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News
May 25, 2026
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.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Reduced grey matter volume of amygdala and hippocampus is associated with the severity of autistic symptoms and language abilities in school-aged children with Autism Spectrum Disorder: an exploratory study

Brain Structure and Function. 2023. Vol. 228. No. 6. P. 1573–1579.
Arutiunian V., Davydova E., Pereverzeva D., Sorokin A., Tyushkevich S., Mamokhina U., Danilina K., Dragoy O.

The core symptoms of Autism Spectrum Disorder (ASD) are impairments in social interaction/communication and the presence of stereotyped and repetitive behaviour. The amygdala and hippocampus are involved in core functions in the “social brain” and, thus, may be of particular interest in ASD. Previous studies demonstrated inconsistent results, revealing both increased and reduced volume of these brain structures in individuals with ASD. In this study, we investigated the grey and white matter volumes of amygdala and hippocampus in primary-school-aged children with and without ASD. Also, we assessed the relationships between the volume of brain structures and behavioural measures in children with ASD. A total of 36 children participated in the study: 18 children with ASD (13 boys, age range 8.01–14.01 years, mean age (Mage) = 10.02, standard deviation (SD) = 1.76) and 18 age- and sex-matched typically developing controls (13 boys, age range 7.06–12.03 years, Mage = 10.00, SD = 1.38). The whole-brain structural magnetic resonance imaging (MRI) was applied to acquire T1 images for each child. The results showed a bilateral reduction in grey matter volume of amygdala and hippocampus in children with ASD, but no difference was found in white matter volume. Importantly, pathological reduction in grey matter volume of amygdala was associated with lower language skills and more severe autistic traits; also, a reduced grey matter volume of the left hippocampus was related to lower language skills in the ASD group.

Research target: Philology and Linguistics
Language: English
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DOI
Text on another site
Keywords: языкlanguageкоммуникацияcommunicationаутизмгиппокампhippocampusамигдалаamygdalaAutism Spectrum Disorder (ASD)
Publication based on the results of:
Neuroanatomical foundations of language disorders in children and adults (2023)
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