Terminology development towards harmonizing multiple clinical neuroimaging research repositories

Area of Science:

• Neuroscience
• Data Science
• Psychiatry

Background:

• Sharing neuroimaging data across repositories is complex due to varied terminology.
• Schizophrenia research requires integrated neuroimaging, clinical, and cognitive data.

Purpose of the Study:

• To develop a standardized terminology model for querying decentralized neuroimaging data.
• To facilitate data sharing and automated analysis in schizophrenia studies.
• To improve user experience and enable future computational reasoning.

Main Methods:

• Developed domain-specific models for neuroimaging, clinical, and cognitive data.
• Identified common, agreed-upon terms across repositories.
• Linked identified terms to standard ontological terms where feasible.

Main Results:

• Existing terminologies were found to be insufficient for cross-repository querying.
• A model was created to address the need for common terms in clinical neuroimaging data sharing.
• The project facilitated querying across decentralized schizophrenia data repositories.

Conclusions:

• Standardized terminology is essential for effective data sharing and analysis in multi-repository neuroimaging studies.
• The developed model provides a framework for harmonizing data across clinical neuroimaging repositories.
• This work supports advanced computational analysis and reasoning on schizophrenia-related data.