Language Mapping in the Operating Room: HSE Neurolinguists Assist Surgeons in Complex Brain Surgery
Researchers from the HSE Center for Language and Brain took part in brain surgery on a patient who had been seriously wounded in the SMO. A shell fragment approximately five centimetres long entered through the eye socket, penetrated the cranial cavity, and became lodged in the brain, piercing the temporal lobe responsible for language. Surgeons at the Burdenko Main Military Clinical Hospital removed the foreign object while the patient remained conscious. During the operation, neurolinguists conducted language tests to ensure that language function was preserved.
Researchers from the HSE Center for Language and Brain, Maria Protopova (Prokopyeva) and Irina Provlotskaya, worked in the operating room as part of a multidisciplinary team and administered standardised language tasks during direct cortical stimulation. During the operation, the patient was asked to name objects shown in pictures and to repeat pseudowords. These tasks help identify brain regions that are critically important for language and allow clinicians to detect in a timely manner any changes that may occur during the intervention.
Research Assistant, Center for Language and Brain
'If a patient begins to make errors while performing language tasks, this may indicate that the stimulation is affecting areas critical for language. Once these areas are identified, surgeons avoid them during subsequent stages of the operation.'
In this case, the procedure differed in that the patient remained conscious throughout the entire intervention, including the stage of opening the skull. More commonly, during such operations, the patient is kept under anaesthesia until cortical stimulation and language testing begin, after which they are placed back under anaesthesia.
Junior Research Fellow, Center for Language and Brain
'The language testing procedure was generally structured in the same way as in standard awake surgeries, such as the removal of an epileptic lesion or a mass, but in this case it was particularly important to monitor language continuously. We used object-naming tasks during cortical stimulation to identify language-related areas so that surgeons could bypass them during resection. Pseudoword repetition was used directly during the removal of the foreign body to ensure timely detection of any language impairments that might arise while manipulating tissue near the language pathways.'
The HSE Center for Language and Brain has been developing a clinical focus and actively collaborating with leading hospitals and medical centres in Moscow, Nizhny Novgorod, Novosibirsk, and other cities in Russia, as well as abroad. This collaboration helps introduce modern neurolinguistic methods into clinical practice, increasing the safety of neurosurgical interventions and improving the precision with which functionally significant brain areas are preserved.
Director, Center for Language and Brain
'Tests developed by the centre’s specialists for intraoperative language mapping—including object and action naming from pictures and other standardised language tasks—have been successfully used in awake surgeries for nearly 10 years. It is essential for us that these techniques function not only as research tools but also as clinical standards, effectively applied in neurosurgical practice.'
See also:
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
‘Entering Robotics Now Means Growing with the Area’
Unmanned vehicles, courier robots, and smart speakers are rapidly becoming a part of our lives. In 2026, the HSE Faculty of Computer Science opens its new Bachelor’s Programme ‘Design of Intelligent Robotic Systems’ (DIRS). It will train specialists at the intersection of IT, artificial intelligence, and robotics. Academic Supervisor of DIRS Vadim Morgachev explains how studies are organised and why graduates of the programme ‘will definitely be accepted into the future.’
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
MIEM Tech Day at Pokrovka: Exploring HSE’s Engineering DNA Together
On May 26, 2026, the central atrium of the building at 11 Pokrovsky Bulvar will host the annual large-scale festival of engineering developments created by project teams from the HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM). The programme includes presentations of the best student technological projects, stands from partner companies and joint workshops, a lecture series featuring practising engineers, a round table on the development of engineering education, and presentations of MIEM master’s degree programmes.
The 'Second Shift' Is Not Why Women Avoid News
Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.
Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty
Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.