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DOI 10.1016/j.neuroimage.2019.116230

Медицинская библиотека · NeuroImage · 2019

Stability of dynamic functional architecture differs between brain networks and states.

Li Le, Lu Bin, Yan Chao-Gan

Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

Stable representation of information in distributed neural connectivity is critical to function effectively in the world. Despite the dynamic nature of the brain's functional architecture, characterizing its temporal stability within a continuous state has been largely neglected. Here we characterized stability of functional architecture at a dynamic timescale (~1 min) for each brain voxel by measuring the concordance of dynamic functional connectivity (DFC) over time, compared between association and unimodal regions, and established its reliability using test-retest resting-state fMRI data of adults from an open dataset. After the measure of functional stability was established, we further employed another fMRI open dataset which included movie-watching and resting-state data of children and adolescents, to explore how stability was modified by natural viewing from its intrinsic form, with specific focus on the associative and primary visual cortices. The results showed that high-order association regions, especially the default mode network, demonstrated high stability during resting-state scans, while primary sensory-motor cortices revealed relatively lower stability. During movie watching, stability in the primary visual cortex was decreased, which was associated with larger DFC variation with neighboring regions. By contrast, higher-order regions in the ventral and dorsal visual stream demonstrated increased stability. The distribution of functional stability and its modification describes a profile of the brain's stability property, which may be useful reference for examining distinct mental states and disorders.

Для цитирования: Li Le, Lu Bin, Yan Chao-Gan. Stability of dynamic functional architecture differs between brain networks and states.. NeuroImage. 2019. DOI: 10.1016/j.neuroimage.2019.116230.

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