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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Methodological limitations of the BrainStructureAges pipeline from the volBrain platform: a brief report of the normative pediatric group

Pediatric Radiology. 2026. P. 1–9.
Korotkova E., Kulikova S., Lvova O., Tarasov D., Meshkov A., Kovtun O.

Background

Predicted brain age is a widely used neuroimaging biomarker of brain maturation. However, limited normative pediatric datasets in model training raise concerns about systematic biases when applied to children. This study evaluates the hypothesis of predicted brain age overestimation in typically developing pediatric populations.

Objective

To analyze predicted brain age and brain delta in a large, age- and sex-matched sample of typically developing children and to test whether there is any potential systematic overestimation of predicted brain age in children using the BrainStructureAges pipeline (LaBRI, CNRS/University of Bordeaux, Bordeaux, France).

Materials and methods

We estimated predicted brain age from 105 brain MRI scans of healthy and typically developing children aged 6–9 years with well-documented medical histories. All scans were performed on a Magnetom Amira 1.5-T scanner (Siemens Healthineers, Erlangen, Germany), using T1-weighted MPRAGE sequences. Brain age prediction utilized the BrainStructureAges pipeline on the volBrain (ITACA Institute, Universitat Politècnica de València, Valencia, Spain, and LaBRI, CNRS/University of Bordeaux, Bordeaux, France) platform. The brain delta was defined as the difference between chronological age and predicted brain age. Regional estimates were obtained for 133 brain structures.

Results

The mean predicted brain age was 9.3±1.2 years, compared to a chronological age of 7.7±0.4 years, resulting in a mean brain delta of 1.6±1.2 years. No significant sex differences were observed for chronological age, predicted brain age, or delta (boys, 1.55 years; girls, 1.61 years; t=0.27, P=0.79). Positive brain delta values were seen in 95.2% of participants across all structures, with the largest deviations in the left cerebellar exterior (+3.3 years), left occipital fusiform gyrus (+3.3 years), cerebellar lobules VI–VII (+2.9 years), left inferior occipital gyrus (+2.9 years), and right frontal pole (+2.8 years).

Conclusions

These findings suggest a systematic overestimation of predicted brain age in children, highlighting the need for caution in clinical interpretations to prevent misinterpretations in pediatric populations.

Research target: Medical and Health Sciences
Language: English
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Keywords: мозгМРТbiological ageбиологический возрастBrain MRI
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