CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study.

OBJECTIVES: The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS: A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS: 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION: The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.

Activation network improves spatiotemporal modelling of human brain communication processes.

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Development of a Clinical Risk Score Prediction Tool for 5-, 9-, and 13-Year Risk of Dementia.

Importance: Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression. Objective: To develop a point risk score prediction model of dementia. Design, Setting, and Participants: This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model. Main Outcomes and Measures: Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk. Results: A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women. Conclusions and Relevance: In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.

Risk of inflammatory bowel disease appears to vary across different frequency, amount, and subtype of alcoholic beverages.

Objective: Inflammatory bowel disease (IBD) and alcohol use has become a significant and growing public health concern. Alcohol use has been reported to be the most-avoided diet item among IBD patients. However, knowledge regarding the impact of different classes of alcoholic beverages on the management of IBD is limited. Our study aims to evaluate the association of different frequencies, amounts, and subtypes of alcoholic beverages with IBD risk. Methods: The UK Biobank comprised 7,095 subjects with IBD and 4,95,410 subjects without IBD. Multivariate Logistic regression, stratifying analysis, and interaction terms were used to estimate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of IBD. A generalized additive model was used to evaluate the linearity associations of the total amount of all alcoholic beverages or that of each of five alcoholic beverages with IBD risk. Results: Compared with non-drinkers, the IBD risk was 12 to 16% lower in red wine consumers (1-2 glasses/week, OR [95%CI], 0.88 [0.80, 0.97]; 3-4 glasses/week, 0.84 [0.76, 0.93]; ≥5 glasses/week, 0.86 [0.78, 0.95]), whereas 12% higher in white wine and champagne consumers (1-2 glasses/week, 1.12 [1.03, 1.22]). Stratifying analysis showed low-frequency red wine consumers were associated with a lower IBD risk (0.85 [0.74, 0.97]), whereas spirits consumers were associated with a higher risk (1.28 [1.03, 1.59]). High doge of red wine consumers were associated with a lower IBD risk (above guidelines, 0.80 [0.67, 0.97]; double above, 0.83 [0.71, 0.97]), whereas high doge white wine and champagne (1.32 [1.09, 1.61]) and beer and cider (1.26 [1.02, 1.54]) consumers were associated with a higher IBD risk. White wine and champagne showed a significant interaction effect with high doge alcohol consumption (1.27 [1.03-1.58], p = 0.029). The dose-response association showed an increased IBD risk with more number of alcohol consumption of white wine and champagne, beer and cider, or the total amount of all alcoholic beverages. However, red wine is at low risk across the whole dose cycle. Conclusions: The IBD risk appears to vary across different frequencies, amounts, and subtypes of alcoholic beverages. Overall, alcohol intake is not recommended.

Association Between Sleep Duration and Parkinson's Disease Varied Across Related Orphan Receptor A rs2028122 Genotypes.

Background: The purpose of the study was to examine the association of long and short sleep duration with risk of Parkinson's disease (PD) across RORA rs2028122 genotypes. Methods: In the present prospective study with a large sized UK Biobank cohort, we performed multivariate logistic regression analyses, generalized additive model, interaction terms, stratification analysis, and mediation analysis to evaluate the association of long and short sleep duration with risk of PD across RORA rs2028122 genotypes. Results: The GG genotype [1.16 (1.01, 1.33)], a short sleep duration [1.23 (1.10, 1.37)], and a long sleep duration [1.19 (1.03, 1.37)] were identified as the independent risk factors for PD. Sleep duration exhibited a curvilinear U-shaped correlation with the risk of PD; first, the risk of PD gradually decreased as the length of sleep increase, but then, the risk began to increase as the length of sleep increase. Among habitual long sleepers, AG carriers had a higher risk of PD compared with AA carriers [1.67 (1.09, 2.55)]. Among AG carriers, both habitual short [1.28 (1.09, 1.50)] and long [1.38 (1.13, 1.69)] sleepers increased the risk of PD compared with habitual normal sleepers. Among GG carriers, habitual short sleepers have a higher risk of PD [1.26 (1.06, 1.50)] compared with habitual normal sleepers. A mediation model suggested that the rs2028122 genotype partially mediated the causal pathway of sleep duration leading to the development of PD on a positive effect. Conclusion: Our study demonstrated that the association between sleep duration and PD risk varied across different RORA rs2028122 genotypes. Our findings could help individuals to identify their potential risk profile and take timely actions to prevent the PD.

Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS.

To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.

Risk factors of COVID-19 in subjects with and without mental disorders.

BACKGROUND: Subjects with mental disorders are at a higher risk of various pandemic, but no specific studies concerning on screening and comparing the risk factors of COVID-19 for subjects with and without mental disorders, and the role of different classes of mental disorders with respect to the COVID-19. METHODS: This study comprised 42,264 subjects with mental disorders and 431,694 subjects without. Logistic regression was used to evaluate the associations of exposure factors with COVID-19 risk. Interaction terms were employed to explore the potential interaction effect between mental disorders and each exposure factor on COVID-19 risk. RESULTS: Mental disorders increased 1.45-fold risk of COVID-19 compared with non-mental disorders. There were significant interaction effects between mental disorders and age, sex, ethnicity, health ratings, socioeconomic adversity, lifestyle habits or comorbidities on COVID-19 risk. Subjects with and without mental disorders shared some overlapping risk factors of COVID-19, including the non-white ethnicity, socioeconomic adversity and comorbidities. Subjects without mental disorders carry some specific risk and protective factors. Among subjects with mental disorders, the COVID-19 risk was higher in subjects with a diagnosis of organic/symptomatic mental disorders, mood disorders, and neurotic, stress-related and somatoform disorders than that of their counterparts. Age, amount of alcohol consumption, BMI and Townsend deprivation showed non-linear increase with COVID-19 risk. LIMITATIONS: Absence of replication. CONCLUSIONS: Subjects with mental disorders are vulnerable populations to whom more attention should be paid. Public health guidance should focus on reducing the COVID-19 risk by advocating healthy lifestyle habits and preferential policies in populations with comorbidities.

Brain network excitatory/inhibitory imbalance is a biomarker for drug-naive Rolandic epilepsy: A radiomics strategy.

OBJECTIVE: Seizure occurs when the balance between excitatory and inhibitory (E/I) inputs to neurons is perturbed, resulting in abnormal electrical activity. This study investigated whether an existing E/I imbalance in neural networks is a useful diagnostic biomarker for Rolandic epilepsy by a resting-state dynamic causal modeling-based support vector machine (rs-DCM-SVM) algorithm. METHODS: This multicenter study enrolled a discovery cohort (76 children with Rolandic epilepsy and 76 normal controls [NCs]) and a replication cohort (59 children with Rolandic epilepsy and 60 NCs). Spatial independent component analysis was used to seven canonical neural networks, and a total of 25 regions of interest were selected from these networks. The rs-DCM-SVM classifier was used for individual classification, consensus feature selection, and feature ranking. RESULTS: The rs-DCM-SVM classifier showed that the E/I imbalance in brain networks is a useful neuroimaging biomarker for Rolandic epilepsy, with an accuracy of 88.2% and 81.5% and an area under curve of .92 and .83 in the discovery and the replication cohorts, respectively. Consensus brain regions with the highest contributions to the classification were located within the epilepsy-related networks, indicating that this classifier was suitable. Consensus functional connection pairs with the highest contributions to the classification were associated with an excitation network loop and an inhibition network loop. The excitation loop mediated the integration of advanced cognitive networks (subcortex, dorsal attention, default mode, executive control, and salience networks), whereas the inhibition loop was involved in the segregation of sensorimotor and language networks. The two loops showed functional segregation. SIGNIFICANCE: Brain E/I imbalance has potential to serve as a biomarker for individual classification in children with Rolandic epilepsy, and might be an important mechanism for causing seizures and cognitive impairment in children with Rolandic epilepsy.

Preexisting Mental Disorders Increase the Risk of COVID-19 Infection and Associated Mortality.

Coronavirus disease 2019 (COVID-19), a respiratory disease of unknown origin, has a high rate of morbidity and mortality. Individuals with mental disorders may have a higher risk of infection and worse clinical outcomes because of a variety of factors such as poorer general resilience and lower immune function. However, there have been no studies to date specifically investigating the risk of COVID-19 and associated mortality in these patients. This was addressed in the present study by analyzing the data of 473,958 subjects included in the UK Biobank, 14,877 of whom tested positive for COVID-19 infection. Logistic regression analysis was performed to evaluate the associations between mental disorders and risks of COVID-19 infection and associated mortality. The results showed that subjects who were diagnosed with a mental disorder had a significantly higher risk of developing COVID-19 and a worse outcome as evidenced by higher rates of COVID-19-related mortality, with the strongest effects observed for dementia. Among dementia subtypes, Alzheimer disease patients had the highest risks of COVID-19 infection (7.39-fold increase) and associated mortality (2.13-fold increase). Late-life anxiety only increased the risk of developing COVID-19 while late-life depression not only was associated with a higher risk of infection but also a worse outcome. These findings highlight the need to prioritize patients with mental disorders-especially those who experience these disorders later in life-when implementing preventive strategies such as vaccinations.

BCCT: A GUI Toolkit for Brain Structural Covariance Connectivity Analysis on MATLAB.

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex-subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Agitation and apathy increase risk of dementia in psychiatric inpatients with late-onset psychiatric symptoms.

BACKGROUND: A diagnosis of dementia in middle-aged and elder people is often complicated by physical frailty and comorbid neuropsychiatric symptoms (NPSs). Previous studies have identified NPSs as a risk factor for dementia. The aim of this study was to figure out to what extent individual NPS and certain demographic factors increased the risk of dementia in middle-aged and senior psychiatric inpatients. METHODS: One hundred twenty-seven middle-aged and senior patients admitted to psychiatric wards for late-onset (age ≥ 50 years) psychiatric symptoms were included and categorized into dementia or non-demented psychiatric disorders (NDPD). The patients' demographic information and medical records were collected during the first hospitalization and subjected to statistical analyses. RESULTS: 41.73% of the registered psychiatric inpatients were diagnosed as dementia in which Alzheimer's disease (AD) was the dominant subtype. The NDPD group consisted of nine individual diagnoses, except for schizophrenia. The frequencies of dementia inpatients increased with first episode age while that of NDPD inpatients decreased with first episode age. In the enrolled inpatients, most of dementia patients were males while females accounted for a higher proportion of NDPD patients. 58.49% of enrolled dementia inpatients presented cognitive deficit (CD) as the initial symptom while the remaining 41.51% showed NPS as initial symptom. Of the 12 NPSs, agitation and apathy greatly and significantly increased risk of dementia in psychiatric inpatients with late-onset psychiatric symptoms. CONCLUSIONS: These results added evidence that the demented patients admitted to psychiatric ward are more likely to be male, older first episode age, and have characteristic NPS including aberrant motor behavior (AMB), hallucinations, agitation, irritability and apathy. Further, this study emphasized the importance of agitation and apathy of NPSs functioning as risk factors of dementia in these inpatients.

Prognostic Value of S100P Expression in Patients With Digestive System Cancers: A Meta-Analysis.

BACKGROUND: Digestive system cancers (DSCs) are associated with high morbidity and mortality. S100P has been reported as a prognostic biomarker in DSCs, but its prognostic value remains controversial. Accordingly, we conducted a meta-analysis to investigate whether S100P is correlated with overall survival (OS) of patients with DSCs. The relationship between S100P and clinicopathological features was also evaluated. METHODS: We systematically searched PubMed, Embase, Web of Science and Cochrane Library for eligible studies up to January 2020. In total, 16 publications with 1,925 patients were included. RESULTS: S100P overexpression was associated with poor OS of patient with DSCs (HR=1.54, 95% CI: 1.14-2.08, P=0.005). When stratified by anatomic structure, S100P overexpression was associated with poor prognosis in non-gastrointestinal tract cancers (HR=1.98, 95% CI: 1.44-2.72, P<0.001) but not in gastrointestinal tract cancers (HR=1.09, 95% CI: 0.66-1.81, P=0.727). When stratified by tumor type, S100P overexpression predicted poor OS in cholangiocarcinoma (HR=2.14, 95% CI: 1.30-3.50, P=0.003) and hepatocellular carcinoma (HR=1.91, 95% CI: 1.22-2.99, P =0.005) but not in gastric cancer (HR=0.97, 95% CI: 0.65-1.45, P=0.872), colorectal cancer (HR=1.18, 95% CI: 0.32-4.41, P=0.807), gallbladder cancer (HR=1.40, 95% CI: 0.84-2.34, P=0.198), and pancreatic cancer (HR=1.92, 95% CI: 0.99-3.72, P=0.053). Furthermore, high S100P expression was significantly associated with distant metastasis (OR=3.58, P=0.044), advanced clinical stage (OR=2.03, P=0.041) and recurrence (OR=1.66, P=0.007). CONCLUSION: S100P might act as a prognostic indicator of non-gastrointestinal tract cancers.

Associations of sleep phenotypes with severe intentional self-harm: a prospective analysis of the UK Biobank cohort.

STUDY OBJECTIVES: We aimed to investigate the prospective associations of sleep phenotypes with severe intentional self-harm (ISH) in middle-aged and older adults. METHODS: A total of 499,159 participants (mean age: 56.55 ± 8.09 years; female: 54.4%) were recruited from the UK Biobank between 2006 and 2010 with follow-up until February 2016 in this population-based prospective study. Severe ISH was based on hospital inpatient records or a death cause of ICD-10 codes X60-X84. Patients with hospitalized diagnosis of severe ISH before the initial assessment were excluded. Sleep phenotypes, including sleep duration, chronotype, insomnia, sleepiness, and napping, were assessed at the initial assessments. Cox regression analysis was used to estimate temporal associations between sleep phenotypes and future risk of severe ISH. RESULTS: During a follow-up period of 7.04 years (SD: 0.88), 1,219 participants experienced the first hospitalization or death related to severe ISH. After adjusting for demographics, substance use, medical diseases, mental disorders, and other sleep phenotypes, short sleep duration (HR: 1.50, 95% CI: 1.23-1.83, p < .001), long sleep duration (HR: 1.56, 95% CI: 1.15-2.12, p = .004), and insomnia (usually: HR: 1.57, 95% CI: 1.31-1.89, p < .001) were significantly associated with severe ISH. Sensitivity analyses excluding participants with mental disorders preceding severe ISH yielded similar results. CONCLUSION: The current study provides the empirical evidence of the independent prediction of sleep phenotypes, mainly insomnia, short- and long-sleep duration, for the future risk of severe ISH among middle-aged and older adults.

Decreased modulation of segregated SEEKING and selective attention systems in chronic insomnia.

Sleep-related attentional bias and instinctual craving-sleep status may be associated with value-driven selective attention network and SEEKING system. We hypothesized that the two networks might be important components and underlie etiology of inability to initiate or/and maintain sleep in patients with chronic insomnia (PIs). Our aim is to investigate whether frequency-frequency couplings(temporal and spatial coupling, and differences of a set of imaging parameters) could elevate the sensibility to characterize the two insomnia-related networks in studying their relationships with sleep parameters and post-insomnia emotions. Forty-eight PIs and 48 status-matched good sleepers were requested to complete sleep and emotion-related questionnaires. Receiver operating characteristic curve was used to calculate the discriminatory power of a set of parameters. Granger causality and mediating causality analysis were used to address the causal relationships between the two networks and sleep/emotion-related parameters. Frequency-frequency couplings could characterize the two networks with high discriminatory power (AUC, 0.951; sensitivity, 87.5%; specificity, 95.8%), which suggested that the frequency-frequency couplings could be served as a useful biomarker to address the insomnia-related brain networks. Functional deficits of the SEEKING system played decreased mediator acting in post-insomnia negative emotions (decreased frequency-frequency coupling). Functional hyperarousal of the value-driven attention network played decreased mediator acting in sleep regulation (increased frequency-frequency coupling). Granger causality analysis showed decreased causal effect connectivity between and within the two networks. The between-network causal effect connectivity segregation played decreased mediator acting in sleep regulation (decreased connectivity). These findings suggest that the functional deficits and segregation of the two systems may underlie etiology of PIs.

COVID-19 Risk Appears to Vary Across Different Alcoholic Beverages.

Objectives: To evaluate the associations of status, amount, and frequency of alcohol consumption across different alcoholic beverages with coronavirus disease 2019 (COVID-19) risk and associated mortality. Methods: This study included 473,957 subjects, 16,559 of whom tested positive for COVID-19. Multivariate logistic regression analyses were used to evaluate the associations of alcohol consumption with COVID-19 risk and associated mortality. The non-linearity association between the amount of alcohol consumption and COVID-19 risk was evaluated by a generalized additive model. Results: Subjects who consumed alcohol double above the guidelines had a higher risk of COVID-19 (1.12 [1.00, 1.25]). Consumption of red wine above or double above the guidelines played protective effects against the COVID-19. Consumption of beer and cider increased the COVID-19 risk, regardless of the frequency and amount of alcohol intake. Low-frequency of consumption of fortified wine (1-2 glasses/week) within guidelines had a protective effect against the COVID-19. High frequency of consumption of spirits (≥5 glasses/week) within guidelines increased the COVID-19 risk, whereas the high frequency of consumption of white wine and champagne above the guidelines decreased the COVID-19 risk. The generalized additive model showed an increased risk of COVID-19 with a greater number of alcohol consumption. Alcohol drinker status, frequency, amount, and subtypes of alcoholic beverages were not associated with COVID-19 associated mortality. Conclusions: The COVID-19 risk appears to vary across different alcoholic beverage subtypes, frequency, and amount. Red wine, white wine, and champagne have chances to reduce the risk of COVID-19. Consumption of beer and cider and spirits and heavy drinking are not recommended during the epidemics. Public health guidance should focus on reducing the risk of COVID-19 by advocating healthy lifestyle habits and preferential policies among consumers of beer and cider and spirits.

The abnormal reward network associated with insomnia severity and depression in chronic insomnia disorder.

Previous research has shown that acute sleep deprivation can influence the reward networks. However, it is unclear whether and how the intrinsic reward network is altered in chronic insomnia disorder (CID). In the present study, we aimed to investigate whether the reward network is altered in patients with CID using resting-state functional magnetic resonance imaging (fMRI) data. Forty-two patients with CID and 33 healthy controls (HCs) were enrolled and underwent resting-state fMRI. Nucleus accumbens (NAc) - based functional connectivity (NAFC) was evaluated to explore the differences in the reward network between the CID and HC groups. Pearson correlation analysis was used to evaluate the clinical significance of altered NAFC networks. Compared to those in the HC group, increased NAFC was found in the salience and limbic networks, while decreased NAFC was found in the default mode network (DMN) and within the reward circuit in patients with CID. In addition, decreased FC between the NAc and DMN was associated with insomnia severity, while NAFC within the reward network was associated with depression symptoms in patients with CID. These findings showed that the reward network is dysfunctional and associated with depression symptom in patients with CID. Future studies of CID should consider both insomnia and depression symptoms to disentangle the role of insomnia and depression in the relationship under study.

Reliability and availability of granger causality density in localization of Rolandic focus in BECTS.

A new method, called granger causality density (GCD), could reflect the directed information flow of the epileptiform activity, which is much closely match with excitatory and inhibitory imbalance theory of epilepsy. Here, we investigated if GCD could effectively localize the Rolandic focus in 50 patients with benign childhood epilepsy with central-temporal spikes (BECTS) from 27 normal children. The BECTS were classified into ictal epileptiform discharges (IEDs; 12 females, 15 males;age, 8.15 ± 1.68 years) and non-IEDs (10 females, 13 males; age, 9.09 ± 1.98 years) subgroups depending on the presence of central-temporal spikes. Multiple correlation-modality analyses (Pearson, across-voxel and across-subject correlations) were used to calculate the couplings between the GCD maps and IEDs-related brain activation map. The individual lateralization coefficient of localize IEDs and multiple regression analysis were used to identify the reliability of the GCD method in localizing the Rolandic focus. In this study, multiple correlation-modality analyses showed that the IEDs-related brain activation map and the GCD maps had highly temporal (coefficient ׀r\= 0.56 ~ 0.65) and spatial (\r\=0.53~0.91) (r\=~ couplings. The proposed GCD method and multiple regression analyses showed consistent findings with the clinical EEG recordings in lateralization of Rolandic focus. Furthermore, the GCD method could reflect the epilepsy-related brain activity during non-IEDs substate. Therefore, the proposed GCD method has the potential to be served as an effective and reliable neuroimaging biomarker to localize the Rolandic focus of BECTS. These findings are critical for clinical early diagnosis, and may promote the progression of treatment and management of pediatric epilepsy.