Current Opinion in Neurology最新文献

Purpose of review: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions.

Recent findings: Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation).

Summary: Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Purpose of review: The purpose of this article is to provide a review of neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), with a focus on what renders optic neuritis "atypical" in these two conditions. Clinical features, diagnostic criteria, and epidemiology are outlined. Acute treatments for optic neuritis, as well as immunotherapy for NMOSD and MOGAD are discussed.

Recent findings: Updates in NMOSD and MOGAD are highlighted, with an emphasis on novel work including the new 2023 MOGAD diagnostic criteria, our evolving understanding on the epidemiology of these conditions, and recently FDA-approved NMOSD treatments. Pipeline therapies are also discussed.

Summary: A thorough history and examination, supported by ancillary testing, continues to be the mainstay of optic neuritis diagnosis. Stratifying typical versus atypical optic neuritis is paramount. Within the atypical category, NMOSD and MOGAD are important considerations. Clues can point towards these diagnoses and guide steps for treatment, which is increasingly becoming targeted to individual diseases, as the pathophysiology is different for these disorders.

Purpose of review: Stroke is the second leading cause of death worldwide, and exposure to particulate air pollution is now recognized as one of the major modifiable risk factors. However, air pollution can vary in terms of physicochemical composition and exposition specificities. Therefore, its relationships with stroke outcomes remain under intense investigation.

Recent findings: This review highlights, alongside particles, that short-term and long-term exposure to nitrogen dioxide (NO2) and ozone is likely to be also linked to stroke-related morbidity and mortality. Moreover, air pollution may increase the risk of transitioning from a healthy status to incident stroke and morbimortality after stroke. Additionally, relationships may vary depending on the air pollution mixture (e.g., particle-related components, pollutant interactions), pollutant sources (e.g., traffic-related or not), stroke etiology (ischemic or hemorrhagic), or exposed individual's characteristics (e.g., age, sex, genetic predisposition, weight status). Nonlinear dose-response functions and short-term effect lags have been reported, but these features need further refinement.

Summary: The relationship between stroke and air pollution is now well established. Nonetheless, future research should further consider the physicochemical properties of air pollutants, multiple exposures, and individual vulnerabilities. Moreover, advanced statistical methods should be more commonly used to better describe the relationship shapes.

Purpose of review: The purpose of this article is to discuss the global impact of stroke, the disparities and barriers to implement stroke care, and the global efforts to improve access to acute treatments in low and middle-income countries (LMICs).

Recent findings: Disparities in access to stroke care are influenced by socioeconomic inequalities, geographic disparities, and limited healthcare infrastructure, particularly in LMICs. Effective stroke care requires a coordinated approach involving emergency services, rapid diagnosis, timely treatment, and early rehabilitation. However, there are significant delays in implementing evidence-based practices, particularly in areas where stroke care resources are scarce.Key barriers include geographic disparities, economic constraints, insufficient healthcare infrastructure, low public awareness, and weak policy frameworks. Addressing these challenges requires strengthening health systems, promoting universal health coverage, enhancing public and healthcare provider education, leveraging technology like telemedicine, and fostering international collaboration. Global efforts, including initiatives by the World Stroke Organization, focus on improving stroke care through infrastructure development, workforce training, and policy advocacy.

Summary: These recommended strategies aim to make stroke care accessible and effective for everyone, regardless of location or socioeconomic status, ultimately helping to reduce the global burden of stroke.

Purpose of review: This review intends to systematize the diagnostic and treatment approach to cerebral venous thrombosis (CVT), highlighting key studies that have been recently published.

Recent findings: In light of the recent pandemic, new risk factors for CVT have emerged. Contrast-enhanced MRI and susceptibility-weighted imaging have been shown to offer increased sensitivity for detecting cortical vein thrombosis.Dabigatran seems to be as effective and well tolerated as warfarin for long-term anticoagulation. Partial venous recanalization often occurs in patients treated with anticoagulation only, as early as 8 days after treatment onset. For patients with CVT and impending brain herniation, two-thirds of those who undergo decompressive craniectomy survive, with one-third being functionally independent 6 months after diagnosis.

Summary: CVT is an unusual type of cerebrovascular disease that mostly affects women of fertile age. Risk factors should be identified and addressed. Diagnosis relies on confirmation of venous sinus and/or vein thrombosis, usually by CT venography or MRI. Anticoagulation is the cornerstone of treatment. Despite the lack of high-quality evidence, endovascular treatment is often considered in severe cases. Special populations require tailored approaches. About 80% achieve mRS 0-1, but residual symptoms often affect quality of life and the ability to return to work.

Purpose of review: Alongside motor and cognitive symptoms, amyotrophic lateral sclerosis (ALS) and ALS with frontotemporal dementia (ALSFTD) present with behavioural symptoms, which can be challenging for all affected by the disease. A scoping review of studies published between 2011 and 2024 was conducted to present the breadth of behavioural symptoms in ALS and ALSFTD, explore how they are described and assessed, and identify patterns in the literature.

Findings: This scoping review identified 3939 articles, with 111/3939 meeting eligibility criteria. Most studies were from Australia (23.22%), Italy (16.94%) and the UK (14.29%); 75.67% were cross-sectional. Sample size ranged from 1 to 1013, as case studies were included. Overall mean age (100/111 studies) was 61.32 (SD = 4.15). Proportion of male patients (reported 102/111 studies) was 61.49%; mean disease duration (reported in 86/111 records) was 32.63 months (SD = 24.72). Papers described a broad range of behavioural symptoms (465 examples), which were thematically collated into seven categories: disinhibition (27.74%), apathy (25.16%), perseverative/compulsive behaviours (17.42%), hyperorality (10.53%), loss of sympathy or empathy (8.6%), psychotic symptoms (7.74%), and loss of insight about disease and changes (2.8%). Most studies (78.37%) used validated behavioural assessments that elicited carer's perspectives.

Summary: Despite extensive evidence of behavioural symptoms in ALS, implementation of assessments and management of behavioural symptoms in clinical care remain limited. Clinicians must assess behavioural symptoms, as these can negatively affect disease prognosis, patient treatment engagement and increase family distress. Measures capturing carers' perspectives through interviews are ideal as they can reveal anosognosia, lack of sympathy and lack of empathy.

Purpose of review: Neuroimaging has been instrumental in shaping current understanding of the pathoanatomical signature of amyotrophic lateral sclerosis (ALS) across clinically well defined patient cohorts. The potential utility of imaging as an objective disease marker, however, remains poorly defined.

Recent findings: Increasingly advanced quantitative and computational imaging studies have highlighted emerging clinical applications for neuroimaging as a complementary clinical modality for diagnosis, monitoring, and modelling disease propagation. Multimodal neuroimaging has demonstrated novel approaches for capturing primary motor disease. Extra-motor subcortical dysfunction is increasingly recognized as key modulators of disease propagation.

Summary: The neural signature of cortical and subcortical dysfunction in ALS has been well defined at the population level. Objective metrics of focal primary motor dysfunction are increasingly sensitive and translatable to the individual patient level. Integrity of extra-motor subcortical abnormalities are recognized to represent critical pathways of the ALS disease 'connectome', predicting pathological spread. Neuroimaging plays a pivotal role in capturing upper motor neuron pathology in ALS. Their potential clinical role as objective disease markers for disease classification, longitudinal monitoring, and prognosis in ALS have become increasingly well defined.

Purpose of review: In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field.

Recent findings: The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy.

Summary: Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community.

Purpose of review: There is no diagnostic biomarker that can reliably detect Guillain-Barré syndrome (GBS) or chronic inflammatory demyelinating polyneuropathy (CIDP). Diagnosis relies upon integrating key clinical characteristics and relevant supportive data. Consequently, misdiagnosis and delayed diagnosis are common. Diagnostic criteria have proven valuable resources to improve diagnosis, but are underutilized during routine clinical care.

Recent findings: In 2021, the EAN/PNS CIDP criteria was published, and were followed by the EAN/PNS GBS criteria in 2023. Both guidelines utilized GRADE methodology to formulate evidence-based recommendations that are intended to be used by adult and paediatric clinicians across diverse care settings to optimize diagnostic accuracy and improve patient outcomes during routine clinical care.

Summary: The EAN/PNS GBS and CIDP criteria detail specific clinical, electrophysiological, and laboratory features that raise diagnostic confidence, and call attention to diagnostic mimics. The sensitivity of EAN/PNS and other modern criteria to detect GBS and CIDP is high, but utilization during clinical practice is low. Complexity is one factor limiting widespread application. Strategies are needed to optimize criteria adoption during routine clinical care such that GBS and CIDP diagnosis can be achieved with greater speed and accuracy.