Cyclic Alternating EEG Patterns: From Sleep to Encephalopathy.

SUMMARY: In the 2021 version of the Standardized Critical Care EEG Terminology, the American Clinical Neurophysiology Society introduced new definitions, including for the cyclic alternating pattern of encephalopathy (CAPE). CAPE refers to changes in background EEG activity, with two patterns alternating spontaneously in a regular manner. CAPE shares remarkable similarities with the cyclic alternating pattern, a natural EEG phenomenon occurring in normal non-rapid eye movement sleep, considered the main electrophysiological biomarker of sleep instability. This review explores similarities and differences between cyclic alternating pattern and CAPE and, leveraging the existing expertise on cyclic alternating pattern, aims to extend knowledge on CAPE. A standardized assessment of CAPE features is key to ascertain its prevalence and clinical significance among critically ill patients and to encompass the impact of confounding factors such as anesthetic and sedative agents. Although the preservation of non-rapid eye movement sleep-related elements has a well-known prognostic value in the critical care setting, the clinical importance of cyclic oscillating patterns and the prognostic significance of CAPE remain to be elucidated.

Difficulties in the Diagnostics and Treatment of Hashimoto's Encephalopathy-A Systematic and Critical Review.

Hashimoto's encephalopathy (HE) has been a poorly understood disease. It has been described in all age group, yet, there is no specific HE marker. Additionally, the treatment data in the available studies are frequently divergent and contradictory. Therefore, the aim of our systematic and critical review is to evaluate the diagnosis and treatment of HE in view of the latest findings. The databases browsed comprised PubMed, Scopus, and Google Scholar as well as Cochrane Library, and the search strategy included controlled vocabulary and keywords. A total of 2443 manuscripts were found, published since the beginning of HE research until February 2024. In order to determine validity of the data collected from studies, bias assessment was performed using RoB 2 tool. Ultimately, six studies were included in our study. HE should be considered in the differential diagnosis in patients with psychiatric and neurological symptoms. According to our findings, negative thyroid peroxidase antibodies (anti-TPOs) may represent a valuable parameter in ruling out HE. Nonetheless, this result cannot be used to confirm HE. Furthermore, the proposed anti NH2-terminal-α-enolase (anti-NAE) is non-specific for HE. The effectiveness of glucocorticoid therapy is 60.94%, although relapse occurs in 31.67% of patients following the treatment. Our review emphasizes the significance of conducting further large-scale research and the need to take into account the potential genetic factor.

Clinical characteristics and prognosis of paediatric respiratory syncytial virus-related encephalopathy.

BACKGROUND: To understand the clinical characteristics and prognosis of respiratory syncytial virus (RSV)-related encephalopathy in children. METHODS: A retrospective analysis of the data of children who were diagnosed with RSV-related encephalopathy and admitted to the paediatric intensive care unit (PICU) of Beijing Children's Hospital between November 2016 and November 2023 was performed. RESULTS: Four hundred and sixty-four children with RSV infection were treated in the PICU, and eight of these patients (1.7%) were diagnosed with RSV-related encephalopathy. The mean age of the patients was 24.89 (5.92 ∼ 36.86) months. Two patients had underlying diseases. The time from the onset of illness to impaired consciousness was 3 (1.88-3.75) days. Five patients had convulsions, and three patients had an epileptic status. The serum procalcitonin (PCT) level was 1.63 (0.24, 39.85) ng/ml for the eight patients, and the cerebrospinal fluid (CSF) protein level was 232 (163 ∼ 848) g/L. Among the 8 patients, four patients underwent electroencephalogram (EEG) monitoring or examination. One patient showed continuous low-voltage, nonresponsive activity, and another patient displayed persistent slow waves, the remaining two patients had negative results. One patient had a combination of acute necrotizing encephalopathy (ANE) and acute encephalopathy with biphasic seizures and late reduced diffusion (AESD). Additionally, one patient had ANE, and another had acute brain swelling (ABS). One patient died in the hospital, and the other seven patients were discharged with improvement. Routine follow-up was conducted for 4.58(0.5 ∼ 6.50) years, and all patients fully recovered. CONCLUSIONS: RSV-related encephalopathy could have varying clinical manifestations, and some types, such as ANE and ABS, are dangerous and can lead to death.

Clinical performance of automated machine learning: A systematic review.

Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35-1.00, F1-score 0.16-0.99, area under the precision-recall curve (AUPRC) 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.

The clinical value of amplitude-integrated electroencephalography in a historical cohort with neonatal encephalopathy: A comparison of short-term versus prolonged-period monitoring.

BACKGROUND: To compare the amplitude-integrated electroencephalography (aEEG) monitoring (short-term versus prolonged-period) for neonatal seizure detection and outcome. METHODS: The aEEG monitoring in a historical cohort (n = 88, preterm:42, and term:46) with neonatal encephalopathy between 2010-2022 was re-evaluated for neonatal seizures (electrographic, electro-clinical, and clinical seizures) and EEG background scoring. The cohort was dichotomized: group I (short-period with 6-12 h, n = 36) and group II (prolonged-period with 24-48 h, n = 52). Both monitoring types were evaluated for the diagnostic accuracy of the "patients with seizures" and for outcome characteristics (early death as well as adverse outcomes at 12 months of age). RESULTS: A total of 67 (76 %) neonates of the cohort were diagnosed as "patients with seizures": electrographic-only seizures in 10 (15 %), electro-clinical seizures in 22 (33 %), and clinical-only seizures in 35 (52 %). The aEEG provides the "patients with seizures" in neonates with a 36.5 % rate with both types of monitoring: 17/36 (47.2 %) with short-term and 15/52 (28.8 %) with prolonged-period monitoring. The prolonged period aEEG had higher diagnostic values for seizure detection (sensitivity = 0.73 and negative predictivity value = 0.81). However, the aEEG background scores were similar for both types of aEEG monitoring, respectively (the mean ± SD: 4.73 ± 2.9 versus 4.4 ± 4. p = 0.837). The aEEG scoring was correlated with the magnitude of brain injury documented with MRI, the early death, and the adverse outcome at 12 months of age. CONCLUSIONS: Both aEEG types are valuable for monitoring the "patients with seizures" and outcome characteristics.

Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis.

BACKGROUND AND OBJECTIVE: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future. METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study. RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis. CONCLUSION: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy.

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Use of neurofilament light chain to identify structural brain diseases in dogs.

BACKGROUND: Neurofilament light chain (NfL) is released into the peripheral circulation by damaged axons. OBJECTIVES: To evaluate the diagnostic value of serum NfL concentration in dogs with intracranial diseases. ANIMALS: Study included 37 healthy dogs, 31 dogs with idiopathic epilepsy (IE), 45 dogs with meningoencephalitis of unknown etiology (MUE), 20 dogs with hydrocephalus, and 19 dogs with brain tumors. METHODS: Cohort study. Serum NfL concentrations were measured in all dogs using single-molecule array technology. RESULTS: Serum NfL concentration in dogs with each structural disease was significantly higher than in healthy dogs and dogs with IE (P = .01). The area under the receiver operating characteristic curve of NfL for differentiating between dogs with structural diseases and IE was 0.868. An optimal cutoff value of the NfL 27.10 pg/mL had a sensitivity of 86.67% and a specificity of 74.19% to differentiate the dogs with IE from those with structural brain diseases. There were significant correlations between NfL concentrations and lesion size: (1) MUE, P = .01, r = 0.429; (2) hydrocephalus, P = .01, r = 0.563. CONCLUSIONS AND CLINICAL IMPORTANCE: Serum NfL could be a useful biomarker for distinguishing IE from structural diseases in dogs and predicting the lesion sizes of MUE and hydrocephalus.

Acute Encephalopathy in a Patient with Raoultella Ornithinolytica Infection: A Challenging Presentation.

Raoultella ornithinolytica is a rare, gram-negative environmental enterobacterium. Although infections in humans caused by R. ornithinolytica are uncommon, there are increasing reports implicating it in urinary tract infections, hepatobiliary infections, and bacteremia, designating it as an emerging pathogen. Its habitat is primarily in aquatic environments and soil, with seafood frequently identified as a potential source of infection. While these infections have predominantly been described in immunocompromised patients previously, our case suggests that advanced age may be a significant risk factor. We describe a case of a 73-year-old man presenting with encephalopathy who then was found to have R. ornithinolytica bacteremia from a genitourinary source. Following antibiotic treatment, the infection resolved and the neurologic symptoms improved. To the best of our knowledge, this is the first documented case in the medical literature of R. ornithinolytica featuring a primary neurologic presentation.

Fluorescent Probes for Disease Diagnosis.

The identification and detection of disease-related biomarkers is essential for early clinical diagnosis, evaluating disease progression, and for the development of therapeutics. Possessing the advantages of high sensitivity and selectivity, fluorescent probes have become effective tools for monitoring disease-related active molecules at the cellular level and in vivo. In this review, we describe current fluorescent probes designed for the detection and quantification of key bioactive molecules associated with common diseases, such as organ damage, inflammation, cancers, cardiovascular diseases, and brain disorders. We emphasize the strategies behind the design of fluorescent probes capable of disease biomarker detection and diagnosis and cover some aspects of combined diagnostic/therapeutic strategies based on regulating disease-related molecules. This review concludes with a discussion of the challenges and outlook for fluorescent probes, highlighting future avenues of research that should enable these probes to achieve accurate detection and identification of disease-related biomarkers for biomedical research and clinical applications.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

Identification of neurological complications in childhood influenza: a random forest model.

BACKGROUND: Among the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1-2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE). OBJECTIVE: To develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza. METHODS: This retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model. RESULTS: Of the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions. CONCLUSION: This model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza.

Transplantation-Associated Altered Mentation and Encephalopathy: A New Classification for Acute Neurocognitive Changes Associated with Hematopoietic Cell Transplantation from the ASTCT Committee on Practice Guidelines.

Acute encephalopathy, manifesting clinically as delirium, is a common but often unrecognized complication of hematopoietic cell transplantation (HCT). Delirium can occur in patients of any age and is observed after autologous or allogeneic HCT. Although delirium has been studied primarily during initial HCT hospitalizations in recipients of myeloablative conditioning, recent investigations have identified delirium later post-transplantation and in recipients of reduced-intensity conditioning. Acute encephalopathy can be driven by infectious complications, medications, tissue damage, and/or organ dysfunction. Altered consciousness, either mild or profound, is often its only clinical manifestation. Identifying delirium is essential to overall HCT care, because patients who experience delirium have longer hospitalization and recovery times and are at risk for other poor post-HCT outcomes. Given the critical nature of this common complication and the ongoing expansion of HCT for more vulnerable populations, the American Society of Transplantation and Cellular Therapy (ASTCT) recommends intensifying research into post-HCT cognitive changes and establishing standardized definitions that encompass the full spectrum of altered consciousness for clinical care purposes and to provide benchmark endpoints for future research studies. To capture a range of acute neurocognitive changes specifically found in HCT patients (often referred to as acute encephalopathy), the ASTCT proposes a new diagnosis, transplantation-associated altered mentation and encephalopathy (TAME). The TAME diagnosis includes HCT patients who meet Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for delirium and those with acute neurocognitive changes who do not meet all the DSM-5 criteria for delirium (subsyndromal delirium). Early TAME is defined as occurring during conditioning or ≤100 days post-HCT, whereas late TAME occurs >100 days post-HCT in patients with additional HCT-related complications. This manuscript establishes clear diagnostic criteria and discusses factors that can potentially impact the development of TAME, as well as the workup and management of TAME.

Congenital infectious encephalopathies from the intrapartum period to postnatal life.

Congenital infections are a common but often underrecognized cause of fetal brain abnormalities, as well as fetal-neonatal morbidity and mortality, that should be considered by all healthcare professionals providing neurological care to fetuses and newborns. Maternal infection with various pathogens (cytomegalovirus, Toxoplasmosis, Rubella virus, Parvovirus B19, lymphocytic choriomeningitis virus, syphilis, Zika virus, varicella zoster virus) during pregnancy can be transmitted to the developing fetus, which can cause multisystem dysfunction and destructive or malformative central nervous system lesions. These can be recognized on fetal and neonatal imaging, including ultrasound and MRI. Imaging and clinical features often overlap, but some distinguishing features can help identify specific pathogens and guide subsequent testing strategies. Some pathogens can be specifically treated, and others can be managed with targeted interventions or symptomatic therapy based on expected complications. Neurological and neurodevelopmental complications related to congenital infections vary widely and are likely driven by a combination of pathophysiologic factors, alone or in combination. These include direct invasion of the fetal central nervous system by pathogens, inflammation of the maternal-placental-fetal triad in response to infection, and long-term effects of immunogenic and epigenetic changes in the fetus in response to maternal-fetal infection. Congenital infections and their neurodevelopmental impacts should be seen as an issue of public health policy, given that infection and the associated complications disproportionately affect woman and children from low- and middle-income countries and those with lower socio-economic status in high-income countries. Congenital infections may be preventable and treatable, which can improve long-term neurodevelopmental outcomes in children.

Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Uraemic brainstem encephalopathy mimicking ocular myasthenia: a case report.

BACKGROUND: Uraemia causes a generalised encephalopathy as its most common neurological complication. Isolated brainstem uraemic encephalopathy is rare. We report a case of fatigable ptosis and complex ophthalmoplegia in brainstem uraemic encephalopathy. CASE PRESENTATION: A 22-year-old Sri Lankan man with end stage renal failure presented with acute onset diplopia and drooping of eyelids progressively worsening over one week. The patient had not complied with the prescribed renal replacement therapy which was planned to be initiated 5 months previously. On examination, his Glasgow coma scale score was 15/15, He had a fatigable asymmetrical bilateral ptosis. The ice-pack test was negative. There was a complex ophthalmoplegia with bilateral abduction failure and elevation failure of the right eye. The diplopia did not worsen with prolonged stare. The rest of the neurological examination was normal. Serum creatinine on admission was 21.81 mg/dl. The repetitive nerve stimulation did not show a decremental pattern. Magnetic resonance imaging (MRI) of the brain demonstrated diffuse midbrain and pontine oedema with T2 weighted/FLAIR hyperintensities. The patient was haemodialyzed on alternate days and his neurological deficits completely resolved by the end of the second week of dialysis. The follow up brain MRI done two weeks later demonstrated marked improvement of the brainstem oedema with residual T2 weighted/FLAIR hyperintensities in the midbrain. CONCLUSIONS: Uraemia may rarely cause an isolated brainstem encephalopathy mimicking ocular myasthenia, which resolves with correction of the uraemia.