Semin Neurol 2025; 45: 432-433
DOI: 10.1055/a-2623-4880

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Semin Neurol 2025; 45: 503-511
DOI: 10.1055/a-2632-9941

The practice of neurology is undergoing significant transformation due to evolving economic pressures, workforce shortages, and increasing demand for neurologic care. Lower reimbursements, increasing operating expenses, and complexity of care challenge the sustainability of existing practice models. This study outlines critical considerations for practice and staffing models in neurology, focusing on strategies to optimize access to care, contain costs for patients and the practice, and enhance operational efficiency. Key topics discussed include integrating advanced practice providers, expanding teleneurology and intravisit care, exploring value-based care models, and enhancing workflows via technology to improve patient experience and clinic efficiency. As the field continues to evolve, neurology practices must adopt agile strategies that balance clinical excellence with economic sustainability in order to meet the demands of a challenging healthcare landscape.
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Semin Neurol 2025; 45: 303-303
DOI: 10.1055/a-2592-0663

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Semin Neurol 2025; 45: 304-304
DOI: 10.1055/a-2601-9576

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Semin Neurol 2025; 45: 401-409
DOI: 10.1055/a-2624-5696

Insomnia is highly prevalent in clinical practice and can present independently or alongside other medical and mental health disorders. Insomnia is a risk factor for the development and exacerbation of medical and mental health conditions. Behavioral and pharmacological treatments for insomnia are available. In this article, we review important mechanisms associated with insomnia including the hyperarousal model of insomnia and the neurobiology of insomnia. We then review the treatment approaches and management strategies for insomnia including cognitive behavioral therapy for insomnia, transcranial magnetic stimulation, pharmacologic treatment, and adjunctive treatments for insomnia.
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Semin Neurol
DOI: 10.1055/a-2713-6126

The administration of a Neurology training program requires dynamic leadership. Training programs will have many internal and external challenges. The ability to prepare for these challenges is variable. This paper reviews three cases: (1) The resident who is failing to meet competency in the program, (2) the impact of the growing Vascular Neurology workload, and (3) the impact of the coronavirus disease 2019 (COVID-19) pandemic on neurology training, and how these were handled within our system. The objective of this paper is to provide a road map for addressing these challenges by learning how to identify the problem, utilize available resources, and maximize communication.
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Semin Neurol
DOI: 10.1055/a-2713-6622

Artificial intelligence (AI) is transforming neurology and stroke education through applications like automated feedback, adaptive simulations, and enhanced exposure to critical events. This narrative review explores foundational AI concepts, current educational uses in professional and patient training, virtual patients, tutoring tools, and personalized assessment. We evaluate the growing evidence for AI's effectiveness in improving knowledge, skills, and learner engagement, alongside implementation strategies. Key challenges include accuracy, bias, ethics, resource gaps, and potential skill decay. Conclusions emphasize that while AI shows promise for personalized learning and objective assessment, realizing its potential requires addressing barriers like cost-effectiveness, faculty readiness, and an evolving curriculum. Thoughtful integration requires rigorous validation, ethical standards, and further research into long-term outcomes. Ultimately, AI can complement traditional mentorship, preparing neurologists for data-driven practice.
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Semin Neurol
DOI: 10.1055/a-2709-6750

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.
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Semin Neurol
DOI: 10.1055/a-2693-0547

The convergence of artificial intelligence (AI) and neuroscience represents one of medicine's most profound intellectual partnerships. Neuronal architecture has inspired computational methods, while computational models, evolving from theoretical constructs to transformative clinical tools, are reshaping neurological practice. As AI systems attempt to augment diagnostic accuracy, treatment planning, and patient care, neurologists must develop fluency in these technologies to harness their potential while navigating their limitations and dangers. AI-related publications have exponentially increased in recent years, yet many neurologists lack the foundational computer science background needed to critically evaluate and most safely and effectively implement these tools in clinical practice. This article serves to outline the historical foundations linking neuroscience to computing, examine core concepts of the past and current AI landscape in neurology, and describe methodologies that aim to revolutionize neurological care.
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Semin Neurol
DOI: 10.1055/a-2694-4877

Magnetic resonance imaging (MRI) remains an integral diagnostic tool in multiple sclerosis (MS), for both making the initial diagnosis and monitoring for disease relapse and progression. Despite the applied use of MRI according to the revised McDonald's criteria in 2017, there has been persistent low diagnostic specificity, especially as it pertains to differentiating radiologically isolated syndrome from alternative diagnoses that mimic demyelination. This report will provide an overview of recent paraclinical innovations, with a focus on MRI biomarkers and parameters such as the central vein and paramagnetic rim signs. Utilizing these tools in clinical practice will hopefully improve precision in the diagnosis and monitoring of MS and reduce the risk of false-positive diagnoses.
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Semin Neurol
DOI: 10.1055/a-2681-6597

Cerebrovascular imaging has undergone significant advances, enhancing the diagnosis and management of cerebrovascular diseases such as stroke, aneurysms, and arteriovenous malformations. This chapter explores key imaging modalities, including non-contrast computed tomography, computed tomography angiography, magnetic resonance imaging (MRI), and digital subtraction angiography. Innovations such as high-resolution vessel wall imaging, artificial intelligence (AI)-driven stroke detection, and advanced perfusion imaging have improved diagnostic accuracy and treatment selection. Additionally, novel techniques like 7-T MRI, molecular imaging, and functional ultrasound provide deeper insights into vascular pathology. AI and machine learning applications are revolutionizing automated detection and prognostication, expediting treatment decisions. Challenges remain in standardization, radiation exposure, and accessibility. However, continued technological advances, multimodal imaging integration, and AI-driven automation promise a future of precise, non-invasive cerebrovascular diagnostics, ultimately improving patient outcomes.
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Semin Neurol
DOI: 10.1055/a-2685-3141

Carotid artery stenosis is a major cause of acute ischemic stroke, accounting for approximately 15% of cases. Although optimal medical therapy remains the cornerstone of management, current guidelines recommend consideration of surgical intervention for symptomatic patients with ≥50% stenosis and asymptomatic patients with ≥70% stenosis. Extensive evidence supports carotid endarterectomy (CEA) as the gold standard procedure, whereas transfemoral carotid angioplasty and stenting (TF-CAS) and transcarotid artery revascularization (TCAR) offer safe alternatives for patients with high surgical risk. Emerging data suggest that TCAR provides safety and efficacy profiles comparable to CEA and superior to TF-CAS in select patients. Considering these findings, selecting an appropriate revascularization strategy should rely on a multidisciplinary, individualized risk–benefit assessment. This article aims to provide a comparative review of the latest evidence on clinical indications, surgical techniques, and outcomes for current carotid revascularization strategies.
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Semin Neurol
DOI: 10.1055/a-2683-6482

Stroke is a major global health burden, requiring time-sensitive diagnosis and treatment to improve patient outcomes. This urgency has created a compelling role for artificial intelligence in the stroke imaging workflow to accelerate diagnosis and treatment. Artificial intelligence has demonstrated a significant impact across multiple aspects of stroke care, including automated detection of acute findings, expedited triage and notification of findings, quantitative assessment of infarcts, predictive prognostication of outcomes, as well as acceleration of image acquisition. However, these advances are accompanied by important limitations including introduction of biases and challenges in the real-world clinical integration of such tools. In this review, we examine the current applications of artificial intelligence in stroke imaging and evaluate the limitations and real-world implementation challenges.
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Semin Neurol
DOI: 10.1055/a-2645-2914

Neuroimaging plays a key role in the diagnosis of central nervous system (CNS) infections, as well as common infectious mimics. Standard imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as advanced techniques such as vessel wall imaging and MR spectroscopy, are becoming widely used in many areas of the world and are valuable tools to aid neurologists in clinical practice. This review seeks to elucidate patterns of infectious pathogen tropism in the brain and spine, detail key imaging features of specific neuroinfectious pathogens such as patterns of enhancement and formation of mass lesions, and improve understanding of the sequential development of CNS infections and their complications including stroke and hydrocephalus. Here, we focus on a clinically relevant approach, categorizing pathogens in detail based on clinical syndrome and neuroanatomical imaging findings.
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Semin Neurol
DOI: 10.1055/a-2601-9030

An exceptionally broad array of diseases can affect the spinal cord, often in ways that are nonspecific with significant overlap in symptomatology and neurologic exam findings. Neuroimaging is essential in determining the underlying cause and is usually the first diagnostic test to meaningfully reshape the differential diagnosis and adjust which investigations are prioritized. In combination with disease time course, the differential diagnosis can be narrowed by determining a lesion's morphological characteristics, pattern of enhancement, predilection for certain tracts, longitudinal length, and associated radiographic abnormalities. This review provides a brief overview of spinal anatomy using normal spinal cord imaging, followed by a suggested approach to analyzing images and highlighting the radiographic abnormalities unique to each pathology that affects the spinal cord (i.e., autoimmune, infectious, neoplastic, nutritional, structural, and vascular).
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Semin Neurol
DOI: 10.1055/a-2596-5950

Artificial intelligence (AI) has emerged as a transformative force in the management of movement disorders. This review explores the various applications of AI across the spectrum of care, from diagnosis to clinical workflows, treatment, and monitoring. Recent advancements include deep phenotyping tools like the Next Move in Movement Disorders (NEMO) project for hyperkinetic disorders, diagnostic platforms such as DystoniaNet, and biomarker identification systems for early Parkinson's disease detection. AI may revolutionize treatment selection through technologies like DystoniaBoTXNet and adaptive deep brain stimulation systems. For symptom monitoring, innovations like the Emerald device and smartphone-based assessment tools enable continuous, objective evaluation. AI may also enhance patient care through improved telemedicine capabilities and ambient listening. Despite these promising developments, recent critiques highlight methodological concerns in AI research, emphasizing the need for rigorous validation and transparency. The future of AI in movement disorders requires balancing technological innovation with clinical expertise to improve patient outcomes.
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