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This article presents a case study indicating that "Herpes simplex" virus (HSV) encephalitis may cause permanent learning disabilities due to damage to the temporal lobes, as it discusses the results of a case study extending over 10 years to determine the long-term effects on both the anatomy of the brain and the intellectual functioning of the subject. Magnetic resonance imaging (MRI) scans were taken of the subject during the initial HSV encephalitis infection and subsequently six months after recovery. Follow-up MRI scans were taken 10 years later. At this time the Senior South African Individual Scale--Revised IQ test was administered to determine any residual neuropsychological impairment due to HSV encephalitis infection. Follow-up MRI analysis indicated permanent bilateral necrotic areas in the medial temporal lobe, while the IQ test revealed marked impairment of the short-term memory, verbal memory, visual memory, visual scanning abilities and gestalt formation, which can be ascribed to temporal lobe lesions.

• TMAS enhances spatial learning and memory abilities and novelty-seeking desire in mice. • Real-time TMAS promotes neural signal transmission in the hippocampal CA1 region of mice. • After TMAS, hippocampal CA1 neurons remain active for a period of time. • TMAS does not cause excessive anxiety in mice. As a new means of brain neuroregulation and research, transcranial magneto-acoustic stimulation (TMAS) uses the coupling effect of ultrasound and a static magnetic field to regulate neural activity in the corresponding brain areas. Calcium ions can promote the secretion of neurotransmitters and play a key role in the transmission of neural signals in brain cognition. In this study, to explore the effects of TMAS on cognitive function and neural signaling in the CA1 region of the hippocampus, TMAS was applied to male 2-month-old C57 mice with a magnetic field strength of 0.3 T and ultrasound intensity of 2.6 W/cm2. First, the efficiency of neural signaling in the CA1 region of the mouse hippocampus was detected by fiber photometry. Second, the effects of TMAS on cognitive function in mice were investigated through multiple behavioral experiments, including spatial learning and memory ability, anxiety and desire for novelty. The experimental results showed that TMAS could improve cognitive function in mice, and the efficiency of neural signaling in the CA1 area of the hippocampus was significantly increased during stimulation and maintained for one week after stimulation. In addition, the neural signaling efficiency in the CA1 area of the hippocampus increased in the open field (OF) experiment and recovered after one week, the neural signaling efficiency in the new object exploration (NOE) experiment was significantly enhanced, and the intensity slowed after one week. In conclusion, TMAS enhances cognitive performance and promotes neural signaling in the CA1 region of the mouse hippocampus. [ABSTRACT FROM AUTHOR]

Postmortem human subject (PMHS) studies are essential to brain injury research in motor vehicle safety. However, postmortem deterioration reduces the similarity between postmortem test results and in vivo response in material testing of brain tissue and in biomechanical testing of the whole head. This pilot study explores the effect of potential preservatives on brain tissue breakdown to identify promising preservatives that warrant further investigation. To identify preservatives with potential to slow postmortem degradation, samples from an initial PMHS were refrigerated at 10°C to qualitatively compare tissue breakdown from 58 to 152 h postmortem after storage in candidate solutions. On brain tissue samples from a second PMHS, compressive stiffness was measured on six samples immediately after harvest for comparison to the stiffness of 23 samples that were stored at 10°C in candidate solutions for 24 h after harvest. The candidate solutions were artificial cerebrospinal fluid (ACSF) without preservatives; ACSF with a combination of antibiotics and antifungal agents; ACSF with added sodium bicarbonate; and ACSF with both the antibiotic/antifungal combination and sodium bicarbonate. Results were analyzed using multiple linear regression of specimen stiffness on harvest lobe and storage solution to investigate potential differences in tissue stiffness. Qualitative evaluation suggested that samples stored in a solution that contained both the antibiotic/antifungal combination and sodium bicarbonate exhibited less evidence of tissue breakdown than the samples stored without preservatives or with only one of those preservatives. In compression testing, samples tested immediately after harvest were significantly stiffer than samples tested after 24 h of storage at 10°C in ACSF (difference: −0.27 N/mm, 95% confidence interval (CI): −0.50, −0.05) or ACSF with antibiotics/antifungal agents (difference: −0.32 N/mm, 95% CI: −0.59, −0.04), controlling for harvest lobe. In contrast, the stiffness of samples tested after storage in either solution containing sodium bicarbonate was not significantly different from the stiffness of samples tested at harvest. There was no significant overall difference in the mean tissue stiffness between samples from the frontal and parietal lobes, controlling for storage solution. Given the importance of PMHS studies to brain injury research, any strategy that shows promise for helping to maintain in vivo brain material properties has the potential to improve understanding of brain injury mechanisms and tolerance to head injury and warrants further investigation. These pilot study results suggest that sodium bicarbonate has the potential to reduce the deterioration of brain tissue in biomechanical testing. The results motivate further evaluation of sodium bicarbonate as a preservative for biomechanical testing using additional test subjects, more comprehensive material testing, and evaluation under a broader set of test conditions including in whole‐head testing. The effect of antibiotics and antifungal agents on brain tissue stiffness was minimal but may have been limited by the cold storage conditions in this study. Further exploration of the potential for microbial agents to preserve tissue postmortem would benefit from evaluation of the effects of storage temperature. [ABSTRACT FROM AUTHOR]

Research in the field of traumatic brain injury has until now heavily relied on the use of animal models to identify potential therapeutic approaches. However, a long series of failed clinical trials has brought many scientists to question the translational reliability of pre-clinical results obtained in animals. The search for an alternative to conventional models that better replicate human pathology in traumatic brain injury is thus of the utmost importance for the field. Recently, orthotopic xenotransplantation of human brain organoids into living animal models has been achieved. This review summarizes the existing literature on this new method, focusing on its potential applications in preclinical research, both in the context of cell replacement therapy and disease modelling. Given the obvious advantages of this approach to study human pathologies in an in vivo context, we here critically review its current limitations while considering its possible applications in traumatic brain injury research. [ABSTRACT FROM AUTHOR]

The Polish Institute for Brain Research was established in Warsaw in 1928 to support scientific research on the brain and its functions. The director of the institute was Maksymilian Rose (1883–1937), a distinguished Polish neurologist and neuroanatomist, a disciple of Oskar Vogt and Korbinian Brodmann. In 1931, the Institute was moved from Warsaw to Vilnius. The Institute was well-known in Europe at the time because of the research in the fields of neuroscience, clinical neurology, and psychiatry, as well as the cytoarchitectonic analysis of social activists’ brains—a fashionable, neophrenological way to link the mental functions of deceased geniuses with the cellular composition of their central nervous systems. In 1939, the work of the Institute was interrupted by World War II; some of the preparations and materials were moved from Vilnius to Warsaw, some were stored in Vilnius, and some were lost. In this article, we analyze the primary and secondary sources, some of which were obscure for over 80 years, and evaluate the most important scientific achievements of the Polish Institute for Brain Research, as well as its legacy in the early period of modern neuroscience and neurology in interwar Vilnius. [ABSTRACT FROM AUTHOR]

Background: Radiation-induced brain injury (RIBI) is a debilitating sequela after cranial radiotherapy. Research on the topic of RIBI has gradually entered the public eye, with more innovations and applications of evidence-based research and biological mechanism research in the field of that. This was the first bibliometric analysis on RIBI, assessing brain injury related to radiation articles that were published during 1998–2023, to provide an emerging theoretical basis for the future development of RIBI. Methods: Literature were obtained from the Web of Science Core Collection (WOSCC) from its inception to December 31, 2023. The column of publications, author details, affiliated institutions and countries, publication year, and keywords were also recorded. Results: A total of 2543 journal articles were selected. The annual publications on RIBI fluctuated within a certain range. Journal of Neuro-oncology was the most published journal and Radiation Oncology was the most impactful one. LIMOLI CL was the most prolific author with 37 articles and shared the highest h-index with BARNETT GH. The top one country and institutions were the USA and the University of California System, respectively. Clusters analysis of co-keywords demonstrated that the temporal research trends in this field primarily focused on imaging examination and therapy for RIBI. Conclusion: This study collects, visualizes, and analyzes the literature within the field of RIBI over the last 25 years to map the development process, research frontiers and hotspots, and cutting-edge directions in clinical practice and mechanisms related to RIBI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI's ability to accelerate drug discovery for Alzheimer's, predict neurotoxicity, personalize treatments for Parkinson's, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research. [ABSTRACT FROM AUTHOR]

Objective: This study investigated influence of biological sex on postconcussive symptoms (PCS) following concussion using the Federal Interagency Traumatic Brain Injury Research (FITBIR) database. Method: All studies with publicly released data as of 4/7/21 that included both males and females, enough information to determine severity of injury consistent with concussion, a measure of PCS, and objective measures of neurocognitive functioning were used. This resulted in 6 studies with a total of 9890 participants (3206 females, 6684 males); 815 participants completed the Neurobehavioral Symptom Inventory (NSI), 471 completed the Rivermead Post-Concussion Symptoms Questionnaire (RPSQ), and 8604 completed the Sport Concussion Assessment Tool–3rd Edition (SCAT 3). Questionnaires were harmonized and the following symptom composite scores were computed: total score, somatic, cognitive, and affective. Data were analyzed using linear mixed-effects models. Results: Females endorsed higher total symptoms relative to males and that military personnel endorsed higher symptoms relative to civilians. Additionally, there was a small but significant interaction effect, such that female military personnel endorsed even higher symptoms than would be predicted by the main effects. Similar patterns were observed for somatic, cognitive, and affective symptom domains. Conclusions: Further understanding sex differences in PCS reporting is key to informing the most appropriate treatment options. Future work will need to examine whether sex differences in symptom reporting is due to sex differences in endorsement styles or genuine differences in symptom presentation, as well as the relationship between study population (e.g., military, civilian, sport) and sex on objective cognitive functioning and other functional outcomes. [ABSTRACT FROM AUTHOR]

Pathological health anxiety (PHA) (e.g., hypochondriasis and illness anxiety disorder) is common in medical settings and associated with increased healthcare costs. However, the psychological and neurobiological mechanisms contributing to the development and maintenance of PHA are incompletely understood. We performed a systematic review to characterize the mechanistic understanding of PHA. PubMed, PsycINFO, and Embase databases were searched to find articles published between 1/1/1990 and 12/31/2022 employing a behavioral task and/or physiological measures in individuals with hypochondriasis, illness anxiety disorder, and PHA more broadly. Out of 9141 records identified, fifty-seven met inclusion criteria. Article quality varied substantially across studies, and was overall inadequate. Cognitive, behavioral, and affective findings implicated in PHA included health-related attentional and memory recall biases, a narrow health concept, threat confirming thought patterns, use of safety-seeking behaviors, and biased explicit and implicit affective processing of health-related information among other observations. There is initial evidence supporting a potential overestimation of interoceptive stimuli in those with PHA. Neuroendocrine, electrophysiology, and brain imaging research in PHA are particularly in their early stages. Included articles evaluated PHA categorically, suggesting that sub-threshold and dimensional health anxiety considerations are not contextualized. Within an integrated cognitive-behavioral-affective and predictive processing formulation, we theorize that sub-optimal illness and health concepts, altered interoceptive modeling, biased illness-based predictions and attention, and aberrant prediction error learning are mechanisms relevant to PHA requiring more research. Comprehensively investigating the pathophysiology of PHA offers the potential to identify adjunctive diagnostic biomarkers and catalyze new biologically-informed treatments. • Systematic review of mechanistic studies in pathological health anxiety. • Cognitive, behavioral and affective constructs are implicated. • Neurobiological research in pathological health anxiety remains limited. • A predictive processing framework for pathological health anxiety is offered. [ABSTRACT FROM AUTHOR]

Voltammetry, which is at the core of electroanalytical chemistry, is an analytical method that investigates and evaluates the current-potential relationship obtained at a given working electrode. If it is used dropping mercury as working electrode, the method is called as polarography. The current year 2022 marks the 100th anniversary of the discovery of polarography by Czech Jaroslav Heyrovský. He received the Nobel Prize in Chemistry in 1959 for this discovery and his contribution to the scientific world. A hundred years, within the endless existence of the universe is maybe nothing. A hundred years, in the history of mankind is a line, maybe a short paragraph. But, in science, a hundred years can lead to very significant advances in a field and often to the birth and establishment of an entirely new scientific discipline. Indeed, in the last hundred years, the design and use of new electrochemical devices, depending on the progress in microelectronics and computer technologies, has almost revolutionized voltammetry. Besides these developments, due to the fact that the redox (oxidation/reduction) process is very basic for living organisms; the voltammetry, especially with the beginning of the 21st century, has started to be used as a very powerful tool in neuroscience to solve the mystery of the brain (the basic problems of biomolecules with physiological and genetic importance in brain tissue). This review article is an overview of the 100-year history and fascinating development of voltammetry from Heyrovský to the present. [ABSTRACT FROM AUTHOR]

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional. [ABSTRACT FROM AUTHOR]

Infrared (IR) neuromodulation holds an increasing potential in brain research, which is fueled by novel neuroengineering approaches facilitating the exploration of the biophysical mechanism in the microscale. The group lays down the fundamentals of spatially controlled optical manipulation of inherently temperature‐sensitive neuronal populations. The concept and in vivo validation of a multifunctional, optical stimulation microdevice is presented, which expands the capabilities of conventional optrodes by coupling IR light through a monolithically integrated parabolic micromirror. Heat distribution in the irradiated volume is experimentally analyzed, and the performance of the integrated electrophysiological recording components of the device is tested in the neocortex of anesthetized rodents. Evoked single‐cell responses upon IR irradiation through the novel microtool are evaluated in multiple trials. The safe operation of the implanted device is also presented using immunohistological methods. The results confirm that shift in temperature distribution in the vicinity of the optrode tip can be controlled by the integrated photonic components, and in parallel with the optical stimulation, the device is suitable to interrogate the evoked electrophysiological activity at the single neuron level. The customizable and scalable optrode system provides a new pathway to tailor the location of the heat maximum during infrared neural stimulation. [ABSTRACT FROM AUTHOR]

Brain metastases challenge cancer treatments with poor prognoses, despite ongoing advancements. Immunotherapy effectively alleviates advanced cancer, exhibiting immense potential to revolutionize brain metastasis management. To identify research priorities that optimize immunotherapies for brain metastases, 2164 related publications were analyzed. Scientometric visualization via R software, VOSviewer, and CiteSpace showed the interrelationships among literature, institutions, authors, and topic areas of focus. The publication rate and citations have grown exponentially over the past decade, with the US, China, and Germany as the major contributors. The University of Texas MD Anderson Cancer Center ranked highest in publications, while Memorial Sloan Kettering Cancer Center was most cited. Clusters of keywords revealed six hotspots: 'Immunology', 'Check Point Inhibitors', 'Lung Cancer', 'Immunotherapy', 'Melanoma', 'Breast Cancer', and 'Microenvironment'. Melanoma, the most studied primary tumor with brain metastases offers promising immunotherapy advancements with generalizability and adaptability to other cancers. Our results outline the holistic overview of immunotherapy research for brain metastases, which pinpoints the forefront in the field, and directs researchers toward critical inquiries for enhanced mechanistic insight and improved clinical outcomes. Moreover, governmental and funding agencies will benefit from assigning financial resources to entities and regions with the greatest potential for combating brain metastases through immunotherapy. [ABSTRACT FROM AUTHOR]

We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological data. The goal of Motion-BIDS is to make motion data interoperable across different laboratories and with other data modalities in human brain and behavioral research. To this end, Motion-BIDS standardizes the data format and metadata structure. It describes how to document experimental details, considering the diversity of hardware and software systems for motion data. This promotes findable, accessible, interoperable, and reusable data sharing and Open Science in human motion research. [ABSTRACT FROM AUTHOR]

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Background: Transcranial magnetic stimulation (TMS) is a noninvasive neurostimulation modality that has been used to study human synaptic plasticity. Leveraging work in ex vivo preparations, mechanistically informed pharmacological adjuncts to TMS have been used to improve our fundamental understanding of TMS-induced synaptic plasticity. Methods: We systematically reviewed the literature pairing pharmacological adjuncts with TMS plasticity-induction protocols in humans. We searched MEDLINE, PsycINFO, and Embase from 2013 to Mar. 10, 2023. Studies published before 2013 were extracted from a previous systematic review. We included studies using repetitive TMS, theta-burst stimulation, paired associative stimulation, and quadripulse stimulation paradigms in healthy and clinical populations. Results: Thirty-six studies met our inclusion criteria (28 in healthy and 8 in clinical populations). Most pharmacological agents have targeted the glutamatergic N-methyl-D-aspartate (NMDA; 15 studies) or dopamine receptors (13 studies). The NMDA receptor is necessary for TMS-induced plasticity; however, sufficiency has not been shown across protocols. Dopaminergic modulation of TMSinduced plasticity appears to be dose-dependent. The GABAergic, cholinergic, noradrenergic, and serotonergic neurotransmitter systems have small evidence bases supporting modulation of TMS-induced plasticity, as do voltage-gated calcium and sodium channels. Studies in clinical populations suggest that pharmacological adjuncts to TMS may rescue motor cortex plasticity, with implications for therapeutic applications of TMS and a promising clinical trial in depression. Limitations: This review is limited by the predominance in the literature of studies with small sample sizes and crossover designs. Conclusion: Pharmacologically enhanced TMS largely parallels findings from ex vivo preparations. As this area expands and novel targets are tested, adequately powered samples in healthy and clinical populations will inform the mechanisms of TMS-induced plasticity in health and disease., Introduction Transcranial magnetic stimulation (TMS) is a noninvasive neurostimulation technique that induces electrical currents in underlying brain parenchyma through electromagnetic induction. (1) It is one of the most established noninvasive [...]

Can we again learn about ourselves and our surrounding world through dance as we age, thereby promoting our own health? This article documents facts of life showing that "older adults" do not have to learn to be cognitive of their movement, affective dispositions, or surrounding world; they have been experientially cognitive of all by way of tactility, kinesthesia, and affectivity from the beginning. Present-day cognitive neuroscience, concentrating and theorizing as it does on the brain's neuroplasticity, is however deficient in recognizing these experiential realities. Research studies on the brain and behavior, in contrast, demonstrate that coordination dynamics are the defining feature of both neurological and kinesthetic coordination dynamics. These dynamics are central to corporeal concepts, to the recognition of if-then relationships, and to thinking inmovement. In effect, the brain is part of a whole-body nervous system. The study proceeds to show that the qualitative dynamics of movement that subtend coordination dynamics are basic to not only everyday movement but also to dancing--to experiencing movement kinesthetically and to being a mindful body. When Merce Cunningham writes that dance gives you that "single fleeting moment when you feel alive" and is not for "unsteady souls" and English writer D. H. Lawrence writes that "[w]e ought to dance with rapture that we are alive, and in the flesh, and part of the living incarnate cosmos," their words are incentives to those who are aging to awaken tactilely, kinesthetically, and affectively to the existential realities of dance. [ABSTRACT FROM AUTHOR]

The article focuses on the ethical challenges and responsibilities associated with innovative brain research, particularly in the context of post-trial care for participants with brain implants. It highlights the necessity of addressing the long-term welfare of patients who rely on these devices, discussing the importance of informed consent, ongoing support, and the broader implications of brain data privacy and potential misuse.

Electrocorticogram (ECoG) is an electrophysiological signal that results from the summation of neuronal activity near the cortical surface. To record ECoG signals, the scalp and skull are surgically opened and electrodes are placed on the cortical surface, either epidurally or subdurally. Owing to its improved spatiotemporal resolution and signal quality compared with electroencephalography, it is widely used to diagnose and treat neurological disorders in clinical settings for several decades, despite the invasiveness of ECoG. Recently, ECoG is applied in research to explore brain functions and connectivity, brain‐computer interfaces, and brain‐machine interfaces. In addition to the need for ECoG in neuroscience research, ECoG devices have advanced in terms of materials, fabrication, and function to overcome the limitations of commercially available ECoG arrays. Here, the conventional use of ECoG in clinical medicine, the new applications of ECoG in basic neuroscience research, and the future challenges in translating recent developments in ECoG devices for clinical use are described. [ABSTRACT FROM AUTHOR]

Implantable neural probes, essential for brain electrophysiological research, have advanced with ultra-flexible designs to mitigate immune responses and postoperative complications. Strategies of shuttle-assisted implantation and temporary stiffening address issues in penetrating these probes into the target region, avoiding undesired bending. However, the risk of intraoperative bleeding remains due to these implants' necessary rigidity during insertion. Here, we describe a neural probe with mechanical compliance accompanying self-implantation along the principal axis in the absence of bleeding. Crucial to the behavior is its anisotropic relaxation, which is dominated by the cross-sectional in-plane deformation inhibition due to interchain interactions between the parallel backbones in the globally aligned polymer system. We observed the ensured upright insertion of the probe into the brain while avoiding angiorrhexis with a two-photon microscope and a high-speed camera. The probes permit electrophysiological studies with minimal foreign body responses and imageological compatibility, underscoring their clinical potential. [ABSTRACT FROM AUTHOR]

Brain research has progressed with anesthetized animal experiments for a long time. Recent progress in research techniques allows us to measure neuronal activity in awake animals combined with behavioral tasks. The trends became more prominent in the last decade. This new research style triggers the paradigm shift in the research of brain science, and new insights into brain function have been revealed. It is reasonable to consider that awake animal experiments are more ideal for understanding naturalistic brain function than anesthetized ones. However, the anesthetized animal experiment still has advantages in some experiments. To take advantage of the anesthetized animal experiments, it is important to understand the mechanism of anesthesia and carefully handle the obtained data. In this minireview, we will shortly summarize the molecular mechanism of anesthesia in animal experiments, a recent understanding of the neuronal activities in a sensory system in the anesthetized animal brain, and consider the advantages and disadvantages of the anesthetized and awake animal experiments. This discussion will help us to use both research conditions in the proper manner. [ABSTRACT FROM AUTHOR]

Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males. This observed preponderance of males in autistic populations has served as a focussing framework in all spheres of autism-related issues, from recognition and diagnosis through to theoretical models and research agendas. One related issue is the near total absence of females in key research areas. For example, this paper reports a review of over 120 brain-imaging studies of social brain processes in autism that reveals that nearly 70% only included male participants or minimal numbers (just one or two) of females. Authors of such studies very rarely report that their cohorts are virtually female-free and discuss their findings as though applicable to all autistic individuals. The absence of females can be linked to exclusionary consequences of autism diagnostic procedures, which have mainly been developed on male-only cohorts. There is clear evidence that disproportionately large numbers of females do not meet diagnostic criteria and are then excluded from ongoing autism research. Another issue is a long-standing assumption that the female autism phenotype is broadly equivalent to that of the male autism phenotype. Thus, models derived from male-based studies could be applicable to females. However, it is now emerging that certain patterns of social behaviour may be very different in females. This includes a specific type of social behaviour called camouflaging or masking, linked to attempts to disguise autistic characteristics. With respect to research in the field of sex/gender cognitive neuroscience, there is emerging evidence of female differences in patterns of connectivity and/or activation in the social brain that are at odds with those reported in previous, male-only studies. Decades of research have excluded or overlooked females on the autistic spectrum, resulting in the construction of inaccurate and misleading cognitive neuroscience models, and missed opportunities to explore the brain bases of this highly complex condition. A note of warning needs to be sounded about inferences drawn from past research, but if future research addresses this problem of male bias, then a deeper understanding of autism as a whole, as well as in previously overlooked females, will start to emerge. Plain Language Summary: Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males, with oft-quoted ratios of 4M: 1F. This has been reflected in the development of diagnostic criteria for autism and, consequently, of measures of eligibility for autism research programmes, with females being (as is now emerging) disproportionately excluded. As outlined in this review, this issue has been particularly problematic in brain-based studies of autism. Many studies have only tested male autistic participants, or minimal numbers of autistic females. By default, sex differences were not examined. But the impression given by such research reports has commonly been that the findings would be applicable to all autistic individuals. Recent psychological and clinical research has shown that there are a significant number of autistic females who have been missed by traditional diagnostic practices. Their inclusion has increased their eligibility for autism research studies. With respect to brain research, it has become possible to devise studies with matched numbers of autistic females and males, and to replicate studies that have previously only tested males. Newly emerging findings from such studies are demonstrating that the 'robust' autism-related differences previously observed in autistic male-only cohorts do not fully generalise to autistic females. It will be necessary to exercise caution in drawing inferences from previous male-biased studies of the autistic brain. However, the identification and inclusion of previously excluded female autistic participants hopefully offers more accurate insights into this highly complex and heterogeneous condition. Highlights: Several decades of neuroimaging research into autism has been based almost entirely on males; even big data sets show strong evidence of male bias. There is clear evidence that autistic females are being excluded from the research process by failures in diagnostic practices that have been developed on male-biased cohorts. Models of autism which inform research protocols are based on male autism phenotypes; it appears to have been assumed that the substantially fewer females that are diagnosed will present with equivalent, if milder, patterns of autistic differences. Newly emerging findings from social cognitive neuroscience research studies investigating sex/gender differences in autistic behaviour and associated biological correlates are demonstrating that the 'robust' autism-related differences previously observed in autistic male-only cohorts do not fully generalise to autistic females. Autism research programmes should prioritise the exploration of sex/gender effects. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought attention to the long-term consequences of the virus, particularly the persistent symptoms that characterize long COVID. This syndrome, which can last for months after the initial infection, includes a range of neurological and neuropsychiatric manifestations that have significant implications for brain health and dementia research. This review explores the current understanding of long COVID's cognitive, neurological, and psychiatric symptoms and their potential impact on brain stimulation and neuroimaging studies. It argues that researchers must adapt their study designs and screening processes to account for the confounding effects of long COVID and ensure the accuracy and reliability of their findings. To advance the understanding of this condition and its long-term effects on brain health, the review proposes a series of strategies, including the development of standardized screening tools, the investigation of underlying mechanisms, and the identification of risk factors and protective factors. It also emphasizes the importance of collaborative research efforts and international data sharing platforms in accelerating the pace of discovery and developing targeted interventions for individuals with long COVID. As the prevalence of this condition continues to grow, it is imperative that the neuroscience community comes together to address this challenge and support those affected by long COVID. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) technology is rapidly advancing in medical research and application. As an emerging biomedical engineering technology, it has garnered significant attention in the clinical research of brain disease diagnosis and treatment, neurological rehabilitation, and mental health. However, BCI also raises several challenges and ethical concerns in clinical research. In this article, the authors investigate and discuss three aspects of BCI in medicine and healthcare: the state of international ethical governance, multidimensional ethical challenges pertaining to BCI in clinical research, and suggestive concerns for ethical review. Despite the great potential of frontier BCI research and development in the field of medical care, the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI. To ensure "responsible innovation" in BCI research in healthcare and medicine, the creation of an ethical global governance framework and system, along with special guidelines for cutting-edge BCI research in medicine, is suggested. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843–0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814–0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826–0.832] for Marshall and 0.838 [95% CI: 0.835–0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857–0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798–0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Zinc Oxide nanoparticles (ZnO NPs) have dualistic properties due to their advantage and toxicity. However, the impact and mechanisms of ZnO NPs on the prefrontal lobe have limited research. This study investigates the behavioral changes following exposure to ZnO NPs (34 mg/kg, 30 days), integrating multiple behaviors and bioinformatics analysis to identify critical factors and regulatory mechanisms. The essential differentially expressed genes (DEGs) were identified, including ORC1, DSP, AADAT, SLITRK6, and STEAP1. Analysis of the DEGs based on fold change reveals that ZnO NPs primarily regulate cell survival, proliferation, and apoptosis in neural cells, damaging the prefrontal lobe. Moreover, disruption of cell communication, mineral absorption, and immune pathways occurs. Gene set enrichment analysis (GSEA) further shows enrichment of behavior, neuromuscular process, signal transduction in function, synapses-related, cAMP signaling, and immune pathways. Furthermore, alternative splicing (AS) genes highlight synaptic structure/function, synaptic signal transduction, immune responses, cell proliferation, and communication. [Display omitted] • ZnO NPs alter mice's PFC function, behavior, emotion, and learning memory. • Identified DEGs regulate neural survival, proliferation, and communication. • ZnO NPs adversely affect neuronal apoptosis, synaptic transmission, immune response, and ion homeostasis. • Alternative splicing of key genes/molecules should be considered for brain research. [ABSTRACT FROM AUTHOR]

Background: As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide‐distributed, high‐expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders. Aim s : To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases. Method: We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place‐occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues. Results: The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment. Conclusions: Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases. [ABSTRACT FROM AUTHOR]

Delirium is a severe postoperative complication associated with poor overall and especially neurocognitive prognosis. Altered brain mineralization is found in neurodegenerative disorders but has not been studied in postoperative delirium and postoperative cognitive decline. We hypothesized that mineralization‐related hypointensity in susceptibility‐weighted magnetic resonance imaging (SWI) is associated with postoperative delirium and cognitive decline. In an exploratory, hypothesis‐generating study, we analysed a subsample of cognitively healthy patients ≥65 years who underwent SWI before (N = 65) and 3 months after surgery (N = 33). We measured relative SWI intensities in the basal ganglia, hippocampus and posterior basal forebrain cholinergic system (pBFCS). A post hoc analysis of two pBFCS subregions (Ch4, Ch4p) was conducted. Patients were screened for delirium until the seventh postoperative day. Cognitive testing was performed before and 3 months after surgery. Fourteen patients developed delirium. After adjustment for age, sex, preoperative cognition and region volume, only pBFCS hypointensity was associated with delirium (regression coefficient [90% CI]: B = −15.3 [−31.6; −0.8]). After adjustments for surgery duration, age, sex and region volume, perioperative change in relative SWI intensities of the pBFCS was associated with cognitive decline 3 months after surgery at a trend level (B = 6.8 [−0.9; 14.1]), which was probably driven by a stronger association in subregion Ch4p (B = 9.3 [2.3; 16.2]). Brain mineralization, particularly in the cerebral cholinergic system, could be a pathomechanism in postoperative delirium and cognitive decline. Evidence from our studies is limited because of the small sample and a SWI dataset unfit for iron quantification, and the analyses presented here should be considered exploratory. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a very aggressive and lethal primary brain cancer in adults. The multifaceted nature of GBM pathogenesis, rising from complex interactions between cells and the tumor microenvironment (TME), has posed great treatment challenges. Despite significant scientific efforts, the prognosis for GBM remains very poor, even after intensive treatment with surgery, radiation, and chemotherapy. Efficient GBM management still requires the invention of innovative treatment strategies. There is a strong necessity to complete cancer in vitro studies and in vivo studies to properly evaluate the mechanisms of tumor progression within the complex TME. In recent years, the animal models used to study GBM tumors have evolved, achieving highly invasive GBM models able to provide key information on the molecular mechanisms of GBM onset. At present, the most commonly used animal models in GBM research are represented by mammalian models, such as mouse and canine ones. However, the latter present several limitations, such as high cost and time-consuming management, making them inappropriate for large-scale anticancer drug evaluation. In recent years, the zebrafish (Danio rerio) model has emerged as a valuable tool for studying GBM. It has shown great promise in preclinical studies due to numerous advantages, such as its small size, its ability to generate a large cohort of genetically identical offspring, and its rapid development, permitting more time- and cost-effective management and high-throughput drug screening when compared to mammalian models. Moreover, due to its transparent nature in early developmental stages and genetic and anatomical similarities with humans, it allows for translatable brain cancer research and related genetic screening and drug discovery. For this reason, the aim of the present review is to highlight the potential of relevant transgenic and xenograft zebrafish models and to compare them to the traditionally used animal models in GBM research. [ABSTRACT FROM AUTHOR]

Background: There continues to be growing interest in the Science of Learning including identifying applications for findings from this work outside the laboratory to support learning. Presently, there exists a gap in our understanding of learning during healthy adulthood as well as effective ways in which that learning can be improved. Developing a more comprehensive understanding of learning during adulthood, and effective ways of improving that learning, are crucial goals given the impact of a rapidly aging global population. The main objective of the proposed systematic review is to identify and synthesize all recent cognitive and brain research investigating learning across the adult lifespan. Methods: Searches will be performed across Scopus, Web of Science, and ProQuest databases. Both published and unpublished literature will be screened for inclusion. Included articles will be limited to research in healthy adult samples reporting measures of learning-related cognition, brain structure or function and their relationship with age, or the impact of interventions to improve learning. All steps of the review will be performed by three trained reviewers. Tabular, narrative, and quantitative syntheses will be provided based on the characteristics of included studies. Discussion: Findings from the proposed review will contribute to our understanding of learning in adulthood. Additionally, this review will identify research gaps in need of further investigation and relevant findings for translation, informing the scope of future funding priorities in the Science of Learning. [ABSTRACT FROM AUTHOR]

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl. [ABSTRACT FROM AUTHOR]

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks. [ABSTRACT FROM AUTHOR]

In addressing the challenge of assessing healthy brain aging across diverse interventions, this study introduces the use of MRI-derived Brain Healthcare Quotients (BHQ) for comprehensive evaluation. We analyzed BHQ changes in 319 participants aged 24–69, who were allocated into dietary (collagen peptide, euglena, matcha, isohumulone, xanthophyll) and physical activity (hand massage with lavender oil, handwriting, office stretching, pink lens, clinical art) groups, alongside a control group, over a month. These interventions were specifically chosen to test the efficacy of varying health strategies on brain health, measured through BHQ indices: GM-BHQ for gray matter volume, and FA-BHQ for white matter integrity. Notably, significant improvements in FA-BHQ were observed in the collagen peptide group, with marginal increases in the hand massage and office stretching groups. These findings highlight BHQ's potential as a sensitive tool for detecting brain health changes, offering evidence that low-intensity, easily implemented interventions can have beneficial effects on brain health. Moreover, BHQ allows for the systematic evaluation of such interventions using standard statistical approaches, suggesting its value in future brain healthcare research. [ABSTRACT FROM AUTHOR]

BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. OBJECTIVE: The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. METHODS: This paper proposes a novel U-Net + ⁣ + network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. RESULTS: The proposed segmentation model is trained and tested on Kaggle's LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. CONCLUSION: This paper conducts research on brain tumor segmentation, using the U-Net + ⁣ + network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180. [ABSTRACT FROM AUTHOR]

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro‐Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi‐echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi‐echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase‐quality‐based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity‐based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting. [ABSTRACT FROM AUTHOR]

For two decades, the field of Mind, Brain and Education (MBE) has shed light on learning mechanisms. However, the direct involvement of young learners in the design and creation of research projects, while steadily emerging, is still rare. More researchers are beginning to see the benefits of co‐construction of not only research questions but also experimental design and analysis based on young learners' ideas. Recognizing that learning is inseparable from social relationships and well‐being, the papers in this special issue respond to two main questions: (1) Why co‐production? (2) How is co‐production achieved? We hope that this special issue will invite discussions about the different domains within MBE research in which learners' input and collaboration can be valuable. We invite readers to take into consideration the diversity of methods which are represented in this special issue to engage young learners and collect their perspectives. The integration of young learners into the research process has only recently became more prevalent. In this special issue, we present five articles in which young learners shared their views on and/or engaged in Mind, Brain and Education research. These papers show that young learners' views can complement, and sometimes question, those of adult researchers. This involvement can lead to productive, well‐rounded projects incorporating both scientific methods and social values. [ABSTRACT FROM AUTHOR]

Background: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. Methods: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. Results: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. Conclusions: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting. [ABSTRACT FROM AUTHOR]

Background: Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine's molecular mechanisms connect to its neural and behavioral effects. Methods: We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets. Results: We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine's data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. Conclusions: These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry. Funding: This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016-0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 - 2056) (FXV). [ABSTRACT FROM AUTHOR]

Introduction: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli. Methods: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain. Results: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways. Discussion: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications. [ABSTRACT FROM AUTHOR]

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation. [ABSTRACT FROM AUTHOR]

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences titled "Advances in Research on Brain Health and Dementia: Prevention and Early Detection of Cognitive Decline and Dementia." The issue focuses on maintaining brain health and early detection and intervention of dementia and its precursor conditions. It includes research on various topics such as cognitive screening methods using Lego® Duplo® building blocks and odor identification tests, the use of digital technology for screening, neuroimaging studies, body fluid biomarkers for early detection, interventions for physical inactivity, and the relationship between motor signs and cognitive performance. The issue also covers other types of dementia and high-risk conditions of dementia. The authors hope that this special issue will be useful in clinical practice and provide suggestions for future research. [Extracted from the article]

Background/Objectives: Premenstrual dysphoric disorder (PMDD) is an understudied psychiatric condition affecting reproductive-age women who experience negative mood in the luteal phase of the menstrual cycle. Cognitive functions in PMDD are not well understood as patients have been tested in the luteal phase. This may confound study results due to noted emotional interferences, as well as the potential opposing effects of the sex hormones estradiol and progesterone. In the present study, we evaluated visuospatial function in the follicular phase in women with PMDD and healthy controls, and further examined the effect of estradiol as research into the hormonal mediation of visuospatial function in reproductive-age women has produced mixed results. Methods: To this end, we analyzed estradiol concentrations using the gold standard mass spectrometry. Serum samples were collected in the early follicular and mid/late follicular subphases when estradiol is low and high, respectively, while progesterone is low and steady. We assessed visuospatial function using the classic mental rotation task. Results: Women with PMDD had a higher mental rotation total score (t = 2.17; p < 0.05). The addition of six demographic, biological, and anthropomorphic variables in a hierarchical fashion accounted for 45.3% of the total variance in the final model with diagnosis remaining statistically significant (t = 4.36; p < 0.001). Estradiol did not mediate the group difference and was not significantly associated with visuospatial function. Conclusions: The present results provide support for new research directions into the potential biological mechanisms that underlie the pathophysiology of PMDD, represented as enhanced visuospatial ability in women with PMDD in the follicular phase. We review the theory that PMDD is a disorder of the enhanced excitation-to-inhibition ratio, with a focus on findings to date from brain imaging research. [ABSTRACT FROM AUTHOR]

In 2015, a groundswell of brain tumour patient, carer and charity activism compelled the UK Minister for Life Sciences to form a brain tumour research task and finish group. This resulted, in 2018, with the UK government pledging £20m of funding, to be paralleled with £25m from Cancer Research UK, specifically for neuro‐oncology research over the subsequent 5 years. Herein, we review if and how the adult brain tumour research landscape in the United Kingdom has changed over that time and what challenges and bottlenecks remain. We have identified seven universal brain tumour research priorities and three cross‐cutting themes, which span the research spectrum from bench to bedside and back again. We discuss the status, challenges and recommendations for each one, specific to the United Kingdom. [ABSTRACT FROM AUTHOR]

Organoid technologies are a potent tool for investigating human biology, modelling diseases and developing novel therapies. In this Viewpoint, experts in metabolic and endocrine research in the brain, pituitary, skeletal muscle, bone and gastrointestinal system discuss how organoids and related bioengineered systems are currently used in their field and how innovations in these technologies could transform future research. [ABSTRACT FROM AUTHOR]

Calcium flux has been successfully verified to play an important role in the malignant proliferation and progression of brain tumors, which can serve as an important diagnosis guide. However, clinical diagnosis based on calcium information remains challenging because of the highly complex and heterogeneous features in calcium signals. Here we propose a calcium feature-based tumor diagnosis and treatment guidance platform (CA-TDT-GP) using random forest analysis framework for the efficient prediction of complex tumor behaviors for clinical therapy guidance. Multiple important features associated with brain tumor biological malignancy were screened out through comprehensive feature importance analysis. It provided useful guidance for understanding the biological process and the selection of drugs of brain tumors. Further clinical validation confirmed the accurate prediction of tumor biological characteristics by the model, with a coefficient of determination of over 0.86 in the same cohort of patients and over 0.77 for the new cohort of patients. We further verified the clinical malignant assessment by this model, which performed a 100% prediction match with diagnosed WHO grades, indicating great potential of the platform for clinical guidance. This promising model provides a new diagnostic and therapeutic tool for brain tumor research and preclinical treatment. [ABSTRACT FROM AUTHOR]

Stem cell-derived organoid technology is a powerful tool that revolutionizes the field of biomedical research and extends the scope of our understanding of human biology and diseases. Brain organoids especially open an opportunity for human brain research and modeling many human neurological diseases, which have lagged due to the inaccessibility of human brain samples and lack of similarity with other animal models. Brain organoids can be generated through various protocols and mimic whole brain or region-specific. To provide an overview of brain organoid technology, we summarize currently available protocols and list several factors to consider before choosing protocols. We also outline the limitations of current protocols and challenges that need to be solved in future investigation of brain development and pathobiology. [ABSTRACT FROM AUTHOR]