Researchers at HSE Centre for Language and Brain Reveal Key Factors Determining Language Recovery in Patients After Brain Tumour Resection
Alina Minnigulova and Maria Khudyakova at the HSE Centre for Language and Brain have presented the latest research findings on the linguistic and neural mechanisms of language impairments and their progression in patients following neurosurgery. The scientists shared insights gained from over five years of research on the dynamics of language impairment and recovery.
The surgical removal of brain tumours located near language areas can result in various postoperative language disorders. Typically, language functions recover in the immediate postoperative period; however, two out of three patients may experience disorders in the chronic phase.
To understand the dynamics of language impairments and recovery after tumour resection, scientists at the HSE Centre for Language and Brain in Moscow and the Centre for Language and Brain Studies in Nizhny Novgorod have been assessing patients using the Russian Aphasia Test (RAT) at three key time points: before surgery, a few days post-surgery, and three to four months later.
By comparing comprehensive language test results with magnetic resonance imaging (MRI) data, it was possible to evaluate the importance of specific grey and white matter areas for language function.
The researchers examined how well key clinical factors—such as postoperative declines in language processing, tumour malignancy, resection volume and location, and the use of intraoperative language mapping—can predict long-term language recovery after surgery. Beyond predicting overall recovery, the researchers identified the brain regions involved in language processing that are most vulnerable to persistent deficits.
Maria Khudyakova, Director of the Centre for Language and Brain Studies at the HSE Campus in Nizhny Novgorod, studied the postoperative restoration of both comprehension and production of individual words: nouns and verbs.
'By testing the comprehension and production of nouns and verbs, we expected to observe a significant decline in performance on all tasks immediately after surgery, followed by a substantial improvement three to four months later. It was interesting to note that while the comprehension and production of nouns did not fully recover to preoperative levels, verbs—terms denoting actions—and the comprehension of all words were restored,' according to Khudyakova.
Maria Khudyakova added that although object naming had not fully recovered to its preoperative level, the naming rate for objects that were correctly identified had increased, meaning they were named more quickly.
Alina Minnigulova, Research Fellow at the HSE Centre for Language and Brain in Moscow, has participated in surgeries since 2017, initially testing patients in Nizhny Novgorod. In 2020, she joined a neurosurgical project in Moscow, which included a study on predictors of language recovery.
‘In 2024, we completed a five-year study on the impact of clinical and behavioural characteristics on language recovery in patients following the resection of glial tumours in the left hemisphere after a delay of three to six months,' explains Minnigulova. 'We found that smaller declines in language test scores and awake surgery involving brain mapping and speech monitoring both predict better language recovery in patients three to six months after surgery.'
Additionally, the scientists determined that verbal working memory and vocabulary retrieval, in particular verbs, are most vulnerable to persistent deficits.
'This study has been completed, but we continue to work with the neurosurgical population, investigating the neural correlations of language disorders, lateralisation, and the specification of language functions,' says Minnigulova.
See also:
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.