Cortex2vector: anatomical embedding of cortical folding patterns.

Current brain mapping methods highly depend on the regularity, or commonality, of anatomical structure, by forcing the same atlas to be matched to different brains. As a result, individualized structural information can be overlooked. Recently, we conceptualized a new type of cortical folding pattern called the 3-hinge gyrus (3HG), which is defined as the conjunction of gyri coming from three directions. Many studies have confirmed that 3HGs are not only widely existing on different brains, but also possess both common and individual patterns. In this work, we put further effort, based on the identified 3HGs, to establish the correspondences of individual 3HGs. We developed a learning-based embedding framework to encode individual cortical folding patterns into a group of anatomically meaningful embedding vectors (cortex2vector). Each 3HG can be represented as a combination of these embedding vectors via a set of individual specific combining coefficients. In this way, the regularity of folding pattern is encoded into the embedding vectors, while the individual variations are preserved by the multi-hop combination coefficients. Results show that the learned embeddings can simultaneously encode the commonality and individuality of cortical folding patterns, as well as robustly infer the complicated many-to-many anatomical correspondences among different brains.

An integrated finite element method and machine learning algorithm for brain morphology prediction.

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

Probe the nanoparticle-nucleus interaction via coarse-grained molecular model.

The present study reports on a computational model that systematically evaluates the effect of physical factors, including size, surface modification, and rigidity, on the nuclear uptake of nanoparticles (NPs). The NP-nucleus interaction is a crucial factor in biomedical applications such as drug delivery and cellular imaging. While experimental studies have provided evidence for the influence of size, shape, and surface modification on nuclear uptake, theoretical investigations on how these physical factors affect the entrance of NPs through the nuclear pore are lacking. Our results demonstrate that larger NPs require a higher amount of energy to enter the nucleus compared to smaller NPs. This highlights the importance of size as a critical factor in NP design for nuclear uptake. Additionally, surface modification of NPs can impact the nuclear uptake pathway, indicating the potential for tailored NP design for specific applications. Notably, our findings also reveal that the rigidity of NPs has a significant effect on the transport process. The interplay between physicochemical properties and nuclear pore is found to determine nuclear uptake efficiency. Taken together, our study provides new insights into the design of NPs for precise and controllable NP-nucleus interaction, with potential implications for the development of efficient and targeted drug delivery systems and imaging agents.

Gyral peaks: Novel gyral landmarks in developing macaque brains.

Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.

Marmoset Brain ISH Data Revealed Molecular Difference Between Cortical Folding Patterns.

Literature studies have demonstrated the structural, connectional, and functional differences between cortical folding patterns in mammalian brains, such as convex and concave patterns. However, the molecular underpinning of such convex/concave differences remains largely unknown. Thanks to public access to a recently released set of marmoset whole-brain in situ hybridization data by RIKEN, Japan; this data's accessibility empowers us to improve our understanding of the organization, regulation, and function of genes and their relation to macroscale metrics of brains. In this work, magnetic resonance imaging and diffusion tensor imaging macroscale neuroimaging data in this dataset were used to delineate convex/concave patterns in marmoset and to examine their structural features. Machine learning and visualization tools were employed to investigate the possible transcriptome difference between cortical convex and concave patterns. Experimental results demonstrated that a collection of genes is differentially expressed in convex and concave patterns, and their expression profiles can robustly characterize and differentiate the two folding patterns. More importantly, neuroscientific interpretations of these differentially expressed genes, as well as axonal guidance pathway analysis and gene enrichment analysis, offer novel understanding of structural and functional differences between cortical folding patterns in different regions from a molecular perspective.

Geometrical nonlinear elasticity of axon under tension: A coarse-grained computational study.

Axon bundles cross-linked by microtubule (MT) associate proteins and bounded by a shell skeleton are critical for normal function of neurons. Understanding effects of the complexly geometrical parameters on their mechanical properties can help gain a biomechanical perspective on the neurological functions of axons and thus brain disorders caused by the structural failure of axons. Here, the tensile mechanical properties of MT bundles cross-linked by tau proteins are investigated by systematically tuning MT length, axonal cross-section radius, and tau protein spacing in a bead-spring coarse-grained model. Our results indicate that the stress-strain curves of axons can be divided into two regimes, a nonlinear elastic regime dominated by rigid-body like inter-MT sliding, and a linear elastic regime dominated by affine deformation of both tau proteins and MTs. From the energetic analyses, first, the tau proteins dominate the mechanical performance of axons under tension. In the nonlinear regime, tau proteins undergo a rigid-body like rotating motion rather than elongating, whereas in the nonlinear elastic regime, tau proteins undergo a flexible elongating deformation along the MT axis. Second, as the average spacing between adjacent tau proteins along the MT axial direction increases from 25 to 125 nm, the Young's modulus of axon experiences a linear decrease whereas with the average space varying from 125 to 175 nm, and later reaches a plateau value with a stable fluctuation. Third, the increment of the cross-section radius of the MT bundle leads to a decrease in Young's modulus of axon, which is possibly attributed to the decrease in MT numbers per cross section. Overall, our research findings offer a new perspective into understanding the effects of geometrical parameters on the mechanics of MT bundles as well as serving as a theoretical basis for the development of artificial MT complexes potentially toward medical applications.

Twining plant inspired pneumatic soft robotic spiral gripper with a fiber optic twisting sensor.

The field of soft robotics has been significantly advanced with the recent developments of pneumatic techniques, soft materials, and high-precision motion control. While comprehensive motions can be achieved by sophisticated soft robots, multiple coordinated pneumatic controls are usually required to achieve even the simplest motions. Furthermore, most soft robotics are lacking the ability to sense the environment and provide feedback to the pneumatic control system. In this work, we design a twining plant inspired soft-robotic spiral gripper that requires only one single pneumatic control to perform the twining motion and to firmly hold onto a target object. The soft-robotic spiral gripper has an embedded high-birefringence fiber optic twisting sensor to provide critical information, including twining angle, presence of external perturbation, and physical parameter of the target object. Furthermore, finite element analyses (FEA) in parametric studies of the spiral gripper are performed for module design optimization. The unique single pneumatic channel design enables easy manipulation of the soft spiral gripper with a maximum of 540° twining angle and allows a firm grip of a target object as small as 1-mm in diameter. The embedded fiber optic sensor provides useful information of the target object as well as the twining angle of the soft robotic spiral gripper with high twining angle sensitivity of 0.03nm. The unique fiber-optic sensor embedded single-channel pneumatic spiral gripper that is made from non-toxic silicone rubber allows parallel and soft gripping of elongated objects located in a confined area, which is an essential building block for twining and twisting motions in soft robot.

A coarse-grained model for mechanical behavior of phosphorene sheets.

The popularity of phosphorene (known as monolayer black phosphorus) in electronic devices relies on not only its superior electrical properties, but also its mechanical stability beyond the nanoscale. However, the mechanical performance of phosphorene beyond the nanoscale remains poorly explored owing to the spatiotemporal limitation of experimental observations, first-principles calculations, and atomistic simulations. To overcome this limitation, here a coarse-grained molecular dynamics (CG-MD) model is developed via a strain energy conservation approach to offer a new computational tool for the investigation of the mechanical properties of phosphorene beyond the nanoscale. The mechanical properties of a single phosphorene sheet are first characterized by all-atom molecular dynamics (AA-MD) simulations, followed by a force-field parameter optimization of the CG-MD model by matching these mechanical properties from AA-MD simulations. The intrinsic out-of-plane puckered feature is conserved in our CG-MD model, rendering mechanical anisotropy and heterogeneity in both the in-plane and out-of-plane directions preserved. The results indicate that our coarse-grained model is able to accurately capture the anisotropic in-plane mechanical performance of phosphorene and quantitatively reproduce Young's modulus, ultimate strength, and fracture strain under various environmental temperatures. Our CG-MD model can also capture the anisotropic out-of-plane bending stiffness of phosphorene. We demonstrate the applicability of our model in capturing the fracture toughness of phosphorene in both the armchair and zigzag directions by comparison with the results from AA-MD simulations. This CG-MD model proposed here offers greater capability to perform mechanical mesoscale simulations for phosphorene-based systems, allowing for a deeper understanding of the mechanical properties of phosphorene beyond the nanoscale, and the potential transferability of the developed force-field can help design hybrid phosphorene devices and structures.

Denser Growing Fiber Connections Induce 3-hinge Gyral Folding.

Recent studies have shown that quantitative description of gyral shape patterns offers a novel window to examine the relationship between brain structure and function. Along this research line, this paper examines a unique and interesting type of cortical gyral region where 3 different gyral crests meet, termed 3-hinge gyral region. We extracted 3-hinge gyral regions in macaque/chimpanzee/human brains, quantified and compared the relevant DTI-derived fiber densities in 3-hinge and 2-hinge gyral regions. Our observations consistently showed that DTI-derived fiber densities in 3-hinge regions are much higher than those in 2-hinge regions. Therefore, we hypothesize that besides the cortical expansion, denser fiber connections can induce the formation of 3-hinge gyri. To examine the biomechanical basis of this hypothesis, we constructed a series of 3-dimensional finite element soft tissue models based on continuum growth theory to investigate fundamental biomechanical mechanisms of consistent 3-hinge gyri formation. Our computational simulation results consistently showed that during gyrification gyral regions with higher concentrations of growing axonal fibers tend to form 3-hinge gyri. Our integrative approach combining neuroimaging data analysis and computational modeling appears effective in probing a plausible theory of 3-hinge gyri formation and providing new insights into structural and functional cortical architectures and their relationship.

Exploring 3-hinge gyral folding patterns among HCP Q3 868 human subjects.

Comparison and integration of neuroimaging data from different brains and populations are fundamental in neuroscience. Over the past decades, the neuroimaging field has largely depended on image registration to compare and integrate neuroimaging data from individuals in a common reference space, with a basic assumption that the brains are similar. However, the intrinsic neuroanatomical complexity and huge interindividual cortical folding variation remain underexplored. Here we focus on a specific cortical convolution pattern, termed 3-hinge gyral folding, which is the conjunction of gyri from multiple orientations and has unique and consistent anatomically, structurally, and functionally connective patterns across subjects. By developing a novel shape descriptor and a two-stage clustering pipeline, we devise an automatic method to identify 3-hinges in the Human Connectome Project Q3 868 human brains, and further parameterize the complexity of such a pattern and quantify its regularity and variation in terms of 3-hinge number, position, and morphology. Our results not only exhibit the huge interindividual variations, but also reveal regular relationship between gyral hinges and other factors, such as their locations and cortical morphologies. It is found that "line-shape" cortices have relatively more consistent 3-hinge shape pattern distributions, and certain types of 3-hinge patterns favor particular cortical morphologies. In addition, more 3-hinges are found on "line-shape" cortices while their numbers vary more across subjects than those on "non-line-shape" cortices. This study adds new insights into a better understanding of the regularity and variability of human brain anatomy, and their functional aspects.

Stretchable fiber-Bragg-grating-based sensor.

A fiber-Bragg-grating-(FBG)-based sensor is a very popular fiber optic sensor due to its simplicity, mature fabrication technology, and sensitivity to a number of physical stimuli. However, due to the stiff nature of optical fiber, it is impossible to heavily stretch a FBG sensor. A stretchable sensor is highly desired in soft robotics, human motion detection, and biomedical applications, which could involve motions like tension, bending, and twisting. In this Letter, we demonstrate a stretchable FBG-based fiber optic sensor by embedding a FBG-written optic fiber sinusoidally in a soft silicone film at an off-center position. This unique structure enables 30% of elongation in length that facilitates tension, bending, and twisting measurement.

Layer-by-layer assembly of nanorods on a microsphere via electrostatic interactions.

Combining coarse-grained molecular dynamics simulations and experiments, a systematic study on both the dynamics and equilibrium behavior of the layer-by-layer (LbL) assembly of charged nanorods (NRs) onto a charged microsphere (MS) via electrostatic interactions has been carried out. The adsorption of the first layer of NRs on the MS follows a growth-saturation dynamics. The adsorption rate is governed by a diffusion limited process when the NR concentration (CNR) is low, while the rate is independent of CNR when CNR is high. The equilibrium NR coverage on the microsphere is found to follow a Langmuir adsorption model. For multilayer LbL assembly, when CNR is low, the number (N) of NRs adsorbed onto the MS follows a linear relationship with the number of dips M; while when CNR is high, in each dip the MS surface is fully covered with NRs, and the N follows a quadratic relationship with M. Most simulation results have been confirmed by experiments using α-Fe2O3 NRs and magnetic microspheres modified by poly(diallyldimethylammonium chloride) and poly(styrenesulfonate, sodium salt). These findings provide useful guidelines for designing complex superparticles via charged building nanoblocks based on electrostatic interactions, and therefore open up a novel avenue to exploit the capability of self-assembled charged nanostructures for potential applications such as surface modifications, sensors, drug delivery vehicles, etc.

Abnormal linear elasticity in polycrystalline phosphorene.

Phosphorene, also known as monolayer black phosphorous, has been widely used in electronic devices due to its superior electrical properties. However, its relatively low Young's modulus, low fracture strength and susceptibility to structural failure has limited its application in nano devices. Therefore, in order to design more mechanically reliable devices that utilize phosphorene, it is necessary to explore the mechanical properties of polycrystalline phosphorene. Here molecular dynamics simulations are performed to study the effect of grain size on the mechanical performance of polycrystalline phosphorene sheets. Unlike other two-dimension materials with planar crystalline structure, polycrystalline phosphorene sheets are almost linear elastic, resulting from its high bending stiffness due to its intrinsic buckled crystalline structure. Moreover, the percentage increase of stiffness for polycrystalline phosphorene associated with the increase of grain size from 2 to 12 nm is only 15.9%, much smaller than that for other two-dimension materials with planar crystalline structure. This insensitivity could be attributed to the small difference between the elastic modulus of the crystalline phase and amorphous phase of polycrystalline phosphorene. In addition, the strength deduction obeys well a logarithm relation of grain size, well explained by the dislocation pile-up theory analogous to that of polycrystalline graphene. Overall, our findings provide a better understanding of mechanical properties of polycrystalline phosphorene and establish a guideline for manufacturing and designing novel phosphorene-based nano devices and nano structures.

Forces, energetics, and dynamics of conjugated-carbon ring tethers adhered to CNTs: a computational investigation.

The strength and nature of the interactions between carbon nanotubes (CNTs) and molecular tethers plays a vital role in technology such as CNT-enzyme sensors. Tethers that attach noncovalently to CNTs are ideal for retaining the electrical properties of the CNTs since they do not degrade the CNT surface and effect its electrical conductivity. However, leaching due to weak CNT-tether attachment is very common when using noncovalent tethers, and this has limited their use in commercial products including biosensors. Thus, understanding the fundamental mechanics governing the strength of CNT-tether adhesion is crucial for the design of highly sensitive, viable sensors. Here, we computationally investigate the adhesion strength of CNT-tether complexes with 8 different tethering molecules designed to adhere noncovalently to the CNT surface. We study the effects of CNT diameter, CNT chirality, and the size/geometry of the tethering molecule on the adhesion energy and force. Our results show an asymptotic relationship between adhesion strength and CNT diameter. Calculations show that noncovalent tethers tested here can reach adhesion forces and energies that are up to 21% and 54% of the strength of the carbon-carbon single bond force and bond energy respectively. We anticipate our results will help guide CNT-enzyme sensor design to produce sensors with high sensitivity and minimal leaching.

Mechanisms of circumferential gyral convolution in primate brains.

Mammalian cerebral cortices are characterized by elaborate convolutions. Radial convolutions exhibit homology across primate species and generally are easily identified in individuals of the same species. In contrast, circumferential convolutions vary across species as well as individuals of the same species. However, systematic study of circumferential convolution patterns is lacking. To address this issue, we utilized structural MRI (sMRI) and diffusion MRI (dMRI) data from primate brains. We quantified cortical thickness and circumferential convolutions on gyral banks in relation to axonal pathways and density along the gray matter/white matter boundaries. Based on these observations, we performed a series of computational simulations. Results demonstrated that the interplay of heterogeneous cortex growth and mechanical forces along axons plays a vital role in the regulation of circumferential convolutions. In contrast, gyral geometry controls the complexity of circumferential convolutions. These findings offer insight into the mystery of circumferential convolutions in primate brains.

4D Origami by Smart Embroidery.

There exist many methods for processing of materials: extrusion, injection molding, fibers spinning, 3D printing, to name a few. In most cases, materials with a static, fixed shape are produced. However, numerous advanced applications require customized elements with reconfigurable shape. The few available techniques capable of overcoming this problem are expensive and/or time-consuming. Here, the use of one of the most ancient technologies for structuring, embroidering, is proposed to generate sophisticated patterns of active materials, and, in this way, to achieve complex actuation. By combining experiments and computational modeling, the fundamental rules that can predict the folding behavior of sheets with a variety of stitch-patterns are elucidated. It is demonstrated that theoretical mechanics analysis is only suitable to predict the behavior of the simplest experimental setups, whereas computer modeling gives better predictions for more complex cases. Finally, the applicability of the rules by designing basic origami structures and wrinkling substrates with controlled thermal insulation properties is shown.

Fracture mechanisms in multilayer phosphorene assemblies: from brittle to ductile.

The outstanding mechanical performance of nacre has stimulated numerous studies on the design of artificial nacres. Phosphorene, a new two-dimensional (2D) material, has a crystalline in-plane structure and non-bonded interaction between adjacent flakes. Therefore, multi-layer phosphorene assemblies (MLPs), in which phosphorene flakes are piled up in a staggered manner, may exhibit outstanding mechanical performance, especially exceptional toughness. Therefore, molecular dynamics simulations are performed to study the dependence of the mechanical properties on the overlap distance between adjacent phosphorene layers and the number of phosphorene flakes per layer. The results indicate that when the flake number is equal to 1, a transition of fracture patterns is observed by increasing the overlap distance, from a ductile failure controlled by interfacial friction to a brittle failure dominated by the breakage of covalent bonds inside phosphorene flakes. Moreover, the failure pattern can be tuned by changing the number of flakes in each phosphorene layer. The results imply that the ultimate strength follows a power law with the exponent -0.5 in terms of the flake number, which is in good agreement with our analytical model. Furthermore, the flake number in each phosphorene layer is optimized as 2 when the temperature is 1 K in order to potentially achieve both high toughness and strength. Moreover, our results regarding the relations between mechanical performance and overlap distance can be explained well using a shear-lag model. However, it should be pointed out that increasing the temperature of MLPs could cause the transition of fracture patterns from ductile to brittle. Therefore, the optimal flake number depends heavily on temperature to achieve both its outstanding strength and toughness. Overall, our findings unveil the fundamental mechanism at the nanoscale for MLPs as well as provide a method to design phosphorene-based structures with targeted properties via tunable overlap distance and flake number in phosphorene layers.

Grain-size dependence of mechanical properties in polycrystalline boron-nitride: a computational study.

The field of research in polycrystalline hexagonal boron nitride (PBN) has been enjoying extraordinary growth recently, in no small part due to the rise of graphene and the technical advancement of mass production in polycrystalline 2D materials. However, as the grain size in 2D materials can strongly affect their materials properties and the performance of their relevant devices, it is highly desirable to investigate this effect in PBN and leverage the service capability of PBN-based devices. Here we employ molecular dynamics simulations to explore the effects of grain size in PBN on its mechanical properties such as Young's modulus, yield strength, toughness, and energy release rate as well as its failure mechanism. By visualizing and comparing the tensile failure of PBN with and without a predefined crack we have shown that the grain size of PBN is positively correlated with its elastic modulus, yield strength and toughness. Through inclusion of a crack with varying length in the PBN samples, the energy release rate is determined for each grain size of PBN and it is concluded that the energy release rate increases with an increase in the average grain size of PBN. These findings offer useful insights into utilizing PBN for mechanical design in composite materials, abrasion resistance, and electronic devices etc.

On the crumpling of polycrystalline graphene by molecular dynamics simulation.

Crumpled graphene has been emerging as a valuable component for a variety of devices such as supercapacitors or hydrophobic surface coatings due to its geometric change from a 2D to a 3D structure accompanied by changes in its material behavior. As polycrystalline graphene is easier to produce than pristine graphene, certain applications of crumpled graphene may be better suited to polycrystalline graphene. However, the crumpling process of polycrystalline graphene and its relevant mechanical properties remain poorly understood. Here we employ molecular dynamics simulation to model the behavior of polycrystalline graphene under geometric confinement and elucidate the effect of grain size, with a focus on the mechanical stabilizing mechanisms and properties of the crumpled structures in comparison to pristine graphene. Simulation results show that crumpled polycrystalline graphene exhibits a slight negative correlation between average grain size and measured hardness, bulk modulus, and crumpled size. As the size of the grains decreases, the crumpled structures formed are harder and smaller, with sharp edges caused by the grain boundaries. These findings provide evidence towards the feasibility of using polycrystalline graphene in place of pristine graphene in applications involving crumpled carbon structures.

Molecular dynamics study of a CNT-buckyball-enabled energy absorption system.

An energy absorption system (EAS) composed of a carbon nanotube (CNT) with nested buckyballs is put forward for energy dissipation during impact owing to the outstanding mechanical properties of both CNTs and buckyballs. Here we implement a series of molecular dynamics (MD) simulations to investigate the energy absorption capabilities of several different EASs based on a variety of design parameters. For example, the effects of impact energy, the number of nested buckyballs, and of the size of the buckyballs are analyzed to optimize the energy absorption capability of the EASs by tuning the relevant design parameters. Simulation results indicate that the energy absorption capability of the EAS is closely associated with the deformation characteristics of the confined buckyballs. A low impact energy leads to recoverable deformation of the buckyballs and the dissipated energy is mainly converted to thermal energy. However, a high impact energy yields non-recoverable deformation of buckyballs and thus the energy dissipation is dominated by the strain energy of the EAS. The simulation results also reveal that there exists an optimal value of the number of buckyballs for an EAS under a certain impact energy. Larger buckyballs are able to deform to a larger degree yet also need less impact energy to induce plastic deformation, therefore performing with a better overall energy absorption ability. Overall, the EAS in this study shows a remarkably high energy absorption density of 2 kJ g(-1), it is a promising candidate for mitigating impact energy and sheds light on the research of buckyball-filled CNTs for other applications.