The relationship between stress, HPA axis functioning and brain structure in first episode psychosis over the first 12 weeks of treatment

Stress and abnormal hypothalamic-pituitary-adrenal axis functioning have been implicated in the early phase of psychosis and may partly explain reported changes in brain structure. This study used magnetic resonance imaging to investigate whether biological measures of stress were related to brain structure at baseline and to structural changes over the first 12 weeks of treatment in first episode patients (n=22) compared with matched healthy controls (n=22). At baseline, no significant group differences in biological measures of stress, cortical thickness or hippocampal volume were observed, but a significantly stronger relationship between baseline levels of cortisol and smaller white matter volumes of the cuneus and anterior cingulate was found in patients compared with controls. Over the first 12 weeks of treatment, patients showed a significant reduction in thickness of the posterior cingulate compared with controls. Patients also showed a significant positive relationship between baseline cortisol and increases in hippocampal volume over time, suggestive of brain swelling in association with psychotic exacerbation, while no such relationship was observed in controls. The current findings provide some support for the involvement of stress mechanisms in the pathophysiology of early psychosis, but the changes are subtle and warrant further investigation.

The molecular structure of chloritoid: A mid-infrared and near-infrared spectroscopic study

The mineral chloritoid collected from the argillite in the bottom of Yaopo Formation of Western Beijing was characterized by mid-infrared (MIR) and near-infrared (NIR) spectroscopy. The MIR spectra showed all fundamental vibrations including the hydroxyl units, basic aluminosilicate framework and the influence of iron on the chloritoid structure. The NIR spectrum of the chloritoid showed combination (ν + δ)OH bands with the fundamental stretching (ν) and bending (δ) vibrations. Based on the chemical component data and the analysis result from the MIR and NIR spectra, the crystal structure of chloritoid from western hills of Beijing, China, can be illustrated. Therefore, the application of the technique across the entire infrared region is expected to become more routine and extend its usefulness, and the reproducibility of measurement and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for the unit cell structural analysis.

Infrared and Raman spectroscopic characterization of the carbonate bearing silicate mineral aerinite – Implications for the molecular structure

The mineral aerinite is an interesting mineral because it contains both silicate and carbonate units which is unusual. It is also a highly colored mineral being bright blue/purple. We have studied aerinite using a combination of techniques which included scanning electron microscopy, energy dispersive X-ray analysis, Raman and infrared spectroscopy. Raman bands at 1049 and 1072 cm−1 are assigned to the carbonate symmetric stretching mode. This observation supports the concept of the non-equivalence of the carbonate units in the structure of aerinite. Multiple infrared bands at 1354, 1390 and 1450 cm−1 supports this concept. Raman bands at 933 and 974 cm−1 are assigned to silicon–oxygen stretching vibrations. Multiple hydroxyl stretching and bending vibrations show that water is in different molecular environments in the aerinite structure.

The molecular structure of kaolinite–potassium acetate intercalation complexes: A combined experimental and molecular dynamic simulation study

The kaolinite (Kaol) intercalated with potassium acetate (Ac) was prepared and characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and thermogravimetry. Molecular dynamic simulation was performed to investigate the structure of Kaol–Ac intercalation complex and the hydrogen bonds between Kaol and intercalated Ac andwater using INTERFACE forcefield. The acetate anions andwater arranged in a bilayer structure in the interlayer space of Kaol. The potassium cations distributed in the interlayer space and strongly coordinated with acetate anions aswell aswater rather than keyed into the ditrigonal holes of tetrahedral surface of Kaol. Strong hydrogen bonds formed between the hydrogen atoms of hydroxyl on the octahedral surface and oxygen atoms of both acetate anions and water. The acetate anions andwater also weakly bonded hydrogen to the silica tetrahedral surface through their hydrogen atoms with the oxygen atoms of silica tetrahedral surface.

New insights into the molecular structure of kaolinite–methanol intercalation complexes

A series of kaolinite–methanol complexes with different basal spacings were synthesized using guest displacement reactions of the intercalation precursors kaolinite–N-methyformamide (Kaol–NMF), kaolinite–urea (Kaol–U), or kaolinite–dimethylsulfoxide (Kaol–DMSO), with methanol (Me). The interaction of methanol with kaolinite was examined using X-ray diffraction (XRD), infrared spectroscopy (IR), and nuclear magnetic resonance (NMR). Kaolinite (Kaol) initially intercalated with N-methyformamide (NMF), urea (U), or dimethylsulfoxide (DMSO) before subsequent reaction with Me formed final kaolinite–methanol (Kaol–Me) complexes characterized by basal spacing ranging between 8.6 Å and 9.6 Å, depending on the pre-intercalated reagent. Based on a comparative analysis of the three Kaol–Me displacement intercalation complexes, three types of Me intercalation products were suggested to have been present in the interlayer space of Kaol: (1) molecules grafted onto a kaolinite octahedral sheet in the form of a methoxy group (Al-O-C bond); (2) mobile Me and/or water molecules kept in the interlayer space via hydrogen bonds that could be partially removed during drying; and (3) a mixture of types 1 and 2, with the methoxy group (Al-O-C bond) grafted onto the Kaol sheet and mobile Me and/or water molecules coexisted in the system after the displacement reaction by Me. Various structural models that reflected four possible complexes of Kaol–Me were constructed for use in a complimentary computational study. Results from the calculation of the methanol kaolinite interaction indicate that the hydroxyl oxygen atom of methanol plays the dominant role in the stabilization and localization of the molecule intercalated in the interlayer space, and that water existing in the intercalated Kaol layer is inevitable.

Anodized nanoporous WO3 Schottky contact structure for hydrogen and ethanol sensing

This paper reports the development of nanoporous tungsten trioxide (WO3) Schottky diode-based gas sensors. Nanoporous WO3 films were prepared by anodic oxidation of tungsten foil in ethylene glycol mixed with ammonium fluoride and a small amount of water. Anodization resulted in highly ordered WO3 films with a large surface-to-volume ratio. Utilizing these nanoporous structures, Schottky diode-based gas sensors were developed by depositing a platinum (Pt) catalytic contact and tested towards hydrogen gas and ethanol vapour. Analysis of the current–voltage characteristics and dynamic responses of the sensors indicated that these devices exhibited a larger voltage shift in the presence of hydrogen gas compared to ethanol vapour at an optimum operating temperature of 200 °C. The gas sensing mechanism was discussed, associating the response to the intercalating H+ species that are generated as a result of hydrogen and ethanol molecule breakdowns onto the Pt/WO3 contact and their spill over into nanoporous WO3.

Extending the validity of the Feeding Practices and Structure Questionnaire

Background Feeding practices are commonly examined as potentially modifiable determinants of children’s eating behaviours and weight status. Although a variety of questionnaires exist to assess different feeding aspects, many lack thorough reliability and validity testing. The Feeding Practices and Structure Questionnaire (FPSQ) is a tool designed to measure early feeding practices related to non-responsive feeding and structure of the meal environment. Face validity, factorial validity, internal reliability and cross-sectional correlations with children’s eating behaviours have been established in mothers with 2-year-old children. The aim of the present study was to further extend the validity of the FPSQ by examining factorial, construct and predictive validity, and stability. Methods Participants were from the NOURISH randomised controlled trial which evaluated an intervention with first-time mothers designed to promote protective feeding practices. Maternal feeding practices (FP) and child eating behaviours were assessed when children were aged 2 years and 3.7 years (n=388). Confirmatory Factor analysis, group differences, predictive relationships, and stability were tested. Results The original 9-factor structure was confirmed when children were aged 3.7±0.3 years. Cronbach’s alpha was above the recommended 0.70 cut-off for all factors except Structured Meal Timing, Over Restriction and Distrust in Appetite which were 0.58, 0.67 and 0.66 respectively. Allocated group differences reflected behaviour consistent with intervention content and all feeding practices were stable across both time points (range of r= 0.45-0.70). There was some evidence for the predictive validity of factors with 2 FP showing expected relationships, 2 FP showing expected and unexpected relationships and 5 FP showing no relationship. Conclusions Reliability and validity was demonstrated for most subscales of the FPSQ. Future validation is warranted with culturally diverse samples and with fathers and other caregivers. The use of additional outcomes to further explore predictive validity is recommended as well as testing construct validity and test-retest reliability of the questionnaire.

Using decision trees to understand structure in missing data

Objectives Demonstrate the application of decision trees – classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs) – to understand structure in missing data. Setting Data taken from employees at three different industry sites in Australia. Participants 7915 observations were included. Materials and Methods The approach was evaluated using an occupational health dataset comprising results of questionnaires, medical tests, and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the Type of data (medical or environmental), the site in which it was collected, the number of visits and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusion Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Versatile single-layer sodium phosphidostannate(II): Strain-tunable electronic structure, excellent mechanical flexibility, and an ideal gap for photovoltaics

Density functional theory (DFT) calculations were performed to study the structural, mechanical, electrical, optical properties, and strain effects in single-layer sodium phosphidostannate(II) (NaSnP). We find the exfoliation of single-layer NaSnP from bulk form is highly feasible because the cleavage energy is comparable to graphite and MoS2. In addition, the breaking strain of the NaSnP monolayer is comparable to other widely studied 2D materials, indicating excellent mechanical flexibility of 2D NaSnP. Using the hybrid functional method, the calculated band gap of single-layer NaSnP is close to the ideal band gap of solar cell materials (1.5 eV), demonstrating great potential in future photovoltaic application. Furthermore, strain effect study shows that a moderate compression (2%) can trigger indirect-to-direct gap transition, which would enhance the ability of light absorption for the NaSnP monolayer. With sufficient compression (8%), the single-layer NaSnP can be tuned from semiconductor to metal, suggesting great applications in nanoelectronic devices based on strain engineering techniques.

Editorial: Structure of the case report

The CJNN is one of only two international nursing journals with a focus on neuroscience nursing. We at CJNN (the editorial staff and CANN board of directors) have had to make the difficult decision to reduce publication frequency from quarterly (four times per year) down to three editions per year. The reason behind this decision relates to the current lack of submitted articles for peer review and potential publication in the journal; it is difficult to put out a quality edition with only one or two new manuscripts. We would like to encourage Canadian neuroscience nurses to share their insights and expertise with colleagues by writing about challenges and achievements in patient care, experiences encountered on a daily basis, or about unique/interesting cases that may inform others in their practice.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.