Seminars in Neurology最新文献

Over the past four decades, numerous advances in imaging modalities have improved the diagnosis and treatment of neuromuscular diseases. Magnetic resonance imaging (MRI) and ultrasound (US), the most common imaging modalities in neuromuscular medicine, each have benefits and limitations. MRI has a large field of view, is better at identifying deeper structures, and can incorporate contrast and different imaging sequences. However, it involves higher costs and requires patients to lie flat for prolonged periods of time. Conversely, US is faster, well-tolerated, and cost-effective with high spatial resolution, but identifying deeper structures is limited. Consequently, MRI is more useful for imaging nerve roots and inherited myopathies, whereas US has advantages in peripheral neuropathies. This review focuses on imaging of nerves and muscles in common neuromuscular conditions.

Traumatic brain injury and traumatic spinal cord injury are major causes of morbidity and mortality, necessitating rapid and accurate diagnostic evaluation. Neuroimaging plays a critical role in the early assessment and management of these conditions, allowing for the timely identification of hemorrhagic lesions, cerebral edema, vascular injuries, and spinal cord pathology that may require urgent intervention. In this review, we use a time-based approach to appraise the role of imaging in the hyperacute (first 24 hours) and acute (up to 1 week) periods postinjury. Although computed tomography imaging guides most decision-making in trauma, we also highlight the role of ultrasound imaging modalities such as transcranial Doppler and optic nerve sheath diameter monitoring for noninvasive ICP monitoring, and magnetic resonance imaging for prognostication. Cases are used to highlight imaging findings that may change management in the hyperacute and acute period.

Functional neuroimaging has revolutionized our understanding of brain physiology and pathophysiology, providing dynamic insights into neural activity that complement structural imaging. This review examines the principal functional neuroimaging modalities used in routine clinical practice, positron emission tomography (PET), single-photon emission computed tomography, magnetic resonance imaging, and their clinical applications in neurology. We present an evidence-based approach to modality selection and interpretation, emphasizing the integration of multimodal imaging data to enhance diagnostic accuracy. Recent advances in molecular imaging have transformed the evaluation of neurodegenerative diseases, moving the field from observing the downstream consequences of disease to visualizing the primary molecular pathologies in vivo. The development of specific PET ligands for amyloid-β plaques, tau neurofibrillary tangles, neurotransmitter system components, and markers of synaptic density and neuroinflammation represents a paradigm shift toward a biological definition of these disorders. This review provides practical algorithms for clinical decision-making, critically evaluates the strengths and limitations of each technique, and highlights emerging applications that promise to further advance neurological diagnosis and management toward an era of precision medicine.

Neuroimaging is an essential part of epilepsy diagnosis and management. In this review, we examine the shift in our understanding of epilepsy from a focal, lesional-centric process to a broader, network-based disorder and how it has affected the acquisition and interpretation of neuroimaging for epilepsy diagnosis and management. We discuss the current clinical practice of neuroimaging in epilepsy, both in terms of acquisitions and interpretation, through computed tomography, magnetic resonance imaging (including structural, diffusion, functional, and high-field), magnetoencephalography, and nuclear and metabolic imaging. Additionally, we look ahead to some of the latest research advances in both the acquisition and analysis of these different neuroimaging modalities to help promote the eventual goal of a consolidated, multi-modality imaging approach to understanding, diagnosing, and treating epilepsy.

As the U.S. population ages, Alzheimer's disease and related dementias (ADRD) cases are increasing, resulting in long wait times for specialist care. We review state-of-the-art artificial intelligence (AI) applications in ADRD care, from streamlining clinical diagnosis to pioneering novel digital biomarkers. Near-term AI applications include neuroimaging interpretation, conversational agents for patient interviews, and digital cognitive assessments. Large language models show promise as collaborative partners, helping clinicians interpret complex data while supporting patients and caregivers. Emerging digital biomarkers-speech analysis, passive monitoring through wearable devices, electronic health record analysis, and multiomics-offer potential for continuous monitoring to detect cognitive decline years before traditional assessments. Despite the acceleration of AI innovation, most of these systems are inaccessible in clinical practice. Implementation bottlenecks include limited external validation, technical challenges, model biases, infrastructure, and regulatory requirements. This review aims to help neurologists navigate this rapidly evolving AI landscape and prepare for implementation in ADRD care.

This review provides an overview of the emergence and application of transformer-based language models in electronic health records in neurology. Transformer architectures are well-suited for neurological data due to their ability to model complex spatiotemporal patterns and capture long-range dependencies, both characteristic of neurological conditions and their documentation. We introduce the foundational principles of transformer models and outline the model training and evaluation frameworks commonly used in clinical text processing. We then examine current applications of transformers in neurology, spanning disease detection and diagnosis, phenotyping and symptom extraction, and outcome and prognosis prediction, and synthesize emerging patterns in model adaptation and evaluation strategies. Additionally, we discuss the limitations of current models, including generalizability, model bias, and data privacy, and propose future directions for research and implementation. By synthesizing recent advances, this review aims to guide future efforts in leveraging transformer-based language models to improve neurological care and research.

Carotid webs are increasingly recognized as an underdiagnosed etiology of ischemic stroke, especially in young, otherwise healthy patients. These fibrous intimal protrusions create regions of flow stasis within the internal carotid artery, predisposing to thromboembolism. Diagnosis remains challenging due to their subtle radiographic appearance and underappreciation in clinical practice. While antiplatelet therapy or anticoagulation used to be the cornerstone of management, medical therapy alone has been found to be insufficient for stroke prevention in symptomatic patients. Definitive intervention includes carotid artery stenting or carotid endarterectomy; both have demonstrated excellent safety and efficacy. Risk stratification for symptomatic and asymptomatic carotid webs remains an area of active research, with emerging evidence suggesting that specific anatomic features, termed the carotid web angioarchitecture, may help predict stroke risk. Further studies are needed to determine the role of preventative intervention. A deeper understanding of carotid web pathogenesis, natural history, and hemodynamic impact is critical for guiding clinical decision-making.

Cervical artery dissection is one of the leading causes of ischemic stroke in young adults, and poses unique diagnostic and therapeutic challenges due to an often nonspecific clinical presentation. Prompt recognition is essential, as early ischemic events are common within the first 2 to 4 weeks. This review summarizes current evidence on the epidemiology, pathophysiology, clinical features, diagnostic strategies, and management of cervical carotid artery dissections. While antithrombotic therapy is the mainstay of secondary stroke prevention, the optimal choice between antiplatelet and anticoagulation remains uncertain. Randomized trials and large cohort studies suggest similar efficacy between antiplatelet and anticoagulant therapies, though anticoagulation may confer benefit in patients with vessel occlusion. Recurrent dissection and ischemic events are rare, and dissecting aneurysms generally have a benign course. Endovascular intervention is reserved for select cases. A tailored, risk-based approach to therapy-guided by clinical and radiographic features-is essential to improve outcomes in this complex and heterogeneous population.

Carotid artery atherosclerosis is an important etiology of carotid artery stenosis and subsequent cerebrovascular events. Carotid atherosclerosis follows a pattern that begins with endothelial dysfunction, marked by impaired nitric oxide-mediated vasodilation and increased endothelial permeability, and is followed by intimal low-density lipoprotein (LDL) accumulation. Retained oxidized LDL results in a pro-inflammatory environment that results in inflammatory cell inflammation and foam cell formation, the basis of the fatty streak. Migrating medial vascular smooth muscle cells, which undergo phenotypic switching, lead to plaque growth and fibrous cap formation. The unique geometry of the carotid bifurcation contributes to the complex local hemodynamic environment and predisposes the carotid bifurcation to endothelial dysfunction. In later stages of atherosclerosis, higher wall shear stress erodes the fibrous cap and increases the risk of plaque rupture. Several parameters of carotid bifurcation geometry, including the bifurcation angle and relative diameters of the internal and common carotid arteries, also contribute to disturbed flow and atherosclerotic plaque development.

Artificial intelligence (AI) in multiple sclerosis (MS) is an area of growing importance of growing importance. We review the historical context, current applications, and future prospects of AI and machine learning (ML) in MS. The review highlights AI's potential to address critical challenges in MS management, including early and accurate diagnosis, individualized treatment strategies, prognostication, and efficient patient monitoring. By leveraging large datasets and high-dimensional data, AI promises profound insights and augments clinical decision-making processes. Additionally, the manuscript covers potential limitations and challenges facing AI use in MS clinical practice and research.