Skip to main content

Analytical Model: Growing Cortex on growing subcortex

In the analytical model of brain development, the scenario of a growing cortex on a growing subcortex is considered. Here are the key aspects of this analytical model:


1. Model Description: The model involves representing the cortex as a morphogenetically growing outer layer and the subcortex as a strain-driven growing inner core. This dual-layered approach captures the dynamic nature of both layers as they interact and influence the folding patterns of the brain.


2.  Mechanical Interactions: The model accounts for the mechanical interactions between the growing cortex and subcortex, considering how their respective growth rates and properties influence the deformation and folding of the brain tissue. This approach integrates both axonal tension-driven and differential growth-driven hypotheses of cortical folding.


3.  Continuum Theory of Finite Growth: The model is based on the continuum theory of finite growth, which describes the growth and deformation of biological tissues over time. By incorporating growth mechanisms into the model, researchers can simulate the evolving morphology of the brain surface during development.


4.  Parameter Exploration: The model explores the effects of varying parameters such as cortical thickness, stiffness ratios, and growth rates between the cortex and subcortex. By systematically varying these parameters, researchers can analyze how different growth dynamics impact the folding patterns and surface morphologies of the brain.


5. Analytical Estimates: The model provides analytical estimates for critical parameters such as the critical time, pressure, and wavelength at the onset of folding. These estimates offer insights into the conditions under which cortical folding initiates and how the growth dynamics of the cortex and subcortex contribute to this process.


6. Integration with Cellular Mechanisms: The model aims to connect the macroscopic mechanical behavior of the cortex-subcortex system with underlying cellular mechanisms such as axon elongation. By bridging the gap between macroscopic and microscopic scales, researchers can better understand the biological processes driving cortical folding.


In summary, the analytical model of a growing cortex on a growing subcortex offers a comprehensive framework for studying the mechanical and morphological aspects of brain development. By incorporating growth dynamics and mechanical interactions into the model, researchers can simulate the complex folding patterns observed in the developing brain and gain insights into the underlying mechanisms shaping brain morphology.

 

Comments

Popular posts from this blog

How do pharmacological interventions targeting NMDA glutamate receptors and PKCc affect alcohol drinking behavior in mice?

Pharmacological interventions targeting NMDA glutamate receptors and PKCc can have significant effects on alcohol drinking behavior in mice. In the context of the study discussed in the PDF file, the researchers investigated the impact of these interventions on ethanol-preferring behavior in mice lacking type 1 equilibrative nucleoside transporter (ENT1). 1.   NMDA Glutamate Receptor Inhibition : Inhibition of NMDA glutamate receptors can reduce ethanol drinking behavior in mice. This suggests that NMDA receptor-mediated signaling plays a role in regulating alcohol consumption. By blocking NMDA receptors, the researchers were able to observe a decrease in ethanol intake in ENT1 null mice, indicating that NMDA receptor activity is involved in the modulation of alcohol preference. 2.   PKCc Inhibition : Down-regulation of intracellular PKCc-neurogranin (Ng)-Ca2+-calmodulin dependent protein kinase type II (CaMKII) signaling through PKCc inhibition is correlated with reduced CREB activity

How the Neural network circuits works in Parkinson's Disease?

  In Parkinson's disease, the neural network circuits involved in motor control are disrupted, leading to characteristic motor symptoms such as tremor, bradykinesia, and rigidity. The primary brain regions affected in Parkinson's disease include the basal ganglia and the cortex. Here is an overview of how neural network circuits work in Parkinson's disease: 1.      Basal Ganglia Dysfunction: The basal ganglia are a group of subcortical nuclei involved in motor control. In Parkinson's disease, there is a loss of dopamine-producing neurons in the substantia nigra, leading to decreased dopamine levels in the basal ganglia. This dopamine depletion results in abnormal signaling within the basal ganglia circuitry, leading to motor symptoms. 2.      Cortical Involvement: The cortex, particularly the motor cortex, plays a crucial role in initiating and coordinating voluntary movements. In Parkinson's disease, abnormal activity in the cortex, especially in the beta and gamma

Force-Velocity Relationship

The force-velocity relationship in muscle physiology describes how the force a muscle can generate is influenced by the velocity of muscle contraction. Here are key points regarding the force-velocity relationship: 1.     Inverse Relationship : o     The force-velocity relationship states that the force a muscle can generate is inversely related to the velocity of muscle shortening. o     At higher contraction velocities (faster shortening), the force-generating capacity of the muscle decreases. o     Conversely, at lower contraction velocities (slower shortening), the muscle can generate higher forces. 2.     Factors Influencing Force-Velocity Relationship : o     Cross-Bridge Cycling : The rate at which cross-bridges form and detach during muscle contraction affects the force-velocity relationship. At higher velocities, there is less time for cross-bridge formation, leading to reduced force production. o     Energy Availability : The availability of ATP, which powers muscle contracti

How can a better understanding of the physical biology of brain development contribute to advancements in neuroscience and medicine?

A better understanding of the physical biology of brain development can significantly contribute to advancements in neuroscience and medicine in the following ways: 1.    Insights into Neurodevelopmental Disorders:  Understanding the role of physical forces in brain development can provide insights into the mechanisms underlying neurodevelopmental disorders. By studying how disruptions in mechanical cues affect brain structure and function, researchers can identify new targets for therapeutic interventions and diagnostic strategies for conditions such as autism, epilepsy, and intellectual disabilities. 2.   Development of Novel Treatment Approaches:  Insights from the physical biology of brain development can inspire the development of novel treatment approaches for neurological disorders. By targeting the mechanical aspects of brain development, such as cortical folding or neuronal migration, researchers can design interventions that aim to correct abnormalities in brain structure and

Complex Random Sampling Designs

Complex random sampling designs refer to sampling methods that involve a combination of various random sampling techniques to select a sample from a population. These designs often incorporate elements of both probability and non-probability sampling methods to achieve specific research objectives. Here are some key points about complex random sampling designs: 1.     Definition : o     Complex random sampling designs involve the use of multiple random sampling methods, such as systematic sampling, stratified sampling, cluster sampling, etc., in a structured manner to select a sample from a population. o     These designs aim to improve the representativeness, efficiency, and precision of the sample by combining different random sampling techniques. 2.     Purpose : o    The primary goal of complex random sampling designs is to enhance the quality of the sample by addressing specific characteristics or requirements of the population. o     Researchers may use these designs to increase