Modeling of human brain diseases in experiments on rodents (brief overview)

Depressive disorders are one of the leading problems of modern health care. Their prevalence in the world ranges from 4.4 to 20 % of the population, and in Russia the incidence of depression reaches 20 %. The study of depressive disorders is relevant not only from the point of view of psychiatry and behavioral therapy, but also occupies a leading place in neurological practice, since more than a quarter of the population with chronic neurological diseases suffer from depression. Currently, most basic biomedical research on depressive disorders is carried out using laboratory animals. However, the protocols for experimental modeling and analysis of behavioral patterns are very diverse, which creates certain difficulties in planning research. This review is aimed at describing, analyzing, comparing and systematizing possible approaches to creating experimental models of depression in animals, and also contains criteria for model validation and a description of behavioral tests to assess anxiety-depressive disorders.

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Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets—On an Example of Diagnosing Alzheimer’s Disease

Alekseev

Kozhemyakin

Никитин

et al. 2023

Algorithms

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This paper aimed to increase accuracy of an Alzheimer’s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer’s disease. The obtained decision root is a discrete switching function of several variables applicated to aggregation of a few indicators to one integrated assessment presents as a superposition of few functions of two variables. Magnetic susceptibility values of the basal veins and veins of the thalamus were used as indicators. Two categories of patients were used as function values. To increase accuracy, the idea of using artificial neural networks was suggested, but a feature of medical data is its limitation. Therefore, neural networks based on limited training datasets may be inefficient. The solution to this problem is proposed to preprocess initial datasets to determine the parameters of the neural networks based on decisions’ roots, because it is known that any can be represented in the incompletely connected neural network form with a cascade structure. There are no publicly available specialized software products allowing the user to set the complex structure of a neural network, which is why the number of synaptic coefficients of an incompletely connected neural network has been determined. This made it possible to predefine fully connected neural networks, comparable in terms of the number of unknown parameters. Acceptable accuracy was obtained in cases of one-layer and two-layer fully connected neural networks trained on limited training sets on an example of diagnosing Alzheimer’s disease. Thus, the scientific hypothesis on preprocessing initial datasets and neural network architecture selection using special methods and algorithms was confirmed.

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Modeling of human brain diseases in experiments on rodents (brief overview)

Cited by 3 publications

References 52 publications

Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

Current methods of modeling depression in animals and approaches to the study of their efficiency in biomedical research (review of the problem)

Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets—On an Example of Diagnosing Alzheimer’s Disease

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