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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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SchizConnect: Virtual Data Integration in Neuroimaging.

Jose Luis Ambite1, Marcelo Tallis2, Kathryn Alpert3

  • 1University of Southern California, Los Angeles, California, USA { ambite@isi.edu , tallis@isi.edu , konstant@isi.edu }

Data Integration in the Life Sciences : ... International Workshop, DILS ... : Proceedings. DILS (Conference)
|December 22, 2015
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Summary
This summary is machine-generated.

This study introduces a virtual data integration architecture for neuroimaging research, enabling scalable cohort expansion by harmonizing data from distributed sources without centralizing it. This approach facilitates larger sample sizes for schizophrenia studies.

Keywords:
Data integrationMediationNeuroimagingSchema Mappings

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Area of Science:

  • Neuroimaging
  • Data Science
  • Psychiatric Research

Background:

  • Increasingly large cohorts are required for statistical power in neuroimaging studies.
  • The economics of imaging studies limit the sample sizes achievable by single studies or consortia.
  • A scalable architecture is needed to integrate data from multiple, geographically distributed studies.

Purpose of the Study:

  • To present a virtual data integration architecture for neuroimaging data.
  • To enable the incorporation of additional studies as they become available.
  • To facilitate the creation of larger cohorts for psychiatric research, specifically schizophrenia.

Main Methods:

  • Developed a virtual data integration approach where data remains at its original source.
  • Implemented a system (SchizConnect) that retrieves and harmonizes data in response to user queries.
  • Integrated data from three schizophrenia neuroimaging consortia: FBIRN (HID), MRN (COINS), and NUSDAST (XNAT Central).

Main Results:

  • Demonstrated a functional architecture for distributed neuroimaging data integration.
  • Successfully harmonized data from multiple, disparate neuroimaging consortia.
  • Deployed a public portal (schizconnect.org) offering harmonized access to integrated schizophrenia neuroimaging data.

Conclusions:

  • Virtual data integration offers a scalable solution for building large neuroimaging cohorts.
  • This architecture overcomes the limitations of centralized data warehousing for multi-site studies.
  • SchizConnect provides a valuable resource for schizophrenia research by enabling access to integrated, harmonized neuroimaging data.