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Abstract Understanding the processes of axonal growth and pathfinding during cortical folding in the brain is crucial to unravel the mechanisms underlying brain disorders that disturb connectivity throughout human brain development. However, this topic remains incompletely understood, highlighting the need for further investigation. Here, we propose and evaluate a diffusion based-mechanistic model to understand how axons grow and navigate in the folding brain. To do so, a bilayer growth model simulating the brain was devised involving a thin gray matter overlying a thick white matter. Innovatively, the stochastic model of axonal growth was linked with the stress and deformation fields of the folding bilayer system. The results showed that the modulus ratio of the gray matter to the white matter and the axonal growth rate are two potentially critical parameters that significantly influence axon pathfinding in the folding brain. The model demonstrated robust predictability in identifying axonal termination points and offered a potential mechanism explaining why axons settle more in gyri (ridges) than sulci (valleys) of the brain. Importantly, the results explain how alterations in the mechanical properties of the folding system can impact the underlying connectivity patterning. This mechanistic insight not only enhances our understanding of brain connectivity development during the fetal stage but also sheds light on brain disorders characterized by linked abnormalities in cortical folds and disruptions in connectivity. 

Abstract Understanding the mechanics linking cortical folding and brain connectivity is crucial for both healthy and abnormal brain development. Despite the importance of this relationship, existing models fail to explain how growing axon bundles navigate the stress field within a folding brain or how this bidirectional and dynamic interaction shapes the resulting surface morphologies and connectivity patterns. Here, we propose the concept of “axon reorientation” and formulate a mechanical model to uncover the dynamic multiscale mechanics of the linkages between cortical folding and connectivity development. Simulations incorporating axon bundle reorientation and stress-induced growth reveal potential mechanical mechanisms that lead to higher axon bundle density in gyri (ridges) compared to sulci (valleys). In particular, the connectivity patterning resulting from cortical folding exhibits a strong dependence on the growth rate and mechanical properties of the navigating axon bundles. Model predictions are supported by in vivo diffusion tensor imaging of the human brain. 

Abstract Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress–strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments. 

Abstract 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. 

Abstract Tissue interfaced electronics have become promising candidates for transcending beyond conventional diagnostic technology, enabling chronic, quantitative health monitoring possibilities; however, these systems have primarily relied on impenetrable materials that contribute to the mechanical and physical mismatch of bioelectronic interfaces. Inspired by the soft mechanics and physical architecture of the epidermal extracellular matrix, this study presents a 3D microporous, fibrous mesh of polydimethylsiloxane for epidermal electronics. The resulting elastic microfiber mat, exhibits a minimal mechanical footprint with analogous viscoelastic behavior, cytocompatibility, and biofluid‐permeable interface capable of small molecule, gas, and transdermal water diffusion. Electrocardiography electrodes heterogeneously integrate within the synthetic electronic‐extracellular matrix (e‐ECM) membrane and achieve chronic high resolution biopotential monitoring during typically debilitating environments (e.g., vigorous sweating) for conventional bioelectronics. The e‐ECM platform provides a substrate template for open‐mesh electronics, enabling advanced implementations in long‐term quantitative analysis monitoring for wearable and implantable devices.