To assess the associations among several anthropometric measures, as well as BMI trajectories and colorectal cancer (CRC) risk in older women.

Prospective cohort study.

Forty clinical centres in the USA.

Totally, 79 034 postmenopausal women in the Women’s Health Initiative Observational Study.

During an average of 15·8 years of follow-up, 1514 CRC cases were ascertained. Five BMI trajectories over 18–50 years of age were identified using growth mixture model. Compared with women who had a normal BMI at age 18, women with obesity at age 18 had a higher risk of CRC (HR 1·58, 95 % CI 1·02, 2·44). Compared with women who kept relatively low normal body size during adulthood, women who progressed from normal to obesity (HR 1·29, 95 % CI 1·09, 1·53) and women who progressed from overweight to obesity (HR 1·37, 95 % CI 1·13, 1·68) had higher CRC risks. A weight gain > 15 kg from age 18 to 50 (HR 1·20, 95 % CI 1·04, 1·40) and baseline waist circumference > 88 cm (HR 1·33, 95 % CI 1·19, 1·49) were associated with higher CRC risks, compared with stable weight and waist circumference ≤ 88 cm, respectively.

Women who have a normal weight in early adult life and gain substantial weight later, as well as those who are persistently heavy over adulthood, demonstrated a higher risk of developing CRC. Our study highlights the importance of maintaining a healthy body weight over the life course for reducing the risk of developing CRC in women.

This study aimed to analyse the temporal and spatial trends in the burden of anxiety disorders and major depressive disorder related to bullying victimisation on global, regional and country scales.

Data were from the 2019 Global Burden of Disease (GBD) Study. We assessed the global disability-adjusted life years (DALYs, per 100 000 population) of anxiety disorders and major depressive disorder attributable to bullying victimisation by age, sex and geographical location. The percentage changes in age-standardised rates of DALYs were used to quantify temporal trends, and the annual rate changes across 204 countries and territories were used to present spatial trends. Furthermore, we examined the relationship between the sociodemographic index (SDI) and the burden of anxiety disorders as well as major depressive disorder attributable to bullying victimisation and its spatial and temporal characteristics globally.

From 1990 to 2019, the global DALY rates of anxiety disorders and major depressive disorder attributable to bullying victimisation increased by 23.31 and 26.60%, respectively, with 27.27 and 29.07% for females and 18.88 and 23.84% for males. Across the 21 GBD regions, the highest age-standardised rates of bullying victimisation-related DALYs for anxiety disorders were in North Africa and the Middle East and for major depressive disorder in High-income North America. From 1990 to 2019, the region with the largest percentage increase in the rates of DALYs was High-income North America (54.66% for anxiety disorders and 105.88% for major depressive disorder), whereas the region with the slowest growth rate or largest percentage decline was East Asia (1.71% for anxiety disorders and −25.37% for major depressive disorder). In terms of SDI, this study found overall upward trends of bullying-related mental disorders in areas regardless of the SDI levels, although there were temporary downward trends in some stages of certain areas.

The number and rates of DALYs of anxiety disorders and major depressive disorder attributable to bullying victimisation increased from 1990 to 2019. Effective strategies to eliminate bullying victimisation in children and adolescents are needed to reduce the burden of anxiety disorders and major depressive disorder. Considering the large variations in the burden by SDI and geographic location, future protective actions should be developed based on the specific cultural contexts, development status and regional characteristics of each country.

The non-selective serotonin 2A (5-HT2A) receptor agonist lysergic acid diethylamide (LSD) holds promise as a treatment for some psychiatric disorders. Psychedelic drugs such as LSD have been suggested to have therapeutic actions through their effects on learning. The behavioural effects of LSD in humans, however, remain incompletely understood. Here we examined how LSD affects probabilistic reversal learning (PRL) in healthy humans.

Healthy volunteers received intravenous LSD (75 μg in 10 mL saline) or placebo (10 mL saline) in a within-subjects design and completed a PRL task. Participants had to learn through trial and error which of three stimuli was rewarded most of the time, and these contingencies switched in a reversal phase. Computational models of reinforcement learning (RL) were fitted to the behavioural data to assess how LSD affected the updating (‘learning rates’) and deployment of value representations (‘reinforcement sensitivity’) during choice, as well as ‘stimulus stickiness’ (choice repetition irrespective of reinforcement history).

Raw data measures assessing sensitivity to immediate feedback (‘win-stay’ and ‘lose-shift’ probabilities) were unaffected, whereas LSD increased the impact of the strength of initial learning on perseveration. Computational modelling revealed that the most pronounced effect of LSD was the enhancement of the reward learning rate. The punishment learning rate was also elevated. Stimulus stickiness was decreased by LSD, reflecting heightened exploration. Reinforcement sensitivity differed by phase.

Increased RL rates suggest LSD induced a state of heightened plasticity. These results indicate a potential mechanism through which revision of maladaptive associations could occur in the clinical application of LSD.

Apart from the psychiatric symptoms, cognitive deficits are also the core symptoms of schizophrenia. Brain network control theory provided information on the role of a specific brain region in the cognitive control process, helping understand the neural mechanism of cognitive impairment in schizophrenia.

To characterize the control properties of functional brain network in first-episode untreated patients with schizophrenia and the relationships between controllability and psychiatric symptoms, as well as exploring the predictive value of controllability in differentiating patients from healthy controls (HCs).

Average and modal controllability of brain networks were calculated and compared between 133 first-episode untreated patients with schizophrenia and 135 HCs. The associations between controllability and clinical symptoms were evaluated using sparse canonical correlation analysis. Support vector machine (SVM) and SVM-recursive feature elimination combined with the controllability were performed to establish the individual prediction model.

Compared to HCs, the patients with schizophrenia showed increased average controllability and decreased modal controllability in dorsal anterior cingulate cortex (dACC). Brain controllability predominantly in somatomotor, default mode, and visual networks was associated with the positive symptomatology of schizophrenia. The established model could identify patients with an accuracy of 0.68. Furthermore, the most discriminative features were located in dACC, medial prefrontal lobe, precuneus and superior temporal gyrus.

Altered controllability in dACC may play a critical role in the neuropathological mechanisms of cognitive deficit in schizophrenia, which could drive the brain function to different states to cope with varied cognitive tasks. As symptom-related biomarkers, controllability could be also beneficial to individual prediction in schizophrenia.

No significant relationships.

There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.

To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.

Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).

Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.

The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).

Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.

No significant relationships.

One of the most perplexing and characteristic symptoms of the schizophrenia (SZ) patients is hallucination. The occurrence of hallucinations to be associated with altered activity in the auditory and visual cortex but is not well understood from the brain functional network dynamics in SZ.

To explore the brain abnormal basis of hallucinations in SZ with the dynamic functional connectivity (dFC).

Using magnetic resonance imaging for 83 SZ patients and 83 matched healthy controls and independent component analysis, 52 independent components (ICs) were identified as nodes and assigned into eight intrinsic connectivity networks (Figure 1A). Subsequently, we established dFC matrices and clustered them into four discrete states (Figure 1B) and three state transition metrics were obtained. To further explore the changes in the centrality of each component, eigenvector centrality (EC) was calculated and its time-varying was evaluated.

Compared to controls with FDR correction, we found that patients had more mean dwell times and fractional time in state 1 (P=0.0081 and P=0.0018), mainly with hypoconnectivity between auditory and visual network and other networks and hyperconnectivity between language and default-mode network (DMN). While, patients had less dwell times and fractional time in state 3 (P=0.0018 and P=0.0009), and decreased FC between visual network and executive control network (ECN) and increased FC between ECN and DMN than controls (Figure 2).

EC statistics showed that SZs displayed increased temporal dynamics in visual-related regions (Figure 3).

SZ was mainly manifested as altered dFC and temporal variability of nodal centrality in auditory and visual networks.

No significant relationships.

Though schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.

To explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.

Resting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.

Static analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).

These changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.

Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) are both severe psychiatric disorders. Though these two disorders have distinct typical symptoms, there are partial polygenic overlap and comorbidity between the two disorders. However, few studies have explored the shared and disorder-specific brain function underlying the neural pathophysiology of the two disorders, especially in the aspect of dynamics.

To explore the abnormal characteristics of the dynamic functional connectivity (dFC) in OCD and SZ as well as the association between dFC metrics and symptom severity.

The resting state functional magnetic resonance imaging data of 31 patients with OCD, 49 patients with SZ, and 45 healthy controls were analyzed using independent component analysis to obtain independent components (ICs) and assigned them into eight brain networks (Figure 1), then used the sliding-window approach to generate dFC matrices. Using k-means clustering, we obtained three reoccurring dFC states (Figure 2), and state transition metrics were obtained

In a sparsely connected state (state 1), SZ showed both increased fractional time and mean dwell time than controls (P=0.047 and P=0.033) and OCD (P=0.001 and P=0.003). In a state characterized by negative FC between networks (state 2), OCD showed both increased fractional time and mean dwell time than controls (P=0.032 and P=0.013) and SZ (P=0.005 and P=0.003). Moreover, the fractional time of state 2 was positively correlated with anxiety scores in OCD (r=0.535, P=0.021, FDR corrected) (Figure 3).

OCD and SZ patients showed distinct alternations of brain functional dynamics.

No significant relationships.

Lifestyle interventions are an important and viable approach for preventing cognitive deficits. However, the results of studies on alcohol, coffee and tea consumption in relation to cognitive decline have been divergent, likely due to confounds from dose–response effects. This meta-analysis aimed to find the dose–response relationship between alcohol, coffee or tea consumption and cognitive deficits.

Prospective cohort studies or nested case-control studies in a cohort investigating the risk factors of cognitive deficits were searched in PubMed, Embase, the Cochrane and Web of Science up to 4th June 2020. Two authors searched the databases and extracted the data independently. We also assessed the quality of the studies with the Newcastle-Ottawa scale. Stata 15.0 software was used to perform model estimation and plot the linear or nonlinear dose–response relationship graphs.

The search identified 29 prospective studies from America, Japan, China and some European countries. The dose–response relationships showed that compared to non-drinkers, low consumption (<11 g/day) of alcohol could reduce the risk of cognitive deficits or only dementias, but there was no significant effect of heavier drinking (>11 g/day). Low consumption of coffee reduced the risk of any cognitive deficit (<2.8 cups/day) or dementia (<2.3 cups/day). Green tea consumption was a significant protective factor for cognitive health (relative risk, 0.94; 95% confidence intervals, 0.92–0.97), with one cup of tea per day brings a 6% reduction in risk of cognitive deficits.

Light consumption of alcohol (<11 g/day) and coffee (<2.8 cups/day) was associated with reduced risk of cognitive deficits. Cognitive benefits of green tea consumption increased with the daily consumption.

Cognitive deficits at the first episode of schizophrenia are predictive of functional outcome. Interventions that improve cognitive functioning early in schizophrenia are critical if we hope to prevent or limit long-term disability in this disorder.

We completed a 12-month randomized controlled trial of cognitive remediation and of long-acting injectable (LAI) risperidone with 60 patients with a recent first episode of schizophrenia. Cognitive remediation involved programs focused on basic cognitive processes as well as more complex, life-like situations. Healthy behavior training of equal treatment time was the comparison group for cognitive remediation, while oral risperidone was the comparator for LAI risperidone in a 2 × 2 design. All patients were provided supported employment/education to encourage return to work or school.

Both antipsychotic medication adherence and cognitive remediation contributed to cognitive improvement. Cognitive remediation was superior to healthy behavior training in the LAI medication condition but not the oral medication condition. Cognitive remediation was also superior when medication adherence and protocol completion were covaried. Both LAI antipsychotic medication and cognitive remediation led to significantly greater improvement in work/school functioning. Effect sizes were larger than in most prior studies of first-episode patients. In addition, cognitive improvement was significantly correlated with work/school functional improvement.

These results indicate that consistent antipsychotic medication adherence and cognitive remediation can significantly improve core cognitive deficits in the initial period of schizophrenia. When combined with supported employment/education, cognitive remediation and LAI antipsychotic medication show separate significant impact on improving work/school functioning.