How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson’s correlation and sparse representation) and the commonly used feature selection methods (two-sample t-test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.
摘要:目的: 观察免费治疗社区精神分裂症患者的疗效。 方法 :纳入贫困家庭精神分裂症患者140例,随机分为免费服药组和对照组,每组70例。随访1年,采用精神分裂症阳性与阴性症状量表(PANSS)\社会功能缺陷量表(SDSS)等评估。 结果 :对实验组与对照组的基线、6个月后及1年后随访的PANSS总分、各因子分、SDSS总分分别进行比较,结果显示基线、6月后均无统计学差异;1年后SDSS总分、PANSS总分、阳性因子分、一般病理因子、思维障碍、偏执因子分差异有显著性;免费治疗组1年后各指标与入组前相比分值降低(P＜001)。 结论 :精神分裂症患者免费服药后精神症状缓解明显,同时其社会功能缺陷也得到改善。Abstract: Objective: To observe the effect of the free treatment on schizophrenics from community. Methods : Totally 140 subjects from poor family were divided into the free treated group and the control group at random. They were followed up for 1 year. The treatment effects were evaluated by PANSS and SDSS. Results : There were no significant difference in all examinations at baseline and after 6 months; at the following end point, significant difference existed in the score of SDSS, the total scores of the PANSS, the positive factor, the general pathology factor, the thinking factor and the paranoid ideation factor between two groups. There was decrease in the scores for all examinations in the free treated group. Conclusion : The symptoms of schizophrenics by free treatment relieve significantly, and the social function improves.
The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
ObjectiveTo systematically review the effectiveness of Tai Chi for improving negative symptoms and activity participation in patients with schizophrenia. MethodsDatabases including PubMed, The Cochrane Library (Issue 3, 2016), EMbase, CBM, CNKI, VIP and WanFang Data were electronically searched to collect the randomized controlled trials (RCTs) and quasi-randomized controlled trials (quasi-RCT) about Tai Chi for improving negative symptoms and activity participation in patients with schizophrenia from inception to Apirl 1st 2016. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then meta-analysis was performed by using RevMan 5.3 software. ResultsA total of three RCTs and two quasi-RCTs were included. The result of meta-analyses showed that no significant difference was found in negative symptom scores (MD=–0.95, 95% CI –3.78 to –1.89, P=0.51) and positive symptoms scores of PANSS (MD=–0.02, 95% CI –0.50 to 0.46, P=0.94) between two groups. However, the Tai Chi group was superior to the control group in items including attention, avolition, anhedonia-asociality, alogia and affective flattening/blunting of SANS (all P values<0.05). ConclusionTai Chi may have positively influence on various negative symptoms in patients with schizophrenia, but no evidence to support the Tai Chi's effects for activities participation. Larger and higher quality studies are needed.
Objective To systematically review the health state utility values in patients with schizophrenia, and to provide references for subsequent studies on the health economics of schizophrenia. Methods The PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, and VIP databases were searched from inception to December 1st, 2021 to collect studies on health state utility values in patients with schizophrenia. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed by Stata 15.0 software. Results A total of 19 studies were included. Patients’ utility values were 0.68 (95%CI 0.59 to 0.77) for direct measures, and 0.77 (95%CI 0.75 to 0.80) and 0.66 (95%CI 0.61 to 0.70) for indirect measures with the EQ-5D-5L and EQ-5D-3L as the primary scales. Utility values varied with measures, tariffs, regions, and populations. Conclusion Studies on health state utility value in schizophrenia are diversified in measurement methods, showing high inter-study heterogeneity. Therefore, it is necessary to promote the study on utility value measurement in schizophrenia in China.
In different stages of schizophrenia (SZ), alterations in gray matter volume (GMV) of patients are normally regulated by various pathological mechanisms. Instead of analyzing stage‐specific changes, this study employed a multivariate structural covariance model and sliding‐window approach to investigate the illness duration‐related developmental trajectory of GMV in SZ. The trajectory is defined as a sequence of brain regions activated by illness duration, represented as a sparsely directed matrix. By applying this approach to structural magnetic resonance imaging data from 145 patients with SZ, we observed a continuous developmental trajectory of GMV from cortical to subcortical regions, with an average change occurring every 0.208 years, covering a time window of 20.176 years. The starting points were widely distributed across all networks, except for the ventral attention network. These findings provide insights into the neuropathological mechanism of SZ with a neuroprogressive model and facilitate the development of process for aided diagnosis and intervention with the starting points.
Objective To investigate the difference in first onset age, family history and medication compliance between male and female patients with schizophrenia in communities. Methods We used self-designed questionnaire to survey and analyze 372 cases of schizophrenia between June to August 2014. Results There were no significant differences between male and female schizophrenic patients in the family history, personality before the disease, education level, age, and the onset type and disease course (P > 0.05). The first onset age of male patients [(24.92±8.22) years] was significantly earlier than female patients [(27.02±11.28) years] and the difference was statistically significant (P < 0.05). The number of unmarried male patients (115, 58.97%) was significantly more than unmarried females (81, 45.76%) and the difference was statistically significant (P < 0.05). The full medication compliance rate of female patients (127, 71.75%) was significantly better than that of male patients (115, 58.97%) (P < 0.05). Conclusion The first onset age, marital status and medication compliance are significantly different between the two genders of patients with schizophrenia, which indicates that prevention, treatment and recovery measures for male and female patients should be differentiated.