Seminars in Neurology最新文献

Tremor is a hyperkinetic movement disorder that consists of rhythmic, involuntary oscillatory movement of a body part. It may be present at birth or develop later during childhood. Pediatric tremor may be an isolated disorder or a sign of an underlying genetic and/or neurologic disease. This review provides a summary of the features and classification of pediatric tremors, recommended clinical evaluation, and currently available treatments.

Functional neurological disorder (FND) is a disabling neuropsychiatric condition characterized by altered voluntary motor or sensory functions and cognitive symptoms. Unlike other neurological disorders, FND is not caused by structural brain damage but by disruptions across brain networks involved in agency, attention, emotion processing, sensory-motor control, and interoception. These alterations align with alterations in predictive coding, which propose that abnormal prior beliefs override sensory input, contributing to symptom generation. Early-life trauma is a significant risk factor, interacting with genetic and epigenetic vulnerabilities that influence emotional regulation, stress sensitivity, and brain connectivity. Psychiatric comorbidities are also common and may affect symptom severity and prognosis. This review synthesizes recent research to clarify the complex mechanisms underlying FND by integrating neurobiological, environmental, and psychological factors. By doing so, we aim to advance the understanding of FND pathophysiology and promote a more comprehensive conceptual framework that highlights the role of individual vulnerability.

Functional neurological disorders (FNDs) are neurological conditions resulting from altered brain network activity causing physical symptoms that are genuine but not explained by structural changes in the brain. FND results from abnormal connectivity in the limbic system and overlapping circuitry dysfunction in salience networks. The autonomic nervous system (ANS) refers to the part of the nervous system devoted to unconscious processes, the viscera and homeostasis. The ANS has afferent pathways, central nuclei and networks, and efferent pathways. Since unconscious neural processing and automatic behaviors are under the purview of the ANS, there is great interest in understanding the role of abnormal ANS activity in FND. To date, the overlap between ANS dysfunction and FND has been relatively underexplored. Here, we discuss the role of the ANS in FND and the overlap between autonomic dysfunction and FND.

Functional neurological disorder (FND) in children and adolescents presents distinct challenges and opportunities compared with adult populations. Pediatric FND frequently affects high-achieving youth without significant trauma histories or psychiatric illness, highlighting demographic and etiologic differences that challenge many of the classic psychodynamic assumptions. The developing brain's heightened plasticity may predispose to maladaptive functional patterns, yet also makes recovery particularly attainable when diagnosis and treatment are timely. Positive rule-in signs and clear, developmentally attuned explanations are central to reframing symptoms as real, reversible, and brain-based. Evidence from the retraining and control therapy randomized trial and recent telehealth cohorts demonstrates that multidisciplinary, family- and school-engaged approaches can achieve high remission rates. Adjunctive strategies that target network dynamics and plasticity may further amplify recovery. With coordinated care, pediatric FND is highly reversible, restoring agency, alleviating disability, and giving back decades of ability, opportunity, and thriving identity formation.

Neurological disorders affect hundreds of millions globally, yet translating artificial intelligence (AI) advances into clinical practice remains challenging due to fragmented, privacy-sensitive datasets. Federated learning (FL) has emerged as a promising paradigm, enabling collaborative model training across institutions without sharing raw patient data. This review synthesizes FL applications in neurology from 2020 to 2025, spanning neuroimaging, electrophysiology, and electronic health records. We analyze real-world deployments, highlight algorithmic trends, and discuss technical, regulatory, and organizational barriers to clinical translation. While FL demonstrates feasibility in tasks such as brain tumor segmentation, multiple sclerosis lesion detection, and electronic health record-based predictive modeling, verified clinical implementations remain scarce. We outline strategies to enhance adoption, including privacy-preserving techniques, standardized infrastructures, domain-adaptive algorithms, and cross-disciplinary collaboration. By bridging technical innovation with regulatory compliance and operational scalability, FL holds significant potential to advance precision neurology while safeguarding patient privacy.

Child neurology training has undergone minimal change over the decades, despite a rapid growth in subspecialty knowledge, patient volumes, and complexity. The current 5-year structure, which was established due to necessary historical compromises between pediatrics and neurology, is increasingly misaligned with modern clinical practice and educational priorities. Most child neurologists no longer pursue dual pediatrics certification, and few provide neurologic care to adult patients. Meanwhile, the field has expanded significantly in complexity and volume, making it a large enough specialty to sustain an independent curriculum. We propose a streamlined 4-year categorical residency model that integrates relevant components of pediatrics and adult neurology while centering training around child neurology from the start. This model, which aligns better with structures seen in comparable specialties, prioritizes flexibility and increases the opportunities for longitudinal mentorship and professional development. Thoughtful planning and collaboration will be essential to surmount challenges during the transition, including changes in board certification and alterations to institutional funding. Modernizing child neurology training is essential to better prepare future specialists, support recruitment and resident development, and meet the evolving needs of children with neurologic disorders.

Functional tremor (FT) is the most prevalent subtype of functional movement disorders, characterized by variability, distractibility, and entrainability on clinical examination. Diagnosis relies on positive clinical and electrophysiological signs, shifting emphasis from a diagnosis of exclusion to "rule-in" criteria. Surface electromyography and tremor analysis are essential tools in establishing the diagnosis. Pathophysiology involves abnormal motor co-activation, disrupted volitional awareness, and impaired predictive processing, resulting in tremor perceived as involuntary despite intact motor pathways. Management requires a multidisciplinary approach, including physiotherapy, occupational therapy, cognitive-behavioral interventions, biofeedback, and transcranial magnetic stimulation. FT often results in persistent disability, with limited treatment response. Management is hindered by diagnostic challenges, especially in functional overlay, limited training, cultural misconceptions, and underutilization of neurophysiological and rehabilitation interventions. Improving clinician training, expanding access to neurophysiology, and multidisciplinary care, along with high-quality prospective specific research and standardized care pathways, is essential to optimize outcomes.

Functional neurological disorder (FND) most commonly affects females. As such, there is biological evidence that sex differences contribute to the onset and development of this disorder. Gender can also impact FND. In this article, we will address some important considerations for FND, including the history of sex differences and gender-related factors in FND, common comorbidities, hormonal and reproductive considerations, and future directions for research.

Acute stroke alerts are frequently triggered by conditions unrelated to cerebrovascular disease, resulting in false positives that burden clinical teams and contribute to diagnostic ambiguity. At a large academic center, we developed ScanNER v2, a machine learning (ML) model based on large-language models (LLMs) and structured clinical data to predict the presence of acute cerebrovascular disease (ACD) in approximately 16,000 stroke alerts occurring over 10 years with an area under the receiver-operating curve and F1 score of 0.72 and overall positive predictive value of 0.68. In this perspective, we outline a practical framework for operationalizing this model within hospital-based stroke systems. We first describe our health-system experience developing and validating an AI-enabled pipeline, named "ScanNER 2," then take the point of view of two implementation angles (high sensitivity and high specificity), outlining the operational and clinical tradeoffs for each approach. We also highlight challenges related to implementation, clinical governance, workflow integration, and equity, emphasizing guardrails required for responsible deployment. As stroke centers increasingly adopt AI-assisted tools, this type of thought experiment is essential to ensure that such ML-based innovations effectively enhance the core mission of delivering timely, high-quality acute stroke care.

Tremor is one of the most visible and distressing symptoms of Parkinson's disease (PD), ranking high among patients' most bothersome features. While levodopa is the most effective pharmacological therapy, approximately half of patients report insufficient tremor control, and refractory tremor remains a major therapeutic challenge. The psychosocial impact of tremor is profound: its visibility leads to embarrassment, stigma, and emotional distress, while its interference with daily activities and professional life compounds disability. Prevalence studies suggest that nearly all individuals with PD experience tremor at some point, though its severity fluctuates over time and may plateau or improve in later disease stages. Tremor pathophysiology differs from bradykinesia and rigidity, involving both basal ganglia and cerebello-thalamo-cortical circuits, supporting the "dimmer-switch" model. Management strategies include dopaminergic and nondopaminergic medications, deep brain stimulation, and emerging interventions such as MR-guided focused ultrasound. Optimizing therapy remains crucial to alleviating tremor-related burden in PD.