Role of deep learning in infant brain MRI analysis

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them

A segmentation editing framework based on shape change statistics

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user

STUDENTS’ LOYALTY: DOES VALUE CO-CREATION IN HIGHER EDUCATION INSTITUTIONS MATTER?

This study investigates the influence for intrinsic and extrinsic motives on customers` participation in value co-creation activities (CPVCA), beside examining the direct and indirect impact for CPVCA on customers` loyalty. Quantitative research approach is used, while the study population encompasses all Lebanese private universities students. A questionnaire was developed to gather data from 403 universities` students who were chosen using the convenience sampling technique. PLS-SEM was adopted to examine the study proposed scale validity and the relationships between its latent variables. The current study results indicate a positive influence for both intrinsic and extrinsic motives on CPVCA. Also, the findings reveal a significant direct relationship among CPVCA and customer loyalty, as well as an indirect relationship via mediating brand experience

Non-Euclidean, convolutional learning on cortical brain surfaces

In recent years there have been many studies indicating that multiple cortical features, extracted at each surface vertex, are promising in the detection of various neurodevelopmental and neurodegenerative diseases. However, with limited datasets, it is challenging to train stable classifiers with such high-dimensional surface data. This necessitates a feature reduction that is commonly accomplished via regional volumetric morphometry from standard brain atlases. However, current regional summaries are not specific to the given age or pathology that is studied, which runs the risk of losing relevant information that can be critical in the classification process. To solve this issue, this paper proposes a novel data-driven approach by extending convolutional neural networks (CNN) for use on non-Euclidean manifolds such as cortical surfaces. The proposed network learns the most powerful features and brain regions from the extracted large dimensional feature space; thus creating a new feature space in which the dimensionality is reduced and feature distributions are better separated. We demonstrate the usability of the proposed surface-CNN framework in an example study classifying Alzheimers disease patients versus normal controls. The high performance in the cross-validation diagnostic results shows the potential of our proposed prediction system

Model selection for spatiotemporal modeling of early childhood sub-cortical development

Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach

Mental health, suicide attempt, and family function for adolescents' primary health care during the COVID-19 pandemic

Background: The study's purpose was to identify associations between mental health risk, suicide attempts, and family function. Methods: A correlational, descriptive, and cross-sectional study was carried out in a group of adolescents in the last grade of secondary school to establish the association between mental health risk, suicide attempt, and family functionality. The instruments used were the self-report questionnaire, the suicide risk assessment scale, and the family APGAR. Data analysis was performed using the artificial intelligence algorithm (gower clustering). Results: 246 adolescents responded to the three instruments, which made it possible to select those with correlations of sensitive interest and, based on these, an intervention plan. Psychological distress was found in 28%, psychotic symptoms in 85%, and problematic alcohol use in 9%. Good family functioning was identified in 34% and some type of family dysfunction in 66%. In terms of suicide risk, there was a low suicide risk of 74%, 24% medium risk, and 2% high risk. It could be shown that there is a correlation in a group of 15% of the respondents. Conclusions: The risk of suffering mental health deterioration and the suicide risk, during this pandemic period, seems to be related to family functionality

Solitons on compact and noncompact spaces in large noncommutativity

We study solutions at the minima of scalar field potentials for Moyal spaces and torii in the large non-commutativity and interprete these solitons in terms of non-BPS D-branes of string theory. We derive a mass spectrum formula linking different D-branes together on quantum torii and suggest that it describes general systems of D-brane bound states extending the D2-D0 one. Then we propose a shape for the effective potential approaching these quasi-stable bound states. We give the gauge symmetries of these systems of branes and show that they depend on the quantum torii representations.Comment: 25 pages, Latex, 1 figure (use epsfig.sty), corrected formul

A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development

New nitroindazole-porphyrin conjugates: synthesis, characterization and antibacterial properties

The synthesis of new porphyrin-indazole hybrids by a Knoevenagel condensation of 2-formyl-5,10,15,20-tetraphenylporphyrin and N-methyl-nitroindazolylacetonitrile derivatives is reported. The target compounds were isolated in moderate to good yields (32-57%) and some of the isolated porphyrin-indazole conjugates showed good performance in the generation of singlet oxygen when irradiated with visible light. Their efficiency as photosensitizers in the photoinactivation of methicillin resistant Staphylococcus aureus-MRSA was evaluated. All derivatives showed to be able to photoinactivate the MRSA bacteria. Compound 3a appears to be the most promising photosensitiser (PS) in the photoinactivation of these bacteria, despite being the least efficient in singlet oxygen generation. The addition of potassium iodide (KI) significantly potentiated the antimicrobial Photodynamic Therapy (aPDT) process mediated by all the analysed porphyrin-indazole conjugates. The combined action of nitroindazole-porphyrins with potassium iodide (KI) action appears to be promising in the photoinactivation of MRSA.publishe