Perpendicular crossing chains enable high mobility in a noncrystalline conjugated polymer.

The nature of interchain π-system contacts, and their relationship to hole transport, are elucidated for the high-mobility, noncrystalline conjugated polymer C16-IDTBT by the application of scanning tunneling microscopy, molecular dynamics, and quantum chemical calculations. The microstructure is shown to favor an unusual packing motif in which paired chains cross-over one another at near-perpendicular angles. By linking to mesoscale microstructural features, revealed by coarse-grained molecular dynamics and previous studies, and performing simulations of charge transport, it is demonstrated that the high mobility of C16-IDTBT can be explained by the promotion of a highly interconnected transport network, stemming from the adoption of perpendicular contacts at the nanoscale, in combination with fast intrachain transport.

Ultrahigh toughness zirconia ceramics.

The attainment of both high strength and toughness is the ultimate goal for most structural materials. Although ceramic material has been considered for use as a structural material due to its high strength and good chemical stability, it suffers from the limitation of low toughness. For instance, although Y2O3-stabilized tetragonal ZrO2 polycrystals (Y-TZPs) exhibit remarkable toughness among ceramics due to their phase transformation toughening mechanism, this toughness is still much weaker than that of metals. Here, we report Y-TZP-based ceramic materials with toughnesses exceeding 20 MPa m1/2, which is comparable to those of metals, while maintaining strengths over 1,200 MPa. The superior mechanical properties are realized by reducing the phase stability of tetragonal zirconia by tailoring the microstructure and chemistry of the Y-TZP. The proposed ceramic materials can further advance the design and application of ceramic-based structural materials.

Diffusion MRI of the Hippocampus.

The hippocampus is a brain structure that plays key roles in a variety of cognitive processes. Critically, a wide range of neurological disorders are associated with degeneration of the hippocampal microstructure, defined as neurons, dendrites, glial cells, and more. Thus, the hippocampus is a key target for methods that are sensitive to these microscale properties. Diffusion MRI is one such method, which can noninvasively probe neural architecture. Here we review the extensive use of diffusion MRI to capture hippocampal microstructure in both health and disease. The results of these studies indicate that (1) diffusion tensor imaging is sensitive but not specific to the hippocampal microstructure; (2) biophysical modeling of diffusion MRI signals is a promising avenue to capture more specific aspects of the hippocampal microstructure; (3) use of ultra-short diffusion times have shown unique laminar-specific microstructure and response to hippocampal injury; (4) dispersion of microstructure is likely abundant in the hippocampus; and (5) the angular richness of the diffusion MRI signal can be leveraged to improve delineation of the internal hippocampal circuitry. Overall, extant findings suggest that diffusion MRI offers a promising avenue for characterizing hippocampal microstructure.

The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood.

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer's disease.

Over the past several years, there has been a surge in blood biomarker studies examining the value of plasma or serum neurofilament light (NfL) as a biomarker of neurodegeneration for Alzheimer's disease. However, there have been limited efforts to combine existing findings to assess the utility of blood NfL as a biomarker of neurodegeneration for Alzheimer's disease. In addition, we still need better insight into the specific aspects of neurodegeneration that are reflected by the elevated plasma or serum concentration of NfL. In this review, we survey the literature on the cross-sectional and longitudinal relationships between blood-based NfL levels and other, neuroimaging-based, indices of neurodegeneration in individuals on the Alzheimer's continuum. Then, based on the biomarker classification established by the FDA-NIH Biomarker Working group, we determine the utility of blood-based NfL as a marker for monitoring the disease status (i.e. monitoring biomarker) and predicting the severity of neurodegeneration in older adults with and without cognitive decline (i.e. a prognostic or a risk/susceptibility biomarker). The current findings suggest that blood NfL exhibits great promise as a monitoring biomarker because an increased NfL level in plasma or serum appears to reflect the current severity of atrophy, hypometabolism and the decline of white matter integrity, particularly in the brain regions typically affected by Alzheimer's disease. Longitudinal evidence indicates that blood NfL can be useful not only as a prognostic biomarker for predicting the progression of neurodegeneration in patients with Alzheimer's disease but also as a susceptibility/risk biomarker predicting the likelihood of abnormal alterations in brain structure and function in cognitively unimpaired individuals with a higher risk of developing Alzheimer's disease (e.g. those with a higher amyloid-ß). There are still limitations to current research, as discussed in this review. Nevertheless, the extant literature strongly suggests that blood NfL can serve as a valuable prognostic and susceptibility biomarker for Alzheimer's disease-related neurodegeneration in clinical settings, as well as in research settings.

Microstructural associations between locus coeruleus, cortical, and subcortical regions are modulated by astrocyte reactivity: a 7T MRI adult lifespan study.

The locus coeruleus-norepinephrine system plays a key role in supporting brain health along the lifespan, notably through its modulatory effects on neuroinflammation. Using ultra-high field diffusion magnetic resonance imaging, we examined whether microstructural properties (neurite density index and orientation dispersion index) in the locus coeruleus were related to those in cortical and subcortical regions, and whether this was modulated by plasma glial fibrillary acidic protein levels, as a proxy of astrocyte reactivity. In our cohort of 60 healthy individuals (30 to 85 yr, 50% female), higher glial fibrillary acidic protein correlated with lower neurite density index in frontal cortical regions, the hippocampus, and the amygdala. Furthermore, under higher levels of glial fibrillary acidic protein (above ~ 150 pg/mL for cortical and ~ 145 pg/mL for subcortical regions), lower locus coeruleus orientation dispersion index was associated with lower orientation dispersion index in frontotemporal cortical regions and in subcortical regions. Interestingly, individuals with higher locus coeruleus orientation dispersion index exhibited higher orientation dispersion index in these (sub)cortical regions, despite having higher glial fibrillary acidic protein levels. Together, these results suggest that the interaction between locus coeruleus-norepinephrine cells and astrocytes can signal a detrimental or neuroprotective pathway for brain integrity and support the importance of maintaining locus coeruleus neuronal health in aging and in the prevention of age-related neurodegenerative diseases.

Revealing the Effect of the [CoO]6 Microstructure in Pseudocapacitance by Controlled Delithium of LiCoO2.

Revealing the in-depth structure-property relationship and designing specific capacity electrodes are particularly important for supercapacitors. Despite many efforts made to tune the composition and electronic structure of cobalt oxide for pseudocapacitance, insight into the [CoO]6 octahedron from the microstructure is still insufficient. Herein, we present a tunable [CoO]6 octahedron microstructure in LiCoO2 by a chemical delithiation process. The c-strained strain of the [CoO]6 octahedron is induced to form higher valence Co ions, and the (003) crystalline layer spacing increases to allow more rapid participation of OH- in the redox reaction. Interestingly, the specific capacity of L0.75CO2 is nearly four times higher than that of LiCoO2 at 10 mA g-1. The enhanced activity originated from the asymmetric strain [CoO]6 octahedra, resulting in enhanced electronic conductivity and Co-O hybridization for accelerated redox kinetics. This finding provides new insights into the modification strategy for pseudocapacitive transition metal oxides.

Machine Learning on Microstructure-Property Relationship of Lithium-Ion Conducting Oxide Solid Electrolytes.

Understanding the structure-property relationship of lithium-ion conducting solid oxide electrolytes is essential to accelerate their development and commercialization. However, the structural complexity of nonideal materials increases the difficulty of study. Here, we develop an algorithmic framework to understand the effect of microstructure on the properties by linking the microscopic morphology images to their ionic conductivities. We adopt garnet and perovskite polycrystalline oxides as examples and quantify the microscopic morphologies via extracting determined physical parameters from the images. It directly visualizes the effect of physical parameters on their corresponding ionic conductivities. As a result, we can determine the microstructural features of a Li-ion conductor with high ionic conductivity, which can guide the synthesis of highly conductive solid electrolytes. Our work provides a novel approach to understanding the microstructure-property relationship for solid-state ionic materials, showing the potential to extend to other structural/functional ceramics with various physical properties in other fields.

Microstructure-Reconfigured Graphene Oxide Aerogel Metamaterials for Ultrarobust Directional Sensing at Human-Machine Interfaces.

Graphene aerogels hold huge promise for the development of high-performance pressure sensors for future human-machine interfaces due to their ordered microstructure and conductive network. However, their application is hindered by the limited strain sensing range caused by the intrinsic stiffness of the porous microstructure. Herein, an anisotropic cross-linked chitosan and reduced graphene oxide (CCS-rGO) aerogel metamaterial is realized by reconfiguring the microstructure from a honeycomb to a buckling structure at the dedicated cross-section plane. The reconfigured CCS-rGO aerogel shows directional hyperelasticity with extraordinary durability (no obvious structural damage after 20 000 cycles at a directional compressive strain of ≤0.7). The CCS-rGO aerogel pressure sensor exhibits an ultrahigh sensitivity of 121.45 kPa-1, an unprecedented sensing range, and robust mechanical and electrical performance. The aerogel sensors are demonstrated to monitor human motions, control robotic hands, and even integrate into a flexible keyboard to play music, which opens a wide application potential in future human-machine interfaces.

A New Strategy for the Design of Triple-Phase Eutectic High-Entropy Alloys Based on Infinite Solid Solution and Pseudo-Ternary Method.

Eutectic high-entropy alloys (EHEAs) have combined both high-entropy alloys and eutectic alloy contributions, with excellent castability and high-temperature application potential. Yet, multielement/triple-phase eutectic high-entropy alloy (TEHEA) designs remain puzzling. This work proposed a new strategy based on an infinite solid solution and pseudo-ternary model to reveal the puzzle of TEHEAs. The designed triple-phase eutectic high-entropy alloys (TEHEAs) with more than seven elements were identified as face-centered cubic (FCC), ordered body-centered cubic (B2), and Laves phase structures. In this work, the alloy C showcases outstanding comprehensive mechanical properties, offering a novel avenue for the design of high-performance EHEAs.

High-Throughput Two-Photon 3D Printing Enabled by Holographic Multi-Foci High-Speed Scanning.

The emerging two-photon polymerization (TPP) technique enables high-resolution printing of complex 3D structures, revolutionizing micro/nano additive manufacturing. Various fast scanning and parallel processing strategies have been proposed to promote its efficiency. However, obtaining large numbers of uniform focal spots for parallel high-speed scanning remains challenging, which hampers the realization of higher throughput. We report a TPP printing platform that combines galvanometric mirrors and liquid crystal on silicon spatial light modulator (LCoS-SLM). By setting the target light field at LCoS-SLM's diffraction center, sufficient energy is acquired to support simultaneous polymerization of over 400 foci. With fast scanning, the maximum printing speed achieves 1.49 × 108 voxels s-1, surpassing the existing scanning-based TPP methods while maintaining high printing resolution and flexibility. To demonstrate the processing capability, functional 3D microstructure arrays are rapidly fabricated and applied in micro-optics and micro-object manipulation. Our method may expand the prospects of TPP in large-scale micro/nanomanufacturing.

Texture Transfer in Dielectric Layers via Nanocrystalline Networks: Insights from in Situ 4D-STEM.

Transition metal oxide dielectric layers have emerged as promising candidates for various relevant applications, such as supercapacitors or memory applications. However, the performance and reliability of these devices can critically depend on their microstructure, which can be strongly influenced by thermal processing and substrate-induced strain. To gain a more in-depth understanding of the microstructural changes, we conducted in situ transmission electron microscopy (TEM) studies of amorphous HfO2 dielectric layers grown on highly textured (111) substrates. Our results indicate that the minimum required phase transition temperature is 180 °C and that the developed crystallinity is affected by texture transfer. Using in situ TEM and 4D-STEM can provide valuable insights into the fundamental mechanisms underlying the microstructural evolution of dielectric layers and could pave the way for the development of more reliable and efficient devices for future applications.

Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy.

Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.

Changes in Cortical Microstructure of the Human Brain Resulting from Long-Term Motor Learning.

The mechanisms subserving motor skill acquisition and learning in the intact human brain are not fully understood. Previous studies in animals have demonstrated a causal relationship between motor learning and structural rearrangements of synaptic connections, raising the question of whether neurite-specific changes are also observable in humans. Here, we use advanced diffusion magnetic resonance imaging (MRI), sensitive to dendritic and axonal processes, to investigate neuroplasticity in response to long-term motor learning. We recruited healthy male and female human participants (age range 19-29) who learned a challenging dynamic balancing task (DBT) over four consecutive weeks. Diffusion MRI signals were fitted using Neurite Orientation Dispersion and Density Imaging (NODDI), a theory-driven biophysical model of diffusion, yielding measures of tissue volume, neurite density and the organizational complexity of neurites. While NODDI indices were unchanged and reliable during the control period, neurite orientation dispersion increased significantly during the learning period mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor areas. Importantly, reorganization of cortical microstructure during the learning phase predicted concurrent behavioral changes, whereas there was no relationship between microstructural changes during the control phase and learning. Changes in neurite complexity were independent of alterations in tissue density, cortical thickness, and intracortical myelin. Our results are in line with the notion that structural modulation of neurites is a key mechanism supporting complex motor learning in humans.SIGNIFICANCE STATEMENT The structural correlates of motor learning in the human brain are not fully understood. Results from animal studies suggest that synaptic remodeling (e.g., reorganization of dendritic spines) in sensorimotor-related brain areas is a crucial mechanism for the formation of motor memory. Using state-of-the-art diffusion magnetic resonance imaging (MRI), we found a behaviorally relevant increase in the organizational complexity of neocortical microstructure, mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor regions, following training of a challenging dynamic balancing task (DBT). Follow-up analyses suggested structural modulation of synapses as a plausible mechanism driving this increase, while colocalized changes in cortical thickness, tissue density, and intracortical myelin could not be detected. These results advance our knowledge about the neurobiological basis of motor learning in humans.

Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging.

Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Fast and robust quantification of uncertainty in non-linear diffusion MRI models.

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

Cortical microstructure and hemispheric specialization-A diffusion-imaging analysis in younger and older adults.

Characterizing cortical plasticity becomes increasingly important for identifying compensatory mechanisms and structural reserve in the ageing population. While cortical thickness (CT) largely contributed to systems neuroscience, it incompletely informs about the underlying neuroplastic pathophysiology. In turn, microstructural characteristics may correspond to atrophy mechanisms in a more sensitive way. Fractional anisotropy, a diffusion tensor imaging (DTI) measure, is inversely related to cortical histologic complexity. Axial diffusivity and radial diffusivity are assumed to be linked to the density of structures oriented perpendicular and parallel to the cortical surface, respectively. We hypothesized (1) that cortical DTI will reveal microstructural correlates for hemispheric specialization, particularly in the language and motor systems, and (2) that lateralization of cortical DTI parameters will show an age effect, paralleling age-related changes in activation, especially in the prefrontal cortex. We analysed data from healthy younger and older adult participants (N = 91). DTI and CT data were extracted from regions of the Destrieux atlas. Diffusion measures showed lateralization in specialized motor, language, visual, auditory and inferior parietal cortices. Age-dependent increased lateralization for DTI measures was observed in the prefrontal, angular, superior temporal and lateral occipital cortex. CT did not show any age-dependent alterations in lateralization. Our observations argue that cortical DTI can capture microstructural properties associated with functional specialization, resembling findings from histology. Age effects on diffusion measures in the integrative prefrontal and parietal areas may shed novel light on the atrophy-related plasticity in healthy ageing.

Spatiotemporal patterns of cortical microstructural maturation in children and adolescents with diffusion MRI.

Neocortical maturation is a dynamic process that proceeds in a hierarchical manner; however, the spatiotemporal organization of cortical microstructure with diffusion MRI has yet to be fully defined. This study characterized cortical microstructural maturation using diffusion MRI (fwe-diffusion tensor imaging [DTI] and neurite orientation dispersion and density imaging [NODDI] multicompartment modeling) in a cohort of 637 children and adolescents between 8 and 21 years of age. We found spatially heterogeneous developmental patterns broadly demarcated into functional domains where NODDI metrics increased, and fwe-DTI metrics decreased with age. By applying nonlinear growth models in a vertex-wise analysis, we observed a general posterior-to-anterior pattern of maturation, where the fwe-DTI measures mean diffusivity and radial diffusivity reached peak maturation earlier than the NODDI metrics neurite density index. Using non-negative matrix factorization, we found occipito-parietal cortical regions that correspond to lower order sensory domains mature earlier than fronto-temporal higher order association domains. Our findings corroborate previous histological and neuroimaging studies that show spatially varying patterns of cortical maturation that may reflect unique developmental processes of cytoarchitectonically determined regional patterns of change.

Localized Ionic Reinforcement of Double Network Granular Hydrogels.

Nature produces soft materials with fascinating combinations of mechanical properties. For example, the mussel byssus embodies a combination of stiffness and toughness, a feature that is unmatched by synthetic hydrogels. Key to enabling these excellent mechanical properties are the well-defined structures of natural materials and their compositions controlled on lengths scales down to tens of nanometers. The composition of synthetic materials can be controlled on a micrometer length scale if processed into densely packed microgels. However, these microgels are typically soft. Microgels can be stiffened by enhancing interactions between particles, for example through the formation of covalent bonds between their surfaces or a second interpenetrating hydrogel network. Nonetheless, changes in the composition of these synthetic materials occur on a micrometer length scale. Here, 3D printable load-bearing granular hydrogels are introduced whose composition changes on the tens of nanometer length scale. The hydrogels are composed of jammed microgels encompassing tens of nm-sized ionically reinforced domains that increase the stiffness of double network granular hydrogels up to 18-fold. The printability of the ink and the local reinforcement of the resulting granular hydrogels are leveraged to 3D print a butterfly with composition and structural changes on a tens of nanometer length scale.