Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20–25 for the young group; N = 32, ages 60–85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.

Diffusion kurtosis imaging is a newly developed diffusion magnetic resonance imaging technique, which is becoming increasingly valuable in clinical practice. Although low-resolution sampling is commonly used to compensate the unsteadiness of kurtosis estimation, the influence of the sampling shape has not been investigated. In this study, by using two different acquisition protocols, isotropic and anisotropic sampling voxels were acquired and their influence on various white matter structures was observed. Fiber tracking, T-tests, and correlation analysis were used to quantify the difference between the anisotropic and isotropic sampling. A significant difference (p<0.01) was found in the fractional anisotropic level but not in kurtosis. The results presented here can provide a basis for higher resolution as well as higher quality kurtosis mapping, which may be of great significance in clinical examinations.

Nowadays, cultivated variants and adulterants of Astragali Radix (AR) have flooded the market, causing the quality assurance of AR to be challenging. To address this issue, we combined network pharmacology with chromatographic fingerprinting and multicomponent quantitative analysis for the quality evaluation of AR. Specifically, through network pharmacology, a complete understanding of the active components and pharmacological activities of AR was established. In addition, establishing fingerprint profiles and multicomponent quantitation by high-performance liquid chromatography (HPLC) is convenient and comprehensive, and can more fully reflect the overall situation of the distribution of various chemical components. To evaluate and differentiate AR from different origins, hierarchical cluster analysis and principal component analysis were performed. The result showed that AR acts synergistically through multiple targets and pathways. The content of chemical components in AR from different origins varied significantly. Combining network pharmacology and multicomponent quantification results, astragaloside II and IV and formononetin can be used as quality markers for the quality control of AR. This study provides a comprehensive and reliable strategy for the quality evaluation of AR and identifies its quality markers to ensure the quality of the herb.

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