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Razavi, M. J.

Publications and source records attributed to Razavi, M. J..

2 recordsLinked to original sources

A Mechanical Theory for the Formation of Short Association Fibers in the Brain

The development of neural connections in the brain results from a complex interplay between biological processes and mechanical forces. A key question in neuroscience is how physical forces and the mechanical properties of brain tissue influence the formation of structural connections. Here, we demonstrate that mechanical forces play an essential role in shaping the emergence of short-range connections, particularly U-shaped fibers that link neighboring regions of the cortex. Using a computational model that incorporates our "stress-dependent axon reorientation" hypothesis, we simulate how growing axons respond to the mechanical stress field generated by cortical folding. Our results suggest that axonal growth and reorientation may be strongly influenced by local mechanical cues, helping establish the organization of these short-range pathways. Supported by in vivo diffusion tensor imaging and histological observations, our findings provide a physical explanation for why these fibers predominantly adopt U-shaped trajectories, and why connections between gyri (ridges) are more prevalent than those between sulci (valleys) or spanning gyri and sulci. These results suggest that understanding the mechanics of brain folding is critical for fully explaining the formation of brain connectivity and its variations in health and disorder. Teaser: Mechanical forces during cortical folding guide the formation of short association fibers in the brain.

bioengineering↗

Mechanics of the Spatiotemporal Evolution of Sulcal Pits in the Folding Brain

Understanding the development of complex brain surface morphologies during the fetal stage is essential for uncovering mechanisms behind brain disorders linked to abnormal cortical folding. However, knowledge of the spatiotemporal evolution of fetal brain landmarks is limited due to the lack of longitudinal data capturing multiple timepoints for individual brains. In this study, we develop and validate a true-scale, image-based mechanical model to explore the spatiotemporal evolution of brain sulcal pits in individual fetal brains. Our model, constructed using magnetic resonance imaging (MRI) scans from the first timepoint of longitudinal data, predicts the brains surface morphology by comparing the distribution of sulcal pits between predicted models and MRI scans from a later timepoint. This dynamic model elucidates how a smooth fetal brain with primary folds evolves to form secondary and tertiary folds. Our results align with imaging data, showing that sulcal pits are stable during brain development and can serve as key markers linking prenatal and postnatal brain characteristics. The model provides a robust platform to study the evolution of sulcal pits in both healthy and disordered brains, which is crucial as altered sulcal pits patterns are seen in disorders such as autism spectrum disorder (ASD), polymicrogyria, down syndrome, and agenesis of the corpus callosum. This research represents a significant advancement in understanding fetal brain development and its connection to disorders that manifest as abnormal sulcal pit patterns later in life.

bioengineering↗