Seminars in Neurology最新文献

There is growing evidence supporting specific approaches to treat functional neurological disorder (FND), which commonly employ a multidisciplinary strategy guided by the biopsychosocial model and holistic perspective. Integrative Medicine and Health is an emerging specialty that brings together conventional and complementary modalities to deliver coordinated and comprehensive care. Although integrative approaches can be incorporated into existing care models, their application in the treatment of FND has not been systematically investigated, and programs employing such approaches to FND remain exceedingly rare. This study outlines the potential role of integrative care in FND. It will characterize the integrative approach and highlight its potential benefits for FND, review current evidence for relevant therapies, propose a potential clinic workflow, and illustrate its application through clinical case vignettes.

Undergraduate medical education has evolved in its adoption of competency-based medical education (CBME) by more frequent implementation of workplace-based assessments (WBAs). However, a number of challenges may arise due to the complexities of the authentic clinical environment. We describe a single institution's approach to implementing WBAs across core clinical clerkships for medical students, with a focus on the neurology clerkship. At our institution, widespread implementation of WBAs has resulted in a significantly increased number of assessment and feedback opportunities, and we discuss how we approached the increased documentation burden, time, effort, and resources that were required for successful implementation. We conclude that effective adoption of CBME and implementation of WBAs can be achieved through a thoughtful and deliberate approach from the outset, investment in the stakeholders, and a commitment to continuous quality improvement.

Orthostatic tremor (OT) is a rare movement disorder characterized by high-frequency (13-18 Hz) tremor in the legs and trunk during standing and relieved by sitting or walking. While OT is frequently an isolated disorder, some patients may have comorbid neurological conditions, such as essential tremor and parkinsonism, the so-called OT Plus. The pathophysiology of OT is not well-understood, and a specific central oscillator remains elusive, but current evidence suggests that ascending spinocerebellar and proprioceptive inputs, cerebellar processing, cerebello-thalamo-cortical signals, and cortico-muscular coupling play a role in the pathophysiology of OT, suggesting that this is a network disorder. The treatment of OT is challenging, but pharmacological therapy includes gabapentin, clonazepam, perampanel, and levodopa. While spinal cord stimulation seems promising for selected patients, deep brain stimulation is mainly employed in severe cases, resulting in modest improvement in standing tolerance.

Deep brain stimulation (DBS) is a highly effective treatment for movement disorders like Parkinson's disease, essential tremor, and dystonia. However, the current multidisciplinary workflow for implanting and programming DBS is often complex, which can lead to improperly placed leads, suboptimal symptom management, and increased procedure time, ultimately resulting in poor patient outcomes. There is a pressing need for a more streamlined, accurate, reproducible, and personalized approach to DBS therapy. Artificial intelligence (AI), which can analyze complex data and identify patterns with remarkable speed, holds significant promise as a tool to address these challenges. This narrative review explores the current and future applications of AI in improving the entire DBS workflow, from surgical planning and lead placement to postoperative programming, with the goal of enhancing clinical efficiency and achieving better, more personalized outcomes for patients with movement disorders.

Functional neurological disorder (FND) is a prevalent, neuropsychiatric condition characterized by symptoms of impaired motor, sensory, cognitive, or perceptual systems. This study reviews the incidence, prevalence, demographic factors, risk factors, comorbidities, prognosis, and economic impact of FND and its subtypes. FND affects individuals across the lifespan and is more common in women, with socioeconomic and cultural factors also playing critical roles. FND is frequently comorbid with other functional syndromes, psychiatric and neurological disorders. Incidence estimates range from 10 to 16 per 100,000, with prevalence estimates between 79 and 144 per 100,000. Prognosis is poor without early intervention, with long diagnostic delays contributing to chronic symptoms and disability. FND accounts for significant healthcare utilization and economic burden. Evidently, there is a clear need for standardized diagnostic approaches and interdisciplinary collaboration to improve epidemiological accuracy and clinical outcomes.

Persistent symptoms after concussion (PSaC) and functional neurological disorder (FND) are frequently encountered in clinical practice and are often challenging to manage due to heterogeneous and polysymptomatic presentations, as well as fragmented care pathways. This review outlines key points of intersection between PSaC and FND across pathophysiology, illness beliefs, rehabilitation models, and emerging treatments. We describe when FND should be considered in the differential diagnosis of patients with PSaC, and provide guidance on history-taking, examination, diagnostic communication, and rehabilitation planning that can be applied to both conditions. We also examine the influence of expectations, clinical messaging, and interactions with the healthcare system on recovery. Integrating principles from FND into concussion care may help clinicians more accurately formulate cases and support individualized rehabilitation pathways.

Large language models (LLMs) have transformative potential in neurology, impacting clinical decision-making, medical training, and research. Prompt engineering, the strategic design of inputs to optimize LLM performance, is essential for neurologists and trainees seeking to effectively integrate these powerful tools into practice. Carefully crafted prompts enable LLMs to summarize complex patient narratives, generate differential diagnoses, and support patient education. In training, structured prompts enhance diagnostic reasoning, board preparation, and interactive case-based learning. Neurological research also benefits, with LLMs aiding in data extraction, computed phenotype generation, and literature synthesis. Despite their promise, challenges remain, including hallucinations, data bias, privacy concerns, and regulatory complexities. This review synthesizes current advances and highlights best practices, including two structured prompt engineering frameworks tailored to neurology: Role-Task-Format (RTF) for routine use and our newly developed BRAIN (Background, Role, Aim, Instructions, Next steps) for complex tasks. We offer practical guidance to maximize accuracy, safety, and equity in LLM outputs, ensuring reliable support for neurologists and trainees.

The utility of neuroimaging in the diagnosis and management of movement disorders has been steadily increasing as both imaging and image analysis technologies have advanced in the last decade. Neuroimaging is also playing a critical role in the search for novel therapies to prevent, slow down, and treat various movement disorders. This article reviews both standard and innovative imaging tools available for both clinicians and researchers. We focus predominantly on the clinician's perspective, discussing imaging tools that are becoming rapidly available and how these may be integrated into the clinic to provide cutting-edge and patient-centered care. We discuss novel and emerging techniques and their potential implications for the field, as well as highlight areas still in need of imaging solutions.

Brain tumors are a diverse group of neoplasms that vary widely in treatment and prognosis. Imaging serves as the cornerstone of diagnosis, monitoring response to treatment and identifying progression of disease in neuro-oncologic care. This review outlines current and emerging imaging modalities with a focus on clinical application in glioma, meningioma, and brain metastasis. We cover standard imaging modalities, advanced magnetic resonance techniques such as perfusion and spectroscopic imaging, and nuclear imaging with positron emission tomography (PET), including amino acid PET. We summarize the standardized Response Assessment in Neuro-Oncology (RANO) criteria, and explore innovations in radiomics, artificial intelligence, and targeted imaging biomarkers. Finally, we address challenges related to equitable access to advanced imaging. This review provides a practical, clinically focused guide to support neurologists in the imaging-based care of patients with primary or metastatic brain tumors.