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:
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.
Genetic Prediction of Cancer Recurrence: Scientists Verify Reliability of Computer Models
In biomedical research, machine learning algorithms are often used to analyse data—for instance, to predict cancer recurrence. However, it is not always clear whether these algorithms are detecting meaningful patterns or merely fitting random noise in the data. Scientists from HSE University, IBCh RAS, and Moscow State University have developed a test that makes it possible to determine this distinction. It could become an important tool for verifying the reliability of algorithms in medicine and biology. The study has been published on arXiv.
Habits Stem from Childhood: School Years Found to Shape Leisure Preferences in Adulthood
Moving to a big city does not necessarily lead to dramatic changes in daily habits. A study conducted at HSE University found that leisure preferences in adulthood are largely shaped during childhood and are influenced by where individuals spent their school years. This conclusion was drawn by Sergey Korotaev, Research Fellow at the HSE Faculty of Economic Sciences, from analysing the leisure habits of more than 5,000 Russians.
Russian Scientists Reconstruct Dynamics of Brain Neuron Model Using Neural Network
Researchers from HSE University in Nizhny Novgorod have shown that a neural network can reconstruct the dynamics of a brain neuron model using just a single set of measurements, such as recordings of its electrical activity. The developed neural network was trained to reconstruct the system's full dynamics and predict its behaviour under changing conditions. This method enables the investigation of complex biological processes, even when not all necessary measurements are available. The study has been published in Chaos, Solitons & Fractals.
Researchers Uncover Specific Aspects of Story Comprehension in Young Children
For the first time, psycholinguists from the HSE Centre for Language and Brain, in collaboration with colleagues from the USA and Germany, recorded eye movements during a test to assess narrative skills in young children and adults. The researchers found that story comprehension depends on plot structure, and that children aged five to six tend to struggle with questions about protagonists' internal states. The study findings have been published in the Journal of Experimental Child Psychology.
Scientists Propose Novel Theory on Origin of Genetic Code
Alan Herbert, Scientific Supervisor of the HSE International Laboratory of Bioinformatics, has put forward a new explanation for one of biology's enduring mysteries—the origin of the genetic code. According to his publication in Biology Letters, the contemporary genetic code may have originated from self-organising molecular complexes known as ‘tinkers.’ The author presents this novel hypothesis based on an analysis of secondary DNA structures using the AlphaFold 3 neural network.