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Investigations of neuronal mechanisms of cognition and decision making: neuroimaging, computational models and non-invasive brain stimulation

Priority areas of development: humanitarian

Goal of research

The goal of our research is to investigate mechanisms of the decision making and cognitive control, perception and generation of speech, perception of the multimodal information by active and passive brain decoding using technologies of dynamical neurovizualization, connectivity assessment, computational modeling and neurointerfaces.


Our methods include navigated Transcranial Magnetic Stimulation (nTMS), Transcranial Direct Current Stimulation (tDCS), Transcranial Alternating Current Stimulation (tACS), Magnetoencephalography (MEG), Electroencephalography (EEG), Eyetracking, functional and structural magnetic resonance imaging (fMRI, MRI).

Empirical base of research

National Research University Higher School of Economics unique equipment “Automated system for non-invasive brain stimulation allows synchronous registration of neural oscillations and tracking of eye-movements fixation”. It includes 2 TMS stimulators: MagPro X100 (MagVenture) with Localite navigation system which uses structural MRI scans for precise localization of the stimulation points, 2 Eye-trackers: SMI REDm and Eyelink 1000 plus, virtual reality eye tracking setup HTC Vive +Pupil labs, 128-channel BRAINAMP DC amplifier for EEG (Brain Products), TES System. Following equipment was also used: Vectorview MEG-system from Elekta Neuromag (MGPPU), 3-T MRI scanner Siemens (FSBNU NTSN).

Results of research

In 2019 we have conducted a wide range of the interdisciplinary research studies, aimed at deepening our knowledge about fundamental mechanisms of the brain functioning, namely, the interaction between different neurocognitive systems, as well as between different levels of the information processing of speech perception, decision making, performing movements and other activities, including resting state. Specific emphasis in our work was on decoding neuronal activity during neurocognitive and neuroeconomy tasks. We have focused on the development of neurocognitive technologies. We have significantly widened approaches to the analysis of multidimensional data of  brain activity. Together with temporal brain state correlations, and MEG and EEG cross-frequencies detection method we implemented intracortical high frequency analysis, representational similarity analysis, running wave method, spike sorting Spyking Circus technique, deep learning and other classification methods, MEG frequency marking changes and many more.

Level of implementation, recommendations on implementation or outcomes of the implementation of the results

We have achieved significant results in the field of the applied research, which led to the development of new biomarker and neurocorrelates of pathological states and neurodegenerative disorders detection: epilepsy, stroke, aphasia etc. Significant results were obtained in the development of the neurocognitive technologies, for example, we created an algorithm for the automatic detection of interconnected spikes and new mathematical methods of measuring cortical representation maps, neuromarketing measures of optimal price and many more.


Bermúdez-Margaretto B., Shcherbakova O., Beltrán D., Cuetos F., Dominguez A. Novel word learning: event-related brain potentials reflect pure lexical and task-related effects // Frontiers in Human Neuroscience. 2019. Vol. 13. No. 347. P. 1-13. doi
Hulme O., Melveille T., Gutkin B. Neurocomputational Theories of Homeostatic Control // Physics of Life Reviews. 2019. Vol. 31. P. 214-232. doi
Zinchenko O. Brain responses to social punishment: a meta-analysis // Scientific Reports. 2019. Vol. 9. No. 12800. P. 1-8. doi
Iemi L., Busch N., Laudini A., Haegens S., Samaha J., Villringer A., Nikulin V. Multiple mechanisms link prestimulus neural oscillations to sensory responses // eLife. 2019. No. 8. P. 1-34. doi
Luna K., Albuquerque P. B., Martín-Luengo B. Cognitive load eliminates the effect of perceptual information on judgments of learning with sentences // Memory and Cognition. 2019. Vol. 47. No. 1. P. 106-116. doi
Martinez-Saito M., Konovalov R., Piradov M. A., Shestakova A., Gutkin B., Klucharev V. Action in auctions: neural and computational mechanisms of bidding behaviour // European Journal of Neuroscience. 2019. Vol. 50. No. 8. P. 3327-3348. doi
Deperrois N., Moiseeva V., Gutkin B. Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations // Frontiers in Neural Circuits. 2019. Vol. 12. No. 116. P. 1-17. doi
Myachykov A., Fischer M. A hierarchical view of abstractness: Grounded, embodied, and situated aspects // Physics of Life Reviews. 2019. Vol. 29. P. 161-163. doi
Myachykov A., Chapman A., Beal J., Scheepers C. Random word generation reveals spatial encoding of syllabic word length // British Journal of Psychology. 2019. P. 1-12. doi
Nazarova M., Novikov P., Иванина Е. О., Kozlova K., Nikulin V. Absolute and relative reliability of TMS motor mapping – how much could we trust the results of TMS somatotopy? // Brain Stimulation. 2019. Vol. 12. No. 2. P. 506-507. doi
Babayan A., Erbey M., Kumral D., Reinelt j., Reiter A., Nikulin V. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. // Scientific data. 2019. No. 6. P. 1-21. doi
Vidaurre C., Nolte G., de V. I., Gómez M., Boonstra T., Müller K., Villringer A., Nikulin V. Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets // NeuroImage. 2019. Vol. 201:116009. P. 1-11. doi
Vidaurre C., Ramos-Murguialday A., Haufe S., Gómez-Fernández M., Müller K., Nikulin V. Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation // NeuroImage. 2019. No. 199. P. 375-386. doi
Maudrich T., Kenville R., Nikulin V., Maudrich D., Villringer A., Ragert P. Inverse relationship between amplitude and latency of physiological mirror activity during repetitive isometric contractions. // Neuroscience. 2019. No. 406. P. 300-313. doi
Shih P., Steele C., Nikulin V., Villringer A., Sehm B. Kinematic profiles suggest differential control processes involved in bilateral in-phase and anti-phase movements. // Scientific Reports. 2019. No. 9. P. 1-12. doi
Ovadia-Caro S., Khalil A., Sehm B., Villringer A., Nikulin V., Nazarova M. Predicting the response to non-invasive brain stimulation in stroke // Frontiers in Neurology. 2019. No. 10:302. P. 1-6. doi
Schaworonkow N., Nikulin V. Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG // PLoS Computational Biology. 2019. No. 15. P. 1-22. doi
Galli G., Vadillo M. A., Sirota M., Feurra M., Medvedeva A. A systematic review and meta-analysis of the effects of transcranial direct current stimulation (tDCS) on episodic memory // Brain Stimulation. 2019. Vol. 12. No. 2. P. 231-241. doi
Martín-Luengo B., Seungah L., Mikhailova L., Kapitsyn M., Myachykov A., Shtyrov Y. Differential neural basis for different levels of metacognitive evaluations, in: Proceedings of the 3rd International Conference Neurobiology of Speech and Language.: Скифия-принт, 2019. С. 90-91. 
Martín-Luengo B., Mikhailova L., Seungah L., Kapitsyn M., Myachykov A., Shtyrov Y. EEG correlates of false information processing, in: Proceedings of the 3rd International Conference Neurobiology of Speech and Language.: Скифия-принт, 2019. С. 53-54. 
Nazarova M., Novikov P., Kozlova K., Иванина Е. О., Nikulin V. MNI NORMALIZATION OF TMS MOTOR MAPS: PROBING WITHIN-LIMB SOMATOTOPY OF THE PRIMARY HAND MOTOR CORTEX, in: Когнитивная наука в Москве: новые исследования. Материалы конференции 19 июня 2019 г.. Moscow : Буки Веди, 2019. С. 559-563. 
Bermúdez-Margaretto B., Shtyrov Y. Rapid acquisition of novel written word-forms: ERP evidence // Behavioral and Brain Functions. 2020. Vol. 16. No. 11. P. 1-17. doi
Rooy M., Novikov N., Zakharov D., Gutkin B. Interaction between PFC neural networks ultraslow fluctuations and brain oscillations // Известия высших учебных заведений. Прикладная нелинейная динамика. 2020. Vol. 28. No. 1. P. 90-97. doi
Lussange J. A., Palminteri S., Bourgeois-Gironde S., Gutkin B. Stock market microstructure inference via multi-agent reinforcement learning / arXiv. Series Stock market microstructure inference via multi-agent reinforcement learning. "arXiv". 2019.