Autism spectrum disorders (ASD) is a complex,behaviorally defined,mental disorder with uncertain causes although it is more likely caused by the interaction of genetic and environmental factors.ASD's heterogeneity in pathology and phenotype makes it difficult to accurately diagnose individual patients.Therefore,it is an urgent need to develop effective tools for screening susceptible individuals for early diagnosis and treatment.This paper reviews recent neuroimaging and imaging genetic studies of ASD.The existing results indicate that proper combination of multimodal neuroimaging techniques and genetic technology is expected to reveal the pathological mechanism of ASD and might help find objective biomarkers for achieving an early and accurate diagnosis of ASD.

Objective To evaluate the validity and reliability of the Chinese version of Alcohol Withdrawal Scale (AWS). Methods Totally 175 patients diagnosed as alcohol dependence according to the criteria of ICD-10 were studied. Intraclass correlation coefficient (ICC) analysis was applied for examining interrater consistency and Cronbach' s α for internal consistency. Factor analysis was used to examine the construct validity. Correlation analysis between AWS and CGI,Revised Clinic Institute Alcohol Withdrawal Syndrome Scale(CIWA-Ar) were conducted to evaluate the criterion validity. Based on clinical criteria,ROC curve was calculated so as to test the discriminative ability and establish the cut-off point of the scale. Results ( 1 ) Reliability: ICC value was 0.93,and Cronbach's α was 0.83,which indicated good interrater and internal consistency. (2) Validity:the correlation coefficients of the two subscale with the total scale score were 0.78,0.83 respectively. The correlation coefficients between the subscale were 0. 81 and factor analysis revealed that each item of the scale had relatively high load on the primary factor (0.409 ～0.926). At the time of admission,the total score of the AWS was positively correlated with that of CGI ( r = 0.71, P < 0.05 ). The total score of the AWS also was positively correlated with that of CIWA-Ar ( r = 0. 86, P<0. 05). As treatment went on,total score of the AWS showed a downward trend,at the end of the first week,the total score of the AWS was positively correlated with that of CGI ( r = 0.62, P<0.05). (3)The cut-off point of AWS for mild alcohol withdrawal state was determined as ≥3. With this cut-off point,AWS had both high sensitivity (92.1% ) and specificity (73.5% ) ,and the area under curve (AUC) was 0. 91. The cut-off point of AWS for moderate withdrawal state was determined as ≥7, and the sensitivity and specificity of AWS were 94.3 % and 89.7 % respectively, with the AUC of 0.94. The cut-off point of AWS for severe withdrawal state was determined as ≥ 10. With this cut-off point AWS had both high sensitivity (94. 9% ) and high specificity (92.6% ) .with the AUC of 0.93. Conclusion AWS has good reliability and validity and can reflect the change of the disease and the efficacy of treatment.

Objective To investigate the prevalence and risk factors of needle-sharing behavior among intravenous drug users (IDUs).Methods Data regarding socio-demographics,drug abuse characteristics and HIV related health literacy of 474 heroin IDUs were collected.Antisocial personality disorder of IDUs was diagnosed through Mini international neuropsychiatric interview.Results The prevalence of needle sharing among IDUs was 53.8％.Needle-sharing behavior of IDUs was significantly associated with male gender,marital status of being single and separated/divorced,local dwelling,an early age of first drug abuse and antisocial personality disorder (OR =1.11 ～ 6.69,P＜0.05).Conclusion Heroin IDUs have high prevalence of needle sharing.A comprehensive social,behavior and psychology based intervention is needed to effectively prevent HIV infection in IDUs.

Objective To assess the clinical utility of P300 for the assessment of cognitive disorders in Chinese patients with Parkinson's disease (PD) by meta-analysis.Methods Studies comparing the P300 between PD patients with different cognitive disorders (dementia,mild cognitive impairment (MCI),and no cognitive impairment;the former two were “cognitive impairments”,while the latter two were “nondementia”) and healthy controls were retrieved by searching within major Chinese and English databases.Studies,whose case and control groups were matched for age and gender,were rated as high quality.Weighted mean differences (WMDs) in P300 latency and amplitude between groups were calculated by using meta-analysis.Results In total,23 studies were included and 20 of which were rated high quality.Demented PD patients had statistically significant longer P300 latency (WMD:56.14,95％ CI:19.06,93.23) and lower amplitude (WMD:-1.30,95％CI:-2.08,-0.51) than non-demented PD patients.PD patients with MCI had statistically significant longer P300 latency (WMD:13.71,95％CI:5.13,22.29) and lower amplitude (WMD:-1.25,95 ％ CI:-2.29,-0.21) than PD patients without cognitive disorders.However,no significant differences in P300 latency (WMD:4.16,95％ CI:-13.85,22.17) and amplitude (WMD:0.23,95％ CI:-1.72,2.17) were found between PD patients with dementia and MCI.Conclusion Among Chinese PD patients,there are significant P300 differences between those with dementia and without dementia,and between MCI and those without cognitive impairments,however,but no significant P300 differences between those with dementia and MCI.

Objective:To investigate the brain characteristics of chronic schizophrenia by voxel-mirrored homotopic connectivity (VMHC).Methods:Forty seven patients with chronic schizophrenia from Tianjin Anding Hospital and 31 healthy controls from nearby communities were included in the study. The MRI data of the subjects were collected by Siemens Skyra 3.0 T MRI.Positive and negative symptoms scale (PANSS) was used to assess the illness severity of patients with chronic schizophrenia.SPM12 was used to process the collected MRI data. DPARSF was used to calculate the VMHC values of subjects in the two groups. The independent sample t test based on SPM12 was used to compare the VMHC values of the two groups. The significant brain regions in VMHC were regarded as regions of interest (ROI), and VBM8 was used to further analyze the gray matter volume of the ROI of the two groups. Results:Compared with healthy controls, the patients with chronic schizophrenia demonstrated decreased VMHC mainly located in lingual gyrus(voxel=208, T=4.98), occipital middle occipital gyrus(voxel=156, T=3.75) and postcentral gyrus(voxel=237, T=4.36) (FDR correction, q=0.05). Compared with healthy controls, the gray matter volumes in bilateral lingual gyrus(left(0.0034±0.0008), (0.0028±0.0013), t=-2.141, P=0.037; right(0.0025±0.0006), (0.0020±0.0011), t=2.268, P=0.028) and postcentral gyrus((0.0432±0.0051), (0.0372±0.0162), t=-2.070, P=0.045) increased, but non-significant change in postcentral gyrus of the patients with chronic schizophrenia. Conclusion:The abnormal VMHC mainly locate in lingual gyrus, middle occipital gyrus and postcentral gyrus in patients with chronic schizophrenia.

The etiology and pathological mechanism of schizophrenia are very complicated,and the heterogeneity of clinical manifestation and the heterogeneity of treatment reaction are also distinct. The jour-nal of World Journal of psychiatry reported that in the past 20 years,about the schizophrenic biomarker stud-y,either the direction of our research is wrong,or the direction is right,but the method is wrong. It is urgent to adopt new technologies to carry out study about the biomarkers of schizophrenia from multiple perspectives. It can be seen that the precise exploration of the etiology and specific pathological mechanism and therapeutic targets of schizophrenia is still a common research task of the world researchers. Compared with the research results achieved by tumor and heart disease study and the rapid development of clinical transformation,psy-chiatric research needs to consider other disciplines'ideas,methods and techniques to promote the research of schizophrenia. In the past 2 years,many scholars have studied schizophrenia from different perspectives. This paper reviewed the previous studies about omics matrix biomarkers study during the past two years. We brief-ly summarized the previous findings from the brain connectomics,genomics,proteomics,and microbial genom-ics perspective,respectively. We generally described the progress of brain imaging networks,gene networks, protein networks and micro biological networks in schizophrenia to increase our understanding of knowledge in this field.

A single infusion of ketamine has sustained antidepressant effects and significantly de-creases the risk of suicide,and the effects can last up for 7-10 days,but the underlying mechanism is un-clear. The mechanism was reviewed underlying the antidepressant effects of ketamine,and found that ket-amine may exert its antidepressant effect by regulating sleep/wake cycle,synaptic pruning,molecular path-ways,and neural circuits for treatment-refractory depression. Further studies are needed to investigate the ge-netic,molecular mechanisms underlying the sustained antidepressant effect of ketamine,and the associated imaging findings through in vivo imaging of animals and imaging genetics techniques,explore the optimal time for administration of ketamine,and then provide accurate scientific basis for enhancing its anti-depressant effect.

Objective:To explore the risk factors of the incidence of arrhythmia and the prediction of baseline ventricular late potential in patients with first depression episode.Methods:The cohort study was used to observe the relationship between the baseline status of ventricular late potential, the severity of baseline depression symptoms, the extent of remission of depressive symptoms within the treatment duration and arrhythmia incidence in the 3 years progress. For the assessment of the severity of depression symptoms, 17 version of Hamilton depression scale was used to evaluate the baseline ventricular late potential, and DMS lab3.0 ECG platform late potential analysis system was used to determine the assessment (CardioScan 12 NET version). The first depression patients with positive ventricular late potential were followed up for 3 years. The changes of the severity of ventricular late potential and depression symptoms were investigated, and the correlation with the subsequent course of arrhythmia was investigated.SPSS 20.0 software package was used for statistical distraction, chi square test was used for count data, independent samples t test was used for normal distribution measurement data, Mann-Whitney U test was used for non-normal distribution count data, and logistic regression method was used to calculate relative risk( RR). Results:According to the 3-year follow-up of 400 first-episode depression patients, 22.25% (89/400) had malignant arrhythmia. The incidence of malignant arrhythmia was 39.46% (58/147) in ventricular late potential positive group and 12.25% (31/253) in ventricular late potential negative group, and the difference was statistically significant(χ 2=9.578, P<0.01). Logistic regression analysis showed that positive ventricular late potential at baseline (compared with negative ventricular late potential at baseline, RR=10.78, 95% CI=8.34-13.80), having a family history of arrhythmia (compared with no family history of arrhythmia, RR=5.23, 95% CI=2.41-9.85), had a higher severity of depression at baseline (compared with lower severity of depression at baseline, RR=1.73, 95% CI=1.25-2.85), poor first-time efficacy and more repeated hospitalizations (compared with good first-time efficacy and less hospitalizations, RR=1.11, 95% CI=1.04-1.17), and age of onset< 20 (compared with age of onset≥20, RR=1.07, 95% CI=1.02-1.93) were the risk factors of malignant arrhythmia in patients with first-episode depression(all P<0.05). Conclusion:The incidence of arrhythmia is very high in those patients with baseline positive late ventricular potential. Positive late ventricular potential, family history of arrhythmia, younger onset age and poor therapeutic effect were the relative risk of arrhythmia in the patients with depression.

A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.