ObjectivesTo quantify reporting preferences and communication barriers between neuroradiologists and neuro-associated clinicians at a tertiary neuroscience center.Materials and methodsTwo near-identical, multilingual online surveys (17 questions) were distributed to neuroradiologists and clinicians in neurology, neurosurgery, and otorhinolaryngology. The surveys included demographic questions, satisfaction assessments, and report-related preferences. Data analysis was performed using descriptive statistics (frequencies and percentages).ResultsResponses were obtained from 80 physicians (21 neuroradiologists; 59 clinicians). Most clinicians reported reading the entire report (59.3%); subspecialty differences were not significant (chi-square test, p = 0.59). In a stroke scenario, context-based structured reports were preferred by 45.8% of clinicians, in contrast to only by 9.5% of neuroradiologists. Standardized classifications and quantitative measurements were endorsed often/always by 59.3%, and key images were considered useful routinely or in complex cases by 89.8% of clinicians. For the clinical neuro-related subspecialties, no significant between-group difference in impact ratings was observed (chi-square test, p = 0.25). Neuroradiologists indicated that more detailed clinical information would be beneficial.ConclusionClinicians favor context-based structured reporting with classifications, quantitative measurements, and key images, whereas neuroradiologists predominantly prefer conventional formats. Adopting context-based templates with clear governance may help align expectations and support interdisciplinary workflows.Critical relevance statementMore focused and detailed clinical information, together with greater use of context-based structured reports incorporating classifications, quantitative measurements, and key images, may facilitate clearer communication between radiologists and clinicians and better support tailored clinical decision-making workflows.Key PointsInterdisciplinary reporting needs in neuroradiology were assessed by surveys.Clinicians favor context-based structured reports and standardized content.Referral quality and report design are modifiable barriers to improving workflow.Graphical

Introduction and Problem StatementNeurology residents are often tasked with incorporating impromptu medical student teaching into their clinical workflow, yet few resources exist that combine content and pedagogical guidance in an efficient format. Residents are also learning themselves and may experience anxiety around teaching, compounded by the additional cognitive load of identifying and addressing student learning needs. We developed a guided worksheet tool to make high-yield, effective clinical neurology teaching accessible to busy residents.ObjectivesWe piloted 4 self-guided worksheets on core neurology topics and examined student learning and the resident teaching experience. We aimed to (1) demonstrate successful student knowledge acquisition, retention, and satisfaction and (2) demonstrate improved resident teacher (RT) comfort, efficacy, and/or efficiency in clinical teaching on inpatient services.Methods and Curriculum DesignFour worksheets were developed: ischemic stroke basics, reading magnetic resonance imaging of the brain, intracranial hemorrhage, and seizures. Residents were asked to guide students through filling out a partially blank version, using a completed ‘master’ version as a guide, while providing targeted teaching when knowledge gaps were apparent. Student learning was assessed using presession, postsession, and two-week postsession quizzes. Qualitative analysis was performed on semistructured interviews with RTs to understand their experience teaching with the tool.Results and Assessment DataSix RTs taught 27 medical students a total of 47 lessons. Learner knowledge scores improved immediately after lessons (1.24/3 ± 0.92 before lesson vs 2.75/3 ± 0.49 after lesson, p < 0.001) and were sustained at 2 weeks after lesson (1.24/3 ± 0.92 before lesson vs 2.47 ± 0.61 2 weeks after lesson, p < 0.001). RTs believed that the tool improved their clinical teaching, with themes of reducing stress around impromptu teaching, improving basic content delivery, the power of microlearning, and developing self-efficacy identified in qualitative interviews.Discussion and Lessons LearnedGuided worksheets offer content and can support RTs in providing effective clinical teaching while improving their comfort and self-efficacy. Expansion of these tools to additional high-yield topics may offer further support for resident teaching in the clinical setting.

Anti-inflammatory and anti-oxidative effects of vanadium on motor and cerebellar cortices of juvenile hydrocephalic mice.

The discovery of pathogenic neuroglial-surface directed autoantibodies (NGSAbs) has fundamentally transformed clinical neurology, by enabling molecular-level diagnoses in potentially treatable, yet previously unrecognised, diseases. Annual descriptions of novel CNS-targeting antibodies create a continuous stream of new conditions in which to evaluate distinct phenotypes, specific tumour associations and immunotherapy responses. Alongside this clinical growth, increasing basic knowledge has highlighted origins and mechanisms underlying disease causation, most comprehensively interrogated in the well-established autoantibody-mediated conditions of autoimmune encephalitis (AE), neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). The corresponding most common 'big six' autoantigens are LGI1, the NMDA receptor, CASPR2, IgLON5, in forms of AE, AQP4 and MOG. Each of these autoantigens associates with a homogenous set of basic clinical features, across age, sex, tumour associations and ethnicities, coupled with partly distinctive profiles of triggers and predispositions, paradigms of immune tolerance escape in the periphery, how cells and autoantibodies gain access to the CNS and discrete mechanisms by which the CNS autoantibodies induce neuroglial dysfunction. These observations lead us to reconstruct a proposed chronological series of events as the "cascade to pathogenicity", which together culminate in a rare CNS disease. By extension, we hypothesize elucidating the underlying biology of each condition will present differing precision medicine approaches to optimize patient care. Despite distinctions, there are also clinical and biological overlaps between these diseases, collectively creating opportunities to compare and contrast their individual features. Here, in each condition, we review current knowledge regarding the similarities and differences between the triggering events, underlying immunological processes and pathogenic mechanisms of autoantibodies. In some instances, we identify scientific clues which drive hypothetical pathways of pathogenesis and, for others, highlight striking observations which aim to generate hypothesis-driven next steps. Our aim is to construct a model across the major autoantibody-mediated CNS diseases to highlight distinct components of cascades to pathogenicity which may offer targeted therapeutic approaches to improve patient outcomes, and identify key areas and questions for future research.

Early and correct classification of neurodegenerative diseases like Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is one of the most important challenges in clinical neurology. In this paper, we present a novel electroencephalogram (EEG)-based approach that integrates a rich set of multiresolution features to improve the performance of automatic classification. Our approach fuses the Graph Fourier Transform (GFT), Graph Wavelet Transform (GWT), Discrete Wavelet Transform (DWT), and a newly developed Graph Empirical Mode Decomposition (GEMD) technique to primarily boost the performance of the proposed model. This also retained the complementary spatial, spectral, and temporal information carried by the EEG signals, which are significant for the differentiation of AD, FTD, and HC subjects. The EEG recordings were segmented into fixed lengths with non-overlapping windows of four durations: 1000, 5000, 10,000, and 20,000 samples. Energy and entropy features were obtained for each segment, both individually within domains and combined into a single 388-dimensional feature vector. The features were then normalized and fed into various machine learning (ML) models, including support vector machines (SVMs), k-nearest neighbors (kNNs), decision trees (DTs), random forests (RFs), and an ensemble learning model with the AdaBoost capability. The proposed model was tested using accuracy, precision, recall, specificity, and F1-scores, with results showing that the ensemble model was better than the other benchmark models in every classification task. That is, in this binary classification problem, an accuracy of 98.84% for AD vs. HC, 98.67% for AD vs. FTD, and 98.94% for FTD vs. HC was obtained. In the multiclass task (AD, FTD, HC), the method reached 96.68% accuracy, demonstrating the efficacy of the proposed method for the identification of Alzheimer's disease and frontotemporal dementia. Compared to previous research using the same dataset, our approach has demonstrated improved performance, validating the effectiveness of graph-based multiresolution feature fusion for dementia classification using EEG signals.

James McLeod was an outstanding clinical neuroscientist who achieved Australian and international renown and leadership in two distinct areas of clinical neurology, disorders of peripheral nerve and multiple sclerosis. He introduced and established clinical neurophysiology in Australia, which facilitated the diagnosis and management of neuromuscular disease and multiple sclerosis prior to the advent of magnetic resonance imaging (MRI). His careful and detailed clinical and neurophysiological studies were essential to the discovery in his laboratory of the gene mutation for the commonest hereditary neuropathy Charcot Marie Tooth disease. He and his team were among the first internationally to define a disabling autoimmune neuropathy and collaborate with appropriate hospital departments (immunology and haematology) to introduce effective therapy for it. With Professor Basten (Immunology) he conducted the first clinical trial of immunotherapy for multiple sclerosis (MS) and participated in the first international studies of immune therapies which greatly improved the outlook for patients with this disease. McLeod also made major contributions to the epidemiology of MS. McLeod worked tirelessly not only to improve disease but to support the patients afflicted by these conditions and his students and colleagues working in this endeavour. He was committed to the practice of medicine, education and the improvement of disease outcomes through research. His life of service was extraordinary. McLeod died on 27 June 2022 and the University of Sydney arranged a memorial service in his honour. Jim McLeod was appointed to the first named chair of neurology in Australia, the Bushell Chair in 1978, and in 2025 the University of Sydney established the James McLeod Chair of Neurology in his honour.

Spatial disorientation represents a clinically meaningful vulnerability during aging and is an early manifestation along the Alzheimer's disease continuum. Understanding how aging-related central nervous system (CNS) changes affect the neural computations required for spatial perspective taking (SPT) is essential for characterizing early neurodegenerative processes. In this study, older adults with mild cognitive impairment (MCI) and subjective cognitive decline (SCD) completed a simplified map-based SPT paradigm while electroencephalography (EEG) was recorded. Although behavioral accuracy was largely preserved, MCI participants demonstrated globally slower responses. Crucially, high-demand egocentric-allocentric transformations (180°) revealed a marked loss of frontal N400 modulation in MCI, together with increased posterior delta-band synchronization. This pattern indicates an aging-related frontal-posterior reweighting in spatial integration networks, consistent with early compensatory recruitment as frontal conflict-monitoring mechanisms decline. These findings provide mechanistic insight into how aging and MCI jointly alter CNS function during spatial cognition, and they highlight the translational potential of low-burden SPT-EEG paradigms for detecting subtle neurophysiological changes relevant to clinical neurology, geriatric assessment, and the early monitoring of neurodegenerative disease.

ABSTRACT INTRODUCTION. Orphan diseases are of particular interest in clinical neurology. At the present stage, the available genetic diagnostic methods make it possible to reasonably verify the hereditary diagnosis. This is especially relevant in the presence of comorbid pathology, which complicates the diagnostic search. Spinobulbar muscular atrophy, also known as Kennedy's disease, is one of the types of rare nosologies manifested by a complex of neurological and comorbid manifestations. AIM. To analyze the clinical manifestations of endocrine pathology, bulbar syndrome and peripheral paresis of the proximal muscles of the extremities in a patient with Kennedy's disease. Identify typical clinical symptoms and present the importance of accurate genetic diagnosis of the disease based on the presented clinical case. MATERIALS AND METHODS. The article presents a clinical case of a 43-year-old patient with a typical CAG mutation. RESULTS AND DISCUSSION. The description of this clinical history will allow specialists to include Kennedy's disease in the differential diagnostic series when examining a patient with suspected neuromuscular pathology with extraneural disorders. CASE PRESENTATION. Our own observation of spinobulbar muscular amyotrophy (Kennedy's disease) with the detection of a CAG mutation in the androgen receptor gene is presented. The article examines a clinical case demonstrating the manifestations of this disease. A special feature of the clinical case is the beginning of a diagnostic search with urological pathology for problems in the reproductive sphere. CONCLUSION. A detailed medical history combined with neurological status data, electromyography, and hormonal profile studies (decreased testosterone levels) make it possible to suspect Kennedy's disease. The diagnosis is verified by molecular genetic analysis of the androgen receptor gene. The description of the given clinical case will allow specialists of different specialities to obtain uptodate information about this pathology and to choose the correct diagnostic and therapeutic algorithm in the management of such patients. KEYWORDS: Kennedy's disease, spinobulbar muscular atrophy, molecular genetic analysis, CAG

Evaluating AI performance in Clinical Neurology: The impact of Patient Race/Ethnicity on Large Language Model Accuracy

The eye serves as an important "window" that sensitively reflects neurological lesions. This article reviews the basic anatomy of the eyeball and optic nerve, and explains simple methods for evaluating visual acuity, pupils, ocular movements, color vision, visual fields, and the fundus that can be performed by neurologists in outpatient clinics and at the bedside. It also focuses on optic neuritis, which is closely associated with neurological disorders, outlining examination methods and key clinical precautions that can be undertaken by neurologists, as well as major ophthalmic adverse effects that may occur with drugs used to treat neurological diseases. Finally, the importance of early collaboration with ophthalmologists is emphasized.

Automated EEG interpretation is essential for clinical neurology but is currently hindered by the absence of robust, generalizable biomarkers. While EEG microstates offer insights into brain dynamics, their large-scale clinical utility remains largely unvalidated. In this study, we employed a hierarchical two-stage clustering approach to extract robust microstate features from 2,994 clinical recordings within the TUAB Corpus. A suite of machine learning classifiers was trained for automated abnormality detection, with their decisions elucidated via SHAP analysis. The Support Vector Classifier (SVC) yielded the superior performance with an AUROC of 0.877 [95% CI: 0.833–0.917], consistently outperforming other architectures like MLP and Logistic Regression. Statistical analysis identified 16 discriminative microstate features (p $$\:<\:$$ 0.0022, Bonferroni-corrected), with six exhibiting large effect sizes (|Cohen’s d| $$\:>$$ 0.8), most notably in microstate duration variability and A-D state transition probabilities. Quantitative template comparisons confirmed that discriminative power arises from both topographical reconfiguration (spatial) and sequence fragmentation (temporal). Specifically, the abnormal group displayed a breakdown in temporal stability, characterized by increased duration variability and disrupted bidirectional flow between canonical states. These findings validate microstate dynamics as powerful, interpretable biomarkers, providing a scalable, data-driven framework for clinical EEG diagnosis.

Background The Cognitive Functioning Self-assessment Scale (CFSS) is specifically designed for the self-report measurement of various domains of cognitive functioning (memory, spatio-temporal orientation, and attention). The objective of this study was to validate the CFSS in the Modern Standard Arabic. Methods A community-based, multinational study of cross-sectional design was conducted during September-October 2025 among 4092 adults from Egypt, Jordan, Lebanon, Oman, and Palestine. A combined non-probability convenience and snowball sampling strategy was used to recruit the study participants. Results The Arabic CFSS yielded a unidimensional structure with good internal consistency (Cronbach’s α = 0.93). Evidence of measurement invariance for the CFSS was established on the basis of sex, educational level and country status at metric, configural and scalar levels. Females, those with secondary educational level or less, and Palestinians showed higher cognitive functioning impairment scores compared to males, those with university educational level, and those from other origins. Greater impairment in cognitive functioning positively correlated with insomnia, depression and anxiety symptoms. Conclusion It is hoped that the new scale will expand the applications of and interests in the cognitive assessment outside the restricted clinical neurology field to include broader non-clinical populations, and ultimately enrich the global cognition research by contributing new data from the largely understudied Arabic-speaking region.

Slow motion (SM) videography provides a novel approach to documenting and understanding movement disorders by revealing subtle phenomenological features not easily captured at normal speed. While widely used in sports and wildlife analysis, its application in clinical neurology remains limited. We present a case series of four patients with Parkinson’s disease in whom SM videography uncovered unique motor signatures. Case 1 revealed the “Scoop Sign” of hand tremor, highlighting wrist and forearm rotational components. Case 2 demonstrated incessant progressive micrographia, a distinctive writing pattern of gradual diminution and incessant continuation, atypical for PD. Case 3 illustrated the “Fulcrum Sign” in freezing of gait, where one foot acted as a pivot. Case 4 depicted the “Octopus Sign” in levodopa-induced dyskinesia. These cases highlight the utility of smartphone-based SM videography as an accessible, cost-effective clinical tool to unravel masked PD phenomenology, enhance bedside observation, and improve recognition of subtle motor patterns.

Since the emergence of neuroscience, brain plasticity has been a key topic in cognitive research. Many factors can induce changes in plasticity in the brain. However, the unique neuroplasticity shown by individuals who have undergone long-term motor training (i.e. motor skills experts) has become a key window to understand the mechanism of human learning and adaptation. Although extensive research confirms that continuous physical training can enhance the plasticity of the brain, there is no systematic review that comprehensively describes the development trajectory and key research areas of this field. This article examines 142 publications from the Web of Science Core Collection, PubMed, PsycINFO and CNKI between 1995 and 2025. e between 1995 and 2025. Using literature metrology analysis and scientific knowledge atlas technology, the research progress of brain magnetic resonance imaging research of motor skills experts is systematically presented, aiming to provide reference for future research. The research results show that the annual publication volume shows a significant upward trend; there is insufficient collaborative network between authors in different countries (regions); journals in the fields of neuroscience, clinical neurology and psychology show high publication volume and influence; technical methods have developed from a single MRI technology to a combination of recent red Comprehensive methods of external brain imaging and other cognitive neuroscience and technology. The research theme centred on the knowledge system of sports experts is still prominent, mainly focussing on movement observation, motion prediction and concussion-related research. The research paradigm has transitioned from task-based research to the investigation of brain function in the resting state of athletes, and attention has shifted from local brain areas to key brain networks. Future efforts should enhance research coordination, strengthen interdisciplinary cooperation, promote ecological and longitudinal research design, and expand the depth and breadth of research.

Spreading depression is a neurophysiological phenomenon that isobserved in the central nervous system of many species, from insects to humans. In essence, spreading depression is a slowly propagating wave of mass depolarization (that is, spreading depolarization), aptly termed a 'brain tsunami', which successively engulfs contiguous brain regions, causing transient neuronal hyperexcitability at its leading edge, followed by complete but reversible neuronal silence lasting minutes. This wave cannot be detected in routine scalp EEG recordings, which contributes to its under-recognized status as a disease biomarker. Here, we present an evidence-based view of spreading depression as a probable cause of characteristic neurological signs and symptoms in numerous neurological conditions. Although migraine aura is a widely recognized manifestation of spreading depression, the clinical signs and symptoms of spreading depressions arising from structural brain pathology have remained an orphan concept with no established name or place in clinical terminology. Therefore, clinicians have long used the term 'migraine aura' to describe the transient neurological manifestations of spreading depression that occur entirely outside of the context of a migraine attack. As migraine is a primary headache disorder not caused by known structural pathology, this terminology is not only erroneous but could also lead to serious misdiagnoses. Consequently, we advocate for the clinical adoption of the more specific mechanistic term spreading depression to describe these clinical episodes. We believe it is imperative to recognize spreading depression as a generic mechanism underlying certain inherited or acquired neurological deficits and to differentiate between structural and non-structural aetiologies, as is done in seizure disorders.

In 2025, international neurology celebrates the bicentenary of J.-M. Charcot's birth. As a major medical scientist in Paris and the founder of modern clinical neurology, Charcot became friends with the celebrated literary figure Alphonse Daudet. Discord subsequently intervened, as Daudet, afflicted with tabes dorsalis, was treated by Charcot without success and even underwent suspension therapy that led to serious side effects. Daudet's son, Léon, blamed Charcot for his own failure in medical school and became a bitter social critic of the French medical system, condemning the hospital hierarchy, including Charcot. Further family discord occurred when Léon married the granddaughter of Victor Hugo, instead of Charcot's own daughter. Within this background, after Charcot's death in 1893, Alphonse Daudet incorporated Charcot into a fictional account, A la Salpêtrière, one of 3 short stories in his Trois Souvenirs (3 Recollections). This study dissects Daudet's Charcot depiction where he presents Charcot as mostly silent, passive, and distant within a circus-like atmosphere of disruptive patients, foreign visitors, and interns. The portrait is a striking contrast to the many other first-hand descriptions of Charcot's domineering, autocratic, and patronizing manner. However, the depiction of a quiet and distantly bland master in the fictional office consultation setting is historically anchored in Daudet's life experiences, which included visits to the Salpêtrière, first-hand knowledge of Charcot over many years, and the experience of being a patient with unremitting neurologic disease. The veracity of the actual events is questionable, given the personal antagonism that colored the last years of their lives, but it is also conceivable to see in Charcot a Janus-like figure of dominance and theatrical authority in the teaching amphitheater interfaced with a more passive, reflective observer in the intimacy of an office setting.

ABSTRACT Introduction Large language models are increasingly used in evaluating medical data and clinical decision making. Data on the performance evolution of these models are limited. This study evaluated the intergenerational development of model performance using paired methods in the discipline of neurology, where the need to synthesize and contextualize complex information is high. Methods The scoring system and clinical neurology question set comprising 216 questions used in our previous study were employed using methodological replication. The questions, evenly distributed across 12 subspecialties, were divided into subgroups based on question type, difficulty level, and qualitative characteristics. The responses underwent accuracy and comprehensiveness analyses by three independent academics. Effect sizes were calculated using matched analyses between the two generations. Results For the entire question set, GPT-5.2’s accuracy rate (91.2% vs. 62.5%; p < 0.001; RD = 0.26; OR = 10.3), comprehensiveness scores (2.62 ± 0.47, 2.09 ± 0.89; p < 0.001; Cohen’s d 0.60), and accuracy scores (5.67 ± 0.94, 4.42 ± 2.08; p < 0.001; r:0.78) were significantly higher than the previous version, with effect sizes observed at the medium to high levels. Consistent performance improvement was observed across question types, difficulty levels, and qualitative characteristics. Performance was relatively low in some subspecialties. Conclusion The GPT-5.2 model demonstrated a significant performance increase compared with the previous model when presented with questions in clinical neurology. The performance increase was supported by high effect sizes, indicating potential clinical relevance. Model evolution was not homogeneous across subspecialties. Integrating it into clinical systems with strict control mechanisms may alleviate safety concerns.

Cognitive decline is a common feature of neurologic conditions, with language functions often affected. Word finding difficulties are commonly reported to neurologists in clinic. Receptive language dysfunction (i.e., comprehension) tends to be more difficult to recognize for both the patient and the clinician. Subtle yet pervasive decrements in language may be a key feature (and potential driver) of pathological cognitive decline inherent to neurologic diseases involving a primary or secondary neurodegenerative process. While severe language impairment such as aphasia presenting in the context of stroke or dementia has been studied in detail, mild or insidious presentations remain relatively understudied. In this review, we evaluate neural substrates and clinical manifestations of language deficits noted in four neurologic populations: Alzheimer's disease (AD), stroke, multiple sclerosis (MS), and Parkinson's disease (PD). Despite differences in etiology and pathophysiology, these four neurologic populations each present with prominent language dysfunction. For each, we describe neuroanatomical substrates and networks underlying language dysfunction. We then describe current observations of language dysfunction in each population. We incorporate a discussion of emerging speech measurement tools employing machine learning (ML) and artificial intelligence (AI). Overall, we provide evidence to support a nascent hypothesis of language dysfunction as a potential driver of cognitive decline across neurologic populations with the aim of motivating novel research insights and informing clinical care.

Extended reality in clinical neurology: From interdisciplinary innovations to clinical practice.

Integrating clinical findings with neuroradiological changes is a crucial skill in neurology, particularly for diagnosis. Multiple Sclerosis (MS) lesions in the brainstem are rarely asymptomatic, leading to unique and often localised clinical syndromes. MS lesions exhibit a characteristic perivenular distribution, which in the brainstem is imprinted by the consistent topography of the penetrating veins. This review provides an integrative perspective on the anatomical patterns of MS lesions in the brainstem (midbrain, pons, and medulla). It correlates specific clinical syndromes with radiological appearances, aiding in both diagnosis and functional localisation. We searched the available literature using keywords related to the three brainstem sections (midbrain, pons, medulla) and eloquent anatomical locations (medial longitudinal fasciculus, cerebellar peduncle, nerve fascicle, aqueduct, area postrema), aiming to correlate specific radiological patterns of MS lesions with their consistent clinical syndromes as reported in the literature. Brainstem MS lesions often cause irritative symptoms rather than full functional loss. Unlike other conditions, visible MS lesions on MRI rarely disappear and usually remain as silent lesions following an acute event. The consistent venous architecture creates specific radiological patterns that link to distinct clinical presentations. In contrast, inflammatory disorders like NMOSD and MOGAD cause more aggressive and extensive dysfunction. The visual details of MS brainstem lesions reflect their close relationship to venous anatomy, which can be anticipated even when the central vein sign is not directly visualised. Recognising these specific clinical-radiological syndromes provides a unique and insightful diagnostic tool for MS, underscoring the value of strong functional and radiological-anatomical interpretation skills in clinical neurology.