Razorenova Alexandra
Junior researcher
Publications:
2025
4.
Razorenova, Alexandra; Butorina, Anna; Skidchenko, Ekaterina; Ostras, Maxim; Ossadtchi, Alexei; Koshev, Nikolay (2025). Sparse Sensor Layout Design via Recursive Orthogonalization of the Forward Solution Matrix With a Realistic Noises Environment in MEG. IEEE Transactions on Instrumentation and Measurement, 74, 1-13. https://doi.org/10.1109/TIM.2025.3544372
@article{nokey,
title = {Sparse Sensor Layout Design via Recursive Orthogonalization of the Forward Solution Matrix With a Realistic Noises Environment in MEG},
author = {Alexandra Razorenova and Anna Butorina and Ekaterina Skidchenko and Maxim Ostras and Alexei Ossadtchi and Nikolay Koshev},
doi = {10.1109/TIM.2025.3544372},
year = {2025},
date = {2025-02-28},
urldate = {2025-02-28},
journal = {IEEE Transactions on Instrumentation and Measurement },
volume = {74},
pages = {1-13},
abstract = {The trend toward sensor miniaturization has heightened interest in optimizing sensor array configurations across various scientific and industrial applications that imply multichannel measurements. In this article, we present a novel method for sensor array layout optimization for the needs of the modern real-life magnetoencephalographic (MEG) applications. Starting from a superset of potential locations, we form a multisensor probe comprising sensors placed at the given number of distinct locations so that the signal-to-noise ratio (SNR) of the resulting multichannel array is maximized. We achieve this using a fixed number of iterations equal to the number of available sensors. At each step, the method places a sensor to the location that maximizes the region of interest (ROI)-related SNR and then applies the projection operation to the rows of the forward model matrix to orthogonalize the subsequent stereotypic iterations with respect to the sources served by the already-selected sensors. Within a selected ROI, the developed approach allows for the performance comparable to that of 102-sensor industrial standard (Elekta Neuromag MEG system) in terms of SNR that is from −0.83 to 2.13 dB for different types of compact sensors. Our approach requires significantly less computational resources and is 50× – 70× faster as compared to the previously developed methods. Due to high flexibility, RALFE sparse sensor design, demonstrated in application to MEG, is readily applicable to many other multichannel measurement challenges with linear observation models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The trend toward sensor miniaturization has heightened interest in optimizing sensor array configurations across various scientific and industrial applications that imply multichannel measurements. In this article, we present a novel method for sensor array layout optimization for the needs of the modern real-life magnetoencephalographic (MEG) applications. Starting from a superset of potential locations, we form a multisensor probe comprising sensors placed at the given number of distinct locations so that the signal-to-noise ratio (SNR) of the resulting multichannel array is maximized. We achieve this using a fixed number of iterations equal to the number of available sensors. At each step, the method places a sensor to the location that maximizes the region of interest (ROI)-related SNR and then applies the projection operation to the rows of the forward model matrix to orthogonalize the subsequent stereotypic iterations with respect to the sources served by the already-selected sensors. Within a selected ROI, the developed approach allows for the performance comparable to that of 102-sensor industrial standard (Elekta Neuromag MEG system) in terms of SNR that is from −0.83 to 2.13 dB for different types of compact sensors. Our approach requires significantly less computational resources and is 50× – 70× faster as compared to the previously developed methods. Due to high flexibility, RALFE sparse sensor design, demonstrated in application to MEG, is readily applicable to many other multichannel measurement challenges with linear observation models.
2024
3.
Mikhaylets, Ekaterina; Razorenova, Alexandra M.; Chernyshev, Vsevolod; Syrov, Nikolay; Yakovlev, Lev; Boytsova, Julia; Kokurina, Elena; Zhironkina, Yulia; Medvedev, Svyatoslav; Kaplan, Alexander (2024). SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation. Frontiers in Neuroinformatics, 17. https://doi.org/10.3389/fninf.2023.1301718
@article{Mikhaylets2024,
title = {SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation},
author = {Ekaterina Mikhaylets and Alexandra M. Razorenova and Vsevolod Chernyshev and Nikolay Syrov and Lev Yakovlev and Julia Boytsova and Elena Kokurina and Yulia Zhironkina and Svyatoslav Medvedev and Alexander Kaplan},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.3389_fninf.2023.1301718.pdf},
doi = {10.3389/fninf.2023.1301718},
issn = {1662-5196},
year = {2024},
date = {2024-01-29},
urldate = {2024-01-29},
journal = {Frontiers in Neuroinformatics},
volume = {17},
publisher = {Frontiers Media SA},
abstract = {The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. <jats:italic>Post-hoc</jats:italic> analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. <jats:italic>Post-hoc</jats:italic> analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
2020
2.
Razorenova, Alexandra M.; Chernyshev, Boris V.; Nikolaeva, Anastasia Yu; Butorina, Anna V.; Prokofyev, Andrey O.; Tyulenev, Nikita B.; Stroganova, Tatiana A. (2020). Rapid Cortical Plasticity Induced by Active Associative Learning of Novel Words in Human Adults. Frontiers in Neuroscience, 14, 895. https://doi.org/10.3389/fnins.2020.00895
@article{Razorenova2020b,
title = {Rapid Cortical Plasticity Induced by Active Associative Learning of Novel Words in Human Adults},
author = {Alexandra M. Razorenova and Boris V. Chernyshev and Anastasia Yu Nikolaeva and Anna V. Butorina and Andrey O. Prokofyev and Nikita B. Tyulenev and Tatiana A. Stroganova},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.3389_fnins.2020.00895.pdf},
doi = {10.3389/fnins.2020.00895},
issn = {1662-453X},
year = {2020},
date = {2020-09-11},
urldate = {2020-09-11},
journal = {Frontiers in Neuroscience},
volume = {14},
pages = {895},
publisher = {Frontiers Media SA},
abstract = {Human speech requires that new words are routinely memorized, yet neurocognitive mechanisms of such acquisition of memory remain highly debatable. Major controversy concerns the question whether cortical plasticity related to word learning occurs in neocortical speech-related areas immediately after learning, or neocortical plasticity emerges only on the second day after a prolonged time required for consolidation after learning. The functional spatiotemporal pattern of cortical activity related to such learning also remains largely unknown. In order to address these questions, we examined magnetoencephalographic responses elicited in the cerebral cortex by passive presentations of eight novel pseudowords before and immediately after an operant conditioning task. This associative procedure forced participants to perform an active search for unique meaning of four pseudowords that referred to movements of left and right hands and feet. The other four pseudowords did not require any movement and thus were not associated with any meaning. Familiarization with novel pseudowords led to a bilateral repetition suppression of cortical responses to them; the effect started before or around the uniqueness point and lasted for more than 500 ms. After learning, response amplitude to pseudowords that acquired meaning was greater compared with response amplitude to pseudowords that were not assigned meaning; the effect was significant within 144–362 ms after the uniqueness point, and it was found only in the left hemisphere. Within this time interval, a learning-related selective response initially emerged in cortical areas surrounding the Sylvian fissure: anterior superior temporal sulcus, ventral premotor cortex, the anterior part of intraparietal sulcus and insula. Later within this interval, activation additionally spread to more anterior higher-tier brain regions, and reached the left temporal pole and the triangular part of the left inferior frontal gyrus extending to its orbital part. Altogether, current findings evidence rapid plastic changes in cortical representations of meaningful auditory word-forms occurring almost immediately after learning. Additionally, our results suggest that familiarization resulting from stimulus repetition and semantic acquisition resulting from an active learning procedure have separable effects on cortical activity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human speech requires that new words are routinely memorized, yet neurocognitive mechanisms of such acquisition of memory remain highly debatable. Major controversy concerns the question whether cortical plasticity related to word learning occurs in neocortical speech-related areas immediately after learning, or neocortical plasticity emerges only on the second day after a prolonged time required for consolidation after learning. The functional spatiotemporal pattern of cortical activity related to such learning also remains largely unknown. In order to address these questions, we examined magnetoencephalographic responses elicited in the cerebral cortex by passive presentations of eight novel pseudowords before and immediately after an operant conditioning task. This associative procedure forced participants to perform an active search for unique meaning of four pseudowords that referred to movements of left and right hands and feet. The other four pseudowords did not require any movement and thus were not associated with any meaning. Familiarization with novel pseudowords led to a bilateral repetition suppression of cortical responses to them; the effect started before or around the uniqueness point and lasted for more than 500 ms. After learning, response amplitude to pseudowords that acquired meaning was greater compared with response amplitude to pseudowords that were not assigned meaning; the effect was significant within 144–362 ms after the uniqueness point, and it was found only in the left hemisphere. Within this time interval, a learning-related selective response initially emerged in cortical areas surrounding the Sylvian fissure: anterior superior temporal sulcus, ventral premotor cortex, the anterior part of intraparietal sulcus and insula. Later within this interval, activation additionally spread to more anterior higher-tier brain regions, and reached the left temporal pole and the triangular part of the left inferior frontal gyrus extending to its orbital part. Altogether, current findings evidence rapid plastic changes in cortical representations of meaningful auditory word-forms occurring almost immediately after learning. Additionally, our results suggest that familiarization resulting from stimulus repetition and semantic acquisition resulting from an active learning procedure have separable effects on cortical activity.
1.
Разоренова, А. М.; Тюленев, Н. Б.; Рытикова, А. М.; Чернышев, Б. В.; Скавронская, В. В. (2020). Может ли научение новым словам в слуховой модальности вести к быстрому формированию пластических перестроек в коре больших полушарий у взрослых? Современная зарубежная психология, 9(2), 46-56.
@article{nokey,
title = {Может ли научение новым словам в слуховой модальности вести к быстрому формированию пластических перестроек в коре больших полушарий у взрослых?},
author = {Разоренова, А.М. and Тюленев, Н.Б. and Рытикова, А.М. and Чернышев, Б.В. and Скавронская, В.В.},
year = {2020},
date = {2020-05-08},
urldate = {2020-05-08},
journal = {Современная зарубежная психология},
volume = {9},
number = {2},
pages = {46-56},
publisher = {Московский государственный психолого-педагогический университет},
abstract = {Устойчивая связь между словами и обозначаемыми объектами или событиями лежит в основе человеческой речи. Фундаментальным является вопрос о том, как слово обрабатывается человеческим мозгом
и какие факторы обеспечивают интеграцию незнакомого набора фонем в лексикон. Ответ на него мог бы
произвести прорыв во многих областях, начиная от методик преподавания языка и программ по коррекции речи у детей с поздним развитием и заканчивая новыми методами реабилитации больных с нарушениями речи и нейрофизиологическими тестами для проверки работы речевого аппарата. В данном обзоре рассматривается современное состояние российских и зарубежных исследований по тематике научения новым словам при слуховом предъявлении, выполненных с применением разнообразных методик.
Равное внимание уделено как исследованиям фонологической обработки слова (распознаванию фонетического паттерна), так и работам, посвященным исследованию процессов приобретения словом
семантики. Рассмотрены результаты исследований, выполненных с помощью различных методов —
фМРТ, ЭЭГ/МЭГ и др.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Устойчивая связь между словами и обозначаемыми объектами или событиями лежит в основе человеческой речи. Фундаментальным является вопрос о том, как слово обрабатывается человеческим мозгом
и какие факторы обеспечивают интеграцию незнакомого набора фонем в лексикон. Ответ на него мог бы
произвести прорыв во многих областях, начиная от методик преподавания языка и программ по коррекции речи у детей с поздним развитием и заканчивая новыми методами реабилитации больных с нарушениями речи и нейрофизиологическими тестами для проверки работы речевого аппарата. В данном обзоре рассматривается современное состояние российских и зарубежных исследований по тематике научения новым словам при слуховом предъявлении, выполненных с применением разнообразных методик.
Равное внимание уделено как исследованиям фонологической обработки слова (распознаванию фонетического паттерна), так и работам, посвященным исследованию процессов приобретения словом
семантики. Рассмотрены результаты исследований, выполненных с помощью различных методов —
фМРТ, ЭЭГ/МЭГ и др.
и какие факторы обеспечивают интеграцию незнакомого набора фонем в лексикон. Ответ на него мог бы
произвести прорыв во многих областях, начиная от методик преподавания языка и программ по коррекции речи у детей с поздним развитием и заканчивая новыми методами реабилитации больных с нарушениями речи и нейрофизиологическими тестами для проверки работы речевого аппарата. В данном обзоре рассматривается современное состояние российских и зарубежных исследований по тематике научения новым словам при слуховом предъявлении, выполненных с применением разнообразных методик.
Равное внимание уделено как исследованиям фонологической обработки слова (распознаванию фонетического паттерна), так и работам, посвященным исследованию процессов приобретения словом
семантики. Рассмотрены результаты исследований, выполненных с помощью различных методов —
фМРТ, ЭЭГ/МЭГ и др.