| At present, mental disorders(including suicide) accounted for 20% of the total disease burden in China. Among the mental disorders, schizophrenia is one kind of them. But because of its low incidence and rarely directly lead to death, therefore, its’ dangers has been seriously underestimated. In fact, the high disability of schizophrenia influences family and social a lot, and easy to relapse. In order to effectively alleviate this problem, early screening and treatment of schizophrenia is necessary. Currently, the People’s Liberty Army(PLA) is also facing the problem of mental distress. Under the organization of Department of Defense, the National Recruiting Psychological Assessment Center annually implement the conscription psychological testing work, a large number of young candidates will have to get a “qualified” score through the testing software which was developed by this center, then he or she has the opportunity to join the PLA. An important part of the screening is to detect the presence of mental disorders or discomfort disorders of young candidates, such as schizophrenia, if they have such disorders; we need to stop them from entering the army. The purpose of this study is to take brain imaging method, starting from the screening of schizophrenia and high-risk group with schizophrenia, focusing on abnormalities of brain structure and brain network of them, in order to discover the differences with normal brain structure and connection mode, thereby reducing the lie issue in answering the questionnaire and developing a more intuitive, objective selection for high-risk group and patients with schizophrenia.Significance of the study is that, based on psychological testing methods, we mainly used voxel-based morphometry(VBM), FIRST, SIENAX and pattern recognition techniques to analyze brain imaging data of high-risk group and patients, conducted a relative objective early screening to prevent schizophrenia and high-risk groups entering the armed forces, in order to improve the management level of combat troops and fighting capacity, to reduce unnecessary care, treatment and other military expenditure.Since auditory hallucinationis a common type of positive symptoms of schizophrenia, the incidence of 60%-80%, in order to exclude subjects’ heterogeneity, and other factors, such as delusions, subjects with auditory hallucinations were included in the study, whichwasdivided into four experiments. First one is a study of gray matter volume differences among high-risk group, schizophrenia and normal controls. The high-risk group in this study is defined as normal first-degree siblings of schizophrenic patients; the average age is about 25. First experiment, which was using a voxel-based morphometric analysis to compare the differences of gray matter volume among three groups, and then find the significant brain regions. Further use of FIRST and SIENAX analysis techniques, which are focusing on segmentation and extraction, to extract the volume capacity of deep gray matter, such as thalamus, caudate and so on, from the three groups, and then make a comparison of deep gray matter volume among the three groups. Second one, a study of gray matter volume differences among schizophrenia patients with or without auditory verbal hallucinations(AVHs) and normal controls. Illusion is one common positive symptom of schizophrenia; we further divide the group of schizophrenic patients into two, in order to find whether auditory verbal hallucinations affect gray matter volumes. Third one, pattern recognition study based on brain networks connection of schizophrenia patients and normal controls. Current diagnostic criteria for psychiatric disorders are based on the patient’s description. According to the diagnostic criteria, a psychiatrist makes a decision on the type of disease in combination with the patient’s symptoms and their own experience. This diagnostic model has caused some people to question. In addition, due to changes in the brain structure caused by the disease, will the disease cause changes in brain networks? Therefore, we carried out the experiment. Using multivariate pattern recognition technology, the two groups were tested for automated distinction. Furthermore, we also tried to classify the schizophrenia patients with or without patients. Fourth one, pattern recognition study based on brain networks connection of high-risk group and normal controls. In daily life, we cannot easily find the difference between normal controls and high-risk group. If such kind of people "luckily" passed the test of National Recruiting Psychological Assessment Center, once into army, will bring a series of instabilities to daily training, management, and security work. How to make a relatively objective appraisal of suspicious populations is an urgent and need-to-be solved problem during psychological selection process. Using multivariate pattern recognition technology that can accurately pick out high-risk groups with schizophrenia from the normal controls?The main results of this study and conclusions:1, VBM analysis showed that there are significant differences in the left thalamus, postcentral gyrus, inferior frontal gyrus, medial frontal gyrus, insula, cerebellum anterior lobe, cerebellum postterior lobe and parahippocampal gyrus. And further analysis showed that pairwise comparisons among schizophrenic patients group, normal controls group, high-risk group, the gray matter volumes of brain regions were found significant differences. The method of extracting deep gray matter found thalamus, accumbens volume significantly different among the three groups. These results proved that the existence of abnormalities of brain structure in schizophrenia and high-risk groups, and the reduced gray matter volumes in brain structure of high-risk group have not yet reached the severity degree of patients with schizophrenia group.2, VBM analysis showed that there are significant differences in the bilateral thalamus, left superior temporal gyrus, inferior frontal gyrus and precentral gyrus among schizophrenic patients with or without auditory verbal hallucinations and normal controls. Further analysis found that there were significant volume differences in brain regions between patients with AVHs group or without AVHs group and normal controls, but there was no significant difference between two patients’ groups. We mainly confirmed that there were reduced gray matter volume in language and non-sensory area of first-episode schizophrenic with AVHs.3, Using LOOCV, the linear SVM classifier achieved an accuracy of 81.3% for all first-episode schizophrenic patients and normal subjects(90.2% for first-episode schizophrenic patients and 65.2% for normal controls. Several brain regions, i.e., the Frontal_Sup_Orb_R, thalamus, Temporal_Pole_Sup_L, and Temporal_Pole_Mid, were weighted more than others in classifying patients from normal controls. The results suggest that these areas play more important roles in identifying the patients from normal controls. Similarly, for the two subgroups of the first-episode schizophrenic patients, the overall accuracy of the classifier in LOOCV was 75.6%(77.3% and 73.7% for schizophrenic patients with and without AVHs). We found that the Frontal_Mid_Orb_R, Temporal_Pole_Sup_R, and Parietal_Inf_R exhibited greater weights than others in classifying patients with and without AVHs. These results demonstrated there’re changes of brain network mode, comparing first-episode schizophrenia(whether or not accompanied by auditory verbal hallucinations) with normal controls, and can be easily distinguished from the normal group.4, using LOOCV method, the linear SVM classifier achieved 78.0% for all or the high-risk group and normal subjects(for high-risk group, sensitivity reached 72.2%, while for normal subjects, specificity reached 82.2%). We found that the Frontal_Mid_Orb_R, Temporal_Pole_Sup_R, and Parietal_Inf_R exhibited greater weights than others in classifying. In the present study, we also found that in high-risk groups and to distinguish between normal subjects, there are several major right brain areas than other brain regions, the left parahippocampal gyrus, Pallidum_L, Pallidum_R, Temporal_Pole_Sup_R, this result suggest that these brain regions play more important roles in identifying the high-risk subjects from normal controls. These results show that compared, there are differences in the resting state of the brain network connectivity of high-risk group and normal group.This study is an exploratory application study, aimed at addressing mental disorders during conscription psychological detection. Combined with the current conscription mental screening software, it’s necessary to conduct the early screening for serious mental illness- schizophrenia, including high-risk population of schizophrenia. We demonstrated that there are brain structure abnormalities between first-degree relatives of schizophrenia and normal controls. In future recruitment psychological selection, if the candidates provide real materials of family history, once their parents were diagnosed with schizophrenia, we can consider to refuse such candidate to join the armed forces. In the further study, clinical high-risk subjects will be included, as well as those suspected schizophrenic patients during conscription psychological selection, we will continue to improve the screening studies. |
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