The Stanley Neuropathology Consortium Integrative Database: A novel web-based tool for exploring neuropathological traits, gene expression and associated biological processes in psychiatric disorders

What is the Stanley Neuropathology Consortium Integrative Database (SNCID) To facilitate psychiatric research through data sharing, the SMRI has developed a new web-based database. The SNCID is freely available to all users ( However, for commercial use, agreement and permission should be obtained from the SMRI. The SNCID currently integrates 1747 neuropathology datasets and three expression microarray datasets with statistical modules. All data sets used in the SNCID have been generated with the Stanley Neuropathology Consortium (SNC) tissues. Descriptive statistical and variance analysis modules enable users to identify neuropathological traits in multiple brain areas for psychiatric disorders such as bipolar disorder, depression and schizophrenia. Integration of genome-wide expression microarray data with correlation modules allow users to explore genes correlated with the trait of interest. An interface that links the SNCID to the DAVID database ( allows for the functional annotation of those probe sets that correlated with a particular trait.

 The Stanley Neuropathology Consortium is a collection of 60 brains, consisting of 15 each diagnosed with schizophrenia, bipolar disorder, or major depression, and unaffected controls. The four groups are matched by age, sex, race, postmortem interval, pH, side of brain, and mRNA quality.  All data used in the SNCID were generated with tissues from the SNC. The Stanley Neuropathology Consortium

Overall structure of the SNCID External Database and web tool Neuropathology database 12 brain regions Statistical analysis module Descriptive analysis Variance analysis Correlation analysis Microarray database Frontal cortex Cerebellum Repository database Zipped raw data files NCBI DAVID

Application 1: Identification of Neuropathological Trait Step1-1: Search for trait (marker) of interest Select brain region of interest (12 regions available) and marker type (RNA, Protein, Cell or other)

Application 1: Identification of Neuropathalogical Trait Step1-2: Search trait of interest Refine search by selecting specific marker and/or researcher

Application 1: Identification of Neuropathology Trait Step 2: Basic information of marker, study, and related reference Hyperlink to NCBI’s Entrez gene dbStudy information e.g. methodology Published paper related to the study

Application 1: Identification of Neuropathology Trait Step 3: Descriptive statistics To see the basic statistical information regarding BDNF mRNA levels in layer VIa of temporal cortex choose ‘Analysis’.

Application 1: Identification of Neuropathology Trait Step 3: Descriptive statistics with histogram of the marker ‘Analysis’ gives the basic statistics regarding the dataset, number of cases included (e.g. count) and indicates if the data is normally distributed.

Application 1: Identification of Abnormal Neuropathological Trait Step 4: Statistical analysis: Variance analysis To determine if there is a difference in BDNF mRNA levels between diagnostic groups and controls ‘select a method’ e. g. ANOVA or Non-parametric test.

Application 1: Identification of Abnormal Neuropathology Trait Step 4: Results of variance analysis with box plot Non-parametric test gives basic statistical description of group differences.

Application 1: Identification of Abnormal Neuropathology Trait Step 4: Statistical analysis: Identification of confounding factors To determine which confounding factor (e.g. select brain pH) correlates (e.g. select Parametric or Non-parametric) with BDNF mRNA levels in temporal cortex

Application 1: Identification of Abnormal Neuropathology Trait Step 4: Statistical analysis: Identification of confounding factors Non-parametric test indicates brain pH is significantly correlated with BDNF mRNA levels in temporal cortex

Application 1: Identification of Abnormal Neuropathology Trait Step 4: Statistical analysis: correlation analysis To determine which markers in another brain area (e.g. select frontal cortex) correlate (e.g. select Spearman’s) with BDNF mRNA levels in temporal cortex.

Application 1: Identification of Abnormal Neuropathology Traits Step 4: Statistical analysis: correlation analysis Spearman’s test reveals 47 markers in frontal cortex are significantly correlated (p<0.01) with BDNF mRNA levels in temporal cortex

Application 2: Exploration of genes and pathways associated with dopamine levels in frontal cortex Variance analysis – reveals dopamine levels are significantly increased in the frontal cortex of the schizophrenia group as compared to the control group

Application 2: Exploration of genes and pathways associated with dopamine levels in frontal cortex Genome-wide correlation analysis To determine which genes are correlated with dopamine levels in frontal cortex select microarray dataset (e.g. frontal, MAS5) and method (e.g. Spearman’s)

Application 2: Exploration of genes and pathways associated with dopamine levels in frontal cortex Functional annotation interface Spearman’s test revealed 43 genes significantly correlated (p<0.001) with dopamine levels in frontal cortex. To identify the functional pathways associated with these 43 genes choose ‘functional annotation’

Application 2: Exploration of genes and pathways associated with dopamine levels in frontal cortex Functional annotation interface 43 genes are uploaded onto the DAVID by functional annotation interface and then select functional annotation tool in the DAVID

Application 2: Exploration of genes and pathways associated with dopamine levels in frontal cortex Functional annotation using DAVID database

Application 3: Exploration of genes and pathways associated with glutamate levels in frontal cortex Variance analysis – revealed glutamate levels are significantly elevated in the frontal cortex of groups with bipolar disorder and depression as compared to the normal control group.

Application 3: Exploration of genes and pathways associated with glutamate levels in frontal cortex Confounding factor analysis – reveals a significant difference in glutamate levels between cases that commit suicide and those that do not. suicide

Application 3: Exploration of genes and pathways associated with glutamate levels in frontal cortex Genome-wide correlation analysis suicide 151 genes significantly correlated (p<0.01) with glutamate levels in the frontal cortex

Application 3: Exploration of genes and pathways associated with glutamate levels in frontal cortex Functional annotation using DAVID database

Repository database The SNCID also integrates repository database. We strongly recommend users to download the raw data for further statistical analysis.

Any questions, suggestions, or comments on the SNCID? We hope our new database will contribute to the identification of novel neuropathological markers for psychiatric disorders. We hope that the potential mechanisms that underlie the abnormalities of these markers in psychiatric disorders will be revealed Eventually we hope the SNCID will help researchers find novel drug targets for the major psychiatric disorders. Enjoy data-mining! Please contact Sanghyeon Kim or Maree Webster if you have any questions, suggestions or comments on the SNCID.