New role of platelets in schizophrenia:predicting drug response

Background Elevated platelet count(PLTc)is associated with first-episode schizophrenia and adverse outcomes in individuals with precursory psychosis.However,the impact of antipsychotic medications on PLTc and its association with symptom improvement remain unclear.Aims We aimed to investigate changes in PLTc levels following antipsychotic treatment and assess whether PLTc can predict antipsychotic responses and metabolic changes after accounting for other related variables.Methods A total of 2985 patients with schizophrenia were randomised into seven groups.Each group received one of seven antipsychotic treatments and was assessed at 2,4 and 6 weeks.Clinical symptoms were evaluated using the positive and negative syndrome scale(PANSS).Additionally,we measured blood cell counts and metabolic parameters,such as blood lipids.Repeated measures analysis of variance was used to examine the effect of antipsychotics on PLTc changes,while structural equation modelling was used to assess the predictive value of PLTc on PANSS changes.Results PLTc significantly increased in patients treated with aripiprazole(F=6.00,p=0.003),ziprasidone(F=7.10,p<0.001)and haloperidol(F=3.59,p=0.029).It exhibited a positive association with white blood cell count and metabolic indicators.Higher baseline PLTc was observed in non-responders,particularly in those defined by the PANSS-negative subscale.In the structural equation model,PLTc,white blood cell count and a latent metabolic variable predicted the rate of change in the PANSS-negative subscale scores.Moreover,higher baseline PLTc was observed in individuals with less metabolic change,although this association was no longer significant after accounting for baseline metabolic values.Conclusions Platelet parameters,specifically PLTc,are influenced by antipsychotic treatment and could potentially elevate the risk of venous thromboembolism in patients with schizophrenia.Elevated PLTc levels and associated factors may impede symptom improvement by promoting inflammation.Given PLTc’s easy measurement and clinical relevance,it warrants increased attention from psychiatrists.Trial registration number ChiCTR-TRC-10000934.

Shared and Distinct Topographic Alterations of Alpha-Range Resting EEG Activity in Schizophrenia,Bipolar Disorder,and Depression

DearEditor,Schizophrenia(SCZ),bipolar disorder(BD),and major depressive disorder(MDD)are common psychiatric disorders that share several characteristics such as risk genes,and cognitive,neural,and structural abnormalities[1-3].Despite this,the boundaries between the three disorders are not clearly defined in clinical practice,and the"crosscutting"dimensional assessment of symptoms and clinical phenomena is controversial[2,4].To improve the classification criteria,it is essential to explore the underlying relationships among these disorders and address the fuzzy divergence that exists.

White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging

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 and118 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 typespecific treatment approaches.

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features

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 aone-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.

Functional Striatal Abnormalities:A Distinct Brain Signature of Schizophrenia

Schizophrenia is a severe and complex mental disorder 111.Neuroimaging offers a powerful window for identifying the brain biomarkers and investigating the neuropathological mechanisms of psychiatric disorders.A study led by Professors Jiang and Liu,published recently in Nature Medicine,developed a new neuroimaging biomarker to characterize striatal dysfunction based on a multi-site functional MRI dataset with>1000 individuals.They show that this biomarker can distinguish individuals with schizophrenia and predict the short-term effects of antipsychotic treatmem[2].