Large-scale functional network connectivity mediate the associations of gut microbiota with sleep quality and executive functions.
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ABSTRACT:
SUBMITTER: Cai H
PROVIDER: S-EPMC8193524 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specific cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain networks.
| S-EPMC8789640 | biostudies-literature
Project description:ObjectiveAlthough previous studies have shown that gut microbiota may be involved in the occurrence of deep venous thrombosis (DVT), the specific link between the two remains unclear. The present study aimed to explore this question from a genetic perspective.Materials and methodsGenome-wide association study (GWAS) summary data of DVT were obtained from the UK Biobank (N = 9,059). GWAS summary data of the gut microbiota were obtained from the Flemish Gut Flora Project (N = 2,223) and two German cohorts (FoCus, N = 950; PopGen, N = 717). All the participants were of European ancestry. Linkage disequilibrium score (LDSC) regression has great potential for analyzing the heritability of disease or character traits. LDSC regression was used to analyze the genetic correlation between DVT and the gut microbiota based on the GWAS summary data obtained from previous studies. Mendelian randomization (MR) was used to analyze the genetic causal relationship between DVT and the gut microbiota. We used the random effects inverse variance weighted, MR Egger, weighted median, simple mode, and weighted mode to perform MR analysis. We performed a sensitivity analysis of the MR analysis results by examining heterogeneity and horizontal pleiotropy.ResultsLinkage disequilibrium score analysis showed that Streptococcaceae (correlation coefficient = -0.542, SE = 0.237, P = 0.022), Dialister (correlation coefficient = -0.623, SE = 0.316, P = 0.049), Streptococcus (correlation coefficient = -0.576, SE = 0.264, P = 0.029), and Lactobacillales (correlation coefficient = -0.484, SE = 0.237, P = 0.042) had suggestive genetic correlation with DVT. In addition, the MR analysis showed that Streptococcaceae had a positive genetic causal relationship with DVT (P = 0.027, OR = 1.005). There was no heterogeneity or horizontal pleiotropy in the MR analysis (P > 0.05).ConclusionIn this study, four gut microbes (Streptococcaceae, Dialister Streptococcus, Lactobacillales) had suggestive genetic correlations with DVT, and Streptococcaceae had a positive causal relationship with DVT. Our findings provide a new research direction for the further study of and prevention of DVT.
| S-EPMC9677955 | biostudies-literature
Project description:There is a proof-of-concept that microbial metabolites provide a molecular connection between the gut and the brain. Extensive research has established a link between gut Bacteroides and human cognition, yet the metabolic and neural mechanisms underlying this association remain largely unknown. Here, we collected fecal samples, resting-state functional MRI, and cognitive data from a large and homogeneous sample of 157 healthy young adults. 16S rRNA gene sequencing was conducted with abundances of Bacteroides and metabolic pathways quantified by species annotation and functional prediction analyses, respectively. Large-scale intra- and internetwork functional connectivity was measured using independent component analysis. Results showed that gut Bacteroides were related to multiple metabolic pathways, which in turn were associated with widespread functional network connectivity. Furthermore, functional network connectivity mediated the associations between some Bacteroides-related metabolic pathways and cognition. Remarkably, arginine and proline metabolism, phenylalanine metabolism, and biosynthesis of unsaturated fatty acids act as the key metabolic pathways that are most contributive, and the executive control and sensorimotor systems contribute most strongly at the neural level. Our findings suggest complex poly-pathway and poly-network processes linking Bacteroides to cognition, more generally yielding a novel conceptualization of targeting gut Bacteroides as an intervention strategy for individuals with cognitive impairment.
| S-EPMC8558260 | biostudies-literature
Project description:The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.
| S-EPMC7320173 | biostudies-literature
Project description:Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer's dementia, and it was suggested that sleep disorders such as obstructive sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect [95% CI]: 0.07 [0.03; 0.12], p-value: 0.002; ODI std. effect [95% CI]: 0.09 [0.04; 0.13], p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.
| S-EPMC7953208 | biostudies-literature
Project description:Background Sleep loss is a serious global health concern. Consequences include memory deficits and gastrointestinal dysfunction. Our previous research showed that melatonin can effectively improve cognitive impairment and intestinal microbiota disturbances caused by sleep deprivation (SD). The present study further explored the mechanism by which exogenous melatonin prevents SD-induced cognitive impairments. Here, we established fecal microbiota transplantation, Aeromonas colonization and LPS or butyrate supplementation tests to evaluate the role of the intestinal microbiota and its metabolites in melatonin in alleviating SD-induced memory impairment. Results Transplantation of the SD-gut microbiota into normal mice induced microglia overactivation and neuronal apoptosis in the hippocampus, cognitive decline, and colonic microbiota disorder, manifesting as increased levels of Aeromonas and LPS and decreased levels of Lachnospiraceae_NK4A136 and butyrate. All these events were reversed with the transplantation of SD + melatonin-gut microbiota. Colonization with Aeromonas and the addition of LPS produced an inflammatory response in the hippocampus and spatial memory impairment in mice. These changes were reversed by supplementation with melatonin, accompanied by decreased levels of Aeromonas and LPS. Butyrate administration to sleep-deprived mice restored inflammatory responses and memory impairment. In vitro, LPS supplementation caused an inflammatory response in BV2 cells, which was improved by butyrate supplementation. This ameliorative effect of butyrate was blocked by pretreatment with MCT1 inhibitor and HDAC3 agonist but was mimicked by TLR4 and p-P65 antagonists. Conclusions Gut microbes and their metabolites mediate the ameliorative effects of melatonin on SD-induced cognitive impairment. A feasible mechanism is that melatonin downregulates the levels of Aeromonas and constituent LPS and upregulates the levels of Lachnospiraceae_NK4A136 and butyrate in the colon. These changes lessen the inflammatory response and neuronal apoptosis in the hippocampus through crosstalk between the TLR4/NF-κB and MCT1/ HDAC3 signaling pathways. Video Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s40168-022-01452-3.
| S-EPMC9887785 | biostudies-literature
Project description:To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.
| S-EPMC6965330 | biostudies-literature
Project description:Study Objectives:Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learning methods can approximate the performance of human scorers when supplied with sufficient training cases and to investigate how staging performance depends on the number of training patients, contextual information, model complexity, and imbalance between sleep stage proportions. Methods:A total of 102 features were extracted from six electroencephalography (EEG) channels in routine polysomnography. Two thousand nights were partitioned into equal (n = 1000) training and testing sets for validation. We used epoch-by-epoch Cohen's kappa statistics to measure the agreement between classifier output and human scorer according to American Academy of Sleep Medicine scoring criteria. Results:Epoch-by-epoch Cohen's kappa improved with increasing training EEG recordings until saturation occurred (n = ~300). The kappa value was further improved by accounting for contextual (temporal) information, increasing model complexity, and adjusting the model training procedure to account for the imbalance of stage proportions. The final kappa on the testing set was 0.68. Testing on more EEG recordings leads to kappa estimates with lower variance. Conclusion:Training with a large data set enables automated sleep staging that compares favorably with human scorers. Because testing was performed on a large and heterogeneous data set, the performance estimate has low variance and is likely to generalize broadly.
| S-EPMC6251659 | biostudies-literature
Project description:The gut microbiota has been associated with many diseases, including endometriosis. However, very few studies have been conducted on this topic in human. This study aimed to investigate the association between endometriosis and gut microbiota. Women with endometriosis (N=66) were identified at the Department of Gynaecology and each patient was matched with three controls (N=198) from the general population. All participants answered questionnaires about socioeconomic data, medical history, and gastrointestinal symptoms and passed stool samples. Gut bacteria were analyzed using 16S ribosomal RNA sequencing, and in total, 58 bacteria were observed at genus level in both patients with endometriosis and controls. Comparisons of the microbiota between patients and controls and within the endometriosis cohort were performed. Both alpha and beta diversities were higher in controls than in patients. With the false discovery rate q<0.05, abundance of 12 bacteria belonging to the classes Bacilli, Bacteroidia, Clostridia, Coriobacteriia, and Gammaproteobacter differed significantly between patients and controls. Differences observed between patients with or without isolated ovarian endometriosis, involvement of the gastrointestinal tract, gastrointestinal symptoms, or hormonal treatment disappeared after calculation with false discovery rate. These findings indicate that the gut microbiota may be altered in endometriosis patients.
| S-EPMC8289757 | biostudies-literature
Project description:ObjectivesStudies have shown that obstructive sleep apnea (OSA) is inextricably linked with cardiovascular diseases (CVD). However, the roles of certain common sleep disturbances, such as low slow-wave sleep, excessive daytime sleepiness and short sleep duration, in the pathogenesis and progression of CVD in patients with OSA have not been determined. Therefore, we conducted a large cross-sectional study to explore the effect of low slow-wave sleep, excessive daytime sleepiness and short sleep duration on the risk of CVD in patients with OSA.MethodsSubjects were consecutively enrolled to participate in the sleep center of Shanghai Jiao Tong University Affiliated Sixth People's Hospital. All OSA patients were diagnosed by standard polysomnography, while controls were all simple snorers. A total of 4,475 participants were strictly recruited. The Framingham Risk Score were employed to assess the 10-year risk of CVD, and logistic regression was used to measure the association between sleep disturbances and the moderate-to-high CVD risk.ResultsIn the whole cohort, OSA, excessive daytime sleepiness, and low slow-wave sleep were all risk factors for the moderate-to-high 10-year CVD risk (odds ratio [OR] = 3.012, 95% confidence interval [CI] 2.418-3.751; OR = 1.407, 95% CI: 1.228-1.613, and OR = 0.973,95% CI: 0.967-0.980), but sleep duration did not contribute significantly to that risk. Whether in patients with OSA and controls, low SWS (<12.8%) could increase the risk of CVD. Subjective excessive daytime sleepiness would significantly increase the risk of CVD only in patients with severe OSA.ConclusionIt is important to pay more attention to the impact of sleep on cardiovascular health. Patients with sleep disturbances should adopt a healthy lifestyle and undergo regular follow-up of cardiovascular indicators to prevent cardiovascular complications.Trial registration[http://www.chictr.org.cn/showproj.aspx?proj=43057], identifier [ChiCTr1900025714].
| S-EPMC9659611 | biostudies-literature