This comprehensive review aims to clarify the growing impact of Transformerbased models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and longrange dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence-centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre-trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer-based models in neuroscience, neurology, and psychiatry.
Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) primarily manifests as respiratory dysfunction. However, emerging evidence suggests SARS-CoV-2 can invade the brain, leading to cognitive impairment (CI). It may spread to other brain regions through transsynaptic neurons, including the olfactory, optic, and vagus nerves. Moreover, it may invade the central nervous system through blood transmission or the lymphatic system. This review summarizes the neuroimaging evidence from clinical and imaging studies of COVID-19-associated CIs, including magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomographycomputed tomography. The mechanisms underlying COVID-19-associated CIs are currently being actively investigated. They include nonimmune effects, such as viral proteins, tissue hypoxia, hypercoagulability, and pathological changes in neuronal cells, and immune effects, such as microglia and astrocyte activation, peripheral immune cell infiltration, blood-brain barrier impairment, cytokine network dysregulation, and intestinal microbiota. Inflammation is the central feature. Both central and systemic inflammation may cause acute and persistent neurological changes, and existing evidence indicates that inflammation underlies the elevated risk of Alzheimer’s disease. Finally, potential therapeutic options for COVID-19-associated CIs are discussed. In-depth research into the pathological mechanisms is still needed to help develop new therapies.
This easy-to-follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single-subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single-Subject Data, Basic Python Data Operations, Multiple-Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single-Subject Data chapter provides a standardized procedure for single-subject EEG data preprocessing, primarily using the MNE-Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple-Subject Analysis chapter guides readers on performing event-related potential and time-frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification-based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python-EEG-Handbook; For Chinese version: https://github.com/ZitongLu1996/Python-EEG-Handbook-CN).
High-density neural recordings with superior spatiotemporal resolution powerfully unveil cellular-scale neural communication, showing great promise in neural science, translational medicine, and clinical applications. To achieve such, many design and fabrication innovations enhanced the electrode, chip, or both for biocompatibility improvement, electrical performance upgrade, and size miniaturization, offering several thousands of recording sites. However, an enormous gap exists along the trajectory toward billions of recording sites for brain scale resolution, posing many more design challenges. This review tries to find possible insight into mitigating the gap by discussing the latest progress in high-density electrodes and chips for neural recordings. It emphasizes the design, fabrication, bonding techniques, and in vivo performance optimization of high-density electrodes. It discusses the promising opportunities for circuit-level and architecturelevel multi-channel chip design innovations. We expect that joint effort and close collaboration between high-density electrodes and chips will pave the way to high-resolution neural recording tools supporting cutting-edge neuroscience discoveries and applications.
Sepsis is a life-threatening organ dysfunction syndrome caused by the host's dysregulated response to infection. The leading causes of death in critically ill patients are sepsis-associated encephalopathy (SAE), respiratory dysfunction, circulatory dysfunction, and other multi-organ dysfunctions. SAE is among the most common serious complications of sepsis and is associated with a poor prognosis and longterm cognitive dysfunction. Its clinical manifestations vary, and there are still no unified diagnostic criteria. The incidence of SAE varies from 9% to 71% in critically ill patients due to therapeutic interventions such as sedation, mechanical ventilation, and muscle relaxants. Advances in medical technology have significantly increased the survival rate of patients with sepsis, but up to 21% now experience long-term sequelae or cognitive impairment. The lack of specific early diagnostic and treatment methods leads to increased SAE-associated mortality and complications in patients, which also impose heavy economic burdens. This article reviews the pathogenesis and diagnostic methods of SAE and progress in its treatment, aiming to reduce the mortality and hospitalization lengths of patients with SAE and improve their survival rate and quality of life through early detection, diagnosis, and effective treatment.
In this article, we present the case for the adoption of a neurodiversity paradigm as an essential framework within the brain and behavioral sciences. We challenge the deficit-focused medical model by advocating for the recognition of neurocognitive variances—including autism, ADHD, dyslexia, schizophrenia, and bipolar disorder—as natural representations of human diversity. We call for a shift in research and practice towards valuing neurodivergent individuals' unique strengths and contributions and promoting inclusivity and empathy. In critiquing the tendency to pathologize cognitive differences, we argue for a re-evaluation of therapeutic goals to reflect a more nuanced understanding of neurodiversity. Highlighting the socio-ethical implications of therapy-focused research, we urge an appreciation of the potential for innovation and problem-solving that neurodivergent individuals bring to society. The conclusion is a call to action for an integrated approach in research, policy, and societal attitudes that affirms neurodiversity, fostering an environment in which all forms of cognitive functioning are celebrated as part of human advancement.
