Brain connectomic associations with traditional Chinese medicine diagnostic classification of major depressive disorder: a diffusion tensor imaging study

Lateralized brain activities in subcortical vascular mild cognitive impairment with differential Chinese medicine patterns: A resting-state functional magnetic resonance imaging study

Background

Subcortical vascular mild cognitive impairment (svMCI) is one of the most treatable cognitive impairments, but could be hampered by the high clinical heterogeneities. Further classification by Chinese Medicine (CM) patterns has been proved to stratify its clinical heterogeneities. It remains largely unknown of the spontaneous brain activities regarding deficiency patterns (DPs) and excess patterns (EPs) of svMCI patients based on fMRI data.

Objective

We aim to provide neuroimaging evidence of altered resting-state brain activities associated with DPs and EPs in svMCI patients.

Methods

Thirty-seven svMCI patients (PAs) and 23 healthy controls (CNs) were consecutively enrolled. All patients were categorized into either the EP group (n = 16) and the DP group (n = 21) based on a quantitative CM scale. The fractional amplitude of low-frequency fluctuation (fALFF) value was used to make comparisons between different subgroups.

Results

The DP group showed significant differences of fALFF values in the right middle frontal gyrus and the right cerebellum, while the EP group showed significant differences in the left orbitofrontal gyrus and the left cerebellum, when compared with the CN group. When compared with the EP group, the DP group had markedly increased fALFF values in the left superior temporal gyrus, right middle temporal gyrus and brainstem. The decreased fALFF values was shown in the right anterior cingulate and paracingulate gyri. Among the extensive areas of frontotemporal lobe, the Montreal Cognitive Assessment (MoCA) scores were significantly correlated with the reduced fALFF value of the right middle frontal gyrus and the left orbitofrontal gyrus.

Conclusion

Our results indicated that the DPs and EPs presented the lateralization pattern in the bilateral frontal gyrus, which will probably benefit the future investigation of the pathogenesis of svMCI patients.

Network Pharmacology and Molecular Docking Study of the Chinese Miao Medicine Sidaxue in the Treatment of Rheumatoid Arthritis

Purpose

This study aimed to investigate the molecular mechanisms of Compound Sidaxue (SX), a prescription of Chinese Miao medicine, in treating rheumatoid arthritis (RA) using network pharmacology and in vivo experimental approaches.

Methods

Network pharmacology was adopted to detect the active components of four Traditional Chinese herbal medicine (TCM) of SX, and the key targets and signaling pathways in the treatment of RA were predicted, and the key components and targets were screened for molecular docking. The predicted targets and pathways were validated in bovine type II collagen and incomplete Freund’s adjuvant emulsifier-induced rat RA model.

Results

In this study, we identified 33 active components from SX, predicted to act on 44 RA-associated targets by network pharmacology. PPI network demonstrated that TNF-α, VEGF-A, IL-2, IL-6, AKT, PI3K, STAT1 may serve as the key targets of SX for the treatment of RA. The main functional pathways involving these key targets include PI3K-AKT signaling pathway, TNF signaling pathway, NF-κB signaling pathway. Molecular docking analysis found that the active components β-amyrin, cajanin, eleutheroside A have high affinity for TNF-α, VEGFA, IL-2, AKT, and PI3K, etc. SX can improve joint swelling in Collagen-induced arthritis (CIA) rats, reduce inflammatory cell infiltration and angiogenesis in joint synovial tissue, and down-regulate IL-2, IL-6, TNF-α, VEGF, PI3K, AKT, p-AKT, NF-κBp65, the expression of p-NF-κBp65, STAT1, and PTGS2 are used to control the exacerbation of inflammation and alleviate the proliferation of synovial pannus, and at the same time play the role of cartilage protection to achieve the effect of treating RA.

Conclusion

Through a network pharmacology approach and animal study, we predicted and validated the active compounds of SX and their potential targets for RA treatment. The results suggest that SX can markedly alleviate CIA rat by modulating the VEGF/PI3K/AKT signaling pathway, TNF-α signaling pathway, IL/NF-κB signaling pathway.

White Matter-Based Structural Brain Network of Major Depression

Major depressive disorder (MDD) is frequently characterized as a disorder of the disconnection syndrome. Diffusion tensor imaging (DTI) has played a critical role in supporting this view, with much investigation providing a large amount of evidence of structural connectivity abnormalities in the disorder. Recent research on the human connectome combined neuroimaging techniques with graph theoretic methods to highlight the disrupted topological properties of large-scale structural brain networks under depression, involving global metrics (e.g., global and local efficiencies), and local nodal properties (e.g., degree and betweenness), as well as other related metrics, including a modular structure, assortativity, and (rich) hubs. Here, we review the studies of white matter networks in the case of MDD with the application of these techniques, focusing principally on the consistent findings and the clinical significance of DTI-based network research, while discussing the key methodological issues that frequently arise in the field. The already published literature shows that MDD is associated with a widespread structural connectivity deficit. Topological alteration of structural brain networks in the case of MDD points to decreased overall connectivity strength and reduced global efficiency as well as decreased small-worldness and network resilience. These structural connectivity disturbances entail potential functional consequences, although the relationship between the two is very sophisticated and requires further investigation. In summary, the present study comprehensively maps the structural connectomic disturbances in patients with MDD across the entire brain, which adds important weight to the view suggesting connectivity abnormalities of this disorder and highlights the potential of network properties as diagnostic biomarkers in the psychoradiology field. Several common methodological issues of the study of DTI-based networks are discussed, involving sample heterogeneity and fiber crossing problems and the tractography algorithms. Finally, suggestions for future perspectives, including imaging multimodality, a longitudinal study and computational connectomics, in the further study of white matter networks under depression are given. Surmounting these challenges and advancing the research methods will be required to surpass the simple mapping of connectivity changes to illuminate the underlying psychiatric pathological mechanism.