Severe Pediatric Neurological Manifestations With SARS-CoV-2 or MIS-C Hospitalization and New Morbidity.

Importance: Neurological manifestations during acute SARS-CoV-2-related multisystem inflammatory syndrome in children (MIS-C) are common in hospitalized patients younger than 18 years and may increase risk of new neurocognitive or functional morbidity. Objective: To assess the association of severe neurological manifestations during a SARS-CoV-2-related hospital admission with new neurocognitive or functional morbidities at discharge. Design, Setting, and Participants: This prospective cohort study from 46 centers in 10 countries included patients younger than 18 years who were hospitalized for acute SARS-CoV-2 or MIS-C between January 2, 2020, and July 31, 2021. Exposure: Severe neurological manifestations, which included acute encephalopathy, seizures or status epilepticus, meningitis or encephalitis, sympathetic storming or dysautonomia, cardiac arrest, coma, delirium, and stroke. Main Outcomes and Measures: The primary outcome was new neurocognitive (based on the Pediatric Cerebral Performance Category scale) and/or functional (based on the Functional Status Scale) morbidity at hospital discharge. Multivariable logistic regression analyses were performed to examine the association of severe neurological manifestations with new morbidity in each SARS-CoV-2-related condition. Results: Overall, 3568 patients younger than 18 years (median age, 8 years [IQR, 1-14 years]; 54.3% male) were included in this study. Most (2980 [83.5%]) had acute SARS-CoV-2; the remainder (588 [16.5%]) had MIS-C. Among the patients with acute SARS-CoV-2, 536 (18.0%) had a severe neurological manifestation during hospitalization, as did 146 patients with MIS-C (24.8%). Among survivors with acute SARS-CoV-2, those with severe neurological manifestations were more likely to have new neurocognitive or functional morbidity at hospital discharge compared with those without severe neurological manifestations (27.7% [n = 142] vs 14.6% [n = 356]; P < .001). For survivors with MIS-C, 28.0% (n = 39) with severe neurological manifestations had new neurocognitive and/or functional morbidity at hospital discharge compared with 15.5% (n = 68) of those without severe neurological manifestations (P = .002). When adjusting for risk factors in those with severe neurological manifestations, both patients with acute SARS-CoV-2 (odds ratio, 1.85 [95% CI, 1.27-2.70]; P = .001) and those with MIS-C (odds ratio, 2.18 [95% CI, 1.22-3.89]; P = .009) had higher odds of having new neurocognitive and/or functional morbidity at hospital discharge. Conclusions and Relevance: The results of this study suggest that children and adolescents with acute SARS-CoV-2 or MIS-C and severe neurological manifestations may be at high risk for long-term impairment and may benefit from screening and early intervention to assist recovery.

Myeloid cell activation during Zika virus encephalitis predicts recovery of functional cortical connectivity.

Neurologic complications of Zika virus (ZIKV) infection across the lifespan have been described during outbreaks in Southeast Asia, South America, and Central America since 2016. In the adult CNS ZIKV tropism for neurons is tightly linked to its effects, with neuronal loss within the hippocampus during acute infection and protracted synapse loss during recovery, which is associated with cognitive deficits. The effects of ZIKV on cortical networks have not been evaluated. Although animal behavior assays have been used previously to model cognitive impairment, in vivo brain imaging can provide orthogonal information regarding the health of brain networks in real time, providing a tool to translate findings in animal models to humans. In this study, we use widefield optical imaging to measure cortical functional connectivity (FC) in mice during acute infection with, and recovery from, intracranial infection with a mouse-adapted strain of ZIKV. Acute ZIKV infection leads to high levels of myeloid cell activation, with loss of neurons and presynaptic termini in the cerebral cortex and associated loss of FC primarily within the somatosensory cortex. During recovery, neuron numbers, synapses and FC recover to levels near those of healthy mice. However, hippocampal injury and impaired spatial cognition persist. The magnitude of activated myeloid cells during acute infection predicted both recovery of synapses and the degree of FC recovery after recovery from ZIKV infection. These findings suggest that a robust inflammatory response may contribute to the health of functional brain networks after recovery from infection.

International Prevalence and Mechanisms of SARS-CoV-2 in Childhood Arterial Ischemic Stroke During the COVID-19 Pandemic.

BACKGROUND: Data from the early pandemic revealed that 0.62% of children hospitalized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had an acute arterial ischemic stroke (AIS). In a larger cohort from June 2020 to December 2020, we sought to determine whether our initial point estimate was stable as the pandemic continued and to understand radiographic and laboratory data that may clarify mechanisms of pediatric AIS in the setting of SARS-CoV-2. METHODS: We surveyed international sites with pediatric stroke expertise to determine numbers of hospitalized SARS-CoV-2 patients <18 years, numbers of incident AIS cases among children (29 days to <18 years), frequency of SARS-CoV-2 testing for children with AIS, and numbers of childhood AIS cases positive for SARS-CoV-2 June 1 to December 31, 2020. Two stroke neurologists with 1 neuroradiologist determined whether SARS-CoV-2 was the main stroke risk factor, contributory, or incidental. RESULTS: Sixty-one centers from 21 countries provided AIS data. Forty-eight centers (78.7%) provided SARS-CoV-2 hospitalization data. SARS-CoV-2 testing was performed in 335/373 acute AIS cases (89.8%) compared with 99/166 (59.6%) in March to May 2020, P<0.0001. Twenty-three of 335 AIS cases tested (6.9%) were positive for SARS-CoV-2 compared with 6/99 tested (6.1%) in March to May 2020, P=0.78. Of the 22 of 23 AIS cases with SARS-CoV-2 in whom we could collect additional data, SARS-CoV-2 was the main stroke risk factor in 6 (3 with arteritis/vasculitis, 3 with focal cerebral arteriopathy), a contributory factor in 13, and incidental in 3. Elevated inflammatory markers were common, occurring in 17 (77.3%). From centers with SARS-CoV-2 hospitalization data, of 7231 pediatric patients hospitalized with SARS-CoV-2, 23 had AIS (0.32%) compared with 6/971 (0.62%) from March to May 2020, P=0.14. CONCLUSIONS: The risk of AIS among children hospitalized with SARS-CoV-2 appeared stable compared with our earlier estimate. Among children in whom SARS-CoV-2 was considered the main stroke risk factor, inflammatory arteriopathies were the stroke mechanism.

Prevalence and Risk Factors of Neurologic Manifestations in Hospitalized Children Diagnosed with Acute SARS-CoV-2 or MIS-C.

BACKGROUND: Our objective was to characterize the frequency, early impact, and risk factors for neurological manifestations in hospitalized children with acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or multisystem inflammatory syndrome in children (MIS-C). METHODS: Multicenter, cross-sectional study of neurological manifestations in children aged <18 years hospitalized with positive SARS-CoV-2 test or clinical diagnosis of a SARS-CoV-2-related condition between January 2020 and April 2021. Multivariable logistic regression to identify risk factors for neurological manifestations was performed. RESULTS: Of 1493 children, 1278 (86%) were diagnosed with acute SARS-CoV-2 and 215 (14%) with MIS-C. Overall, 44% of the cohort (40% acute SARS-CoV-2 and 66% MIS-C) had at least one neurological manifestation. The most common neurological findings in children with acute SARS-CoV-2 and MIS-C diagnosis were headache (16% and 47%) and acute encephalopathy (15% and 22%), both P < 0.05. Children with neurological manifestations were more likely to require intensive care unit (ICU) care (51% vs 22%), P < 0.001. In multivariable logistic regression, children with neurological manifestations were older (odds ratio [OR] 1.1 and 95% confidence interval [CI] 1.07 to 1.13) and more likely to have MIS-C versus acute SARS-CoV-2 (OR 2.16, 95% CI 1.45 to 3.24), pre-existing neurological and metabolic conditions (OR 3.48, 95% CI 2.37 to 5.15; and OR 1.65, 95% CI 1.04 to 2.66, respectively), and pharyngeal (OR 1.74, 95% CI 1.16 to 2.64) or abdominal pain (OR 1.43, 95% CI 1.03 to 2.00); all P < 0.05. CONCLUSIONS: In this multicenter study, 44% of children hospitalized with SARS-CoV-2-related conditions experienced neurological manifestations, which were associated with ICU admission and pre-existing neurological condition. Posthospital assessment for, and support of, functional impairment and neuroprotective strategies are vitally needed.

Neuroinflammation During RNA Viral Infections.

Neurotropic RNA viruses continue to emerge and are increasingly linked to diseases of the central nervous system (CNS) despite viral clearance. Indeed, the overall mortality of viral encephalitis in immunocompetent individuals is low, suggesting efficient mechanisms of virologic control within the CNS. Both immune and neural cells participate in this process, which requires extensive innate immune signaling between resident and infiltrating cells, including microglia and monocytes, that regulate the effector functions of antiviral T and B cells as they gain access to CNS compartments. While these interactions promote viral clearance via mainly neuroprotective mechanisms, they may also promote neuropathology and, in some cases, induce persistent alterations in CNS physiology and function that manifest as neurologic and psychiatric diseases. This review discusses mechanisms of RNA virus clearance and neurotoxicity during viral encephalitis with a focus on the cytokines essential for immune and neural cell inflammatory responses and interactions. Understanding neuroimmune communications in the setting of viral infections is essential for the development of treatments that augment neuroprotective processes while limiting ongoing immunopathological processes that cause ongoing CNS disease.

Viruses have multiple paths to central nervous system pathology.

PURPOSE OF REVIEW: Although viral infections of the central nervous system (CNS) are known to acutely cause pathology in the form of cytokine-mediated neural tissue damage and inflammation, the pathophysiology of neurologic sequelae after viral clearance is incompletely understood. RECENT FINDINGS: Alterations in microglial and glial biology in response to initial infiltration of immune cells that persist within the CNS have recently been shown to promote neuronal dysfunction and cognitive deficits in animal models of viral encephalitis. SUMMARY: The current review summarizes the current knowledge on the possible role of innate immune signaling during acute infections as triggers of neurologic sequelae that persist, and may even worsen, after clearance of viral infections within the CNS.

Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.

PURPOSE: To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images. MATERIALS AND METHODS: This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers. RESULTS: For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma. CONCLUSION: Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women.

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

PURPOSE: Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. METHODS: In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. RESULTS: On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05; p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). CONCLUSIONS: In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so that every voxel is characterized by the dominant eigenvectors, capturing the global and local time-intensity curve similarities in the data. This PrEIm allows for the calculation of strong tensor gradients and better region statistics than the original image intensities or alternative image representations such as PCA and FCM. The PrEIm also allows for building a more accurate hybrid AC scheme.

Seizures as a presenting symptom of acute arterial ischemic stroke in childhood.

OBJECTIVES: To define the incidence of seizures as a presenting symptom of acute arterial ischemic stroke (AIS) in children and to determine whether younger age, infarct location, or AIS etiology were risk factors for seizure at AIS presentation. STUDY DESIGN: Children aged 2 months to 18 years presenting with AIS between January 2005 and December 2008 were identified from a single center prospective pediatric stroke registry. Clinical data were abstracted, and a neuroradiologist reviewed imaging studies. RESULTS: Among the 60 children who met our inclusion criteria, 13 experienced seizure at stroke presentation (22%). Median age was significantly younger in children who presented with seizures than in those who did not (1.1 years vs 10 years; P = .0009). Seizures were accompanied by hemiparesis in all patients. Three of 4 children with clinically overt seizures at presentation also had nonconvulsive seizures on continuous electroencephalography monitoring. CONCLUSIONS: Twenty-two percent of children with acute AIS present with seizures. Seizures were always accompanied by focal neurologic deficits. Younger age was a risk factor for seizures at presentation. Seizure at presentation was not associated with infarct location or etiology. Nonconvulsive seizures may occur during the acute period.

Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data.

Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing and applying computerized image analysis and multi-modal data fusion algorithms to digitized patient data (e.g. imaging, tissue, genomic) for helping physicians predict disease outcome and patient survival. While a number of data channels, ranging from the macro (e.g. MRI) to the nano-scales (proteins, genes) are now being routinely acquired for disease characterization, one of the challenges in predicting patient outcome and treatment response has been in our inability to quantitatively fuse these disparate, heterogeneous data sources. At the Laboratory for Computational Imaging and Bioinformatics (LCIB)(1) at Rutgers University, our team has been developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome from multiple modalities including MRI, digital pathology, and protein expression. Additionally, we have been developing novel data fusion algorithms based on non-linear dimensionality reduction methods (such as Graph Embedding) to quantitatively integrate information from multiple data sources and modalities with the overarching goal of optimizing meta-classifiers for making prognostic predictions. In this paper, we briefly describe 4 representative and ongoing CAP projects at LCIB. These projects include (1) an Image-based Risk Score (IbRiS) algorithm for predicting outcome of Estrogen receptor positive breast cancer patients based on quantitative image analysis of digitized breast cancer biopsy specimens alone, (2) segmenting and determining extent of lymphocytic infiltration (identified as a possible prognostic marker for outcome in human epidermal growth factor amplified breast cancers) from digitized histopathology, (3) distinguishing patients with different Gleason grades of prostate cancer (grade being known to be correlated to outcome) from digitized needle biopsy specimens, and (4) integrating protein expression measurements obtained from mass spectrometry with quantitative image features derived from digitized histopathology for distinguishing between prostate cancer patients at low and high risk of disease recurrence following radical prostatectomy.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology.

The identification of phenotypic changes in breast cancer (BC) histopathology on account of corresponding molecular changes is of significant clinical importance in predicting disease outcome. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with nodal metastasis and distant recurrence in HER2+ BC patients. In this paper, we present a computer-aided diagnosis (CADx) scheme to automatically detect and grade the extent of LI in digitized HER2+ BC histopathology. Lymphocytes are first automatically detected by a combination of region growing and Markov random field algorithms. Using the centers of individual detected lymphocytes as vertices, three graphs (Voronoi diagram, Delaunay triangulation, and minimum spanning tree) are constructed and a total of 50 image-derived features describing the arrangement of the lymphocytes are extracted from each sample. A nonlinear dimensionality reduction scheme, graph embedding (GE), is then used to project the high-dimensional feature vector into a reduced 3-D embedding space. A support vector machine classifier is used to discriminate samples with high and low LI in the reduced dimensional embedding space. A total of 41 HER2+ hematoxylin-and-eosin-stained images obtained from 12 patients were considered in this study. For more than 100 three-fold cross-validation trials, the architectural feature set successfully distinguished samples of high and low LI levels with a classification accuracy greater than 90%. The popular unsupervised Varma-Zisserman texton-based classification scheme was used for comparison and yielded a classification accuracy of only 60%. Additionally, the projection of the 50 image-derived features for all 41 tissue samples into a reduced dimensional space via GE allowed for the visualization of a smooth manifold that revealed a continuum between low, intermediate, and high levels of LI. Since it is known that extent of LI in BC biopsy specimens is a prognostic indicator, our CADx scheme will potentially help clinicians determine disease outcome and allow them to make better therapy recommendations for patients with HER2+ BC.

Arterial spin labeling perfusion MRI in pediatric arterial ischemic stroke: initial experiences.

PURPOSE: To investigate the feasibility and utility of arterial spin labeling (ASL) perfusion MRI for characterizing alterations of cerebral blood flow (CBF) in pediatric patients with arterial ischemic stroke (AIS). MATERIALS AND METHODS: Ten children with AIS were studied within 4 to 125 hours following symptom onset, using a pulsed ASL (PASL) protocol attached to clinically indicated MR examinations. The interhemisphere perfusion deficit (IHPD) was measured in predetermined vascular territories and infarct regions of restricted diffusion, which were compared with the degree of arterial stenosis and volumes of ischemic infarcts. RESULTS: Interpretable CBF maps were obtained in all 10 patients, showing simple lesion in nine patients (five hypoperfusion, two hyperperfusion, and two normal perfusion) and complex lesions in one patient. Both acute and follow-up infarct volumes were significantly larger in cases with hypoperfusion than in either hyper- or normal perfusion cases. The IHPD was found to correlate with the degree of stenosis, diffusion lesion, and follow-up T(2) infarct volumes. Mismatch between perfusion and diffusion lesions was observed. Brain regions presenting delayed arterial transit effects were tentatively associated with positive outcome. CONCLUSION: This study demonstrates the clinical utility of ASL in the neuroimaging diagnosis of pediatric AIS.