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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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Atypical Social Behavior is Predicted by Overconnectivity Between Salience and Default Mode Networks in Autism Spectrum Disorder

Journal of Autism and Developmental Disorders. 2025.
Minnigulova A., Prokopeva M., Dragoy O., Arutiunian V.

As Default Mode and Salience networks (DMN, SN) contribute to social behavior and switching between inner and outer attention, they are believed to function and develop differently in individuals with Autism Spectrum Disorder (ASD). However, it remains unclear what alterations of their interactivity are connected to certain autistic traits and how age influences these networks’ maturing. Behavioral (social responsiveness, executive functions and communication skills) and resting-state functional connectivity (FC) data from the Autism Brain Imaging Data Exchange were analyzed comprising individuals with ASD (n = 144) and healthy controls (n = 99). We compared FC between the groups investigating DMN and SN separately and in combination. Finally, we assessed FC-behavior links in the ASD group and age effects on FC across these networks in both samples. Individuals with ASD exhibited increased FC between DMN and SN but decreased one within DMN compared to the control group. FC between right insular and medial prefrontal cortices predicted more severe social responsiveness impairments in ASD but there were no significant associations with executive functions nor adaptive behavior. Additionally, DMN and SN matured in ASD with partly different patterns than in typical development. Our results replicated and expanded previous findings on DMN and SN pointing to robust differences within and between these networks in ASD and their contribution to autistic traits regarding social responsiveness.

Research target: Psychology Biology
Language: English
Full text
DOI
Keywords: salience networkAutistic traitsresting state fMRIDefault mode network (DMN)Autism Spectrum Disorder (ASD)
Publication based on the results of:
Neural foundations of language and its impairments: From basic research to clinical practice (2025)
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