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Ушаков, Вадим Леонидович

Организационные подразделения

Организационная единица

Институт интеллектуальных кибернетических систем

Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.

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Ушаков

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Вадим Леонидович

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Теперь показываю 1 - 8 из 8

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Linguistic parameters for sociomorphic neuronal network models
(2021) Andreyuk, D.; Kuznetsova, E.; Levina, T.; Ushakov, V.; Ушаков, Вадим Леонидович
© 2020 Elsevier B.V.. All rights reserved.Modelling of social interactions by means of mathematical neuronal networks is considered promising, firstly, by imitating and predicting certain social outcomes, dealing with the information processing in social groups and working out a collective decision, and, secondly, by engineering of social groups and building social groups with higher processing capacity in the logic of the neuronal network efficiency. This fact makes it necessary to select special parameters which, on the one side, are essential to keep track in such models since they are totally connected with the fundamentals of the human social nature, and, on the other side, do not excess the model. Such parameters are likely to include speech and linguistic features of social communication. The research is focused on three linguistic factors which seem logical to be included into the sociomorphic neuronal network models: 1) the factor of comprehension or language proximity related to the vector proximity since linguistic variations in social groups are likely to have a continuous nature; 2) the factor of communicative malfunction and distortion; 3) the factor of ideological proximity. The article reviews the possibilities of considering these factors within the structure of neuronal network models.
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Selection of functionally homogeneous brain regions based on correlation-clustering analysis
(2020) Kozlov, S.; Poyda, A.; Orlov, V.; Malakhov, D.; Ushakov, V.; Ушаков, Вадим Леонидович
© 2020 The Authors. Published by Elsevier B.V.In region of interest (ROI) brain analysis, the proper selection of voxels in ROIs plays a crucial role. In existing methods for selection of functionally homogeneous regions of human brain based on fMRI data, each voxel is attributed to some brain region, not taking into account the possibility of the existence of borderline voxels demonstrating transitional dynamics that cannot be clearly attributed to any of the regions between which these voxels are located. As a result, the situation when voxels formally assigned to one region, but located on the opposite borders of the region, have a correlation close to zero or even negative. In such cases the aggregation of voxels activity does not reflect properly the behavior of the whole area. In this article we present two methods for identifying functionally homogeneous regions based on fMRI data, using a correlation-cluster approach: One of the methods allows identifying functionally homogeneous regions, where voxels have a high level of correlation, while the second one allows identifying functionally homogeneous regions, which are stable over time. Both methods assume that not all brain voxels will be assigned to regions.
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Cognitive Brain Signatures of Youth With Early Onset and Relatives With Schizophrenia: Evidence From fMRI Meta-analyses
(2020) Arsalidou, M.; Yaple, Z.; Jurcik, T.; Ushakov, V.; Ушаков, Вадим Леонидович
© The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.Deficits in cognitive function are a major characteristic of schizophrenia. Many functional magnetic resonance imaging (fMRI) studies examine brain correlates of cognitive function in adults with schizophrenia, showing altered implication of associative areas such as the prefrontal cortex and temporal cortex. fMRI studies also examine brain representation of cognitive function in adolescents with early onset schizophrenia and those at risk of the disorder, yet results are often inconsistent. We compile and analyze data from eligible fMRI studies using quantitative meta-analyses to reveal concordant brain activity associated with adolescent relatives of patients with schizophrenia and those with early onset schizophrenia. Results show similar functional hubs of brain activity (eg, precuneus) yet in opposite hemispheres and clusters in ventrolateral rather than dorsolateral prefrontal cortices. Other areas of altered implication include the middle temporal gyrus, insula, and cerebellum. We discuss the findings in reference to the protracted maturation of the prefrontal cortex and possible effects due to the medication status of the two groups.
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New Insights into the Human Brain's Cognitive Organization: Views from the Top, from the Bottom, from the Left and, particularly, from the Right
(2020) Velichkovsky, B.; Nedoluzhko, A.; Goldberg, E.; Efimova, O.; Ushakov, V.; Ушаков, Вадим Леонидович
© 2020 The Authors. Published by Elsevier B.V.The view that the left cerebral hemisphere in humans dominates over the subdominant right hemisphere has been so deeply entrenched in neuropsychology that no amount of evidence seems able to overcome it. In this article, we examine inhibitory cause-and-effect connectivity among human brain structures related to different parts of the triune evolutionary stratification- A rchicortex, paleocortex and neocortex-in relation to early and late phases of a prolonged resting-state functional magnetic resonance imaging (fMRI) experiment. With respect to the evolutionarily youngest parts of the human cortex, the left and right frontopolar regions, we also provide data on the asymmetries in underlying molecular mechanisms, namely on the differential expression of the protein-coding genes and regulatory microRNA sequences. In both domains of research, our results contradict the established view by demonstrating a pronounced right-to-left vector of causation in the hemispheric interaction at multiple levels of brain organization. There may be several not mutually exclusive explanations for the evolutionary significance of this pattern of lateralization. One of the explanations emphasizes the computational advantage of separating the neural substrates for processing novel information ( exploration ) mediated predominantly by the right hemisphere, and processing with reliance on established cognitive routines and representations ( exploitation ) mediated predominantly by the left hemisphere.
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Prediction of trust propensity from intrinsic brain morphology and functional connectome
(2021) Feng, C.; Zhu, Z.; Cui, Z.; Dreher, J. C.; Ushakov, V.; Ушаков, Вадим Леонидович
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.Trust forms the basis of virtually all interpersonal relationships. Although significant individual differences characterize trust, the driving neuropsychological signatures behind its heterogeneity remain obscure. Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures. Our multivariate prediction analyses revealed that trust propensity was predicted by gray matter volume and node strength across multiple regions. The gray matter volume of identified regions further enabled the classification of individuals from an independent sample with the propensity to trust or distrust. Our modular and functional decoding analyses showed that the contributing regions were part of three large-scale networks implicated in calculus-based trust strategy, cost–benefit calculation, and trustworthiness inference. These findings do not only deepen our neuropsychological understanding of individual differences in trust propensity, but also provide potential biomarkers in predicting trust impairment in neuropsychiatric disorders.
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The architecture of neural networks for enhanced autobiographical memory access: A functional MRI study
(2020) Zhigulina, P.; Malakhov, D.; Orlov, V.; Novikov, K.; Ushakov, V.; Kartashov, S.; Ушаков, Вадим Леонидович
© 2020 The Authors. Published by Elsevier B.V.The paper reports results of the first neuroimaging study of a peculiar phenomenon of autobiographical human memory (AM)- A state of enhanced memory access . The objective of this work was to determine the AM network and the AM gate, i.e. the brain structures responsible for access to the entire memory network. In the framework of the experiment, fMRI images of the volunteers' brains were obtained during normal and enhanced autobiographical memory retrieval, then pre-processing and processing of these data were performed, the areas involved in actualization of the AM on the individual and group levels were identified, after that the connections between these areas were exposed and visualized, and a meta-analysis of the data received at individual level were performed. The areas that were identified in the state of enhanced memory retrieval significantly overlap with the already known AM network, but activations were also detected in areas not mentioned earlier in the papers on AM. Also we report evidence, that achievement of the enhanced memory access has individual threshold for each subject.
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Activity of mirror neurons in man in the observation, pronunciation and mental pronunciation of words
(2020) Bushov, Y.; Svetlik, M.; Esipenko, E.; Orlov, V.; Ushakov, V.; Kartashov, S.; Ушаков, Вадим Леонидович
© 2020 The Authors. Published by Elsevier B.V.The purpose of this study was to investigate the activity of mirror neurons in humans during observation, pronunciation and mental pronunciation of emotional and neutral words depending on the lateral organization of the brain. To achieve this goal, the methods of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) were used to examine brain activity in young men when observing, pronouncing and mental pronouncing emotional and neutral words. EEG was recorded monopolar using a 24-channel encephalograph-analyzer Encephalan-131-03 (Medicom, Taganrog, Russia), fMRI were obtained on a 3 T Magnetom Verio tomograph (Siemens). Research have shown that the performance of activities related to observation, pronouncing and mental pronouncing of words is accompanied by a depression of the mu rhythm and, most often, increased levels of cortical communication at the frequency of this rhythm between the central and frontal, temporal, parietal and occipital zones of brain. The results of fMRI-studies showed the participation of a complex of structures represented by neurons of the left inferior frontal gyrus, the right posterior region of the middle temporal gyrus, and the right region of the angular gyrus, which, presumably, is part of the system of mirror neurons and the complex of structures represented by the neurons of the area of precuneus right areas of the gyrus, which are likely to be part of the system of production of speech directly in the provision of cognitive activity associated with observation, associated with the observation, pronunciation and mental pronunciation of emotional and neutral words.
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Comparison of cross-frequency methods such as cross-term deprived covariance (CTDC) and bispectrum
(2020) Skiteva, L.; Ossadtchi, A.; Ushakov, V.; Ушаков, Вадим Леонидович
© 2020 The Authors. Published by Elsevier B.V.Cross-frequency coupling (CFC) is typical for the operation of neural networks from different areas of the brain. This, for example, can be characterized by pacemaker neurons activity, the structure of these part's crust, etc. Thus, the highest interest is not the correlation of those areas, but the synchronous activity of the areas in time at different frequencies. Event-related events can induce the work of neurons, but each at its own frequency. It looks like a synchronous manifestation of activity at different points in time, with a lag, but appearing at different frequencies. Correlation methods and coherence for CFC detection are not suitable, since they are for monofrequencies, and a long time series is required. The connectedness of neurons ensemble's work in time is effectively considered by methods such as bispectrum and CTDC. In this work, we compared these two methods as well as their hybrid. According to the results, the CTDC method proved to be more accurate, both in spatial localization and in inter-frequency.