The overlapping modular organization of human brain functional networks across the adult lifespan.

Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.

Gene expression associated with individual variability in intrinsic functional connectivity.

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.

Predicting visual working memory with multimodal magnetic resonance imaging.

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

Decreased expression of DAB2IP in pancreatic cancer with wild-type KRAS.

BACKGROUND: KRAS mutation plays an important role in the pathogenesis of pancreatic cancer. However, the role of wild-type KRAS in the progression of pancreatic cancer remains unknown. The present study was to investigate the expression of the Ras GTPase activating protein (DAB2IP) in pancreatic cancer and its clinical significance. METHODS: The expression of DAB2IP in pancreatic cancer cell lines and normal human pancreatic ductal epithelial cells was analyzed by Western blotting and real-time quantitative reverse transcription-PCR (qRT-PCR). The KRAS mutational types of pancreatic cancer tissues obtained from pancreatic cancer patients (n=20) were also analyzed. Subsequently, DAB2IP expression was detected in pancreatic cancer tissues, adjacent and normal pancreatic tissues (n=2) by immunohistochemistry, and the relationship between DAB2IP expression and the clinical characteristics of patients was evaluated. RESULTS: Western blotting and qRT-PCR results showed that DAB2IP expression in pancreatic cancer cells with wild-type KRAS was lower than that in those with mutation-type KRAS and normal human pancreatic ductal epithelial cells (P<0.05). Immunohistochemistry showed that DAB2IP expression was lower in pancreatic cancer tissues than that in adjacent and normal pancreatic tissues (Z=-4.000, P=0.000). DAB2IP expression was lower in pancreatic cancer patients with the wild-type KRAS gene than that in those with KRAS mutations (WilcoxonW=35.000, P=0.042). Furthermore, DAB2IP expression in patients with perineurial invasion was lower than that in those without invasion (WilcoxonW=71.500, P=0.028). DAB2IP expression was lower in patients with more advanced stage than that in those with early clinical stage (WilcoxonW=54.000, P=0.002). CONCLUSIONS: DAB2IP expression was reduced in patients with pancreatic cancer compared with those with no cancer. DAB2IP expression was correlated with the KRAS gene, perineurial invasion and clinical stage of the disease. Our data indicated that DAP2IP expression can be used as a potential prognostic indicator and a promising molecular target for therapeutic intervention in patients with pancreatic cancer.

Trade-offs among cost, integration, and segregation in the human connectome.

The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.

Plant Macrofossils Reveal Aquatic Macrophyte Successions of a Typical Shallow Lake (Huanggai Lake, China) in the Past Century.

Aquatic macrophytes are one of the important biotic components of shallow lake ecosystems. Understanding the long-term evolution of the macrophyte community is crucial for lake management. Huanggai Lake, a typical shallow lake in the middle reach of the Yangtze River, was selected as the research site for this study. Based on 210Pb/137Cs dating, aquatic plant macrofossils were used to reconstruct the succession of aquatic macrophytes in the past century. Our results show that the lake maintained a consistent natural state before 1940, with a relatively low abundance of aquatic plants dominated by species such as Najas minor. From 1940 to 1974, human activities gradually intensified in the lake leading to the emergence of eutrophic species such as Potamogeton maackianus, along with the increasing abundance of other emergent and floating aquatic macrophytes. Since 1974, more pollution-resistant, emergent species such as Potamogeton maackianus and Potamogeton crispus have become dominant. The abundance of aquatic macrophytes reached its maximum in the early 1990s. Combined with macrofossil succession and other multiple sedimentary proxy analyses, driving mechanisms for aquatic macrophytes are discussed. Both the nearby Liangzi Lake and Huanggai Lake share many common features of aquatic plant evolution. This study is the first of its kind to use plant macrofossils (with identifiable images) as a proxy for aquatic macrophyte succession in a shallow Yangtze lake. In absence of long-term monitoring records, this study highlights the increased application of plant macrofossils for reconstructing the vegetation dynamics and restoration of degraded lakes exposed to severe anthropogenic impacts over the past century.

Common and distinct neural substrates of the compassionate and uncompassionate self-responding dimensions of self-compassion.

Self-compassion is beneficial for individuals' emotional health, but debates regarding its conceptualization are increasing. The present study aimed to explore the neural basis of self-compassion and its compassionate and uncompassionate dimensions and the indirect path from neural basis to emotional health. Structural MRI and Resting-state fMRI data were used to measure the gray matter volume (GMV) and the amplitude of low-frequency fluctuation (ALFF) in 88 healthy college students. We found that individuals with higher self-compassion had decreased GMV in the prefrontal cortex, cerebellum as well as lower ALFF in the occipital lobe. The compassionate and uncompassionate dimensions of self-compassion shared some similarities (e.g., common correlation with GMV in the medial prefrontal cortex, ALFF in the occipital lobe) but also had some differences (e.g., only uncompassionate dimensions correlated with GMV in the lateral prefrontal cortex, ALFF in medial temporal lobe/striatum). The indirect path analyses revealed that corresponding brain characteristics could have associations with emotional health through self-compassion, as well as its uncompassionate dimension, but not compassionate dimension. This exploratory whole-brain study showed some preliminary findings that compassionate and uncompassionate dimensions of self-compassion were related to distinct brain regions, which are both important to the current conceptualization of self-compassion and intervention study.

Salivary microbiota may predict the presence of esophageal squamous cell carcinoma.

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P < 0.05). The areas under the ROC curves for Streptococcus salivarius, Fusobacterium nucleatum, Porphyromonas gingivalis and the combination of the three bacteria for predicting patients with ESCC were 69%, 56.5%, 61.8% and 76.4%, respectively. The sensitivities corresponding to cutoff value were 69.3%, 22.7%, 35.2% and 86.4%, respectively, and the matched specificity were 78.4%, 96.1%, 90.2% and 58.8%, respectively. These highly expressed Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in the saliva, alone or in combination, indicate their predictive value for ESCC.

[The genetic relation of four ethnic populations in China's Guizhou is revealed by sequence variations of mtDNA D-loop].

OBJECTIVE: Through maternal inheritance, to explore the genetic structures and relationships of Dong, Gelao, Tujia and Yi ethnic population in Guizhou of China. METHODS: The mtDNA D-loop hypervariable segment I (HVS I ) in 108 samples of four ethnic populations were sequenced. Then, the nucleotide diversity was estimated and a phylogenetic tree was constructed by Neighbor-Joining method. RESULTS: In the detected 497 bp fragments, 86 polymorphic sites were found, and 82 different haplotypes were identified. The phylogenetic tree of four ethnic populations showed: Yi, Tujia and Gelao clustered more closely than Dong did. CONCLUSION: Yi and Tujia population are very closely related, the reason may be that they either originate from a common ancestry or frequently undergo the gene exchanges and admixtures. The genetic relationship between Tujia and Gelao population is nearer, perhaps because they have settled in the adjacent regions. Dong and Yi population show the farthest genetic relationship, this is probably due to their different historical origins and geographic segregation.

Genetic polymorphism of mitochondrial DNA in Dong, Gelao, Tujia, and Yi ethnic populations from Guizhou, China.

To reveal the genetic structures and relationships of the four ethnic populations from the maternal inheritance and explore the origins and migrations of nationalities, the genetic polymorphism of mtDNA in Dong, Gelao, Tujia, and Yi populations from Guizhou was studied by direct sequencing of hypervariable segment I(HVS I) and PCR-RFLP of coding region. Thirty-seven (sub-) haplogroups were identified in the classification tree of mtDNA haplogroups. Haplogroup distributions and principal component (PC) analysis showed that the Dong has high frequencies of south-prevalent haplogroups, which indicates that it is a typically southern population. The Yi harbors high frequencies of the south-prevalent and northern-prevalent haplogroups, which demonstrates that it inherits the maternal characteristics from both southern and northern populations. The Yi and Gelao cluster together, the reason for which might be that their ancestries frequently underwent gene exchanges and mixtures.