Biomechanical models and mechanisms of cellular morphogenesis and cerebral cortical expansion and folding

Biophysical basis for brain folding and misfolding patterns in ferrets and humans

A mechanistic understanding of neurodevelopment requires us to follow the multiscale processes that connect molecular genetic processes to macroscopic cerebral cortical formations and thence to neurological function. Using magnetic resonance imaging of the brain of the ferret, a model organism for studying cortical morphogenesis, we create in vitro physical gel models and in silico numerical simulations of normal brain gyrification. Using observations of genetically manipulated animal models, we identify cerebral cortical thickness and cortical expansion rate as the primary drivers of dysmorphogenesis and demonstrate that in silico models allow us to examine the causes of aberrations in morphology and developmental processes at various stages of cortical ontogenesis. Finally, we explain analogous cortical malformations in human brains, with comparisons with human phenotypes induced by the same genetic defects, providing a unified perspective on brain morphogenesis that is driven proximally by genetic causes and affected mechanically via variations in the geometry of the brain and differential growth of the cortex.

Decoding cortical folding patterns in marmosets using machine learning and large language model

Macroscale neuroimaging results have revealed significant differences in the structural and functional connectivity patterns of gyri and sulci in the primate cerebral cortex. Despite these findings, understanding these differences at the molecular level has remained challenging. This study leverages a comprehensive dataset of whole-brain in situ hybridization (ISH) data from marmosets, with updates continuing through 2024, to systematically analyze cortical folding patterns. Utilizing advanced machine learning algorithm and large language model (LLM), we identified genes with significant transcriptomic differences between concave (sulci) and convex (gyri) cortical patterns. Further, gene enrichment analysis, neural migration analysis, and axon guidance pathway analysis were employed to elucidate the molecular mechanisms underlying these structural and functional differences. Our findings provide new insights into the molecular basis of cortical folding, demonstrating the potential of LLM in enhancing our understanding of brain structural and functional connectivity.

Role of data-driven regional growth model in shaping brain folding patterns

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. Recent findings indicate significant regional variations in brain tissue growth, while the role of these variations in cortical development remains unclear. In this study, we explored how regional cortical growth affects brain folding patterns using computational simulation. We first developed growth models for typical cortical regions using machine learning (ML)-assisted symbolic regression, based on longitudinal real surface expansion and cortical thickness data from prenatal and infant brains derived from over 1000 MRI scans of 735 pediatric subjects with ages ranging from 29 postmenstrual weeks to 2 years of age. These models were subsequently integrated into computational software to simulate cortical development with anatomically realistic geometric models. We comprehensively quantified the resulting folding patterns using multiple metrics such as mean curvature, sulcal depth, and gyrification index. Our results demonstrate that regional growth models generate complex brain folding patterns that more closely match actual brains structures, both quantitatively and qualitatively, compared to conventional uniform growth models. Growth magnitude plays a dominant role in shaping folding patterns, while growth trajectory has a minor influence. Moreover, multi-region models better capture the intricacies of brain folding than single-region models. Our results underscore the necessity and importance of incorporating regional growth heterogeneity into brain folding simulations, which could enhance early diagnosis and treatment of cortical malformations and neurodevelopmental disorders such as cerebral palsy and autism.

The Control of Cortical Folding: Multiple Mechanisms, Multiple Models

The cerebral cortex develops through a carefully conscripted series of cellular and molecular events that culminate in the production of highly specialized neuronal and glial cells. During development, cortical neurons and glia acquire a precise cellular arrangement and architecture to support higher-order cognitive functioning. Decades of study using rodent models, naturally gyrencephalic animal models, human pathology specimens, and, recently, human cerebral organoids, reveal that rodents recapitulate some but not all the cellular and molecular features of human cortices. Whereas rodent cortices are smooth-surfaced or lissencephalic, larger mammals, including humans and nonhuman primates, have highly folded/gyrencephalic cortices that accommodate an expansion in neuronal mass and increase in surface area. Several genes have evolved to drive cortical gyrification, arising from gene duplications or de novo origins, or by alterations to the structure/function of ancestral genes or their gene regulatory regions. Primary cortical folds arise in stereotypical locations, prefigured by a molecular "blueprint" that is set up by several signaling pathways (e.g., Notch, Fgf, Wnt, PI3K, Shh) and influenced by the extracellular matrix. Mutations that affect neural progenitor cell proliferation and/or neurogenesis, predominantly of upper-layer neurons, perturb cortical gyrification. Below we review the molecular drivers of cortical folding and their roles in disease.

Mechanobiological insight into brain diseases based on mechanosensitive channels: Common mechanisms and clinical potential

BACKGROUND

As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide-distributed, high-expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders.

AIMS

To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases.

METHOD

We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place-occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues.

RESULTS

The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment.

CONCLUSIONS

Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases.

Axonal tension contributes to consistent fold placement

Cortical folding is a critical process during brain development, resulting in morphologies that are both consistent and distinct between individuals and species. While earlier studies have highlighted important aspects of cortical folding, most existing computational models, based on the differential growth theory, fall short of explaining why folds tend to appear in particular locations. The axon tension hypothesis may provide insight into this conundrum; however, there has been significant controversy about a potential role of axonal tension during the gyrification. The common opinion in the field is that axonal tension is inadequate to drive gyrification, but we currently run the risk of discarding this hypothesis without comprehensively studying the role of axonal tension. Here we propose a novel bi-layered finite element model incorporating the two theories, including characteristic axonal tension in the subcortex and differential cortical growth. We show that axon tension can serve as a perturbation sufficient to trigger buckling in simulations; similarly to other types of perturbations, the natural stability behavior of the system tends to determine some characteristics of the folding morphology (e.g. the wavelength) while the perturbation determines the location of folds. Certain geometries, however, can interact or compete with the natural stability of the system to change the wavelength. When multiple perturbations are present, they similarly compete with each other. We found that an axon bundle of reasonable size will overpower up to a 5% thickness perturbation (typical in the literature) and determine fold placement. Finally, when multiple axon tracts are present, even a slight difference in axon stiffness, representing the heterogeneity of axonal connections, is enough to significantly change the folding pattern. While the simulations presented here are a very simple representation of white matter connectivity, our findings point to urgent future research on the role of axon connectivity in cortical folding.

Volumetric compression by heterogeneous scaffold embedding promotes cerebral organoid maturation and does not impede growth

Although biochemical regulation has been extensively studied in organoid modeling protocols, the role of mechanoregulation in directing stem cell fate and organoid development has been relatively unexplored. To accurately replicate the dynamic organoid development observed in nature, in this study, we present a method of heterogeneous embedding using an alginate-shell-Matrigel-core system. This approach allows for cell-Matrigel remodeling by the inner layer and provides short-term moderate-normal compression through the soft alginate outer layer. Our results show that the time-limited confinement contributes to increased expression of neuronal markers such as neurofilament (NF) and microtubule-associated protein 2 (MAP2). Compared with non-alginate embedding and alginate compression groups, volume growth remains unimpeded. Our findings demonstrate the temporary mechanical regulation of cerebral organoid growth, which exhibits a regular growth profile with enhanced maturation. These results highlight the importance and potential practical applications of mechanoregulation in the establishment of brain organoids. A record of this paper's transparent peer review process is included in the supplemental information.

Mechanobiology in Cells and Tissues

This Editorial is a comment on the success of the Special Issue "Mechanobiology in Cells and Tissues" published in the International Journal of Molecular Sciences [...].