Background: Olanzapine and clozapine are atypical antipsychotics (AAPs) with the greatest risk of weight gain, and changes in feeding behavior are among the most important underlying mechanisms. However, few studies have investigated the role of diet-alone interventions in improving individuals' weight gain by taking AAPs. In closed management mental hospitals of China, family members are allowed to bring food to patients regularly, causing patients to have caloric intake added to their 3 daily meals. However, during the global pandemic of coronavirus disease 2019 (COVID-19), bringing food to the hospital was temporarily prohibited in mental health institutions in China to prevent the spread of the virus. This study sought to compare the body weight and body mass index (BMI) changes of patients taking olanzapine or clozapine undergoing diet-alone interventions caused by this prohibition.Methods: A retrospective self-controlled study was conducted on 90 patients with schizophrenia from a single-center treated with olanzapine or clozapine monotherapy, or combined with aripiprazole or ziprasidone which has a small metabolic impact. A paired-samples t-test was used to compare the changes in body weight and BMI before and after the 3-month prohibition, and general linear regression was used to analyze the effects of gender, age, disease course, duration of drug exposure, and equivalent dose on the BMI improvement. Also, the percentage of people who lost weight and that of individuals who lost 5% of their pre-prohibition body weight were calculated.Results: Paired-samples t-test showed that after 3-month prohibition, the patients' body weight (71.68±6.83 vs. 66.91±7.03, P<0.001) and BMI (26.43±2.11 vs. 24.63±1.81, P<0.001) decreased significantly. Weight loss rate accounted for 99.1%, and weight loss of 5% from the pre-prohibition body weight accounted for 71.8%. General linear regression showed that the duration of drug exposure (β =−0.678, P<0.001) was significantly and negatively correlated with the BMI changes. No significant correlation of gender, age, disease course, or equivalent dose with BMI changes was found.Conclusions: Diet-alone interventions facilitate weight loss in chronically hospitalized schizophrenia patients taking AAPs. Conduction of dietary intervention in the early stages of medication may yield greater benefits.
Empowering computer systems to automatically recognize human emotions has become an urgent need in the field of human-computer interaction (HCI). Two-dimensional emotion (Valence-Arousal) models are commonly used to represent emotions. Up to now, the correlation between emotion dimensions has rarely been investigated, and subject-independent EEG emotion recognition is still a challenging task. For this purpose, we introduce multi-task learning (MTL) into EEG emotion recognition. MTL learns different emotion dimensions simultaneously and extracts correlation information between dimensions in task-sharing space to coordinate the optimization of multiple emotion dimensions. We further propose Identity based Multi-gate Mixture-of-Experts (IDMMOE), which allocates part of model subspace for each subject in a customized manner according to the subject's identity. Extensive experiments were conducted on DEAP dataset. Three MTL models were implemented: Shared-Bottom, Multi-gate Mixture-of-Experts, and Customized Gate Control respectively. They were compared with a single-task learning model trained separately on valence and arousal. Experimental results demonstrate that two emotion dimensions are intrinsically related, and MTL acquires such correlation information and improves prediction accuracy in both emotion dimensions. In addition, IDMMOE achieves average accuracies of 89.5% and 89.7% for valence and arousal respectively and it is effective for subject-independent experiment.
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are both highly prevalent and comorbid psychiatric disorders. Neurocognitive dysfunction has been commonly found in MDD, but the findings in GAD are inconsistent. Few studies have directly compared cognitive performance between GAD and MDD. Therefore, the present study aimed to reveal the similar and distinct cognitive impairments between both disorders. Three non-overlapping and non-comorbid groups were enrolled in the current study including patients with GAD (n = 37), MDD (n = 107) and healthy controls (n = 74). Levels of anxiety and depression were assessed using the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD) respectively. The Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to compare the cognitive performance, including sustained attention, visual memory, executive functions and learning. Both MDD and GAD groups demonstrated common significant deficits in sustained attention, visual memory, working memory and learning when compared to healthy controls. Despite the similarities, the MDD group had significantly greater impairment in learning, particularly generalization, while the GAD group demonstrated more pronounced deficits in visual memory. Patients involved were medicated and the sample size for GAD was relatively small. The significant differences in visual memory and learning between MDD and GAD groups might be indicators to distinguishing both disorders. These results confirm that cognitive function is of great importance as a future target for treatment in order to improve wellbeing, quality of life and functionality in both GAD and MDD.
Abstract In human body, sensory integration plays a significant role as it is a mutually reinforcing and complementary process of the body in general and human brains in particular. This process extensively utilizes the nervous system during individual development for the creation of numerous sensations, which are very helpful in making people act accordingly. It is important to note that body and brain will not be able to perform in unison preferably without sensory integration. This is one of the most challenging issues related to the people especially with autism face. Generally, number of children suffering with autism is increasing day by day at a predominant rate, however, the exact cause of these remains unknown until today. Furthermore, as per our knowledge, positive outcomes or opinions on its onset are not reported yet in literature, which is very alarming, and currently special care behavior training is suggested to be taken by the individuals. By thoroughly analyzing the literature combined with the special psychological characteristics of autistic children, we have observed that sports is one of the possible ways which could possibly help these children in the development process of their brains and bodies, but a necessary measure to intervene and improve their conditions are needed to be adopted as well. In order to solve the problem of sensory integration disorder in children with autism, this paper proposes an effective mechanism for determining the effectiveness of the physical intervention using sensory integration theory on the recovery of children with autism. To investigate this, we have started with sensory integration theory, which is one the most vital factor in the in recovery process of children suffering from the autism. Additionally, it takes a unique approach to designing physical activity for children with autism, and builds a model of physical play assessment followed by an intervention experiment. In order to verify various claims of the proposed scheme, we have carried out numerous experimental studies which conclude that the proposed approach is affective mechanism for solving the problem.
Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.
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