HSE Scientists Develop Application for Diagnosing Aphasia
Specialists at the HSE Centre for Language and Brain have developed an application for diagnosing language disorders (aphasia), which can result from head injuries, strokes, or other neurological conditions. AutoRAT is the first standardised digital tool in Russia for assessing the presence and severity of language disorders. The application is available on RuStore and can be used on mobile and tablet devices running the Android operating system.
Approximately 450,000 stroke cases are reported annually in Russia, with about one-third of patients developing aphasia. Aphasia is an acquired language disorder. A person with aphasia may struggle to understand others, speak, read, or write. Aphasia can result from brain damage caused by stroke, head injury, or tumour removal. Physicians diagnose aphasia based on clinical symptoms and neuropsychological assessment data. Timely diagnosis of aphasia is crucial, as working with a speech therapist and neuropsychologist can significantly accelerate language recovery and improve quality of life.
Researchers at the HSE Centre for Language and Brain have developed a standardised digital tool called AutoRAT (from the Russian Aphasia Test), enabling the detection of aphasia and the assessment of its severity. When creating materials for the application, the developers considered not only the linguistic characteristics of the words, sentences, and texts included in the stimulus set but also psycholinguistic factors. These factors included, for example, the age at which the words were learned, their frequency of use, how easily a person can visualise an object associated with the word, and the complexity of images related to specific stimuli. The AutoRAT application allows diagnostics to be completed in just 60 minutes, providing accurate data for the development of a rehabilitation programme.
The battery of language tests includes 13 different tasks that assess the preservation of all key linguistic levels: phonological, lexico-semantic, syntactic, and discourse. These tasks help identify deficits in comprehension, production, and repetition of speech, provide information about the overall severity of language disorders, and allow comparison of results with age-based norms.
Language comprehension tasks, such as distinguishing sounds and understanding words, sentences, and texts, are automatically processed within the app. To obtain results for tasks involving speech production and repetition—such as naming objects and actions, constructing sentences and stories from drawings, and repeating words and sentences—a detailed manual evaluation system was developed for the user. This allows for the identification of all aspects of language disorders in each participant.
Individual participant profiles are saved in the application for further analysis of the results. AutoRAT enables tracking of the dynamics of language recovery, allowing for the assessment of treatment effectiveness, which is crucial for future prognosis and selecting an appropriate rehabilitation programme. All results are available for download in table format, making them convenient for research purposes.
AutoRAT will be a valuable tool for speech therapists, neuropsychologists, researchers, and clinical specialists. Additionally, it will be useful for healthcare institutions, students, and teachers in medical and linguistic fields, developers of rehabilitation programmes, and research centres focused on cognitive and linguistic processes.
'We aimed to create a tool that would not only help specialists diagnose aphasia but also provide a comprehensive picture of language disorders. AutoRAT is a step toward more precise and personalised patient rehabilitation. This tool combines a strong theoretical linguistic foundation with practical advancements in the field of speech therapy. Our tool enables a detailed description of the core language deficit, making the diagnosis of aphasia even more accurate,' comments Olga Buivolova, one of the project participants and Research Fellow at the HSE Centre for Language and Brain. 'It sets new standards by integrating advanced scientific approaches with practical effectiveness. AutoRAT transforms the aphasia assessment process, making it more convenient, accurate, and highly efficient.'
See also:
Machine Learning Links Two New Genes to Ischemic Stroke
A team of scientists from HSE University and the Kurchatov Institute used machine learning methods to investigate genetic predisposition to stroke. Their analysis of the genomes of over 5,000 people identified 131 genes linked to the risk of ischemic stroke. For two of these genes, the association was found for the first time. The paper has been published in PeerJ Computer Science.
First Digital Adult Reading Test Available on RuStore
HSE University's Centre for Language and Brain has developed the first standardised tool for assessing Russian reading skills in adults—the LexiMetr-A test. The test is now available digitally on the RuStore platform. This application allows for a quick and effective diagnosis of reading disorders, including dyslexia, in people aged 18 and older.
Low-Carbon Exports Reduce CO2 Emissions
Researchers at the HSE Faculty of Economic Sciences and the Federal Research Centre of Coal and Coal Chemistry have found that exporting low-carbon goods contributes to a better environment in Russian regions and helps them reduce greenhouse gas emissions. The study results have been published in R-Economy.
Russian Scientists Assess Dangers of Internal Waves During Underwater Volcanic Eruptions
Mathematicians at HSE University in Nizhny Novgorod and the A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences studied internal waves generated in the ocean after the explosive eruption of an underwater volcano. The researchers calculated how the waves vary depending on ocean depth and the radius of the explosion source. It turns out that the strongest wave in the first group does not arrive immediately, but after a significant delay. This data can help predict the consequences of eruptions and enable advance preparation for potential threats. The article has been published in Natural Hazards. The research was carried out with support from the Russian Science Foundation (link in Russian).
Centre for Language and Brain Begins Cooperation with Academy of Sciences of Sakha Republic
HSE University's Centre for Language and Brain and the Academy of Sciences of the Republic of Sakha (Yakutia) have signed a partnership agreement, opening up new opportunities for research on the region's understudied languages and bilingualism. Thanks to modern methods, such as eye tracking and neuroimaging, scientists will be able to answer questions about how bilingualism works at the brain level.
How the Brain Responds to Prices: Scientists Discover Neural Marker for Price Perception
Russian scientists have discovered how the brain makes purchasing decisions. Using electroencephalography (EEG) and magnetoencephalography (MEG), researchers found that the brain responds almost instantly when a product's price deviates from expectations. This response engages brain regions involved in evaluating rewards and learning from past decisions. Thus, perceiving a product's value is not merely a conscious choice but also a function of automatic cognitive mechanisms. The results have been published in Frontiers in Human Neuroscience.
AI Predicts Behaviour of Quantum Systems
Scientists from HSE University, in collaboration with researchers from the University of Southern California, have developed an algorithm that rapidly and accurately predicts the behaviour of quantum systems, from quantum computers to solar panels. This methodology enabled the simulation of processes in the MoS₂ semiconductor and revealed that the movement of charged particles is influenced not only by the number of defects but also by their location. These defects can either slow down or accelerate charge transport, leading to effects that were previously difficult to account for with standard methods. The study has been published in Proceedings of the National Academy of Sciences (PNAS).
Electrical Brain Stimulation Helps Memorise New Words
A team of researchers at HSE University, in collaboration with scientists from Russian and foreign universities, has investigated the impact of electrical brain stimulation on learning new words. The experiment shows that direct current stimulation of language centres—Broca's and Wernicke's areas—can improve and speed up the memorisation of new words. The findings have been published in Neurobiology of Learning and Memory.
Artificial Intelligence Improves Risk Prediction of Complex Diseases
Neural network models developed at the HSE AI Research Centre have significantly improved the prediction of risks for obesity, type 1 diabetes, psoriasis, and other complex diseases. A joint study with Genotek Ltd showed that deep learning algorithms outperform traditional methods, particularly in cases involving complex gene interactions (epistasis). The findings have been published in Frontiers in Medicine.
Cerium Glows Yellow: Chemists Discover How to Control Luminescence of Rare Earth Elements
Researchers at HSE University and the Institute of Petrochemical Synthesis of the Russian Academy of Sciences have discovered a way to control both the colour and brightness of the glow emitted by rare earth elements. Their luminescence is generally predictable—for example, cerium typically emits light in the ultraviolet range. However, the scientists have demonstrated that this can be altered. They created a chemical environment in which a cerium ion began to emit a yellow glow. The findings could contribute to the development of new light sources, displays, and lasers. The study has been published in Optical Materials.