| Autor: |
Wu, Zhanxiong, Yu, Jiangnan, Chen, Xuanheng, Shen, Jian, Xie, Sangma, Zeng, Yu |
| Předmět: |
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| Zdroj: |
Multimedia Tools & Applications; Jun2024, Vol. 83 Issue 20, p59103-59120, 18p |
| Abstrakt: |
In vivo revealing how brain subregions are structurally connected during neonatal period via diffusion magnetic resonance imaging (dMRI) is critical for understanding brain development and pediatric mental disorders. However, even if preprocess was performed on dMRI images including denoising, eddy and motion correction, and unring, residual artifacts still affect the construction of brain structural networks. In this study, nonlocal estimation of multispectral magnitudes (NESMA) was extended to further smooth the preprocessed brain dMRI images of 46 healthy infants from the developing Human Connectome Project (dHCP). The proposed method smoothed dMRI images by exploiting similar multispectral diffusion-weighted signal pattern and the signal redundance among 3D patches. After structural connectivity networks were constructed from the smoothed dMRI images, network-level and nodal topological measures were estimated. While characteristic path length remained unchanged, significantly higher global efficiency, average clustering coefficient, and transitivity were observed in the infant structural networks built from NESMA-smoothed dMRI images. Additionally, more brain subregions with clustering coefficient > = 0.035 and local efficiency > = 0.05 were identified. In summary, higher efficiency was observed in the structural connectivity networks of healthy infants. Nonlocal estimation of multispectral diffusion-weighted volumes has nonnegligible effect on topological analysis of infant brain structural networks. The code for this algorithm is publicly available at https://github.com/freedom1979/NESMA-dMRI. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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