Understanding Functional Neurological Disorder: Recent Insights and Diagnostic Challenges.

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing.

RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.

Recent advances in understanding the neurobiology of pediatric functional neurological disorder.

INTRODUCTION: Functional neurological disorder (FND) is a neuropsychiatric disorder that manifests in a broad array of functional motor, sensory, or cognitive symptoms, which arise from complex interactions between brain, mind, body, and context. Children with FND make up 10%-20% of presentations to neurology services in children's hospitals and up to 20% of adolescents admitted to hospital for the management of intractable seizures. AREAS COVERED: The current review focuses on the neurobiology of pediatric FND. The authors present an overview of the small but growing body of research pertaining to the biological, emotion-processing, cognitive, mental health, physical health, and social system levels. EXPERT OPINION: Emerging research suggests that pediatric FND is underpinned by aberrant changes within and between neuron-glial (brain) networks, with a variety of factors - on multiple system levels - contributing to brain network changes. In pediatric practice, adverse childhood experiences (ACEs) are commonly reported, and activation or dysregulation of stress-system components is a frequent finding. Our growing understanding of the neurobiology of pediatric FND has yielded important flow-on effects for assessing and diagnosing FND, for developing targeted treatment interventions, and for improving the treatment outcomes of children and adolescents with FND.

Painful Eyes in Neurology Clinic: A Guide for Neurologists.

Eye pain is a common complaint among patients presenting to the neurology clinic. It can be related to neurologic diseases, but it can also be a localized eye condition. Such disorders can be misleading, as their benign appearance might mask more grave underlying conditions, potentially leading to misdiagnoses or delayed treatment. Clinicians should be aware of the specific neurologic or systemic disorders (eg, demyelinating diseases or vascular abnormalities) that might first manifest as eye pain. Formal ophthalmic consultation is recommended for patients presenting with eye pain as the predominant complaint especially when red flags for more serious pathology are present.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD). MATERIALS AND METHODS: The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. RESULTS: The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively. CONCLUSION: The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

Hook-effect in MAGLUMI immunoassay for serum anti-GAD antibodies in neurological disorders: When "wrong" matrix is the right choice.

Antibodies against glutamic acid decarboxylase (anti-GAD) are a valuable diagnostic tool to detect severe autoimmune conditions as type 1 diabetes mellitus (T1DM) and anti-GAD related neurological disorders, having the latter more often anti-GAD concentrations in serum multiple times higher than in the former. Automated immunoassays, either with ELISA or chemiluminescent technology, are validated for diagnostic use in serum with analytical ranges suitable for T1DM diagnosis. In a patient presenting with a suspected autoimmune ataxia, anti-GAD testing on an automated chemiluminescent immunoassay (CLIA) resulted in slightly abnormal concentrations in serum (39.2 KIU/L) and very high concentrations in CSF (>280 KIU/L), thus prompting to proceed to serum dilutions to exclude a false negative result and a misdiagnosis. Different dilutions of serum resulted in nonlinear concentrations with endpoint result of 276,500 KIU/L at dilution 1:1000. CSF dilution was instead linear with endpoint result of 4050 KIU/L. In this case report we found that anti-GAD testing in CSF was essential to establish the clinical diagnosis and to suspect hook-effect in serum due to the excess of autoantibodies in this severe autoimmune condition.

Neurofilaments as biomarkers in neurological disorders - towards clinical application.

Neurofilament proteins have been validated as specific body fluid biomarkers of neuro-axonal injury. The advent of highly sensitive analytical platforms that enable reliable quantification of neurofilaments in blood samples and simplify longitudinal follow-up has paved the way for the development of neurofilaments as a biomarker in clinical practice. Potential applications include assessment of disease activity, monitoring of treatment responses, and determining prognosis in many acute and chronic neurological disorders as well as their use as an outcome measure in trials of novel therapies. Progress has now moved the measurement of neurofilaments to the doorstep of routine clinical practice for the evaluation of individuals. In this Review, we first outline current knowledge on the structure and function of neurofilaments. We then discuss analytical and statistical approaches and challenges in determining neurofilament levels in different clinical contexts and assess the implications of neurofilament light chain (NfL) levels in normal ageing and the confounding factors that need to be considered when interpreting NfL measures. In addition, we summarize the current value and potential clinical applications of neurofilaments as a biomarker of neuro-axonal damage in a range of neurological disorders, including multiple sclerosis, Alzheimer disease, frontotemporal dementia, amyotrophic lateral sclerosis, stroke and cerebrovascular disease, traumatic brain injury, and Parkinson disease. We also consider the steps needed to complete the translation of neurofilaments from the laboratory to the management of neurological diseases in clinical practice.

Neurological Deterioration in Wilson's Disease-Types, Etiology, Course, and Management.

Wilson's disease (WD) is an inherited disorder of copper metabolism in which pathological copper accumulation, mainly in the liver and the brain, leads to hepatic and/or neuropsychiatric signs and symptoms. Chelators and zinc salts can successfully induce negative copper balance in many patients; however, neurological deterioration may still be observed. This phenomenon can be divided into: (1) early 'paradoxical' neurological deterioration, which usually develops in the first 6 months of anti-copper treatment and may be commonly related to drug type, or (2) late neurological deterioration, which mostly occurs after 6 months of treatment and is often related either to non-compliance with treatment, overtreatment resulting in copper deficiency, or adverse drug reactions. Another explanation, especially for early neurological deterioration, is natural WD progression, which can be difficult to differentiate from drug-related deterioration, but usually leads to a worse outcome. There is still no consensus on how to define neurological deterioration in WD using scales or biomarkers, how to distinguish it from the natural disease progression, its risk factors, and optimal management. This narrative review, based on the current literature, aims to provide definitions, prevalence, pathological mechanisms and factors related to neurological deterioration, and also proposes schemes for diagnosis and treatment.

Symptom Management in Children Who Are Neurologically Impaired for the Primary Care Medical Home.

Children with neurologic impairment are a growing population of pediatric patients who require care from a large team of physicians to maintain their health. These children often have similar clinical patterns and symptoms that occur because of their neurologic impairment. Families often seek care first from their primary care home to identify and guide initial steps in management. Identifying the symptoms outlined in the 4 cases in this article will help alleviate consequences of delayed care for these patients and provide opportunities for shared decision-making with the family's goals of care for their child. [Pediatr Ann. 2024;53(3):e82-e87.].

Anger regulation in patients with functional neurological disorder: A systematic review.

BACKGROUND: Functional neurological disorder (FND) has been associated with predisposing psychological factors, including dysregulation of anger-related processes. This paper provides a systematic review of the literature on anger regulation in FND. We evaluated anger-related research on patient self-report, observational, and laboratory based measures in FND. The review also addresses adverse childhood experiences and their relation with anger regulation, and the effects of therapies targeting anger regulation in FND. METHODS: MEDLINE, EMBASE, and PsycINFO were searched for both quantitative and qualitative research, published in a peer-reviewed journal with a sample size of at least 5 (registered under Prospero protocol CRD42022314340). RESULTS: A total of 2200 articles were identified. After screening, 54 studies were included in this review (k = 20 questionnaire-based studies, k = 12 laboratory studies, k = 21 using other methods, and k = 1 used both questionnaires and other methods) representing data of 2502 patients with FND. Questionnaire-based studies indicated elevated levels of state anger and trait hostility in patients with FND. Laboratory studies showed a higher tendency to avoid social threat cues, attentional bias towards angry faces, difficulties reliving anger, and preoccupation with frustrating barriers among FND patients versus controls. No specific childhood experiences were identified related to anger regulation in FND, and too few small and uncontrolled studies were available (k = 2) to assess the effects of anger-related interventions in FND. The overall quality of the studies was fair (k = 31) to poor (k = 18). Five studies (k = 5) were rated as having a good quality. CONCLUSIONS: This review suggests that patients with FND have maladaptive anger regulation compared to individuals without FND. The findings also highlight the need for further research on the prevalence and consequences of anger-related processes in the development, diagnosis and treatment of FND.

Disparities in Genetic Testing for Neurologic Disorders.

BACKGROUND AND OBJECTIVES: Genetic testing is now the standard of care for many neurologic conditions. Health care disparities are unfortunately widespread in the US health care system, but disparities in the utilization of genetic testing for neurologic conditions have not been studied. We tested the hypothesis that access to and results of genetic testing vary according to race, ethnicity, sex, socioeconomic status, and insurance status for adults with neurologic conditions. METHODS: We analyzed retrospective data from patients who underwent genetic evaluation and testing through our institution's neurogenetics program. We tested for differences between demographic groups in 3 steps of a genetic evaluation pathway: (1) attending a neurogenetic evaluation, (2) completing genetic testing, and (3) receiving a diagnostic result. We compared patients on this genetic evaluation pathway with the population of all neurology outpatients at our institution, using univariate and multivariable logistic regression analyses. RESULTS: Between 2015 and 2022, a total of 128,440 patients were seen in our outpatient neurology clinics and 2,540 patients underwent genetic evaluation. Black patients were less than half as likely as White patients to be evaluated (odds ratio [OR] 0.49, p < 0.001), and this disparity was similar after controlling for other demographic factors in multivariable analysis. Patients from the least wealthy quartile of zip codes were also less likely to be evaluated (OR 0.67, p < 0.001). Among patients who underwent evaluation, there were no disparities in the likelihood of completing genetic testing, nor in the likelihood of a diagnostic result after adjusting for age. Analyses restricted to specific indications for genetic testing supported these findings. DISCUSSION: We observed unequal utilization of our clinical neurogenetics program for patients from marginalized and minoritized demographic groups, especially Black patients. Among patients who do undergo evaluation, all groups benefit similarly from genetic testing when it is indicated. Understanding and removing barriers to accessing genetic testing will be essential to health care equity and optimal care for all patients with neurologic disorders.

Validation of a Novel Neurologic Assessment Test for Healthcare Staff.

ABSTRACT: BACKGROUND: To measure the effectiveness of an educational intervention, it is essential to develop high-quality, validated tools to assess a change in knowledge or skills after an intervention. An identified gap within the field of neurology is the lack of a universal test to examine knowledge of neurological assessment. METHODS: This instrument development study was designed to determine whether neuroscience knowledge as demonstrated in a Neurologic Assessment Test (NAT) was normally distributed across healthcare professionals who treat patients with neurologic illness. The variables of time, knowledge, accuracy, and confidence were individually explored and analyzed in SAS. RESULTS: The mean (standard deviation) time spent by 135 participants to complete the NAT was 12.9 (3.2) minutes. The mean knowledge score was 39.5 (18.2), mean accuracy was 46.0 (15.7), and mean confidence was 84.4 (24.4). Despite comparatively small standard deviations, Shapiro-Wilk scores indicate that the time spent, knowledge, accuracy, and confidence are nonnormally distributed ( P < .0001). The Cronbach α was 0.7816 considering all 3 measures (knowledge, accuracy, and confidence); this improved to an α of 0.8943 when only knowledge and accuracy were included in the model. The amount of time spent was positively associated with higher accuracy ( r2 = 0.04, P < .05), higher knowledge was positively associated with higher accuracy ( r2 = 0.6543, P < .0001), and higher knowledge was positively associated with higher confidence ( r2 = 0.4348, P < .0001). CONCLUSION: The scores for knowledge, confidence, and accuracy each had a slightly skewed distribution around a point estimate with a standard deviation smaller than the mean. This suggests initial content validity in the NAT. There is adequate initial construct validity to support using the NAT as an outcome measure for projects that measure change in knowledge. Although improvements can be made, the NAT does have adequate construct and content validity for initial use.

Serial electrodiagnostic testing: Utility and indications in adult neurological disorders.

Although existing guidelines address electrodiagnostic (EDX) testing in identifying neuromuscular conditions, guidance regarding the uses and limitations of serial (or repeat) EDX testing is limited. By assessing neurophysiological change longitudinally across time, serial electrodiagnosis can clarify a diagnosis and potentially provide valuable prognostic information. This monograph presents four broad indications for serial electrodiagnosis in adult peripheral neurological disorders. First, where clinical change has raised suspicion for a new or ongoing lesion, EDX reassessment for spatial spread of abnormality, involvement of previously normal muscle or nerve, and/or evolving pathophysiology can clarify a diagnosis. Second, where diagnosis of a progressive neuromuscular condition is uncertain, electrophysiological data from a second time point can confirm or refute suspicion. Third, to establish prognosis after a static nerve injury, a repeat study can assess the presence and extent of reinnervation. Finally, faced with a limited initial study (as when complicated by patient or environmental factors), a repeat EDX study can supplement missing or limited data to provide needed clarity. Repeat EDX studies carry certain limitations, however, such as with prognostication in the setting of remote or chronic lesions, sensory predominant fascicular injury, or mild axonal injury. Nevertheless, serial electrodiagnosis remains a valuable and underused tool in the diagnostic and prognostic evaluation of neuromuscular conditions.

Neurological Diagnosis: Artificial Intelligence Compared With Diagnostic Generator.

OBJECTIVE: Artificial intelligence has recently become available for widespread use in medicine, including the interpretation of digitized information, big data for tracking disease trends and patterns, and clinical diagnosis. Comparative studies and expert opinion support the validity of imaging and data analysis, yet similar validation is lacking in clinical diagnosis. Artificial intelligence programs are here compared with a diagnostic generator program in clinical neurology. METHODS: Using 4 nonrandomly selected case records from New England Journal of Medicine clinicopathologic conferences from 2017 to 2022, 2 artificial intelligence programs (ChatGPT-4 and GLASS AI) were compared with a neurological diagnostic generator program (NeurologicDx.com) for diagnostic capability and accuracy and source authentication. RESULTS: Compared with NeurologicDx.com, the 2 AI programs showed results varying with order of key term entry and with repeat querying. The diagnostic generator yielded more differential diagnostic entities, with correct diagnoses in 4 of 4 test cases versus 0 of 4 for ChatGPT-4 and 1 of 4 for GLASS AI, respectively, and with authentication of diagnostic entities compared with the AI programs. CONCLUSIONS: The diagnostic generator NeurologicDx yielded a more robust and reproducible differential diagnostic list with higher diagnostic accuracy and associated authentication compared with artificial intelligence programs.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

A Delphi consensus to identify the key screening tests/questions for a digital neurological examination for epidemiological research.

BACKGROUND: Most neurological diseases have no curative treatment; therefore, focusing on prevention is key. Continuous research to uncover the protective and risk factors associated with different neurological diseases is crucial to successfully inform prevention strategies. eHealth has been showing promising advantages in healthcare and public health and may therefore be relevant to facilitate epidemiological studies. OBJECTIVE: In this study, we performed a Delphi consensus exercise to identify the key screening tests to inform the development of a digital neurological examination tool for epidemiological research. METHODS: Twelve panellists (six experts in neurological examination, five experts in data collection-two were also experts in the neurological examination, and three experts in participant experience) of different nationalities joined the Delphi exercise. Experts in the neurological examination provided a selection of items that allow ruling out neurological impairment and can be performed by trained health workers. The items were then rated by them and other experts in terms of their feasibility and acceptability. RESULTS: Ten tests and seven anamnestic questions were included in the final set of screening items for the digital neurological examination. Three tests and five anamnestic questions were excluded from the final selection due to their low ratings on feasibility. CONCLUSION: This work identifies the key feasible and acceptable screening tests and anamnestic questions to build an electronic tool for performing the neurological examination, in the absence of a neurologist.