Longitudinal Assessment of Enhancing Foci of Abnormal Signal Intensity in Neurofibromatosis Type 1.

BACKGROUND AND PURPOSE: Patients with neurofibromatosis 1 are at increased risk of developing brain tumors, and differentiation from contrast-enhancing foci of abnormal signal intensity can be challenging. We aimed to longitudinally characterize rare, enhancing foci of abnormal signal intensity based on location and demographics. MATERIALS AND METHODS: A total of 109 MR imaging datasets from 19 consecutive patients (7 male; mean age, 8.6 years; range, 2.3-16.8 years) with neurofibromatosis 1 and a total of 23 contrast-enhancing parenchymal lesions initially classified as foci of abnormal signal intensity were included. The mean follow-up period was 6.5 years (range, 1-13.8 years). Enhancing foci of abnormal signal intensity were followed up with respect to presence, location, and volume. Linear regression analysis was performed. RESULTS: Location, mean peak volume, and decrease in enhancing volume over time of the 23 lesions were as follows: 10 splenium of the corpus callosum (295 mm3, 5 decreasing, 3 completely resolving, 2 surgical intervention for change in imaging appearance later confirmed to be gangliocytoma and astrocytoma WHO II), 1 body of the corpus callosum (44 mm3, decreasing), 2 frontal lobe white matter (32 mm3, 1 completely resolving), 3 globus pallidus (50 mm3, all completely resolving), 6 cerebellum (206 mm3, 3 decreasing, 1 completely resolving), and 1 midbrain (34 mm3). On average, splenium lesions began to decrease in size at 12.2 years, posterior fossa lesions at 17.1 years, and other locations at 9.4 years of age. CONCLUSIONS: Albeit very rare, contrast-enhancing lesions in patients with neurofibromatosis 1 may regress over time. Follow-up MR imaging aids in ascertaining regression. The development of atypical features should prompt further evaluation for underlying tumors.

Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors.

BACKGROUND AND PURPOSE: B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS: In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS: The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS: Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification. MATERIALS AND METHODS: The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists. RESULTS: Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F1 score were higher than those of 2 of the 4 radiologists. CONCLUSIONS: We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.

MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma.

BACKGROUND AND PURPOSE: Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS: In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS: Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS: This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.

MRI Characteristics of Primary Tumors and Metastatic Lesions in Molecular Subgroups of Pediatric Medulloblastoma: A Single-Center Study.

BACKGROUND AND PURPOSE: Molecular grouping of medulloblastoma correlates with prognosis and supports the therapeutic strategy. We provide our experience with the imaging features of primary and metastatic disease in relation to the molecular groups. MATERIALS AND METHODS: One hundred nineteen consecutive patients (mean age, 7.3 ± 3.8 years at diagnosis; male, 79 [66.4%]) with a confirmed diagnosis of medulloblastoma and interpretable pretreatment MRIs were retrieved from our data base from January 2000 to December 2016. Each patient was assigned to wingless, sonic hedgehog, group 3, or group 4 molecular groups. Then, we determined the imaging features of both primary and metastatic/recurrent disease predictive of molecular groups. RESULTS: In addition to recently reported predictors based on primary tumor, including cerebellar peripheral location for sonic hedgehog (adjusted odds ratio = 9, P < .0001), minimal enhancement of primary group 4 tumor (adjusted odds ratio = 5.2, P < .0001), and cerebellopontine angle location for wingless (adjusted odds ratio = 1.4, P = .03), ependymal metastasis with diffusion restriction and minimal postcontrast enhancement ("mismatching pattern") (adjusted odds ratio = 2.8, P = .001) for group 4 and spinal metastasis for group 3 (adjusted odds ratio = 1.9, P = .01) also emerged as independent predictors of medulloblastoma molecular groups. Specifically, the presence of a metastasis in the third ventricular infundibular recess showing a mismatching pattern was significantly associated with group 4 (P = .02). CONCLUSIONS: In addition to imaging features of primary tumors, some imaging patterns of metastatic dissemination in medulloblastoma seem characteristic, perhaps even specific to certain groups. This finding could further help in differentiating molecular groups, specifically groups 3 and 4, when the characteristics of the primary tumor overlap.

Endoplasmic reticulum stress induces ligand-independent TNFR1-mediated necroptosis in L929 cells.

Endoplasmic reticulum (ER) stress-induced cellular dysfunction and death is associated with several human diseases. It has been widely reported that ER stress kills through activation of the intrinsic mitochondrial apoptotic pathway. Here we demonstrate that ER stress can also induce necroptosis, an receptor-interacting protein kinase 1 (RIPK1)/RIPK3/mixed lineage kinase domain-like protein (MLKL)-dependent form of necrosis. Remarkably, we observed that necroptosis induced by various ER stressors in L929 cells is dependent on tumor necrosis factor receptor 1 (TNFR1), but occurs independently of autocrine TNF or lymphotoxin α production. Moreover, we found that repression of either TNFR1, RIPK1 or MLKL did not protect the cells from death but instead allowed a switch to ER stress-induced apoptosis. Interestingly, while caspase inhibition was sufficient to protect TNFR1- or MLKL-deficient cells from death, rescue of the RIPK1-deficient cells additionally required RIPK3 depletion, indicating a switch back to RIPK3-dependent necroptosis in caspase-inhibited conditions. The finding that ER stress also induces necroptosis may open new therapeutic opportunities for the treatment of pathologies resulting from unresolved ER stress.

No evidence for impairment of venous hemodynamics in children or young adults with pediatric-onset multiple sclerosis.

BACKGROUND AND PURPOSE: Chronic cerebrospinal venous insufficiency is a postulated etiologic factor for multiple sclerosis, but the higher frequency with longer disease duration and progressive disability suggests that chronic cerebrospinal venous insufficiency is secondary to chronic disease. We evaluated the presence of chronic cerebrospinal venous insufficiency in pediatric-onset MS. MATERIALS AND METHODS: Twenty-six pediatric patients with MS (18 years of age or younger), 26 age-matched healthy controls, and 13 young adults with pediatric-onset MS underwent sonography of the internal jugular, vertebral, and deep cerebral veins. Five venous hemodynamic criteria were assessed, with 2 criteria required for chronic cerebrospinal venous insufficiency. MR imaging studies, performed in the pediatric patients with MS and healthy control groups, included intracranial 2D time-of-flight MR venography and velocity-sensitive phase-contrast sequences. Contrast-enhanced brain MR images were obtained in pediatric patients with MS to further evaluate venous patency. We used paired t tests, Wilcoxon matched pairs, McNemar tests, and exact conditional logistic regression to estimate the association of chronic cerebrospinal venous insufficiency with MS. RESULTS: Fifty participants (73.5%) had normal ultrasound findings, 15 (23.1%) met 1 venous hemodynamic criterion, and 2 pediatric patients with MS and 1 young adult with pediatric-onset MS met chronic cerebrospinal venous insufficiency criteria. Chronic cerebrospinal venous insufficiency was not associated with MS (odds ratio, 2.41; 95% CI, 0.19-infinity). Demographic and disease characteristics did not differ between the patients with MS meeting chronic cerebrospinal venous insufficiency criteria (n = 3) and those who did not (n = 36; all, P > .05). The mean (SD) MR imaging measures of intracerebral flow did not differ between the 2 pediatric patients with MS meeting chronic cerebrospinal venous insufficiency criteria (0.85 ± 0.11) and healthy controls (0.87 ± 0.16, P = .50); no child demonstrated venous obstruction. CONCLUSIONS: Chronic cerebrospinal venous insufficiency is rarely observed in children or young adults with pediatric-onset MS. Venous anatomy and flow rates indicate that venous outflow is intact in pediatric patients with MS. Our findings argue against chronic cerebrospinal venous insufficiency as a component of MS etiology.

Development of a standardized MRI scoring tool for CNS demyelination in children.

BACKGROUND AND PURPOSE: The degree to which MR imaging is useful in the diagnosis of MS is predicated on standardized and reliable evaluation of MR imaging parameters. We aimed to devise items for an MR imaging scoring tool that would have high inter-rater agreement and would be straightforward to apply. MATERIALS AND METHODS: On the basis of a literature search and consensus of an expert panel, we identified 48 parameters that describe acute CNS demyelination, predict MS diagnosis, or characterize demyelinating disorder mimics. MR images of children with clinically confirmed MS, monophasic ADEM, and angiography-negative biopsy-positive small-vessel primary angiitis of the CNS were scored by 2 neuroradiologists independently, using the preliminary 48-parameter tool. Parameters with Cohen κ ≥ 0.6 and deemed important in predicting diagnosis were retained. Parameters not visualized on routine clinical imaging or not important in differentiating MS, ADEM, and SV-cPACNS were discarded. RESULTS: Of 65 eligible patients, 55 children were enrolled (16 with monophasic ADEM, 27 with MS, 12 with SV-cPACNS); 10 were excluded (6 had hard-copy films, 4 did not meet MR imaging quality requirements). Of the 48 parameters, 16 were retained in the final scoring tool. The remaining 28 parameters were discarded: 4 had κ < 0.6 and were not deemed useful in predicting diagnosis; 9 were not visible on routinely acquired clinical images; and 15 had inter-rater agreement ≥0.6 but were not useful in differentiating monophasic ADEM, MS, and SV-cPACNS. CONCLUSIONS: We propose a 16-parameter MR imaging scoring tool that is straightforward to apply in the clinical setting and demonstrates high inter-rater agreement.

Comparison of Langevin and Markov channel noise models for neuronal signal generation.

The stochastic opening and closing of voltage-gated ion channels produce noise in neurons. The effect of this noise on the neuronal performance has been modeled using either an approximate or Langevin model based on stochastic differential equations or an exact model based on a Markov process model of channel gating. Yet whether the Langevin model accurately reproduces the channel noise produced by the Markov model remains unclear. Here we present a comparison between Langevin and Markov models of channel noise in neurons using single compartment Hodgkin-Huxley models containing either Na+ and K+, or only K+ voltage-gated ion channels. The performance of the Langevin and Markov models was quantified over a range of stimulus statistics, membrane areas, and channel numbers. We find that in comparison to the Markov model, the Langevin model underestimates the noise contributed by voltage-gated ion channels, overestimating information rates for both spiking and nonspiking membranes. Even with increasing numbers of channels, the difference between the two models persists. This suggests that the Langevin model may not be suitable for accurately simulating channel noise in neurons, even in simulations with large numbers of ion channels.

2D time-of-flight MR venography in neonates: anatomy and pitfalls.

BACKGROUND AND PURPOSE: The dural venous sinuses in neonates differ from those in adults or older children in that the caliber of venous sinuses is smaller and there is skull molding. The aim of this retrospective study is to evaluate the presence of flow gaps in venous sinuses in neonates on 2D time-of-flight (TOF) MR venography (MRV). METHODS: Fifty-one neonates underwent coronal 2D TOF MRV. Nine also had CT venography (CTV) for comparison. In 1 neonate, a further 2D TOF MRV was performed in the sagittal plane; in another neonate, images were captured in the axial plane; and in another, a further coronal TOF MRV with shorter echo time was performed. RESULTS: Flow gap was seen in the posterior aspect of the superior sagittal sinus in 35 of 51 (69%). Focal narrowing of the superior sagittal sinus, in the region of convergence of lambdoid sutures, was detected in 7 of 51 (14%). The right and left transverse sinuses demonstrated flow gap in 13 of 51 (25%) and 32 of 51 (63%) respectively. There was normal filling of contrast on CTV in the superior sagittal sinus, transverse sinus and sigmoid sinus in those cases with flow gap detected on coronal 2D TOF MRV. Right, left, and codominance of the transverse sinuses are as follows: 32 of 51 (63%), 5 of 51 (10%), and 14 of 51 (27%), respectively. The right and left sigmoid sinuses demonstrated flow gap in 7 of 51 (14%) and 8 of 51 (16%), respectively, and the left sigmoid sinus was absent in 1 of 51 (2%). CONCLUSION: The high proportion of flow gap in the venous sinuses of neonates, particularly of the superior sagittal sinus, could be attributed to the smaller caliber venous sinuses, slower venous flow, and skull molding.

Prenatal diagnosis of unusual fetal pial arteriovenous malformation. A case report.

SUMMARY: Cerebral arteriovenous malformations (CAVMs) are rarely diagnosed in utero. Most prenatal imaging of intracranial vascular malformations relates to Vein of Galen aneurysmal malformations (VGAMs) or Dural Arteriovenous Malformations (D-AVMs). We report a case of a fetal pial AVF with multiple fistulae and venous pouches, which appeared as an anechoic lesion on the prenatal ultrasound scan. The patient was asymptomatic with normal postnatal growth. No haemodynmaic disturbance was evident. Postnatal Computed tomography (CT), Magnetic Resonance Imaging (MRI) and catheter Digital Subtraction Angiography (DSA) confirmed the presence of a pial AVF. The angiographic findings and family history of nose bleeds suggests the diagnosis of Hereditary Hemorrhagic Telangiectasia. The largest AVF was embolized with tissue adhesive; the residual AVF subsequently removed by surgical excision.

An energy budget for signaling in the grey matter of the brain.

Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with

Efficiency and complexity in neural coding.

Neural coding in the retina and lamina of fly compound eyes is amenable to detailed anatomical, physiological and theoretical analysis. This approach shows how identified cell signalling systems are optimized to maximize the transmission of information. Optimization reveals three familiar constraints, noise, saturation and bandwidth, and shows how coding can minimize their effects. Experiments reveal a fourth constraint, metabolic cost, whose properties favour the distribution of information among multiple pathways. The advantages of distributed codes will be offset by increasing complexity and the build up of noise. The optimization of coding in fly retina suggests that both noise and complexity will be reduced by matching each step in the system's operations to the input signal, and to the logical requirements of the network's ultimate function, pattern processing. This line of argument suggests tightly organized networks, laid out that information flows freely and independently, yet patterned so that the necessary contacts and transactions are made quickly and efficiently.

Variations in photoreceptor response dynamics across the fly retina.

Gradients in the spatial properties of retinal cells and their relation to image statistics are well documented. However, less is known of gradients in temporal properties, especially at the level of the photoreceptor for which no account exists. Using light flashes and white-noise-modulated light and current stimuli, we examined the spatial and temporal properties of a single class of photoreceptor (R1-6) within the compound eyes of male blowfly, Calliphora vicina. We find that there is a trend toward higher performance at the front of the eye, both in terms of spatiotemporal resolution and signal-to-noise ratio. The receptive fields of frontal photoreceptors are narrower than those of photoreceptors at the side and back of the eye and response speeds are 20% faster. The signal-to-noise ratio at high frequencies is also greatest at the front of the eye, allowing a 30-40% higher information rate. The power spectra of signals and noise indicate that this elevation of performance results both from shorter responses to individual photons and from a more reliable registration of photon arrival times. These distinctions are characteristic of adaptational changes that normally occur on increasing illumination. However, all photoreceptors were absorbing light at approximately the same mean photon rate during our recordings. We therefore suggest that frontal photoreceptors attain a higher state of light adaptation for a given photon rate. This difference may be achieved by a higher density of (Ca2+ permeable) light-gated channels. Consistent with this hypothesis, membrane-impedance measurements show that frontal photoreceptors have a higher specific conductance than other photoreceptors. This higher conductance provides a better temporal performance but is metabolically expensive. Across the eye, temporal resolution is not proportional to spatial (optical) resolution. Neither is it matched obviously to optic flow. Instead we examine the consequences of an improved temporal resolution in the frontal region for the tracking of small moving targets, a behavior exhibited by male flies. We conclude that the temporal properties of a given class of retinal neuron can vary within a single retina and that this variation may be functionally related to the behavioral requirements of the animal.

Energy as a constraint on the coding and processing of sensory information.

Neurons use significant amounts of energy to generate signals. Recent studies of retina and brain connect this energy usage to the ability to transmit information. The identification of energy-efficient neural circuits and codes suggests new ways of understanding the function, design and evolution of nervous systems.

Chickenpox and stroke in childhood: a study of frequency and causation.

BACKGROUND AND PURPOSE: The purpose of this study was to determine whether infection with varicella is causal for arterial ischemic stroke (AIS) in children. METHODS: First, a prospective cohort study was conducted in young children (aged 6 months to 10 years) with AIS at 2 institutions (cohort study). The presence of varicella infection <12 months before AIS was determined and compared with the published frequency of varicella infection in the healthy pediatric population. The clinical and radiographic features of AIS were compared between the varicella and nonvaricella study cohorts. Second, a literature search of varicella-associated AIS was conducted, and the clinical and radiographic features were compared with the study nonvaricella cohort. RESULTS: In the cohort study, 22 (31%) of 70 consecutive children with AIS had a varicella infection in the preceding year compared with 9% in the healthy population. Children in the varicella cohort were more likely to have basal ganglia infarcts (P<0.001), abnormal cerebral vascular imaging (P<0.05), and recurrent AIS or transient ischemic attacks (P<0.05) than those in the nonvaricella cohort. The pooled literature analysis of 51 cases of varicella-associated AIS showed similar findings to the varicella cohort. CONCLUSION: In young children with AIS, there is a 3-fold increase in preceding varicella infection compared with published population rates, and varicella-associated AIS accounts for nearly one third of childhood AIS. Varicella-associated AIS has characteristic features, including a 2-fold increase in recurrent AIS and transient ischemic attacks. Varicella is an important risk factor for childhood AIS.

Accuracy of velocity estimation by Reichardt correlators.

Although a great deal of experimental evidence supports the notion of a Reichardt correlator as a mechanism for biological motion detection, the correlator does not signal true image velocity. This study examines the accuracy with which realistic Reichardt correlators can provide velocity estimates in an organism's natural visual environment. The predictable statistics of natural images imply a consistent correspondence between mean correlator response and velocity, allowing the otherwise ambiguous Reichardt correlator to act as a practical velocity estimator. Analysis and simulations suggest that processes commonly found in visual systems, such as prefiltering, response compression, integration, and adaptation, improve the reliability of velocity estimation and expand the range of velocities coded. Experimental recordings confirm our predictions of correlator response to broadband images.

Colour thresholds and receptor noise: behaviour and physiology compared.

Photoreceptor noise sets an absolute limit for the accuracy of colour discrimination. We compared colour thresholds in the honeybee (Apis mellifera) with this limit. Bees were trained to discriminate an achromatic stimulus from monochromatic lights of various wavelengths as a function of their intensity. Signal-to-noise ratios were measured by intracellular recordings in the three spectral types of photoreceptor cells. To model thresholds we assumed that discrimination was mediated by opponent mechanisms whose performance was limited by receptor noise. Most of the behavioural thresholds were close to those predicted from receptor signal-to-noise ratios, suggesting that colour discrimination in honeybees is affected by photoreceptor noise. Some of the thresholds were lower than this theoretical limit, which indicates summation of photoreceptor cell signals.