White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging / 神经科学通报·英文版

Identifying data-driven biotypes of major depressive disorder (MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and 118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation. Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD. Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1 (25.9% of patient sample), widespread white-matter disruption; subgroup 2 (43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate; and subgroup 3 (31.0% of patient sample), possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory; subgroup 2, dysfunction in delayed memory; and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize type-specific treatment approaches.

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features / 神经科学通报·英文版

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

Comorbidity of autism spectrum disorder and attention-deficit/hyperactivity disorder:A review / 中国心理卫生杂志

Attention-deficit/hyperactivity disorder (ADHD)is one of the most common comorbidities of ASD.This article reviews the assessment tools and clinical research (prevalence,clinical characteristics and treat-ment)and fundamental research (iconography,genetics,neurophychology,electronerophysiology)of ASD with ADHD according to lately related articles.The findings suggested that there was lack of researches on treatment and iconography of ASD with ADHD and the conclusions were inconformity.Furthermore,most of the objects in these researches were children of normal intelligence.Thus future research should expand its objects to patients of adult and children with mental retardation and do further explore in iconography and treatment.

The prevalence and risk factors for non-suicidal self injury among middle school students / 中华行为医学与脑科学杂志

Objective To investigate the prevalence and risk factors of non?suicidal self injury among middle school students. Methods Data were collected from 2 140 middle school students with the mean age of 13.92 by stratified sampling method. All students were evaluated with Beck Depression Scale (BDI),Barratt Im?pulsiveness Scale ( BIS ) , Adolescent Self?Rating Life Events Check List ( ASLEC ) , Adolescents Health related Risk Behaviors Inventory ( AHRBI ) , Chinese version of Family Adaptability and Cohesion Evaluation Scales ( FACES II?CV) ,and self?made investigate questionnaire. Results The prevalence of non?suicidal self injury a?mong local middle school students was 23.2%,total 495 adolescents were endorsed non?suicidal self injury,and 49.7% were girls.The results of in multivariable logistic regression showed the main risk factors for non?suicidal self injury among adolescents were being female,depressive symptoms,impulsiveness,negative life events and health re?lated risk behaviors. Conclusion Non?suicidal self injury is high among Chinese adolescent in the city of Dujian?gyan,and it is necessary to take effective interventions.