Nesmeyanova E.S.

Work place: The Lomonosov Moscow State University, Moscow, Russian Federation



Research Interests: Artificial Intelligence, Speech Recognition, Speech Synthesis, Statistics, Programming Language Theory


Nesmeyanova Elena Sergeevna is Ph.D. a postgraduate student, the Department of Biophysics, Physics Faculty, the Lomonosov Moscow State University (MSU), a researcher at the Institute of Higher Nervous Activity and Neurophysiology of the RAS. She was graduated from MSU in 2017, and she has master’s degree in physics. Theme of the master's thesis (2017) was «Physicochemical mechanisms of antiviral activity of sulfated polysaccharides». Scientific interests: programming theory, artificial intelligence, speech and language processing, statistics, neuroimaging, digital signal processing. Scientific work experience - 5 years.

Author Articles
Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

By Stepanyan I.V. Mayorova L.A. Alferova V.V. Ivanova E.G. Nesmeyanova E.S. Petrushevsky A.G. Tiktinsky-Shklovsky V.M.

DOI:, Pub. Date: 8 Jun. 2019

The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.

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