Enrichment and Training Improve Cognition in Rats with Cortical Malformations

Children with malformations of cortical development (MCD) frequently have associated cognitive impairments which reduce quality of life. We hypothesized that cognitive deficits associated with MCD can be improved with environmental manipulation or additional training. The E17 methylazoxymethanol acetate (MAM) exposure model bears many anatomical hallmarks seen in human MCDs as well as similar behavioral and cognitive deficits. We divided control and MAM exposed Sprague-Dawley rats into enriched and non-enriched groups and tested performance in the Morris water maze. Another group similarly divided underwent sociability testing and also underwent Magnetic Resonance Imaging (MRI) scans pre and post enrichment. A third group of control and MAM rats without enrichment were trained until they reached criterion on the place avoidance task. MAM rats had impaired performance on spatial tasks and enrichment improved performance of both control and MAM animals. Although MAM rats did not have a deficit in sociability they showed similar improvement with enrichment as controls. MRI revealed a whole brain volume decrease with MAM exposure, and an increase in both MAM and control enriched volumes in comparison to non-enriched animals. In the place avoidance task, MAM rats required approximately 3 times as long to reach criterion as control animals, but with additional training were able to reach control performance. Environmental manipulation and additional training can improve cognition in a rodent MCD model. We therefore suggest that patients with MCD may benefit from appropriate alterations in educational strategies, social interaction and environment. These factors should be considered in therapeutic strategies

Composing and Factoring Generalized Green's Operators and Ordinary Boundary Problems

We consider solution operators of linear ordinary boundary problems with "too many" boundary conditions, which are not always solvable. These generalized Green's operators are a certain kind of generalized inverses of differential operators. We answer the question when the product of two generalized Green's operators is again a generalized Green's operator for the product of the corresponding differential operators and which boundary problem it solves. Moreover, we show that---provided a factorization of the underlying differential operator---a generalized boundary problem can be factored into lower order problems corresponding to a factorization of the respective Green's operators. We illustrate our results by examples using the Maple package IntDiffOp, where the presented algorithms are implemented.Comment: 19 page

Chandra follow up of the Hectospec Cluster Survey: Comparison of Caustic and Hydrostatic Masses and Constraints on the Hydrostatic Bias

Clusters of galaxies are powerful probes with which to study cosmology and astrophysics. However, for many applications an accurate measurement of a cluster's mass is essential. A systematic underestimate of hydrostatic masses from X-ray observations (the so-called hydrostatic bias) may be responsible for tension between the results of different cosmological measurements. We compare X-ray hydrostatic masses with masses estimated using the caustic method (based on galaxy velocities) in order to explore the systematic uncertainties of both methods and place new constraints on the level of hydrostatic bias. Hydrostatic and caustic mass profiles were determined independently for a sample of 44 clusters based on Chandra observations of clusters from the Hectospec Cluster Survey. This is the largest systematic comparison of its kind. Masses were compared at a standardised radius ($R_{500}$) using a model that includes possible bias and scatter in both mass estimates. The systematics affecting both mass determination methods were explored in detail. The hydrostatic masses were found to be systematically higher than caustic masses on average, and we found evidence that the caustic method increasingly underestimates the mass when fewer galaxies are used to measure the caustics. We limit our analysis to the 14 clusters with the best-sampled caustics where this bias is minimised ($\ge210$ galaxies), and find that the average ratio of hydrostatic to caustic mass at $R_{500}$ is $M_X/M_C=1.12^{+0.11}_{-0.10}$. We interpret this result as a constraint on the level of hydrostatic bias, favouring small or zero levels of hydrostatic bias (less than $20\%$ at the $3\sigma$ level). However, we find systematic uncertainties associated with both mass estimation methods remain at the $10-15\%$ level, which would permit significantly larger levels of hydrostatic bias.Comment: 15 pages plus appendices. Updated to match version accepted for publication in A&A. Updates include additional tests of systematics. Main results are unchange

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

<p>Abstract</p> <p>Background</p> <p>The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.</p> <p>Methods</p> <p>Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.</p> <p>Results</p> <p>Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (<it>P </it>< 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).</p> <p>Conclusions</p> <p>Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.</p

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

Assessing the Safety of Stem Cell Therapeutics

Unprecedented developments in stem cell research herald a new era of hope and expectation for novel therapies. However, they also present a major challenge for regulators since safety assessment criteria, designed for conventional agents, are largely inappropriate for cell-based therapies. This article aims to set out the safety issues pertaining to novel stem cell-derived treatments, to identify knowledge gaps that require further research, and to suggest a roadmap for developing safety assessment criteria. It is essential that regulators, pharmaceutical providers, and safety scientists work together to frame new safety guidelines, based on “acceptable risk,” so that patients are adequately protected but the safety “bar” is not set so high that exciting new treatments are lost

Epidemiology of a Daphnia-Multiparasite System and Its Implications for the Red Queen

The Red Queen hypothesis can explain the maintenance of host and parasite diversity. However, the Red Queen requires genetic specificity for infection risk (i.e., that infection depends on the exact combination of host and parasite genotypes) and strongly virulent effects of infection on host fitness. A European crustacean (Daphnia magna) - bacterium (Pasteuria ramosa) system typifies such specificity and high virulence. We studied the North American host Daphnia dentifera and its natural parasite Pasteuria ramosa, and also found strong genetic specificity for infection success and high virulence. These results suggest that Pasteuria could promote Red Queen dynamics with D. dentifera populations as well. However, the Red Queen might be undermined in this system by selection from a more common yeast parasite (Metschnikowia bicuspidata). Resistance to the yeast did not correlate with resistance to Pasteuria among host genotypes, suggesting that selection by Metschnikowia should proceed relatively independently of selection by Pasteuria

Toward a Theory of Child Well-Being

Assuring the well-being of children has emerged over the past several decades as an important goal for health and social policymakers. Although the concept of child well-being has been operationalized and measured in different ways by different child-serving entities, there are few unifying theories that could undergird and inform these various conceptual and measurement efforts. In this paper, we attempt to construct a theory of child well-being. We first review the social and policy history of the concept of child well-being, and briefly review its measurement based on these conceptualizations. We then examine three types of theories of well-being extant in philosophy - mental states theories, desire-based theories and needs-based theories - and investigate their suitability to serve as prototypes of a theory of child well-being. We develop a constraint that child well-being is important in and of itself and not merely as a way station to future adult well-being (we call this a non-reduction constraint). Using this constraint, we identify the limitations of each of the three sets of theories to serve as a basis for a theory of child well-being. Based on a developmentalist approach, we then articulate a theory of child well-being that contains two conditions. First, a child's stage-appropriate capacities that equip her for successful adulthood, given her environment; and, second, an engagement with the world in child-appropriate ways. We conclude by reviewing seven implications of this theoretical approach for the measurement of child well-being. Key Words Child well-being, philosophy, social policy, child developmentNoneThis is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s11205-014-0665-

A study of the impact of individual thermal control on user comfort in the workplace: Norwegian cellular vs. British open plan offices

In modern offices, user control is being replaced by centrally operated thermal systems, and in Scandinavia, personal offices by open plan layouts. This study examined the impact of user control on thermal comfort and satisfaction. It compared a workplace, which was designed entirely based on individual control over the thermal environment, to an environment that limited thermal control was provided as a secondary option for fine-tuning: Norwegian cellular and British open plan offices. The Norwegian approach provided each user with control over a window, door, blinds, heating and cooling as the main thermal control system. In contrast, the British practice provided a uniform thermal environment with limited openable windows and blinds to refine the thermal environment for occupants seated around the perimeter of the building. Field studies of thermal comfort were applied to measure users’ perception of thermal environment, empirical building performance and thermal control. The results showed a 30% higher satisfaction and 18% higher comfort level in the Norwegian offices compared to the British practices. However, the energy consumption of the Norwegian case studies was much higher compared to the British ones. A balance is required between energy efficiency and user thermal comfort in the workplace