Ivanova E.G.

Work place: The Center of Speech Pathology and Neurorehabilitation, Moscow, Russian Federation



Research Interests: Analysis of Algorithms, Mathematics of Computing, Models of Computation


Ivanova Elena Georgievna is a clinical psychologist, Ph.D., an associate professor of the Department of Clinical Psychology of the Pirogov Russian National Research Medical University (RNRMU), a specialist in the field of aphasiology, rehabilitation, and recovery of higher mental functions after organic brain damages. She has more than 30 scientific publications. She was graduated from RNRMU, 2008. Her theme of Ph.D. thesis (2015) was «Variability of speech disorders in agraphia under performing different types of written tasks». Scientific interests: aphasiology, neurorehabilitation, neurolinguistics, level structure of mental functions, teleological approach in the study of mental functions, memory disorders.

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: https://doi.org/10.5815/ijisa.2019.06.03, 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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