Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study.

Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study.

BACKGROUND: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients. METHODS: Forty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements. RESULTS: The values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88). CONCLUSIONS: Multi-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.

Targets and mechanisms of Alpinia oxyphylla Miquel fruits in treating neurodegenerative dementia.

The dried and ripe fruits of Alpinia oxyphylla and ripe fruits of Alpinia oxyphylla Miquel (AO) have the effects of tonifying kidney-essence and nourishing intelligence and thus have been widely used in treating dementia. Alzheimer's disease (AD) is a typical form of neurodegenerative dementia with kidney-essence deficiency in Traditional Chinese Medicine (TCM). So far, there is a lack of systematic studies on the biological basis of tonifying kidney-essence and nourishing intelligence and the corresponding phytochemicals. In this study, we investigated the targets of AO in tonifying kidney-essence and nourishing intelligence based on the key pathophysiological processes of neurodegenerative dementia. According to ultra-high-performance liquid chromatography with triple quadrupole mass spectrometry data and Lipinski's rule of five, 49 bioactive phytochemicals from AO were identified, and 26 of them were found to target 168 key molecules in the treatment of neurodegenerative dementia. Nine phytochemicals of AO were shown to target acetylcholinesterase (ACHE), and 19 phytochemicals were shown to target butyrylcholinesterase (BCHE). A database of neurodegenerative dementia with kidney-essence deficiency involving 731 genes was constructed. Furthermore, yakuchinone B, 5-hydroxy-1,7-bis (4-hydroxy-3-methoxyphenyl) heptan-3-one (5-HYD), oxyhylladiketone, oxyphyllacinol, butyl-ß-D-fructopyranoside, dibutyl phthalate, chrysin, yakuchinone A, rhamnetin, and rhamnocitrin were identified as the key phytochemicals from AO that regulate the pathogenesis of neurodegenerative dementia in a multitargeted manner. The approach of studying the pharmacological mechanism underlying the effects of medicinal plants and the biological basis of TCM syndrome may be helpful in studying the translation of TCM.