Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Although poverty associated with severe mental illness (SMI) has been documented in many studies, little long-term evidence of social drift exists. This study aimed to unravel the poverty transitions among persons with SMI in a fast change community in China.

Two mental health surveys, using the International Classification of Disease (ICD-10), were conducted in the same six townships of Xinjin county, Chengdu, China in 1994 and 2015. A total of 308 persons with SMI identified in 1994 were followed up in 2015. The profiles of poverty transitions were identified and regression modelling methods were applied to determine the predictive factors of poverty transitions.

The poverty rate of persons with SMI increased from 39.9% to 49.4% in 1994 and 2015. A larger proportion of them had fallen into poverty (27.3%) rather than moved out of it (17.8%). Those persons with SMI who had lost work ability, had physical illness and more severe mental disabilities in 1994, as well as those who had experienced negative changes on these factors were more likely to live in persistent poverty or fall into poverty. Higher education level and medical treatment were major protective factors of falling into poverty.

This study shows long-term evidence on the social drift of persons with SMI during the period of rapid social development in China. Further targeted poverty alleviation interventions should be crucial for improving treatment and mental recovery and alleviating poverty related to SMI.

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).

This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.

The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.

These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

To alert policy makers early about emerging health technologies that could significantly impact the healthcare system at the clinical, financial and organizational levels, the Agency for Care Effectiveness (ACE) in Singapore established a horizon scanning system (HSS) in 2019. This paper describes the development of the ACE HSS and showcases its application with cell and gene therapy products as the first example.

A literature review of existing HSS methods, including the processes of the EuroScan International Network and other overseas horizon scanning agencies, was done to inform the development of our horizon scanning framework. The framework was first applied to the new and emerging cell and gene therapies.

Identification sources, filtration and prioritization criteria, and horizon scanning outputs for the HSS were developed in alignment to international best practices, with recommendations for technology uptake represented by a traffic light system. For the first horizon scanning exercise on cell and gene therapies, forty therapies passed the filtration step, of which eight were prioritized for further assessment. The few early reports developed were used to inform and prepare the healthcare system for their potential introduction, particularly in terms of the need to develop health and funding policies.

Early assessment of prioritized topics has provided support for strategic efforts within the Ministry of Health. Given that ACE's horizon scanning program is still in its infancy, the framework will continue to evolve to ensure relevance to our stakeholders so that it remains fit for purpose for our healthcare system.

Major depressive disorder is characterized by a high risk of relapse. We aimed to compare the prophylactic effects of different antidepressant medicines (ADMs).

PubMed, Cochrane Central Register of Controlled Trials, Embase and the Web of Science were searched on 4 July 2019. A pooled analysis of parametric survival curves was performed using a Bayesian framework. The main outcomes were hazard ratios (HRs), relapse-free survival and mean relapse-free months.

Forty randomized controlled trials were included. The 1-year relapse-free survival for ADM (76%) was significantly better than that for placebo (56%). Most of the relapse difference (86.5%) occurred in the first 6 months. Most HRs were not constant over time. Proof of benefit after 6 months of follow-up was not established partially because of small differences between the drug and placebo after 6 months. Almost all studies used an ‘enriched’ randomized discontinuation design, which may explain the high relapse rates in the first 6 months after randomization.

The superiority of ADM v. placebo was mainly attributed to the difference in relapse rates that occurred in the first 6 months. Our analysis provided evidence that the prophylactic efficacy was not constant over time. A beneficial effect was observed, but the prevention of new episodes after 6 months was questionable. These findings may have implications for clinical practice.

The condition of caregivers is important to the quality of care received by people with Parkinson’s disease (PD), especially at the late disease stages. This study addresses the distress placed on caregivers by participants’ neuropsychiatric symptoms at different stages of PD in Taiwan

This prospective study enrolled 108 people with PD. All participants were examined with the Unified Parkinson’s Disease Rating Scale (UPDRS), Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI), and Clinical Dementia Rating (CDR) scale. Caregiver distress was measured using the Neuropsychiatric Inventory Caregiver Distress Scale (NPI-D). Statistical analysis was used to explore the PD-related factors that contribute to caregiver distress.

The mean follow-up interval in the 108 PD participants were 24.0 ± 10.2 months with no participant lost to follow-up due to death. NPI-distress (the sum of NPI caregiver distress scale across the 12 domains of the NPI) was positively correlated with NPI-sum (the total score across the 12 domains of the NPI) (r = 0.787, p < 0.001), CDR (r = 0.403, p < 0.001), UPRDS (r = 0.276, p = 0.004), and disease duration (r = 0.246, p = 0.002), but negatively correlated with CASI (r = −0.237, p = 0.043) and MMSE (r = −0.281, p < 0.001). Multiple linear regression analysis showed that only NPI-sum and disease duration were independently correlated with NPI-distress.

The disease duration and NPI-sum are independent predictors of caregiver distress in Taiwanese populations with PD. Early detection and reduction of neuropsychiatric symptoms in people with PD can help decrease caregiver distress.

To investigate the association between dietary patterns (DP) and overweight risk in the Malaysian Adult Nutrition Surveys (MANS) of 2003 and 2014.

DP were derived from the MANS FFQ using principal component analysis. The cross-sectional association of the derived DP with prevalence of overweight was analysed.

Malaysia.

Nationally representative sample of Malaysian adults from MANS (2003, n 6928; 2014, n 3000).

Three major DP were identified for both years. These were ‘Traditional’ (fish, eggs, local cakes), ‘Western’ (fast foods, meat, carbonated beverages) and ‘Mixed’ (ready-to-eat cereals, bread, vegetables). A fourth DP was generated in 2003, ‘Flatbread & Beverages’ (flatbread, creamer, malted beverages), and 2014, ‘Noodles & Meat’ (noodles, meat, eggs). These DP accounted for 25·6 and 26·6 % of DP variations in 2003 and 2014, respectively. For both years, Traditional DP was significantly associated with rural households, lower income, men and Malay ethnicity, while Western DP was associated with younger age and higher income. Mixed DP was positively associated with women and higher income. None of the DP showed positive association with overweight risk, except for reduced adjusted odds of overweight with adherence to Traditional DP in 2003.

Overweight could not be attributed to adherence to a single dietary pattern among Malaysian adults. This may be due to the constantly morphing dietary landscape in Malaysia, especially in urban areas, given the ease of availability and relative affordability of multi-ethnic and international foods. Timely surveys are recommended to monitor implications of these changes.

Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.

To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.

Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.

The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.

These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.