Toward Understanding Parameters That Influence Cortical Folding
Poster No:
1735
Submission Type:
Abstract Submission
Authors:
Monica Hurdal1
Institutions:
1Florida State University, Tallahassee, FL
First Author:
Introduction:
There is considerable debate among biologists as to how folding patterns develop, including biochemical [1], biomechanical [2], and differential growth hypotheses. In previous work we developed a combined biochemical-biomechanical model to elucidate the mechanisms of cortical folding development [3, 4]. In this research, we alter various model parameters and demonstrate how disorders of cortical folding formations can be explained by parameters in our model. MR images of brains with hemimegalencephaly show one hemisphere is enlarged, the hemisphere grows asymmetrically, and the cortex is thicker. MR images of brains with Norman Roberts Syndrome can present with enlarged lateral ventricles, lissencephaly, microcephaly, and reduced head growth rate. Our model allows us to explore possible mechanisms involved in these types of disorders of cortical formation by modifying model parameters and initial conditions.
Methods:
The intermediate progenitor (IP) model is a biochemical biological hypothesis to explain pre-patterning of cortical folding in early development [1]. Several genes have been shown to regulate cortical folding via modulating IP cell development in mice. Our biochemical model uses a dynamically growing domain Turing reaction-diffusion system where the morphogens regulate intermediate progenitor (IP) cell patterning [3].
The biomechanical portion of the model is based on an axonal tension hypothesis [2]. We use a linear stress-strain-elasticity model with biophysical parameters to determine displacements due to external forces [4]. The concentration of neurons from our biochemical model are determined by the Turing patterns and govern the magnitude of the applied axonal tension forces.
We model asymmetrically increased cell proliferation with irregular Turing patterns, which are generated by changing the initial conditions of the model and applying a genetic regulation parameter. We model the smaller than normal lateral ventricles with a growth rate parameter. Changes in cortical thickness can also be captured by one of the model parameters. Various external forces corresponding to the axonal tension-forces are applied to simulate a variety of folding pattern outcomes.
The biomechanical portion of the model is based on an axonal tension hypothesis [2]. We use a linear stress-strain-elasticity model with biophysical parameters to determine displacements due to external forces [4]. The concentration of neurons from our biochemical model are determined by the Turing patterns and govern the magnitude of the applied axonal tension forces.
We model asymmetrically increased cell proliferation with irregular Turing patterns, which are generated by changing the initial conditions of the model and applying a genetic regulation parameter. We model the smaller than normal lateral ventricles with a growth rate parameter. Changes in cortical thickness can also be captured by one of the model parameters. Various external forces corresponding to the axonal tension-forces are applied to simulate a variety of folding pattern outcomes.
Results:
Asymmetrically increased cell proliferation may occur in a brain with hemimegalencaphaly, which we can model with irregular Turing patterns via changing the initial conditions of the model. These irregular patterns lead to asymmetric forces, resulting in a deformed configuration corresponding to an enlarged hemisphere. Increasing the thickness of the cortex reduces the elongation of the asymmetric development.
A growth rate parameter and a genetic control parameter allow domain size to be controlled, as well as the overall level of genetic expression of activator and inhibitor morphogens. Together, these parameters control the complexity of the folding patterns that are produced. We can model various types of lissencephaly, including Norman-Roberts Syndrome, by varying the genetic control parameter or reducing the growth rate parameter (see Figure 1). Changing the magnitude of the applied forces also contributes to the depth and steepness of the folds (see Figure 2).
A growth rate parameter and a genetic control parameter allow domain size to be controlled, as well as the overall level of genetic expression of activator and inhibitor morphogens. Together, these parameters control the complexity of the folding patterns that are produced. We can model various types of lissencephaly, including Norman-Roberts Syndrome, by varying the genetic control parameter or reducing the growth rate parameter (see Figure 1). Changing the magnitude of the applied forces also contributes to the depth and steepness of the folds (see Figure 2).
·Figure 1: Varying the model growth rate parameter can generate fewer cortical folds (left) or more folds (right).
·Figure 2: Modifying the magnitude of the applied forces can generate lissencepahlic folding patterns (top) and steeper folds (bottom).
Conclusions:
Cortical folding is influenced by a variety of factors. Domain size, directions of the pulling forces, strengths of the forces, and cortical thickness influence folding patterns and can be used to capture characteristics of hemimegalencephaly. Various forms of polymicrogyria and lissencephaly can be captured by by altering parameters controlling domain growth, genetic expression levels, and model initial conditions.
Our model represents an important step in improving our understanding of cortical folding pattern formation in the brain. Our results demonstrate that characteristics of cortical folding malformations are influenced by multiple parameters, which can work in tandem to influence and alter the development of cortical folds.
Our model represents an important step in improving our understanding of cortical folding pattern formation in the brain. Our results demonstrate that characteristics of cortical folding malformations are influenced by multiple parameters, which can work in tandem to influence and alter the development of cortical folds.
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal
Methods Development 2
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Neuroanatomy Other
Keywords:
Cortex
Development
DISORDERS
Modeling
STRUCTURAL MRI
Other - Brain Mapping; Cortical Folding; Folding Disorders; Sulcus
1|2Indicates the priority used for review
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[2] Van Essen, D.C. (2020), PNAS, 117: 32868-32879.
[3] Toole, G. & Hurdal, M.K. (2014), J Dyn Diff Eq, 6: 315-332.
[4] Kim, S. & Hurdal, M.K. (2015), A Biomechanical Model of Cortical Folding. In: Leonard, K., Tari, S. (eds) Research in Shape Modeling. Association for Women in Mathematics Series, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-16348-2_4