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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.
Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.
We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.
This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.
This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.
This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.
No studies have reported on how to relieve distress or relax in medical health workers while wearing medical protective equipment in coronavirus disease 2019 (COVID-19) pandemic. The study aimed to establish which relaxation technique, among six, is the most feasible in first-line medical health workers wearing medical protective equipment.
This was a two-step study collecting data with online surveys. Step 1: 15 first-line medical health workers were trained to use six different relaxation techniques and reported the two most feasible techniques while wearing medical protective equipment. Step 2: the most two feasible relaxation techniques revealed by step 1 were quantitatively tested in a sample of 65 medical health workers in terms of efficacy, no space limitation, no time limitation, no body position requirement, no environment limitation to be done, easiness to learn, simplicity, convenience, practicality, and acceptance.
Kegel exercise and autogenic relaxation were the most feasible techniques according to step 1. In step 2, Kegel exercise outperformed autogenic relaxation on all the 10 dimensions among the 65 participants while wearing medical protective equipment (efficacy: 24 v. 15, no space limitation: 30 v. 4, no time limitation: 31 v. 4, no body position requirement: 26 v. 4, no environment limitation: 30 v. 11, easiness to learn: 28 v. 5, simplicity: 29 v. 7, convenience: 29 v. 4, practicality: 30 v. 14, acceptance: 32 v. 6).
Kegel exercise seems a promising self-relaxation technique for first-line medical health workers while wearing medical protective equipment among COVID-19 pandemic.
Data on average iodine requirements for the Chinese population are limited following implementation of long-term universal salt iodisation. We explored the minimum iodine requirements of young adults in China using a balance experiment and the ‘iodine overflow’ hypothesis proposed by our team. Sixty healthy young adults were enrolled to consume a sequential experimental diet containing low, medium and high levels of iodine (about 20, 40 and 60 μg/d, respectively). Each dose was consumed for 4 d, and daily iodine intake, excretion and retention were assessed. All participants were in negative iodine balance throughout the study. Iodine intake, excretion and retention differed among the three iodine levels (P < 0·01 for all groups). The zero-iodine balance derived from a random effect model indicated a mean iodine intake of 102 μg/d, but poor correlation coefficients between observed and predicted iodine excretion (r 0·538 for μg/d data) and retention (r 0·304 for μg/d data). As iodine intake increased from medium to high, all of the increased iodine was excreted (‘overflow’) through urine and faeces by males, and 89·5 % was excreted by females. Although the high iodine level (63·4 μg/d) might be adequate in males, the corresponding level of 61·6 μg/d in females did not meet optimal requirements. Our findings indicate that a daily iodine intake of approximately half the current recommended nutrient intake (120 μg/d) may satisfy the minimum iodine requirements of young male adults in China, while a similar level is insufficient for females based on the ‘iodine overflow’ hypothesis.
Electromagnetic scattering from the sea surface is of great significance in radar detection, target recognition, ocean remote sensing, etc. By introducing the action spectrum, the modified spatio-temporal variation wave spectrum is used to establish a nonlinear sea surface with currents in this paper. Traditional capillary wave modification facet scattering model (CWMFSM) can only calculate the backscattering from the wind-driven sea surface. By using the new spatio-temporal variation wave spectrum to modify the scattering amplitude of every facet, the new CWMFSM can be used to calculate the nonlinear sea surface scattering with surface currents. Therefore, the model simultaneously considers the modulation of sea surface wind and currents to the radar back echo. The dependence of backscattering coefficient from nonlinear sea surface on the incident angle and the polarization are discussed. The results verify that the nonlinear model is more consistent with the measurement data. This paper also investigates the Doppler spectrum characteristics of the sea with currents. It is found that the effect of wave–current interaction on Doppler spectra is weaker than that of wave–wave interaction. The SAR images of nonlinear sea surfaces are also simulated and different bands, polarizations, and baseline length effects on sea current detection performance of along-track interference SAR are analyzed.
Amnestic mild cognitive impairment (aMCI) is characterized by delayed P300 latency and reduced grey matter (GM) volume, respectively. The relationship between the features in aMCI is unclear. This study was to investigate the relationship between the altered P300 latency and the GM volume in aMCI.
Thirty-four aMCI and 34 well-matched normal controls (NC) were studied using electroencephalogram during a visual oddball task and scanned with MRI. Both tests were finished in the same day.
As compared with the NC group, the aMCI group exhibited delayed P300 latency in parietal cortex and reduced GM volumes in bilateral temporal pole and left hippocampus/parahippocampal gyrus. A remarkable negative correlation was found between delayed P300 latency and reduced left hippocampal volume only in the aMCI group. Interestingly, the mediating analysis found P300 latency significantly mediated the association between right supramarginal gyrus volume and information processing speed indicated by Stroop Color and Word Test A scores.
The association between delayed P300 latency and reduced left hippocampal volume in aMCI subjects suggests that reduced left hippocampal volume may be the potential structural basis of delayed P300 latency.
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.
In this paper, we propose a splitting positive definite mixed finite element method for the approximation of convex optimal control problems governed by linear parabolic equations, where the primal state variable y and its flux σ are approximated simultaneously. By using the first order necessary and sufficient optimality conditions for the optimization problem, we derive another pair of adjoint state variables z and ω, and also a variational inequality for the control variable u is derived. As we can see the two resulting systems for the unknown state variable y and its flux σ are splitting, and both symmetric and positive definite. Besides, the corresponding adjoint states z and ω are also decoupled, and they both lead to symmetric and positive definite linear systems. We give some a priori error estimates for the discretization of the states, adjoint states and control, where Ladyzhenkaya-Babuska-Brezzi consistency condition is not necessary for the approximation of the state variable y and its flux σ. Finally, numerical experiments are given to show the efficiency and reliability of the splitting positive definite mixed finite element method.
Glacier and lake variations in the Yamzhog Yumco basin in southern Tibet were studied by integrating series of spatial data from topographic maps and Landsat images at three different times: 1980, 1988/90 and 2000. The results indicate that the total glacier area has decreased from 218 km2 in 1980 to 215 km2 in 2000, a total reduction of 3 km2 (i.e. a 1.5% decrease). Glacier recession rates were clearly larger in the 1990s than the 1980s due to the warmer climate. The total lake area decreased by about 67 km2 during 1980–90 and increased by 32 km2 during 1990–2000. It is suggested that change of lake area in the basin was rapid and most likely caused primarily by the change in precipitation and evaporation in the basin, and secondarily by the increased water supply from melting glaciers.