICC) range from approximately 0.4 to 0.7, indicating that the new method was well adapted to the subjects. The results of intra-class correlation coefficients from the test-retest experiment were consistent with previous research employing KL divergence, but with low computational complexity. Further, differences between AD subjects and HC subjects can be observed in network metrics. The method proposed herein provides a new perspective for investigating individual brain connectivity; it would enable neuroscientists to further understand the functions of the human brain.

Diachronic phenomenology: A methodological thread within media ecology

Abstract This article identifies and outlines a method of thought and line of research that can be found throughout media ecology. The methodology and research line, ‘diachronic phenomenology’, explores the many ways that conscious life and social organization (in its essential dimensions, modalities, properties and functioning) resonates with particular environments and media forms, and correspondingly grows, develops and becomes itself. The article thus synthesizes and co-adapts media ecology and phenomenology: media ecology benefits from more robust phenomenological grounding and methodological considerations, while phenomenology benefits from recognizing how various noetic and eidetic economies, as well as invariant features of contemporary life, have been culturally and historically effected and affected by social environments and communicative forms (writing, currency, mirrors, clocks, drugs, etc.). The article walks through a wide range of samples and illustrations and then briefly identifies possible future directions by considering recent developments within the fields of brain science and embodied cognition.