Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI.

The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current magnetic resonance based neuroimaging techniques. In this work, we used diffusion magnetic resonance imaging (dMRI) and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the neurite orientation dispersion and density imaging (NODDI) model optimized for grey matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the cornu ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibres, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitectonic differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures were separable across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fibre pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo dMRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

Gauge equivariant convolutional neural networks for diffusion mri.

Diffusion MRI (dMRI) is an imaging technique widely used in neuroimaging research, where the signal carries directional information of underlying neuronal fibres based on the diffusivity of water molecules. One of the shortcomings of dMRI is that numerous images, sampled at gradient directions on a sphere, must be acquired to achieve a reliable angular resolution for model-fitting, which translates to longer scan times, higher costs, and barriers to clinical adoption. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers for dMRI that overcome the challenges associated with the signal being acquired on a sphere with antipodal points identified. This is done by noting that the domain is equivalent to the real projective plane, ℝ P 2 , which is a non-euclidean and a non-orientable manifold. This is in stark contrast to a rectangular grid which typical convolutional neural networks (CNNs) are designed for. We apply our method to upsample angular resolution for predicting diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. The symmetries introduced allow gCNNs the ability to train with fewer subjects as compared to a baseline model that involves only 3D convolutions.

What are the mental health changes associated with the COVID-19 pandemic in people with medical conditions? An international survey.

OBJECTIVES: The COVID-19 pandemic has negatively impacted mental health worldwide but there is paucity of knowledge regarding the level of change in mental health in people with a medical condition (physical/psychiatric). The objectives of this study were (1) to compare the change in mental health in people with and without medical conditions, (2) to assess the change in various types of medical conditions, (3) to evaluate the association between change in mental health and number of comorbidities, and (4) to investigate the influence of receiving treatment and activity limitation imposed by the medical condition(s). DESIGN: Cross-sectional. SETTING: Online international survey. PARTICIPANT: English-speaking adults (age ≥18) were included in the study, with no exclusions based on sex/gender or location. 1276 participants (mean age 30.4, 77.7% female) were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Pre and during COVID-19 pandemic symptoms of anxiety (Generalized Anxiety Disorder-2) and depression (Patient Health Questionnaire-9) were assessed. The Self-Administered Comorbidity Questionnaire was used to collect data regarding medical conditions.Repeated-measures analysis of covariance (objectives 1, 2 and 4) and Pearson's correlation coefficient (objective 3). RESULTS: 50.1% of participants had a medical condition. During the COVID-19 pandemic, compared with people with no medical condition, people with both psychiatric and physical conditions experienced significantly higher symptoms of anxiety (12%, p=0.009) and depression (9.4%, p<0.001). Although not statistically significant, the increase in anxiety and depression occurred across seven major categories of conditions. An association was found between having a higher number of medical conditions with higher anxiety and depression symptoms (r=0.16 anxiety, r=0.14 depression, p<0.001). Receiving treatment and being functionally limited by the disease did not have a significant impact on the amount of change (p>0.05). CONCLUSIONS: During the COVID-19 pandemic, people who had a combination of psychiatric and physical conditions experienced greater symptoms of anxiety and depression. Patients with chronic diseases may need extra support to address their mental health as a result of the pandemic.

Natural Cellulosic Fiber Reinforced Concrete: Influence of Fiber Type and Loading Percentage on Mechanical and Water Absorption Performance.

The paper reports experimental research regarding the mechanical characteristics of concrete reinforced with natural cellulosic fibers like jute, sisal, sugarcane, and coconut. Each type of natural fiber, with an average of 30 mm length, was mixed with a concrete matrix in varying proportions of 0.5% to 3% mass. The tensile and compressive strength of the developed concrete samples with cellulosic fiber reinforcement gradually increased with the increasing proportion of natural cellulosic fibers up to 2%. A further increase in fiber loading fraction results in deterioration of the mechanical properties. By using jute and sisal fiber reinforcement, about 11.6% to 20.2% improvement in tensile and compressive strength, respectively, was observed compared to plain concrete, just by adding 2% of fibers in the concrete mix. Bending strength increased for the natural fiber-based concrete with up to 1.5% fiber loading. However, a decrease in bending strength was observed beyond 1.5% loading due to cracks at fiber-concrete interface. The impact performance showed gradual improvement with natural fiber loading of up to 2%. The water absorption capacity of natural cellulosic fiber reinforced concrete decreased substantially; however, it increased with the loading percent of fibers. The natural fiber reinforced concrete can be commercially used for interior or exterior pavements and flooring slabs as a sustainable construction material for the future.

Tractography in Curvilinear Coordinates.

Coordinate invariance of physical laws is central in physics, it grants us the freedom to express observations in coordinate systems that provide computational convenience. In the context of medical imaging there are numerous examples where departing from Cartesian to curvilinear coordinates leads to ease of visualization and simplicity, such as spherical coordinates in the brain's cortex, or universal ventricular coordinates in the heart. In this work we introduce tools that enhance the use of existing diffusion tractography approaches to utilize arbitrary coordinates. To test our method we perform simulations that gauge tractography performance by calculating the specificity and sensitivity of tracts generated from curvilinear coordinates in comparison with those generated from Cartesian coordinates, and we find that curvilinear coordinates generally show improved sensitivity and specificity compared to Cartesian. Also, as an application of our method, we show how harmonic coordinates can be used to enhance tractography for the hippocampus.

Characterization of Hybrid Composites with Polyester Waste Fibers, Olive Root Fibers and Coir Pith Micro-Particles Using Mixture Design Analysis for Structural Applications.

In the present work, hybrid composites were developed by using polyester waste fibers along with natural origin materials: olive root fibers and coir pitch filler. Such composite panels can be used as a potential alternative for fiber glass sunshade panels and room dividers in buildings. Hybrid composites were fabricated by mixing polyester waste fibers and olive root fibers in different ratios (0:100, 33:67, 67:33 and 100:0). Coir pith micro-particles with an average size of 312 d.nm were used as filler in the polyester matrix at three different levels (0%, 5%, and 10%) of the overall matrix weight. Mechanical properties, e.g., tensile strength, flexural strength and impact strength, thermal properties, e.g., coefficient of linear thermal expansion, thermo-gravimetric analysis (TGA) and environmental properties, e.g., water absorption, loss of density after exposure to weathering were characterized. For comparison purposes, a commercially available fiber glass sunshades sample was also investigated. Mixture design analysis was used to optimize the ratio of all components in the composite. Graphical comparison of experimental results using regression models showed a high degree of correlation. An optimized formulation of composite with an objective of maximization of tensile strength, flexural strength, impact strength and minimization of water absorption, density loss, as well as coefficient of linear thermal expansion, was determined at 70.83 wt%, 15.15 wt%, and 14.01 wt% of polyester waste fibers, olive root fibers and coir pith micro-fillers, respectively. Overall, it can be concluded that the developed hybrid composites from waste fibrous materials can be used as a promising alternative and a value-added application in buildings and construction purposes.