Aberrant baseline brain activity and disrupted functional connectivity in patients with vascular cognitive impairment due to cerebral small vessel disease.

Objective: This study aims to examine the alterations in aberrant brain activity and network connectivity between individuals with mild and major vascular cognitive impairment (VCI). Materials and methods: A total of 114 patients with cerebral small vessel disease (CSVD) were included in this study, comprising 61 individuals with mild VCI (mean age, 55.7 ± 6.9 years; male, 42.6%) and 53 cases with major VCI (mean age, 57.6 ± 5.5 years; male, 58.5%). Additionally, 53 age-, gender-, and education-matched healthy subjects were recruited as normal controls (NC) (mean age, 54.9 ± 7.9 years; male, 52.9%). All participants underwent neuropsychological assessments and magnetic resonance imaging scans. One-way analysis of variance was used to compare fractional amplitude of low-frequency fluctuation (fALFF) values among the three groups. Two-sample t-tests were conducted to assess functional connectivity matrices between different groups for each connection. Moreover, mediation analyses were performed to explore the mediating effect of aberrant brain activity on the relationship between cognitive impairment and CSVD total burden. Results: VCI patients exhibited aberrant brain activity in regions such as the right thalamus (THA_R), right cuneus (CUN_R), left postcentral gyrus (PoCG_L), right postcentral gyrus (PoCG_R), right median cingulate, paracingulate gyri (PCG_R), and left precuneus (PCUN_L). Reduced positive functional connectivity was predominantly observed among nodes including PCUN_L, CUN_R, PoCG_L, PoCG_R, right posterior cingulate (PCG_R), and left occipital gyrus (IOG_L) in VCI patients. The aberrant baseline brain activity and disrupted brain network were more pronounced with worsening cognitive function. Increased fALFF values in THA_R, CUN_R, and PoCG_L mediated cognitive impairment in CSVD patients. Conclusion: Abnormal brain activities in THA_R, CUN_R, and PoCG_L, along with their associated abnormal functional connections, play a significant role in VCI. The study revealed a progressive increase in aberrant brain activity and network connectivity with advancing stages of VCI.

Hippocampal amide proton transfer values are associated with cerebral small vessel disease imaging markers and total burden: a community-based cross-sectional study.

Background: Neurodegeneration has been suggested to be associated with cerebral small vessel disease (CSVD). The association between different CSVD imaging markers and the extent of neurodegeneration could be indirectly confirmed by examining the relationship between CSVD imaging markers and the hippocampal amide proton transfer (APT) values. The associations between hippocampal APT values with CSVD imaging markers and CSVD total load need to be further validated. The aim of this study was to investigate potential variations in hippocampal APT values among individuals with CSVD imaging markers and varying degrees of CSVD total burden. Methods: A cross-sectional study (retrospective analysis of prospectively-acquired data) was conducted at Nanxishan Hospital of Guangxi Zhuang Autonomous Region. From May 2020 to June 2021, 165 individuals (age, 40-76 years; male/female, 103/62) were included in this study. The inclusion criteria for the participants were as follows: The presence of lacunar infarction (LI), and/or cerebral microbleed (CMB); moderate-to-severe enlarged perivascular space (EPVS) (>20); deep white matter hyperintensity (WMH) > Fazekas 2 or periventricular WMH > Fazekas. The exclusion criteria comprised the following: History of craniocerebral operation; Cases with significant pathology incidentally identified during magnetic resonance (MR) scan; Drug or alcohol abuse. The differences of hippocampal APT values between CSVD imaging makers presence or absence groups and different CSVD total burden groups were compared using independent t-test and one-way analysis of variance (ANOVA). The correlations between APT values and CSVD imaging markers were analyzed using Pearson correlation analysis. A mediation analysis model was used to investigate the mediating effect of the hippocampal APT values in the association between CSVD total loads and Montreal Cognitive Assessment (MoCA) score was assessed. Results: The hippocampal APT values among different CSVD total load groups were significantly different (P<0.001). The hippocampal APT values were significantly different between the imaging markers presence and absence groups. The P values for the LI, WMH EPVS, and CMB presence or absence groups were <0.001, <0.001, 0.034, and 0.002, respectively. The hippocampal APT values were significantly correlated with CMB (P<0.01), LI (P<0.01) and WMH (P<0.01). The mediation models demonstrated that the APT values of the hippocampus partially mediated the association between CSVD total load and MoCA score, the proportion of mediation attributable was calculated as 17.50%. Conclusions: Hippocampal APT values were associated with CSVD imaging markers and total burden. Hippocampal APT values may serve as a biomarker for the early detection of neurodegeneration in CSVD patients.

Amide proton transfer imaging has added value for predicting extraprostatic extension in prostate cancer patients.

Background: Prostate cancer invades the capsule is a key factor in selecting appropriate treatment methods. Accurate preoperative prediction of extraprostatic extension (EPE) can help achieve precise selection of treatment plans. Purpose: The aim of this study is to verify the diagnostic efficacy of tumor size, length of capsular contact (LCC), apparent diffusion coefficient (ADC), and Amide proton transfer (APT) value in predicting EPE. Additionally, the study aims to investigate the potential additional value of APT for predicting EPE. Method: This study include 47 tumor organ confined patients (age, 64.16 ± 9.18) and 50 EPE patients (age, 61.51 ± 8.82). The difference of tumor size, LCC, ADC and APT value between groups were compared. Binary logistic regression was used to screen the EPE predictors. The receiver operator characteristic curve analysis was performed to assess the diagnostic performance of variables for predicting EPE. The diagnostic efficacy of combined models (model I: ADC+LCC+tumor size; model II: APT+LCC+tumor size; and model III: APT +ADC+LCC+tumor size) were also analyzed. Results: APT, ADC, tumor size and the LCC were independent predictors of EPE. The area under the curve (AUC) of APT, ADC, tumor size and the LCC were 0.752, 0.665, 0.700 and 0.756, respectively. The AUC of model I, model II, and model III were 0.803, 0.845 and 0.869, respectively. The cutoff value of APT, ADC, tumor size and the LCC were 3.65%, 0.97×10-3mm2/s, 17.30mm and 10.78mm, respectively. The sensitivity/specificity of APT, ADC, tumor size and the LCC were 76%/89.4.0%, 80%/59.6%, 54%/78.9%, 72%/66%, respectively. The sensitivity/specificity of model I, Model II and Model III were 74%/72.3%, 82%/72.5% and 84%/80.9%, respectively. Data conclusion: Amide proton transfer imaging has added value for predicting EPE. The combination model of APT balanced the sensitivity and specificity.

Correlation between symptoms and cognitive function changes in patients with primary insomnia and pathways in gut microbiota.

Background: Primary insomnia (PI) refers to syndromes of difficulty falling asleep, poor sleep quality, early awakening, and difficulty falling asleep after waking up. Although there have been numerous studies, the specific etiology and pathogenesis of PI are still misunderstanding. In recent years, the gut microbiota has been proved to be involved in the metabolism of many mental disorders. But the specific mechanisms of its involvement in PI have not been fully elucidated. This study aims to explore the relationship between the gut microbiota and the symptoms, cognitive function changes in PI. Methods: In this study, the gut microbiota of PI patients and healthy controls was profiled by performing stool 16s rRNA gene sequencing. The co-occurrence network was constructed by using Weight Gene Co-expression Network Analysis (WGCNA) algorithm. The correlation between gut microbiota associated pathways and traits in PI were predicted. Results: WGCNA results demonstrated several Operational Taxonomic Units (OTU) modules are correlated to symptoms. By using PICRUSt2 software, we predicted the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of microbiota in modules. For instance, sleep efficiency may be correlated with the presence of Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane and chlorobenzene degradation. Total sleep time may be correlated with the presence of Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation and Biosynthesis of unsaturated fatty acids. The severity of insomnia may be correlated with Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism and RNA polymerase. Change of name score in Montreal Cognitive Assessment (MoCA) may be correlated with Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation in Gut Microbiota (GM). Conclusion: This study revealed the potential relationships between gut microbiota and PI. By using pathway prediction and enrichment analysis, we concluded many metabolic pathways may associated with some important traits of insomnia patients, including sleep efficiency, severe insomnia, total sleep time and change of name score in MoCA. The metabolic pathways include Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane, chlorobenzene degradation, Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation.Our study demonstrated that PI patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and syndromes of PI.