Abstract
Cognitive dysfunction associated with radiotherapy for cancer treatment has been correlated to several factors, one of which is changes to the dendritic morphology of neuronal cells. Alterations in dendritic geometry and branching patterns are often accompanied by deficits that impact learning and memory. The purpose of this study is to develop a novel predictive model of neuronal dendritic damages caused by exposure to low linear energy transfer (LET) radiation, such as X-rays, γ-rays and high-energy protons. We established in silico representations of mouse hippocampal dentate granule cell layer (GCL) and CA1 pyramidal neurons, which are frequently examined in radiation-induced cognitive decrements. The in silico representations are used in a stochastic model that describes time dependent dendritic damage induced by exposure to low LET radiation. Changes in morphometric parameters, such as total dendritic length, number of branch points and branch number, including the Sholl analysis for single neurons are described by the model. Our model based predictions for different patterns of morphological changes based on energy deposition in dendritic segments (EDDS) will serve as a useful basis to compare specific patterns of morphological alterations caused by EDDS mechanisms.
Highlights
Cranial radiotherapy is widely used to treat primary and metastatic brain tumors in children and adults, and while this can effectively extend the lifespan of cancer patients, these treatments are routinely associated with serious complications, including cognitive dysfunction[1,2,3]
We develop a novel predictive model that characterizes the time dependent neuronal dendritic degradation caused by exposure to low linear energy transfer (LET) radiation
Computer simulated mouse hippocampal dentate granule cell layer (GCL) and CA1 pyramidal neurons, which are frequently examined in radiation-induced cognitive detriments, are first generated using simple stochastic growth models that follow the elementary rules of dendrite development[24,26,27,36,37] and adopt specifications that manifest neuron morphometric parameters reported in rodent experimentation
Summary
Cranial radiotherapy is widely used to treat primary and metastatic brain tumors in children and adults, and while this can effectively extend the lifespan of cancer patients, these treatments are routinely associated with serious complications, including cognitive dysfunction[1,2,3]. Computer simulated mouse hippocampal dentate granule cell layer (GCL) and CA1 pyramidal neurons, which are frequently examined in radiation-induced cognitive detriments, are first generated using simple stochastic growth models that follow the elementary rules of dendrite development[24,26,27,36,37] and adopt specifications that manifest neuron morphometric parameters reported in rodent experimentation. Radiation-induced changes in neuronal morphology expressed as reductions in total dendritic length, number of branch points and branch numbers can be obtained using a probabilistic model This model is used to determine if a given branch segment would be damaged and a mathematical model of damaged segment kinetics represented by ordinary differential equations is used to determine whether the number of damaged segments would be eventually “snipped”, a term devised to distinguish this “event” from the neurobiological process of dendritic pruning. Results for a population of neurons are modeled by considering a correction for the fraction of cell loss, which increases with radiation dose
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