SchizConnect: Mediating neuroimaging databases on

NeuroImage xxx (2015) xxx–xxx

YNIMG-12362; No. of pages: 13; 4C: 2, 5, 7

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SchizConnect: Mediating neuroimaging databases on schizophrenia andrelated disorders for large-scale integration

Lei Wang a,b,⁎, Kathryn I. Alpert a, Vince D. Calhoun c,d,e,f,g, Derin J. Cobia a, David B. Keator h, Margaret D. King c,Alexandr Kogan a, Drew Landis c, Marcelo Tallis i, Matthew D. Turner m,o, Steven G. Potkin h,l,Jessica A. Turner c,n,o, Jose Luis Ambite i,j,k

a Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USAb Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USAc The Mind Research Network, Albuquerque, NM, USAd University of New Mexico Health Sciences Center, Albuquerque, NM, USAe Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USAf Department of Psychiatry, University of New Mexico, Albuquerque, NM, USAg Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USAh Brain Imaging Center, University of California, Irvine, CA, USAi Information Sciences Institute, University of Southern California, Marina del Rey, CA, USAj Digital Government Research Center, University of Southern California, Los Angeles, CA, USAk Department of Computer Science, University of Southern California, Los Angeles, CA, USAl Department of Psychiatry & Human Behavior, University of California, Irvine, School of Medicine, Irvine, CA, USAm Department of Computer Science, Georgia State University, Atlanta, GA, USAn Department of Psychology, Georgia State University, Atlanta, GA, USAo Neuroscience Institute, Georgia State University, Atlanta, GA, USA

⁎ Corresponding author at: Northwestern UniversityDepartment of Psychiatry and Behavioral Sciences, 7101322, Chicago, IL 60614, USA.

E-mail address: [email protected] (L. Wang

http://dx.doi.org/10.1016/j.neuroimage.2015.06.0651053-8119/© 2015 Elsevier Inc. All rights reserved.

Please cite this article as:Wang, L., et al., Schiintegration, NeuroImage (2015), http://dx.d

a b s t r a c t
a r t i c l e i n f o
Article history:Accepted 23 June 2015Available online xxxx

Keywords:Data mediation and integrationNeuroinformaticsMega analysisSchizophrenia databases

SchizConnect (www.schizconnect.org) is built to address the issues ofmultiple data repositories in schizophrenianeuroimaging studies. It includes a level of mediation—translating across data sources—so that the user can placeonequery, e.g. for diffusion images frommale individualswith schizophrenia, andfindout fromacross participatingdata sources howmanydatasets there are, aswell as downloading the imaging and relateddata. The current versionhandles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologiesinto a common framework. New data repositories can also be mediated to bring immediate access to existingdatasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database witha focus on schizophrenia and related disorders that can mediate live data as information is being updated at eachdata source. It is our hope that SchizConnect can facilitate testing newhypotheses through aggregateddatasets, pro-moting discovery related to the mechanisms underlying schizophrenic dysfunction.

© 2015 Elsevier Inc. All rights reserved.

Introduction

Schizophrenia is a complex psychiatric disease with heterogeneousclinical, behavioral, cognitive and genetic manifestations, and the litera-ture, especially on neuroimaging studies, has yet to achieve a state ofconsistency and reproducibility. As a result, multi-site consortia havebeen created to coordinate the collection of large datasets in order toaddress these issues (Turner, 2014). Consortia such as the FunctionalBiomedical Informatics Research Network (FBIRN) (Friedman et al.,2008; Helmer et al., 2011a; Keator et al., 2008), theMind Clinical Imaging

Feinberg School of Medicine,N. Lake Shore Dr, Abbott Hall

).

zConnect: Mediating neuroimoi.org/10.1016/j.neuroimage.2

Consortium (MCIC) (Gollub et al., 2013), North American ProdromeLongitudinal Study Consortium (NAPLS) (Addington et al., 2012) andBipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP)(Keshavan et al., 2011; Thaker, 2008) have led to the improvement ofour understanding of brain circuitry, brain function and genetic variabil-ity in schizophrenia (Allen et al., 2011; Arnold et al., 2015; Cannon et al.,2015; Castro et al., 2014; Chen et al., 2012; Chen et al., 2013; Ehrlich et al.,2014;Hass et al., 2015a, 2015b; Kimet al., 2009; Kimet al., 2010;Mathewet al., 2014; Potkin et al., 2009). These efforts were successful in part be-cause they were created with data sharing in mind, and they anticipatedmany of the difficulties in combining data frommultiple sites in their de-sign and schema. Yet, in building data repositories, many decisions weremade thatwere specific to that particular repository or study, aboutwhatthey would call different data types. In one study's data a structural MRIscan may be listed informatively as “T1-weighted scan”, or something

aging databases on schizophrenia and related disorders for large-scale015.06.065

http://www.schizconnect.org

http://dx.doi.org/10.1016/j.neuroimage.2015.06.065

mailto:[email protected]


http://www.sciencedirect.com/science/journal/10538119

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2 L. Wang et al. / NeuroImage xxx (2015) xxx–xxx

as complex as “5MPRAGE-AVG” or just “scan1”. Combining data fromdatasets that do not share the same protocols and data structures is chal-lenging and it remains a barrier to aggregating mega-datasets. Whencombining data across different sources, individual investigators facethe difficult and costly process of understanding the different imaging,subject, assessment, meta-data content (definitions, formats, organiza-tions) and converting the data into standardized terms. A critical andunmet need exists to automate and virtualize this process so that appro-priate data can be retrieved and combined from different databases re-gardless of differences in the their structure and terminology.

In this paper, we describe the initial deployment of “SchizConnect”(publicly available at http://www.schizconnect.org/), a new resourceaimed at establishing mega-datasets (Wang et al., 2014). Building onthe success of previous consortia efforts, SchizConnect creates a re-source that supports structured querying and retrieval of neuroimagingand related data across these consortia repositories.

Methods

SchizConnect architecture

SchizConnect follows the classical virtual data integrationframework (Florescu et al., 1998; Halevy, 2001; Lenzerini, 2002;Ullman, 1997; Wiederhold, 1992), which saw its initial theoretical

SchizConnect M

Data Source Interface

Query Optimizer

Query Rewriter

HID Data SourceFBIRN–Federated data

XNAT DataNUSDAST in XNA

SchizConnect.oWeb Portal

Mediator Interface

Fig. 1. SchizConnect architecture&data flow. The SchizConnect architecture has the following3org web portal. After the user builds the query at the SchizConnect web portal, the query commSchizConnect domainmodel to translate the incoming query into source-specific schemas, andlanguages, and variables. The returns from the sources are handled by themediator engine, whicportal then interacts with the user for further processing, including signing of data use agreem

Please cite this article as:Wang, L., et al., SchizConnect: Mediating neuroimintegration, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2

application on schizophrenia neuroimaging data within the FBIRNconsortium (Ashish et al., 2010). A distinct feature of this frame-work is that the data can reside at and be maintained at the originaldata sources in their original formats and schemas (i.e., their ownways to define the structure, content, and semantics of data).SchizConnect consists of the following three fundamental components(Fig. 1): 1) the data sources that provide the data, structured accordingto native schemas; 2) the mediation software (i.e., mediator) that recon-ciles the semantic differences in the data across the different sourcesthrough domain modeling and inter-schema mapping; and 3) theweb portal that provides a user-friendly interface for querying anddownloading data.

The typical data flow begins with a user query constructed by drag-ging and dropping terms in a graphical user interface (GUI) at theSchizConnect web portal. The graphical query is translated to SQL andpassed to the SchizConnect mediator. The mediator then relies on theSchizConnect domain model and inter-schema mappings to translatethe user query into source-specific terms, and then queries each datasource directly and concurrently, using the available source-specificquery mechanisms, languages and variables. The results from thesources are collated and joined together by the mediator and providedto the SchizConnect web portal as a unified result table containing themediated (harmonized) variables. The SchizConnect web portal theninteracts with the user for further processing, including signing of data

ediator @ USCLogical Schema

Mappings

sSource Cost

Statistics

COINS Data SourceMCIC & COBRE in COINS

SourceT Central

rg Web PortalData Warehouse

Data Source Interface

components: the federated data sources, the SchizConnectmediator, and the SchizConnect.and is passed to the SchizConnect mediator engine. Themediator engine then relies on thequeries each data source directly and in parallel, using source-specific query mechanisms,h provides the SchizConnectweb portalwith a unified results table. The SchizConnectwebents and data download.


http://www.schizconnect.org/

http://SchizConnect.org



Table 1Sample characteristics on modeled terms.

Dx No knowndisorder

Schizophreniabroad

Schizophreniastrict

Schizoaffective Bipolardisorder

Sibling of schizophreniastrict

Sibling of no knowndisorder

Number of current subjectsa 481 117 333 38 10 44 66Gender (m/f) 295/186 87/30 242/91 22/16 5/5 21/23 16/50Age (years) (mean ± s.d.) 34.2 ± 12.9 34.4 ± 11.0 35.4 ± 12.9 39.5 ± 10.0 46.6 ± 14.4 21.6 ± 3.7 20.4 ± 3.5Age range (years) min–max 13–67 18–61 17–66 19–59 21–64 14–28 14–28

a 40 subjects from the FBIRN database contains no diagnosis information and are not listed here.

3L. Wang et al. / NeuroImage xxx (2015) xxx–xxx

use agreements and data download. Below we describe each of thecomponents in detail.

The data sources

SchizConnect accesses the data available at each of the sources inreal time. It maintains a library of connectors (wrappers) to commonsource types, including relational databases, XML (Extensible MarkupLanguage) or JSON (JavaScript Object Notation) databases, SOAP(Simple Object Access Protocol) or REST (Representation State Transfer)web services, or flat files. For previously unseen source types, the archi-tecture is extensible to program new connectors. Each source runs theirown server platforms with their own database schemas, formats andapplication programming interfaces (APIs). SchizConnect imposes norequirements on data sources with regard to databasing or accessmethods, but is flexible to special needs (see results below for specialscase example).

SchizConnect mediator

The SchizConnect mediator is built on the BIRN mediator (Ashishet al., 2010; Helmer et al., 2011b). The core idea is to define a commondomain model/schema and inter-schema mapping rules that mapbetween the domain schema and the different source schemas. Thedomain model defines an integrated view of the shared data in theapplication domain (i.e., SchizConnect). This concept is closely relatedto that of an ontology, but it is more limited in scope, in the sense thatit only models the part of the application domain that is relevant tointegrating a set of sources (Ashish et al., 2010). It can be viewed as anincremental, data-driven, pragmatic approach to domain ontology de-velopment. Inter-schema mappings are logical formulas, or rules, thatrelate the data models at each of the sources with the domain model,reconciling their semantic differences in the description of the data.The SchizConnect domain model consists of terms that are pertinent to

Table 2SchizConnect data sources, including current and planned sample sizes.

Data source FBIRN

Project name FBIRN Phase IIDatabase(language)

HID(PostgreSQL)

URL http://fbirnbdr.nbirn.net:8080/BNumber of current subjects (with imaging data) 255

(251)Image type Structural MRI

Resting-state fMRITask fMRIdMRI

Number of current subjects with multimodal imagingb 208Number of New subjects (timeline) 400–FBIRN Phase 3

(2015)

a The native COINS architecture involves dynamic data packaging following the query, whichfrom the COINS executive committee, an API was built to enable us to extract domain-model ddatabase underlying the duplicated MCICShare/COBRE data is MySQL using the relational data

b Multimodal imaging is defined as having any of the combination of the following: T1, diffu


schizophrenia neuroimaging research (such as those related to imagingprotocol, disease severity or cognitive functioning). Definitions of theseterms are provided via downloadable documents available on theSchizConnect web portal. During development, we have aligned theSchizConnect domain model terms with the standards provided by theontology community: XCEDE (XML-Based Clinical Experiment DataExchange Schema, http://www.xcede.org/XCEDE.html) (Gadde et al.,2012), NeuroLEX (the Neuroscience Lexicon, http://neurolex.org/)(Larson and Martone, 2013), NIF (the Neuroscience Information Frame-work, http://neuinfo.org/) (Gardner et al., 2008) and INCF (the Interna-tional Neuroinformatics Coordinating Facility, http://incf.org/) (BjaalieandGrillner, 2007; De Schutter, 2009),wherever appropriate. The domainterms with links to existing ontology can be found on the SchizConnectwebsite documentations page. For example, the Positive and NegativeSymptom Scale (PANSS) has an entry on NeuroLex (birnlex_3032,http://neurolex.org/wiki/Category:Positive_and_Negative_Symptom_Scale). In cases where no accepted ontology has been defined, such as theUPSA (University of California Performance-based Skills Assessment), wework with the neuroimaging and neuroinformatics community to definethem. Although such effort is ongoing, since there doesn't yet exist a stan-dard process for ontological term contribution, we will work with datacontributors and the neuroinformatics community on a case-by-casebasis.

The mediator performs two core functions. First, it uses the inter-schema mappings to rewrite the user query, e.g., “T1 images from indi-viduals with schizophrenia older than 30 years old,” from the domainschema to the formats and languages specific to each source. It currentlyuses GAV (global-as-view)mappings (Ashish et al., 2010; Florescu et al.,1998; Halevy, 2001; Lenzerini, 2002; Ullman, 1997;Wiederhold, 1992),where each domain predicate is constructed as a query over the sourcepredicates. Second, the mediator constructs, optimizes, and executes adistributed query evaluation plan, based on the rewritten source-levelquery, whichprovides the answers to the user query. The source queriesare executed directly over the data sources in parallel through each

NU MRN

NUSDAST MCIC & COBREXNAT(XML)

COINS Data Exchangea

DR/ https://central.xnat.org/REST/projects/NUDataSharing http://coins.mrn.org/451(368)

423(410)

Structural MRI Structural MRIResting-state fMRITask fMRIdMRI

– 404130(2015)

200(2016)

does not allow for data to be immediately returned to the query engine. With permissionefined variables from the COINS databases and to duplicate them at the mediator site. Themodel.sion MRI, task-paradigm fMRI, and resting-state fMRI.


http://www.xcede.org/XCEDE.html

http://neurolex.org/

http://neuinfo.org/

http://incf.org/

http://neurolex.org/wiki/Category:Positive_and_Negative_Symptom_Scale

http://neurolex.org/wiki/Category:Positive_and_Negative_Symptom_Scale

http://fbirnbdr.nbirn.net:8080/BDR/

https://central.xnat.org/REST/projects/NUDataSharing

http://coins.mrn.org/


Table 3Example queries and returns at SchizConnect.org.

Query terms Number of images & distinct subjects returned

Total fBIRN@ HID

COBRE@ COINS

MCICShare@ COINS

NUSDAST@ XNAT

T1 SCZ + CON 3178 1048 484 264 1382737 185 173 95 284

3T T1 20–60 Yr 1348 750 492 106 –345 112 180 53

T1 healthy controls 1846 655 243 264 684439 113 94 95 137

Sternberg SCZ 3 T b= 30 Yr 210 210 – – –18 18

Resting BP + CON 103 – 103 – –101 101

Sensory motor + T1,SCZ + CON, 30–50Yr (multimodal)

1310 1140 – 170 –119 83 36

All MRI 21,309 13,552 1596 4347 18141029 251 198 212 368

1 This link will be replaced by the SchizConnect web portal in the near future.2 On the COINS website, after user builds a query, data are packaged offline and a link is

provided for downloading. Therefore the user never sees the actual data until the data aredownloaded via the link.


source connector (which wraps the source APIs). Results from sourcequeries are collated and joined together in the mediator, and served tothe SchizConnect web portal as a unified table organized using the do-main schema terms.

The query execution engine used by the mediator for source-levelquery evaluation is based on the Open Grid Services Architecture DataAccess and Integration (OGSA-DAI) and Distributed Query Processing(OGSA-DQP) services (http://www.ogsadai.org.uk/) (Antonioletti et al.,2005; Grant et al., 2008; Lynden et al., 2009; Lynden et al., 2008).OGSA-DAI is a streaming workflow engine in which data resources andweb services are wrapped through a library of connectors. OGSA-DQP isa query engine that optimizes and implements the query evaluationplan as anOGSA-DAIworkflow,which accesses themultiple data sources.

The SchizConnect domain query language is SQL, expressed onthe domain model terms. Each data source query is wrapped as anOGSA-DAI resource through its library of connectors to common datasources such as PostgreSQL, MySQL, or XML. For additional details onthe mediator architecture, see our previous work on the BIRN mediator(Ashish et al., 2010).

SchizConnect web portal

SchizConnect has been conceptualized as a one-stop resource forquerying and retrieving neuroimaging and related data from distributed,heterogeneous repositories. SchizConnect is therefore set up to facilitateuser registration and compliance with data use agreement (DUA) witheach data source. Tomaximize the usage and efficiency, while complyingwithNIHpolicies andbest practices, the SchizConnectwebportal is struc-tured to have two query modes. The first is a preliminary search, forexample, for the number of male subjects between the ages of 20 and60 with diffusion MRI scans. This mode is completely open to the public,allowing for anyone to browse and immediately obtain useful, current in-formation on a current summary count of available data without havingthe need to register. However, no subject-level data at this point aredownloadable. In the second mode, the user registers and signs in, andwith the same query is able to view subject-level data, sign DUAs anddownload the data. Only data forwhichDUAhas been signed are down-loadable. Signed DUAs are forwarded to each respective data source ad-ministrator for reviewing and filing. The administrators at SchizConnectas well as each data source will work with each user's institution fordata use and institutional review board (IRB) terms upon request.Regarding participant privacy, we require that it is the responsibilityof the data source to ensure that data are shared in accordance torules and regulations set forth by their IRB. The responsibility ofSchizConnect is to confirm with each data source that they agree to thisresponsibility.


Results

The initial deployment of SchizConnect makes available data on1129 subjects from its contributing sources. Sample demographicscan be found in Table 1 as well as on the SchizConnect web portal(http://schizconnect.org/#subject-stats). Of these subjects, 1029 havescan data, which include structural MRI (sMRI), resting-state functionalMRI (fMRI), task-paradigm fMRI, anddiffusionMRI (dMRI) scans, totaling21,309 volumes (Tables 2 and 3). Demographic, neuropsychologicalmeasures, and clinical assessments are either available or in the processof becoming available. As the data sources take in new subjects/scans,they can automatically become available for querying throughSchizConnect, depending on the source data repository's policies. Atthe writing of this manuscript, SchizConnect has 49 users and 37 down-loads, and that statistic is steadily growing (http://schizconnect.org/#user-stats).

SchizConnect data sources

Current data sources include the following schizophrenia-relateddatasets, which are all publicly available themselves and have beenextensively curated, documented, and subjected to quality assurance.Detailed explanations of data can be found in the accompanying specialpapers describing the data source repositories, as well as the followingpapers: (Cetin et al., 2014; Glover et al., 2012; Gollub et al., 2013;Wang et al., 2013).

• FBIRN Phase II dataset @ UCI (http://fbirnbdr.nbirn.net:8080/BDR/)1

(Glover et al., 2012; Potkin and Ford, 2009), containing cross-sectional multisite data from 251 subjects, each with two visits. (Seepaper on FBIRN data in this special issue.) Data include sMRI andfMRI scans collected on a variety of 1.5 T and 3 T scanners, includingSternberg Item Recognition Paradigm (SIRP) and Auditory Oddballparadigms, breath-hold and sensorimotor tasks. The underlying data-base is HID (Keator et al., 2009; Ozyurt et al., 2010) in PostgreSQLusing the relational data model. Access is provided via Java databaseconnectivity technology (JDBC), a database-access API for the Javaprogramming language. Results from the query are returned to theMediator as an SQL ResultSet.

• NUSDAST @ XNAT Central (https://central.xnat.org/REST/projects/NUDataSharing) (Wang et al., 2013), containing data from 368subjects, the majority with longitudinal data (~2 years apart). (Seepaper on NU data in this special issue.) Data include sMRI scanscollected on a single Siemens 1.5 T Vision scanner. The underlyingdatabase is XNAT (Marcus et al., 2007), using the XML data model.Access is provided via XNAT REST API. The mediator constructs aquery XML document conforming to the XNAT search web servicespecification. Results from the query are returned to the Mediator asan XML document.

• COBRE & MCICShare @ COINS Data Exchange (http://coins.mrn.org/)(Bockholt et al., 2010; Scott et al., 2011; Wood et al., 2014), con-taining data from 198 and 212 subjects from COBRE (Cetin et al.,2014) and MCICShare (Gollub et al., 2013) projects, respectively.(See paper on COINS data in this special issue.) Data for COBREinclude sMRI and rest-state fMRI scans collected on a single 3 Tscanner. Data for the multisite MCICShare include sMRI, rest-statefMRI and dMRI scans, collected on 1.5 T and 3 T scanners. COINSdata required special handling in order to satisfy SchizConnect'sneeds because the native COINS architecture involves dynamicdata packaging following the query, which does not allow fordata to be immediately returned to the query engine.2 With


http://www.ogsadai.org.uk/

http://schizconnect.org/#subject-stats

http://schizconnect.org/#user-stats

http://schizconnect.org/#user-stats

http://fbirnbdr.nbirn.net:8080/BDR/



http://coins.mrn.org/




permission from the COINS executive committee, an API wasbuilt to enable SchizConnect to extract domain-model definedvariables (see below) from the COINS databases and to dupli-cate them at the Mediator site. The database underlying the du-plicated MCICShare/COBRE data is MySQL using the relationaldata model. Access is provided via JDBC to the MySQL server.Results from the query are returned to the Mediator as an SQLResultSet.

Subject

Min Age

Max Age

Sex

Diagnosis

Project

COINS MCICShare

COINS COBRE

fBIRN Phase II

NUSDAST

A B

Imaging Protocol

Structural

Perfusion

T1

T2

Functional Resting State

Field Mapping

Diffusion

Task Paradigm

MRI

CField

Strength

1.5 T3 T4 T

MakeSiemens

GEPhilips

ModelVisionTrio

Fig. 2. SchizConnectmediator (Virtual) domain Schema and Inter-schemaMappings. (A) Project, cfrom 3 data sources are included in the domain model. (B) Subject, containing demographic anddefined by “minimum age,” “maximum age,” “sex,” and “diagnosis.” Current diagnosis categorieswhich includes “schizophrenia strict” and “schizoaffective.”We note that the precise definition o“control” to label the healthy comparison subjects provided by each data source. Instead, the termscanner platforms and imaging protocols under which subject imaging data were collected. “Immapping.” Both “structural” and “functional” protocols are subdivided for further distinctions inclpsychological assessments. The domain model uses the MATRICS Consensus Cognitive Batteryfunction,” “learning,” “working memory,” “episodic memory,” “intelligence,” “language,” “mo(E) Clinical domain contains full demographics, assessments of symptoms and functional capacity,“SES (socioeconomic status),” and “Handedness.” Symptom measures include “PANSS (Positive“SANS (Scale for the Assessment of Negative Symptoms),” using the Unified Medical Language S“Mood,” “Suicide Ideation,”, and “Extrapyramidal Symptoms.” Medical information includes “M“Nicotine Addiction/Dependence.” (F) Examples of inter-schemamappings. For example, subjectwith a diagnostic codemapping table to harmonize thediagnoses; normalizeddiagnosis of Strict Sctables with subject and assessment data, then selecting the subjects according to a predefined alg


SchizConnect mediator

The SchizConnect mediator domain schema defines the followingconcepts: project, subject, imaging, cognitive, and clinical, described indetail below.

• Project contains the name and description of the 4 projects from3 datasources for which data are collected (Fig. 2A).

No Known Disorder

SchizoaffectiveSchizophrenia Strict

Mental Disorder

Psychotic Disorder

Bipolar Disorder

Schizophrenia Broad

Auditory OddballFinger TappingBreath Hold

Go NoGoMismatch Negativity

Sensory MotorSIRP

Working Memory

FLASHMPRAGE

ontaining description of the research project forwhich data are collected. Currently 4 projectsdiagnostic information for individual participants of the project. “Subject” is subsequentlyare: “no known disorder” (for healthy controls), “bipolar disorder,” “schizophrenia broad,”f “control”may depend on specific studies and study populations, we did not use the term“no known disorders” is used. (C) Imaging_Protocol (MRI), containing information on MRI

aging protocol” is subsequently defined by “structural,” “functional,” “perfusion,” and “fielduding “T1,” “T2,” “resting state,” and “task paradigm.” (D) Cognitive domain contains neuro-(Nuechterlein et al., 2008) terms whenever appropriate, which are “attention,” “executivetor,” “premorbid functioning,” “processing speed,” “social cognition,” and “visuospatial.”andmedical information. Demographic information includes “Race,” “Ethnicity,” “Education,”And Negative Symptoms Scales),” “SAPS (Scale for the Assessment of Positive Symptoms),”ystem (UMLS) terms whenever appropriate. Symptom measures also include “Depression,”edical History/Medication,” “SCID (Structured Clinical Interview for DSM Disorders),” anddata for the NUSDAST source is obtained by calling the XNAT search web service and joininghizophrenia for the subjects in theHID source is computedon thefly, byfirst joining threeHIDorithm on assessment values.



D

Cognitive

Learning & Memory

Attention

Social Cognition

Motor

Executive Function

Premorbid Functioning

IQEmotional IQ

Verbal LearningVisual Learning

Verbal Working Memory Visual Working MemoryVerbal Episodic Memory Visual Episodic Memory

Language

Processing Speed

Visuospatial

Intelligence

Clinical

E

F

Symptoms -Extrapyramidal

Demographics

Symptoms -Psychopathology

Functional Capacity

Medical

RaceEthnicityEducation

SESHandedness

PANSSSAPSSANS

DepressionMood

Suicide Ideation

Medical HistorySCID

Nicotine

Functional Capacity

Quality of Life

Fig. 2 (continued).


• Subject contains demographic and diagnostic information for individualparticipants, including “subject id”, “age”, “sex” and “diagnosis”(Fig. 2B). Current diagnosis categories are: “no known disorder” (for

Fig. 3. SchizConnectwebportal, query, and return. (A) SchizConnet.orgAt thewebportal, the statusitemaintenance orwhatever reason, since each data source is queried independent of the others,and therefore their lack of return. (B, C, D) Query and reviewwithout signing up. Even without sigquery of “T1-weighted 3 T scans of subjects between the ages 20 and 60.” The query is built fromadownloadable images from 345 distinct subjects from COBRE,MCICShare and FBIRN Phase II datasreturned from the query is now provided as an on-screen table. This table lists sortable variablesinformation to decide whether to further refine the query. The user has the ability to modify, savthe data use agreement page and then data downloading. In the casewheremultiple images are reMRI session, these images will be indicated with unique “Datauri's” (such as the first 4 entries seeDUAs to agreewith, and receive data only fromdata sourceswithwhich the DUAs have been agreestaged at SchizConnect.org host together with the mediated meta-data table for a specified limiteindividual segments, easily reconstructablewith the 7-zip utility (http://www.7-zip.org/). They conor NIFTI formats. Links to these files along with unpacking instructions are sent to the user via em


healthy controls), “bipolar disorder,” “schizophrenia broad,” whichincludes “schizophrenia strict” and “schizoaffective.”We note that theprecise definition of “control” may depend on specific studies and

s of the data sources is shown. If one ormore of the data sources are unavailable due to sourcedata sources that are operational can still be queried. The end user iswarned of the down sitesning up, any end user has the ability to query SchizConnect. Here an example is shown for adrag-and-drop user-friendly graphical user interface. This particular querywould return 1348ets. (E) Query and review after signing up. After signing in, detailed subject-level informationincluding provenance, age, sex, and scan parameters. The end user can review and use thise and retrieve queries. A data download button is also active at this point, which will lead toturned for the same subject, for example as the result ofmultiple acquisitionswithin the samen here) fields. (F, G, H) DUA and data download. The user has the option of choosing whichd. The imaging data resulting from the queries are first transferred out of the data sources andd time period for downloading. Imaging data files are compressed and packaged into 10 GBtain the original and/or preprocessed imaging data shared by each source, inDICOM,Analyze,ail and are available on the “MySchizConnect” page of the website.


http://SchizConnet.org


http://www.7-zip.org/



study populations, we did not use the term “control” to label thehealthy comparison subjects provided by each data source. Instead,the term “no known disorders” is used.

• Imaging_Protocol (MRI) contains information on MRI scannerplatforms and imaging protocols. “Imaging protocol” consists of“structural,” “functional,” “perfusion,” and “field mapping” categories.Both “structural” and “functional” protocols are subdivided for furtherdistinctions, e.g., “T1,” “T2,” “resting state,” and “task paradigm”(Fig. 2C).

Query

A

B


• Cognitive domain contains neuropsychological assessments. Thedomain model uses the MATRICS Consensus Cognitive Battery(Nuechterlein et al., 2008) terms whenever appropriate, which are“attention,” “executive function,” “learning,” “working memory,”“episodic memory,” “intelligence,” “language,” “motor,” “premorbidfunctioning,” “processing speed,” “social cognition,” and “visuospatial”(Fig. 2D).

• Clinical domain contains full demographics, assessments of symptomsand functional capacity, and medical information. Demographic



QueryC

DReview

Fig. 3 (continued).


information includes “Race,” “Ethnicity,” “Education,” “SES (socioeco-nomic status),” and “Handedness.” Symptom measures include“PANSS (Positive and Negative Symptoms Scales),” “SAPS (Scale forthe Assessment of Positive Symptoms),” “SANS (Scale for the Assess-ment of Negative Symptoms),”using theUnifiedMedical Language Sys-tem (UMLS) terms whenever appropriate. Symptom measures alsoinclude “Depression,” “Mood,” “Suicide Ideation,”, and “Extrapyramidal


Symptoms.” Medical information includes “Medical History/Medication,” “SCID (StructuredClinical Interview forDSMDisorders),”and “Nicotine Addiction/Dependence” (Fig. 2E).

The SchizConnect mediator relates the above domain modelconcepts to the terms in the source schemas using inter-schema



E

FDUAs

Review

Fig. 3 (continued).


mappings (see Fig. 2F for sample mappings). For example, subjectdata for the NUSDAST source is obtained by calling the XNAT searchweb service and joining with a diagnostic code mapping table to har-monize the diagnoses; normalized diagnosis of Strict Schizophreniafor the subjects in the HID source is computed on the fly, by first join-ing three HID tables with subject and assessment data, then selectingthe subjects according to a predefined algorithm on assessmentvalues.


Currently, the SchizConnect model comprises 7 domain predicates,17 source predicates (from the 4 sources HID, NUSDAST, COBRE andMCICShare, a local database that contains value mapping information,and utility functional sources), and 18 inter-schema mappings (10 forHID, 4 for NUSDAST, and 4 for COBRE/MCICShare). It is worth notingthat since the inter-schema mappings are specified in a declarativerule language, themediator is easily extensible to incorporate additionalsources by writing the appropriate inter-schema mappings.



Download

Download

G

H

Fig. 3 (continued).


SchizConnect.org web portal and example queries

At the http://www.schizconnect.org/ web portal, the operatingstatuses of the data sources are displayed (Fig. 3A). Queries areperformed only on data sources that are operational. Data queries areconstructed using a drag-and-drop GUI, in which a series of logical“AND” and “OR” operators can be concatenated filtering on domainmodel terms.


An example query is shown in Fig. 3B, C, with summary return infor-mation presented Fig. 3D. Additional sample queries are listed inTable 3. Upon signing in, detailed subject-level information returnedfrom the query is presented to the user as an on-screen table (Fig. 3E).This table lists sortable domain model variables including provenance,age, sex, and scan parameters for each subject with downloadableimages. The user can review and use this information to decidewhetherto further refine the query. The user also has the ability to modify, save


http://www.schizconnect.org/



and retrieve queries. A data download button is available that leads tothe DUA page (Fig. 3F). The user must sign the SchizConnect DUA, andhas the option of choosing which participating source DUAs to agreewith, and receives data only from data sources with which the DUAshave been agreed.

For downloading, the imaging data resulting from the queries aretransferred out of the data sources and staged at the SchizConnect.org host, together with the returnedmeta-data table of the mediatedvariables. Transfer is performed via gridFTP, REST API, HTTP, orothers, depending on the specific data transfer protocol at each source.Imaging data files are compressed and packaged into 10-GB easilyreconstructable, individual 7-zip (http://www.7-zip.org/) segments,available for the user to downloadwithin a specified limited time period(currently 2 weeks). Depending on the size of the requested imagingdata, it can take up to a few days for all data to be staged for download.These data files contain the original and/or preprocessed imaging datashared by each source, in DICOM, Analyze, or NIFTI formats. Links tothese files along with unpacking instructions are sent to the user viaemail. The links are also available on the “My SchizConnect” page ofthewebportal (Fig. 3G, H). Documentation for descriptions of image for-mats and directory structures can be found on the SchizConnectwebsite(http://www.schizconnect.org/documentation). All cognitive and clinicaldata (see model description above) can be included in the downloadpackage along with the download imaging data. On the documentationpage, the user can finduseful information including: Tutorials on how touse the website, DUAs of all data sources, data descriptions, data dictio-naries, and peer-reviewed journal papers related to the data. Since notall neuroimaging data (such as scan sequence parameters) have beenmodeled into the SchizConnect domain hierarchy, it is important to pro-vide technical information on our website to facilitate research. In thedata description section, the NUSDAST description lists structural pa-rameters (e.g., 3D MPRAGE: TR = 9.7 ms, TE = 4 ms, flip = 10°,ACQ = 1, 256 × 256 matrix, 1 × l mm in-plane resolution, 128 slices,slice thickness=1.25mm, 5:36min scan time each) and the COBRE de-scription lists resting-state parameters (resting state scans consisting of149 volumes of T2*-weighted functional images, acquired using agradient-echo EPI sequence: TR = 2 s, TE = 29 ms, flip = 75°, slicethickness= 3.5mm, slice gap=1.05mm, field of view=240mm,ma-trix size=64× 64, voxel size=3.75mm×3.75mm×4.55mm). In thePublications section, we have provided a link to a peer-reviewed journalpublication that provide detailed technical descriptions on each datasource. For example, Gollub et al. (2013) describes “theMCIC collection:a shared repository of multi-modal, multi-site brain image data from aclinical investigation of schizophrenia.” Similar papers describing allthe other data sources currently participating in SchizConnect can befound here.

Discussions and future plans

These initial results demonstrate that SchizConnect allows combiningof neuroimaging data from different databases via mediation to formcompatible mega-datasets with accuracy and fidelity. In SchizConnect,data remains with the source rather than warehoused in a central repos-itory. Providers maintain control of their data and do not need to modifythem for sharing. The user query is done over a single, consistent, well-defined model that is translated to the schemas of the sources. This ap-proach unburdens the user and each source from having to make contactand helping to interpret the data each time, especiallywhen newdata arebeing continuously added to the data source repository. Theweb portal isuser-friendly and intuitive, appearing to the user as a single, virtual data-base with real-time query and download performance. As an on-goingproject, we are continuing to define additional domain model terms forneuropsychological and psychopathological variables, make available ad-ditional imagingmodalities and subjects, and identify andevaluate poten-tial new data sources. Work is also under way integrating with clinicalresearch databases such as Research Electronic Data Capture (REDCap)


(Harris et al., 2009) where clinical and cognitive assessment data arestored, further enhancing the functionality of SchizConnect by searchingacross imaging and non-imaging databases for the same subject.

SchizConnect shows considerable potential for overcoming currentbarriers for creating large-scale datasets to increase statistical power,accelerating the testing of new hypotheses and methods, and creatinga resource for developing advanced techniques to better integrate dis-parate data at low cost. As a proof-of-concept for combining datasets,we compared SAPS/SANS and neurocognition across the current datasources. For SAPS/SANS, we found that while fewer schizophreniasubjects in the NU data scored 2 or higher, the distributions were notstatistically different between sites. For neurocognitive measures, wecomputed z-scores on episodic memory, working memory, attentionand executive function domains using each site's own controls as refer-ence sets. No statistical difference between sites was observed (NUrange: −1.23 to −0.71, FBIRN: −1.28 to −0.65), indicating thatschizophrenia subjects across both consortia exhibit similar degrees ofpsychopathology and cognitive impairment. Nonetheless, subtle differ-ences in cohort make-up may still exist upon closer inspection, butusers will have the opportunity to explore the sources of potentialheterogeneity and use such differences to benefit in developing newhypotheses.

SchizConnect was aimed at combining across different types of datarepositories, and its current data sources are examples of classic types ofdata repository: The HID that houses the FBIRN data is a multisite studyrepository with prospectively collected data using pre-determined pro-tocols; The COINS Data Exchange that houses theMCICShare and COBREdata, and the XNAT Central that houses the NUSDAST data, are central-ized data repositories where data from unrelated studies are uploadedand stored, and the database platforms provide the user with queryabledata structures and interfaces. When owners of these datasets are will-ing to share subject-level data (i.e., scans, assessments), SchizConnect isdesigned to interact with such data repositories for mediation. Wheninvestigators are only willing to share measures, meta-analysis effortssuch as the ENIGMA (Gupta et al., 2014; Thompson et al., 2014; vanErp et al., in press; Wright et al., 2015) will be an excellent alternativefor data sharing.

During the initial implementation of SchizConnect using the currentdata sources as test beds, we have established a series of domainmodelsand their translations to the initial data sourcemodels. TheSchizConnectmediation approach simply translates user queries into the querylanguage of each data source (i.e., equivalent to the user performingthe same query at the individual data source's own web portal). TheSchizConnect data models can be extended easily to accommodatenew data structures of a new data source while placing no restrictionsonwhat the data sourcewants to share. Although the process of domainunderstanding and data modeling can be labor intensive, we have laidthe foundation for extending SchizConnect to other schizophrenianeuroimaging data sources, which will build on the existing schemas.A limitation of the current domainmodelwork is that theunderstandingand modeling of source data remains a manual step. In SchizConnect,this data mediation process starts with domain understanding byexperts on the specific data that is being integrated so that we have aclear knowledge on the data terms and their definition. The data media-tion experts can then integrate this new knowledge into existingSchizConnect models to enable the data to be accessedwithout requiringit to bemodified.We have provided a questionnaire on the schizconnect.org web portal that can initiate the process, http://schizconnect.org/questionnaires/1/responses/new. It is important as a next step for us todevelop approaches that learn from existing data to facilitate more auto-mated procedures for inclusion of new data sources (e.g., following ourprevious work (Knoblock et al., 2012)). Another limitation is query eval-uation in the virtual integration approach, is generally slower that queryevaluation on a warehouse-model database.

Finally, SchizConnect is the only data finder (data broker) of itskind for researchers interested in combining neuroimaging data on




http://www.7-zip.org/

http://www.schizconnect.org/documentation

http://schizconnect.org

http://schizconnect.org

http://schizconnect.org/questionnaires/1/responses/new

http://schizconnect.org/questionnaires/1/responses/new



schizophrenia and related disorders from disparate sources. Existingdata portals such as the COINS, FBIRN, and XNAT Central, data sourcesused in this project, contain schizophrenia data but serve as repositoriesof these types of data. When users intent to combine data across thesesources, harmonization of data terms and mediation of these termsremain a critical challenge andbarrier. SchizConnect is fulfilling this crit-ical need, and the data mediation framework that SchizConnect createdcan be readily extended to other clinical domains including BipolarDisorder. Recent developments on data harmonization have led to thecreation of the Research Domain Criteria Database (RDoCdb) at theNational Institute of Mental Health (NIMH) and its associated datarepository, the National Database for Autism Research (NDAR) (Hallet al., 2012). It should be noted that SchizConnect is a virtual databasethat can mediate data sources that make ongoing updates to their re-positories, and SchizConnect is focused on schizophrenia and relateddisorders. Administratively, the SchizConnect web portal is hosted atNorthwestern University, the SchizConnect mediator is hosted at theInformation Science Institute of the University of Southern California.For inclusion into SchizConnect, new data sources will be evaluatedon completeness, quality, and enthusiasm to collaborate. The focus ofthe initial expansion will be the quickest return for the research com-munity, so that larger, richer datasets that have been carefully collectedand checked for completeness and quality (e.g., already analyzed andpublished) would be of the highest priority. Data repositories basedon XNAT, or those are already in COINS can be more readily integrated,up to any new data models and new querying news. For valuabledatasets without a database platform, we can help develop an XNAT,HID, or COINS.

It is our hope that SchizConnect can facilitate the testing of new hy-potheses that are hitherto not possible, thus greatly promote discoveryrelated to themechanisms underlying schizophrenia.We aim to expandto repositories on psychosis and related disorders, thus facilitatinglarge-scale dimensional research. We hope that SchizConnect willbecome the prototype for the study of psychiatric disorders, serving asamodel for broader efforts for the integration and sharing of biomedicalinformation across the greater scientific community.

Acknowledgment

This work was supported in part by NIH grants 1U01 MH097435,1R01 MH084803, P50 MH071616, R01 MH056584, U24 RR025736-01,U24-RR021992, U24GM10420, P20 GM103472.

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