Miasnikova Alexandra


Miasnikova Alexandra, PhD in Philosophical Sciences
Research fellow

Research interests
My academic record includes research into neuronal networks underlying cognitive processes which I combined with the development of EEG functional connectivity methods and their applications (publications in peer-reviewed journals followed).

Being a specialist in brain dynamics, I investigate brain mechanisms underlying cognitive processing in healthy and clinical samples. Using statistical methods along with those from brain dynamics I evaluate and validate EEG functional connectivity networks targeting various cognitive processes including motor response activation in pre-movement processes, mind wandering at rest, the emergence of consciousness in coma. After that I develop EEG biomarkers of normal brain functioning and investigate their changes in clinical samples. To do so I apply machine learning techniques.

During my Master’s degree at Higher School of Economics in I performed analyses of neuronal networks at rest in healthy subjects. My analyses were based on supporting brain dynamics with the aim of obtaining and validating the time courses of interacting signals in various frequency ranges, their topographies and indices of strength of interactions. Together with my colleagues in Neurodynamics Group I was improving and developing a new method of estimating neuronal networks in the human brain which was successfully validated and presented in Frontiers in Neuroinformatics [Volk D, Dubinin I, Myasnikova A, 2018].

When investigating abstract reasoning at Higher Nervous Activity Lab, the Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, we used EEG topographies of neuronal interactions and relevant measurements of strength of interactions as features for the decision support system which allowed to choose between types of reasoning exploited by the participants. The solution and empirical results were presented at the International Engineering in Medicine and Biology Conference in Berlin [Miasnikova et al, EMBC 2019]. That was our pilot project for further investigation of schizophrenia which would exploit functional networks in EEG data obtained from participants who were solving categorization tasks. The goal of that line of research was pinpointing neural grounds of relevance attribution and their alterations in schizophrenia [Miasnikova et al, 2020].

Working at Otago University, New Zealand, at the Laboratory of Action and Motor Control I was conducting research into neuronal interactions in patients with congenital mirror movements compared to healthy controls within a classical Go NoGo paradigm. We were interested in the brain dynamics associated with eliciting prepotent motor activity and inhibiting irrelevant motor response. In reanalyzing those data using the generalized cross-frequency decomposition of EEG rhythms, I performed estimations of brain dynamics in the alpha and beta, theta and alpha frequencies in healthy controls. I isolated a specific role of phase interactions in the alpha and beta frequency ranges with the alpha signal obtained from the motor cortices (C3, Cz, C4) in motor readiness and movement preparation [Miasnikova and Franz, 2020, in revision].

In summary, doing extensive scripting in MATLAB/Python/R and applying statistical approach, I first investigate cortical processes which support healthy and pathological brain functions. I perform analyses of functional interactions of brain signals in EEG data. Then, knowing basics of programming in Python/R, Shell and machine learning approach, I design and validate neurophysiological biomarkers of normal brain functions and their alterations in clinical population.

1. Miasnikova A, Franz EA. Brain dynamics in the alpha and beta frequency ranges in movement readiness and preparation. Journal of Psychophysiology [In review].
2. Miasnikova A, Perevoznyuk G, Martynova O, Baklushev M. Cross-frequency phase coupling of brain oscillations and relevance attribution as saliency detection in abstract reasoning. Neurosci Res. 2020;S0168-0102(19)30577-2. doi:10.1016/j.neures.2020.05.012.
3. Miasnikova A, Troshkov D, Baklushev M, Perevoznyuk G. Predicting States of Abstract Reasoning Using EEG Functional Connectivity Markers. 2019 IEEE EMBC Conference on Engineering in Biomedicine, 2019: 2451-2454.10.1109/EMBC.2019.8857031.
4. Volk, D., Dubinin, I., Myasnikova, A., Gutkin, B., & Nikulin, V. V. Generalized Cross- Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies. Frontiers in Neuroinformatics, 2018, 12, 72 doi:10.3389/fninf.2018.00072.