LncRNA H19 inhibits autophagy by epigenetically silencing of DIRAS3 in diabetic cardiomyopathy

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions—particularly prefrontal–parietal and basal ganglia—whereas the network pattern differs between genders.

Schizophrenia (SCZ) is a common psychiatric disorder with polygenetic pathogenesis. Among the many identified candidate genes and loci, the group of tumour suppressor genes has drawn our interest. In this mini-review article, we describe evidence of a correlation between major tumour suppressor genes and SCZ development. Genetic mutations ranging from single nucleotide polymorphisms to large structural alterations have been found in tumour-related genes in patients with SCZ. Epigenetic mechanisms, including DNA methylation/acetylation and microRNA regulation of tumour suppressor genes, have also been implicated in SCZ. Beyond genetic correlations, we hope to establish causal relationships between tumour suppressor gene function and SCZ risk. Accumulating evidence shows that tumour suppressor genes may mediate cell survival and neural development, both of which contribute to SCZ aetiology. Moreover, converging intracellular signalling pathways indicate a role of tumour suppressor genes in SCZ pathogenesis. Tumour suppressor gene function may mediate a direct link between neural development and function and psychiatric disorders, including SCZ. A deeper understanding of how neural cell development is affected by tumour suppressors may lead to improved anti-psychotic drugs.

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995–2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop “bridges” to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the “bridges” may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the “bridges” will aid in the development of future diagnostics for MDD and other mental disorders.

Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject correspondence limits the clinical utility of rsfMRI and its application to single‐subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi‐spatial‐scale canonical intrinsic connectivity network (ICN) templates via the use of multi‐model‐order independent component analysis (ICA). We also study the feasibility of estimating subject‐specific ICNs via spatially constrained ICA. The results show that the subject‐level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large‐scale ICNs require less data to achieve specific levels of (within‐ and between‐subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject‐level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within‐subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Importance: Patients with schizophrenia are considered to have many risk factors for the development of cancer. However, the incidence of breast cancer in women with schizophrenia compared with the general population remains uncertain.Objective: To perform an updated meta-analysis to evaluate the association between schizophrenia and the risk of breast cancer.Data Sources: A systematic search of the PubMed and EMBASE databases was conducted using the search terms schizophrenia, schizophrenic, psychosis, combined with breast and cancer, tumor, neoplasm, or carcinoma. The final literature search was performed on August 15, 2017.Study Selection: Cohort studies reporting the standardized incidence ratio (SIR) for the risk of breast cancer in women with schizophrenia compared with the general population.Data Extraction and Synthesis: The meta-analysis adhered to Meta-analysis of Observational Studies in Epidemiology and the Cochrane Handbook for Systematic Reviews of Interventions. Data extraction was performed independently. A random-effects model was used to pool the results, and a recently proposed prediction interval was calculated to describe the heterogeneity.Main Outcomes and Measures: The SIR for the risk of breast cancer in women with schizophrenia compared with the general population or those without schizophrenia.Results: Twelve cohorts were included in this meta-analysis. The results of the meta-analysis showed that schizophrenia was associated with a significantly increased risk of breast cancer incidence in women (SIR, 1.32; 95% CI, 1.15-1.51; P < .001), with significant heterogeneity (P < .001; I² = 90%). Substantial between-study variance was also suggested by the wide prediction interval (0.82-2.11), which indicated that it is possible that a future study will show a decreased breast cancer risk in women with schizophrenia compared with the general population. The subgroup analysis results showed that the association was not significantly affected by whether breast cancer cases were excluded at baseline or the sample size of the included studies.Conclusions and Relevance: The incidence of breast cancer in women with schizophrenia is higher than that of the general female population. However, significant heterogeneity exists among the included studies. Women with schizophrenia deserve intensive prevention and treatment of breast cancer.