Evidence map›Paper›PMID 42835355›Full record

ArticleFrontiers in neuroanatomy2026

Age- and sex-divergent neurodevelopmental patterns revealed through structural magnetic resonance imaging features.

O Kabenova, R Zhulduzbayev, I Brak, B Kerimkulov, R Siugzdaite, A Kustubayeva

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Article in Frontiers in neuroanatomy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

O KabenovaDepartment of Biophysics, Biomedicine and Neuroscience, Al-Farabi Kazakh National University, Almaty, Kazakhstan.
R ZhulduzbayevKazakh-British Technical University, Almaty, Kazakhstan.
I BrakNational Centre for Neurosurgery, Nur-Sultan, Kazakhstan.
B KerimkulovSunkar Medical Center, Almaty, Kazakhstan.
R SiugzdaiteGhent University, Ghent, Belgium.
A KustubayevaDepartment of Biophysics, Biomedicine and Neuroscience, Al-Farabi Kazakh National University, Almaty, Kazakhstan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Brain maturation during childhood and adolescence involves complex structural changes that vary among individuals. Characterizing these trajectories is essential for understanding normative development and identifying deviations associated with neurological conditions. Methods: We analyzed structural magnetic resonance imaging data from 179 healthy participants aged 7-20 years. Cortical thickness, regional surface area, and regional brain volumes were assessed to characterize age-related morphometric patterns and sex differences. These features were also used to predict chronological age. Ten regression algorithms spanning regularized linear, Bayesian, kernel-based, and gradient-boosting approaches were evaluated using repeated age-stratified five-fold cross-validation. Results: Total gray matter volume decreased with age, whereas white matter volume increased, reflecting ongoing myelination and brain maturation. The caudate nuclei and nucleus accumbens showed age-related volume reductions, while cerebellar and brainstem volumes increased, suggesting continued maturation of motor and sensory systems. No significant age-related associations were observed for cortical surface area. In contrast, cortical thickness showed widespread age-related decreases, including in the superior temporal sulcus, anterior cingulate cortex, and prefrontal cortex, consistent with functional specialization. Males had significantly larger cortical, subcortical, and white matter volumes, as well as larger surface areas across multiple cortical regions. However, cortical thickness did not differ significantly by sex. A stacking ensemble achieved the best age-prediction performance, with an age-bias-corrected mean absolute error of 1.41 years. Despite the observed morphological sex differences, the corrected brain-age gap did not differ significantly between sexes. Discussion: These findings provide a region-specific reference for normative brain development in a Central Asian pediatric and adolescent sample and establish a preliminary framework for predicting age from standard brain morphometric measures. Independent replication in larger and more diverse samples is required to assess the generalizability of these findings.

Indexed as

age predictionbrain maturationmachine learningneurodevelopmentstructural MRI

Identifiers

PMID42835355
PMCPMC13635291

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