Cortical Surface Area Correlates with STON2 Gene Ser307Pro Polymorphism in First-Episode Treatment-Naïve Patients with Schizophrenia

Xiang, Bo; Wu, Jun-yao; Wang, Qiang; Li, Mingli; Jiang, Lijun; Deng, Wei Q.; Chen, Zhuangfei; He, Zongling; Huang, Cao-Hua; Han, Yuanyuan; Li, Yinfei; Lin, Yin; Liu, Xiang; Wang, Yingcheng; Ma, Xiaoyan; Gong, Qiyong; Li, Tao; Hu, Xun

doi:10.1371/journal.pone.0064090

Cited by 6 publications

(9 citation statements)

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“…18 More detailed information was described in our previous study. 19 The voxel-wise comparisons of fractional anisotropy values were performed with Statistical Parametric Mapping (version SPM8; https://www.fil.ion.ucl.ac.uk/spm/) processing, and two sample t-tests were performed to explore the main effects with gender, age and total intracranial volume as covariates. Statistical inferences were made with a voxel-level threshold of P < 0.05 after false discovery rate correction for multiple comparisons.…”

Section: Mri Data Acquisition Preprocessing and Imaging Processingmentioning

confidence: 99%

Genes in immune pathways associated with abnormal white matter integrity in first-episode and treatment-naïve patients with schizophrenia

et al. 2019

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BackgroundPrevious studies have inferred a strong genetic component in schizophrenia. However, the genetic variants involved in the susceptibility to schizophrenia remain unclear.AimsTo detect potential gene pathways and networks associated with schizophrenia, and to explore the relationship between common and rare variants in these pathways and abnormal white matter integrity in schizophrenia.MethodThe analysis included 100 first-episode treatment-naïve patients with schizophrenia and 140 healthy controls. A network-based analysis was carried out on the data collected from the Psychiatric Genomics Consortium Phase I (PGC-I). Based on our genome-wide association study and whole-exome sequencing data-sets, we performed a gene-set analysis to detect associations between the combining effects of common and rare genetic variants and abnormal white matter integrity in schizophrenia.ResultsPatients had significantly reduced functional anisotropy in the left and right anterior cingulate cortex, left and right precuneus and extra-nuclear (t = 4.61–5.10, PFDR < 0.01), compared with controls. Generated from co-expression network analysis of the PGC-1 summary statistics of schizophrenia, a subnetwork of 207 genes associated with schizophrenia was identified (P < 0.01), and 176 genes were co-expressed in four gene modules. Functional enrichment analysis for genes in each module revealed that the yellow module was enriched with highly co-expressed, innate immune response genes. Furthermore, rare variants of enriched genes in the yellow module were associated with reduced functional anisotropy in the left anterior cingulate cortex (P = 0.006; Padjusted = 0.024) in patients only.ConclusionsThe pathogenesis of schizophrenia may be substantially influenced by genes involved in the immune system, via both pathway and network.Declaration of interestsNone.

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Section: Mri Data Acquisition Preprocessing and Imaging Processingmentioning

confidence: 99%

Genes in immune pathways associated with abnormal white matter integrity in first-episode and treatment-naïve patients with schizophrenia

et al. 2019

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“…Brain structure metrics, such as cortical volume (vol), surface area (area), cortical thickness (thick), mean cortical curvature (curv), local gyrification index (lgi), and sulcus depth (sulc), have been derived from surface modeling of the cerebral cortex (Fischl et al 1999) and widely applied to investigate brain development and brain disorders. For example, the vol, area, and thick metrics are popular measures used to quantify brain morphometric abnormalities in schizophrenia (e.g., Ohtani et al 2014; Sprooten et al 2013; Xiang et al 2013). In particular, these metrics play different roles in distinguishing schizophrenia from bipolar disorder patients (Rimol et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Brain structure–function associations identified in large-scale neuroimaging data

et al. 2016

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The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure–function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.Electronic supplementary materialThe online version of this article (doi:10.1007/s00429-015-1177-6) contains supplementary material, which is available to authorized users.

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“…The STON2 gene has been implicated as being correlated with regional surface area in a model of schizophrenia Xiang et al (2013). The SNP Browser application of the Data Portal provides access to selected SNPs that are located in genes of interest.…”

Section: Resultsmentioning

confidence: 99%

A web-portal for interactive data exploration, visualization, and hypothesis testing

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et al. 2014

Front. Neuroinform.

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Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data.

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Cortical Surface Area Correlates with STON2 Gene Ser307Pro Polymorphism in First-Episode Treatment-Naïve Patients with Schizophrenia

Cited by 6 publications

References 54 publications

Genes in immune pathways associated with abnormal white matter integrity in first-episode and treatment-naïve patients with schizophrenia

Genes in immune pathways associated with abnormal white matter integrity in first-episode and treatment-naïve patients with schizophrenia

Brain structure–function associations identified in large-scale neuroimaging data

A web-portal for interactive data exploration, visualization, and hypothesis testing

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