Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r = 0.267, p = 0.0078) with an accuracy of 64.6%. In addition, the creativity-related functional connectivity network we identified in the first dataset was still significantly correlated with the creativity score in the new dataset (p<10). In summary, our research demonstrates that strong top-down control versus weak bottom-up processes underlie creativity, which is modulated by competition between the glutamate and GABA neurotransmitter systems. Our work provides the first insights into both the neural and the genetic bases of creativity.

It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people’s lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs.

Background:Patients with coronary heart disease (CHD) angina pectoris are in critical condition, which can cause sudden death, myocardial infarction, and other adverse events, and bring serious burden to families and society. Timely treatment should be given to improve the condition. Western medicine treatment of angina pectoris failed to meet the demand of angina symptom control.Objective:It is hoped that the research method with higher evidential value will be adopted to compare the short-term, medium-term, and long-term effects of Chinese patent medicine combined with conventional western medicine and conventional western medicine alone in the treatment of CHD angina pectoris, so as to tap the clinical efficacy advantages of traditional Chinese medicine (TCM) and provide reliable data support for its clinical application.Methods:A prospective cohort study was conducted among patients with CHD angina pectoris who were treated with oral Chinese patent medicine and conventional western medicine. The patients were divided into exposed group and nonexposed group according to whether or not the patients with CHD angina pectoris were treated with Chinese patent medicine. The exposed group was treated with TCM combined with conventional western medicine, while the nonexposed group was treated with conventional western medicine alone. Patients need to be hospitalized for 2 weeks as the introduction period and whether to enter the group is determined according to the treatment and medication conditions of the patients. The follow-up time points were 0th, 4th, 12th, 24th, and 48th weeks. The main events and secondary events were used as the evaluation criteria for clinical efficacy of CHD angina pectoris. In the experimental study, we will use strict indicators to detect standard operation procedure for multinomics and bacterial flora detection.Conclusion:This study will provide evidence for the clinical efficacy advantages of Chinese patent medicine and reliable support for its clinical application through test data.

SummaryAimsTo develop and validate a novel score for prediction of 3‐month functional outcome in neurocritically ill patients.MethodsThe development of the novel score was based on two widely used scores for general critical illnesses (Acute Physiology and Chronic Health Evaluation II, APACHE II; Simplified Acute Physiology Score II, SAPS II) and consideration of the characteristics of neurocritical illness. Data from consecutive patients admitted to neurological ICU (N‐ICU) between January 2013 and June 2016 were used for the validation. The modified Rankin Scale (mRS) was used to evaluate 3‐month functional outcomes. APACHE II scores, SAPS II scores, and our novel scores at 24 hours and 72 hours in N‐ICU were obtained. We compared the prognostic performance of our score with APACHE II and SAPS II.ResultsWe developed a 44‐point scoring system named the INCNS score, and it includes 19 items which were categorized into five parts: inflammation (I), nutrition (N), consciousness (C), neurological function (N), and systemic function (S). We validated the INCNS score with a cohort of 941 N‐ICU patients. The 72‐hours INCNS score achieved an area under the receiver operating characteristic curve (AUC) of 0.828 (95% CI: 0.802‐0.854), and the 24‐hours INCNS score achieved an AUC of 0.788 (95% CI: 0.759‐0.817). The INCNS score exhibited significantly better discriminative and prognostic performance than APACHE II and SAPS II at both 24 hours and 72 hours in N‐ICU.ConclusionWe developed an INCNS score with superior predictive power for functional outcome of neurocritically ill patients.

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

Depression is a mental disorder characterized by persistent unhappiness, lack of interest, with cognitive and sleep disorders. Jiaotaiwan is a traditional Chinese medicine for the treatment of insomnia and depressive‐like symptoms. In this study, the major chemical components in Jiaotaiwan were qualitatively analyzed using ultra high performance liquid chromatography quadrupole time‐of‐flight mass spectrometry, and a model of depression in rats was subsequently established with chronic unpredictable mild stress followed by Jiaotaiwan intervention. Next, the metabolic profile of rat serum samples was analyzed using nontargeted metabolomics, wherein changes in the metabolites in serum samples before and after Jiaotaiwan administration were measured by multiple statistical approaches. Principal component analysis and partial least squares discriminant analysis indicated that the Jiaotaiwan treatment improved the metabolic phenotype depression. Moreover, the heatmap analysis identified the most important ten biomarkers involved in depression. According to the pathway analysis, the therapeutic effect of Jiaotaiwan on depression may involve the regulation of amino acid metabolism, glycerophospholipid metabolism, and energy metabolism. These findings help us understand the pathogenesis of depression in‐depth, and discover targets for clinical diagnosis and treatment. And it also lays a foundation for the use of Jiaotaiwan as an antidepressant agent.

We study the convergence to equilibria, as time tends to infinity, of trajectories of dissipative wave systems with time‐dependent velocity feedbacks and subject to nonlinear potential energies. Estimates for the speed of convergence are obtained in terms of the damping coefficient and the Łojasiewicz–Simon exponent. We allow for both restoring and amplifying effects of exterior forces, which makes our results possess wide applicability. As an example of application, we show that the trajectories of a sine‐Gordon system, with nonautonomous damping, approach equilibria at least polynomially. Copyright © 2016 John Wiley & Sons, Ltd.

Although the diagnoses based on phenomenology have many practical advantages, accumulating evidence shows that schizophrenia and autism spectrum disorder (ASD) share some overlap in genetics and clinical presentation. It remains largely unknown how ASD-associated polygenetic risk contributes to the pathogenesis of schizophrenia. In the present study, we calculated high-resolution ASD polygenic risk scores (ASD PRSs) and selected optimal ten ASD PRS with minimal P values in the association analysis of PRSs, with schizophrenia to assess the effect of ASD PRS on brain neural activity in schizophrenia cases and controls. We found that amplitude of low-frequency fluctuation in left amygdala was positively associated with ASD PRSs in our cohort. Correlation analysis of ASD PRSs with facial emotion recognition test identified the negative correlation of ASD PRSs with negative emotions in schizophrenia cases and controls. Finally, functional enrichment analysis of PRS genes revealed that neural system function and development, as well as signal transduction, were mainly enriched in PRS genes. Our results provide empirical evidence that polygenic risk for ASD contributes to schizophrenia by the intermediate phenotypes of left amygdala function and emotion recognition. It provides a promising strategy to understand the relationship between phenotypes and genotypes shared in mental disorders.