Brain Science Research Research Articles

Neural electrodes for brain‐computer interface system: From rigid to soft

AbstractBrain‐computer interface (BCI) is an advanced technology that establishes a direct connection between the brain and external devices, enabling high‐speed and real‐time information exchange. In BCI systems, electrodes are key interface devices responsible for transmitting signals between the brain and external devices, including recording electrophysiological signals and electrically stimulating nerves. Early BCI electrodes were mainly composed of rigid materials. The mismatch in Young's modulus between rigid electrodes and soft biological tissue can lead to rejection reactions within the biological system, resulting in electrode failure. Furthermore, rigid electrodes are prone to damaging biological tissues during implantation and use. Recently, flexible electrodes have garnered attention in the field of brain science research due to their better adaptability to the softness and curvature of the brain. The design of flexible electrodes can effectively reduce mechanical damage to neural tissue and improve the accuracy and stability of signal transmission, providing new tools and methods for exploring brain function mechanisms and developing novel neural interface technologies. Here, we review the research advancements in neural electrodes for BCI systems. This paper emphasizes the importance of neural electrodes in BCI systems, discusses the limitations of traditional rigid neural electrodes, and introduces various types of flexible neural electrodes in detail. In addition, we also explore practical application scenarios and future development trends of BCI electrode technology, aiming to offer valuable insights for enhancing the performance and user experience of BCI systems.

Research progress of brain-computer interface applied in the rehabilitation of dysarthria and speech function in amyotrophic lateral sclerosis

With the development of brain science research, artificial intelligence technologies such as brain-computer interface (BCI) have begun to be applied in the medical field. People with advanced amyotrophic lateral sclerosis (ALS) lose voluntary control, including the ability to communicate. For ALS patients, BCI research focuses on communication. BCI technology can be used as a communication tool. It records and extracts features of brain signals and translates these features into commands that can be replaced, restored, enhanced, supplemented, or improved by the central nervous system. Some BCIs have been shown to have potential spillover for ALS patients. However, there are still some challenges that need to be addressed before BCI can be clinically useful.

Recent progress of hydrogels in brain-machine interface

The long-term stable monitoring of brain signals, including electroencephalogram (EEG), electrocorticogram (ECoG) and local field potential (LFP), is of great significance for the fundamental research in brain science, artificial intelligence and the diagnosis and treatment of brain-related disorders. Therefore, both non-invasive and invasive brain-machine interfaces based on different materials and structures have been widely studied due to their unique performance. Among these materials, hydrogels have emerged as a promising interface material for brain signal collection systems due to their similar mechanical properties to biological tissues, excellent biocompatibility, strong self-adhesive properties, and exceptional ionic conductive characteristics. This review aims to provide an overview on the recent progress of hydrogel-based brain interfaces in the recording of brain signals with non-invasive and invasive methods. It is expected that this paper will serve as a valuable summary and reference for future research in the hydrogel-based brain interface.

A hypergraph transformer method for brain disease diagnosis.

To address the high-order correlation modeling and fusion challenges between functional and structural brain networks. This paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging. The proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice. The proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.

TCM in post stroke brain remodeling: Research advances

Brain functional remodeling is fundamental to the recovery from stroke and is one of the hot topics in modern brain science research, involving various factors such as neurophysiology, neurochemistry, and neuroimaging. Traditional Chinese Medicine (TCM), with its multi-target and multi-stage effects on the body, has unique advantages in improving neurological functions and promoting brain functional remodeling. Current research mainly focuses on monotherapy with Chinese herbs, compound prescriptions, and acupuncture, among other aspects. This paper summarizes their effects and related mechanisms, aiming to provide theoretical support for the clinical application of TCM in promoting brain function recovery after stroke.

Sample representativeness in psychological and brain science research

Sample representativeness in psychological and brain science research

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Brain Image Segmentation for Ultrascale Neuron Reconstruction via an Adaptive Dual-Task Learning Network.

Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution of background noise, posing an enormous challenge to neuron reconstruction from whole brain images. In this paper, we propose an adaptive dual-task learning network (ADTL-Net) to quickly and accurately extract neuronal structures from ultrascale brain images. Specifically, this framework includes an External Features Classifier (EFC) and a Parameter Adaptive Segmentation Decoder (PASD), which share the same Multi-Scale Feature Encoder (MSFE). MSFE introduces an attention module named Channel Space Fusion Module (CSFM) to extract structure and intensity distribution features of neurons at different scales for addressing the problem of anisotropy in 3D space. Then, EFC is designed to classify these feature maps based on external features, such as foreground intensity distributions and image smoothness, and select specific PASD parameters to decode them of different classes to obtain accurate segmentation results. PASD contains multiple sets of parameters trained by different representative complex signal-to-noise distribution image blocks to handle various images more robustly. Experimental results prove that compared with other advanced segmentation methods for neuron reconstruction, the proposed method achieves state-of-the-art results in the task of neuron reconstruction from ultrascale brain images, with an improvement of about 49% in speed and 12% in F1 score.

Significance of brain science research from pediatric perspectives

Significance of brain science research from pediatric perspectives

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.

Advancing pedagogy through the science of teaching and learning: A vision for educational practitioners

Over recent decades, the science of teaching and learning has started to provide research-based guidance for educational practices as well as a growing collaboration between neuroscientists, practitioners, and psychologists to create a common language in neuro-educational theory and practice (Chang et al, 2021; Dubinsky et al, 2019; Zadina, 2015). This theoretical paper explores the developments of brain science research and its connection to teaching and learning, focusing on the fundamental and essential processes that schools should implement within the framework of the science of teaching and learning. It brings to the forefront the importance of the responsibility of practitioners on the neuroscience level, including everything from challenging the idea of fixed intelligence to content learning to thinking carefully about promoting and enhancing self-regulated learning, growth mindset, executive function skills, emotional intelligence, as well as memory training. Additionally, it highlights the pivotal role of sleep in the context of learner performance and the overall quality of the learning experience. Strategies that support well-being and their impact on the quality of learning are also addressed. This contribution serves to bridge the gap between educational theory and practice, affirming the integral role that neuroscience plays in enhancing the teaching and learning processes.

Human Brain Magnetic Resonance Imaging Studies for Psychiatric Disorders: The Current Progress and Future Directions

With the prevalence of psychiatric disorders and the limitations of the diagnostic scheme and treatment options of these disorders, magnetic resonance imaging (MRI) studies play a significant role in uncovering the pathological basis of psychiatric disorders and potentially using biological markers in clinical settings. The use of MRI in clinical research has grown over the past three decades, and current MRI research continues to provide an avenue to guide the development of diagnostic approaches and therapeutic solutions. However, the current shortcomings of MRI studies derive not only from technical limitations (i.e., the range of contrasts that MRI probes or sensors can create) but also from confounding factors in the current methodological approaches of case-control studies for psychiatric disorders. Thus, by reviewing the recent literature on MRI research on psychiatric disorders, we explain the current progress and limitations of brain MRI methodologies used to study psychiatric disorders. We consider the growing use of cross-disorder methods to identify shared and disease-specific pathological features across psychiatric disorders. In addition, we need to outline healthy developmental and aging changes of the brain and investigate the disorder difference as a deviation of the trajectory. Although these methods have provided us with new insights, the demarcation between psychiatric disorders based on a definitive set of pathologies remains limited. This challenge of disease stratification is further complicated by the presence of multiple different sets of disorder pathologies within a single disorder and the different progressive timelines of different disorders. As such, we introduce the ongoing research projects in Japan, namely, the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) and the Strategic International Brain Science Research Promotion Program (Brain/MINDS Beyond). These collaborative research initiatives across Japan use neuroimaging and travel-subject harmonization to conduct nationwide MRI studies capable of providing large-scale coherent results, which may address the current limitations of MRI psychiatric disorder research.

Evaluation of consciousness rehabilitation via neuroimaging methods.

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

Research on the Mechanism of Music Perception in the Perspective of Neuroscience

Music Perception and creation is a complex cognitive process, from listening to the music sound to understand the music emotion and connotation, and so on. With the development of brain neuroscience, we are now able to better understand the neural basis of music aesthetic perception mechanism and related brain function. The purpose of this study is to review the current brain science research methods on the mechanism of aesthetic perception of music, including fMRI, EEG, ERP and other signal analysis techniques, and the application and research progress of these technologies in music cognition, aesthetics and emotion processing. The conclusion of this paper shows that the brain science method can provide us with new visual angle and clue to deeply understand the neural mechanism of music perception, and has important practical value for strengthening music education and music therapy.

Dimension selection for EEG classification in the SPD Riemannian space based on PSO

Several classification methods in the task of electroencephalogram (EEG) classification represent input features as symmetric positive definite (SPD) matrices. By translating the data into a Riemannian space, the classification methods have obtained superior performance; however, the high dimensionality of the data typically results in low computational efficiency and degraded classification performance. Therefore, this paper proposes a novel dimension selection method named dimension selection for SPD matrices on Riemannian manifold (DSSR). DSSR can eliminate redundant dimensions for SPD matrices in the Riemannian space based on the framework of particle swarm optimization (PSO). First, we treat one column of the SPD matrix and its corresponding row as a group, where upon the PSO is used to find optimal groups. Next, we use Riemannian classifiers to evaluate the goodness of the selected groups. Finally, the dimension selected SPD matrices are classified by the generalized learning Riemannian space quantization (GLRSQ). Experimental results on two motor imagery datasets show the superior performance of the proposed methods over EEG classification. Neuroscience experiments prove that the optimal dimensions selected by our proposed method are consistent with the findings of brain science research.

Research and discovery of "inner GPS" of brain

Since the 20th century, with the progress of brain science research, scientists have discovered the brain GPS, revealing the mechanism of brain spatial cognition. The discovery process of brain GPS has gone through three stages. In 1971, John O'Keefe discovered the position cells in the hippocampus of the brain, which was the beginning of the research on the GPS in the brain; In 1900, James Rank discovered the head direction cells in the medial entorhinal cortex of the brain, and the research on the GPS in the brain made a breakthrough; In 2005, Edvard I. Moser and his wife discovered grid cells, marking the maturity of the research on GPS in the brain. The discovery of intracerebral GPS not only reveals the spatial cognitive function of the brain at the cellular level, but also provides a theoretical basis for the study of diseases related to the nervous system.

MRI Image Fusion Based on Sparse Representation with Measurement of Patch-Based Multiple Salient Features

Multimodal medical image fusion is a fundamental, but challenging, problem in the fields of brain science research and brain disease diagnosis, as it is challenging for sparse representation (SR)-based fusion to characterize activity levels with a single measurement and not lose effective information. In this study, the Kronecker-criterion-based SR framework was applied for medical image fusion with a patch-based activity level, integrating salient features of multiple domains. Inspired by the formation process of vision systems, the spatial saliency was characterized by textural contrast (TC), composed of luminance and orientation contrasts, to promote the participation of more highlighted textural information in the fusion process. As a substitute for the conventional l1-norm-based sparse saliency, the sum of sparse salient features (SSSF) was used as a metric for promoting the participation of more significant coefficients in the composition of the activity level measurement. The designed activity level measurement was verified to be more conducive to maintaining the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verified the effectiveness of the proposed fusion method in terms of both visual quality and objective assessment. Furthermore, this study will be helpful for the further detection and segmentation of medical images.

Brain Network Analysis of Schizophrenia Patients Based on Hypergraph Signal Processing.

Since high-order relationships among multiple brain regions-of-interests (ROIs) are helpful to explore the pathogenesis of neurological diseases more deeply, hypergraph-based brain networks are more suitable for brain science research. Unlike the existing hypergraph based brain network (brain hypernetwork), where hyperedges containing the same number of ROIs are assumed to have equal weights (to some extent, the network is unweighted), and the underlying structure is described only by an incidence/adjacency matrix, in this paper, we propose a framework for constructing a truly weighted brain hypernetwork described by an adjacency tensor. Considering the relationships among vertices within a hyperedge, we propose a novel hyperedge weight estimation method and convert the incidence matrix into a weighted adjacency tensor. On the basis of tensor decomposition, we apply hypergraph signal processing tools, such as hypergraph Fourier transform, to analyze and compare the spectrum between schizophrenia patients and normal controls. It is found that there are more high frequency components in the spectrum of patients than controls, and the average amplitude is significantly greater than that of controls. Instead of extracting some simple topological features from brain hypernetworks for classification, we innovatively use the hypergraph spectrum and the spectral signal as classification features, and the classification results on two public datasets demonstrate the effectiveness of our proposed method.

The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface.

A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.

Recent Development of Neural Microelectrodes with Dual-Mode Detection

Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.