Tiktinsky-Shklovsky V.M.

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



Research Interests: Medicine & Healthcare, Medicine


Tiktinsky-Shklovsky Viktor Markovich is Ph.D, holder of an Advanced Doctorate in Psychology, a Professor, a full member of the Russian Academy of Education (RAE), a leading Russian expert in the field of speech and language pathology and neurorehabilitation.

The main fields of Prof. V.M. Tiktinsky-Shklovsky’s scientific activity are the development of problems of the clinic, diagnosis, pathogenesis and therapy of language, speech, and communicative disorders. He began his professional activity in 1951 as a defectologist. In 1958, he organized the first outpatient department for adult patients in the USSR - the Speech Pathology Center. V.M. Tiktinsky-Shklovsky developed the concept of interdisciplinary approach to diagnosis, treatment and neurorehabilitation of patients with stroke, brain injuries, and other diseases of the central nervous system, the system and algorithm for organizing specialized care at different stages of the disease.

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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