INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations

Abstract

Background: Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses.

Aims: To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.

Methods: INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.

Results: Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.

Conclusions: Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.

Keywords: cloud computing; computer-aided diagnosis system; computing system; data analysis system; data sharing; neuroimaging.

Figure 1

The overall architecture of INCloud. INCloud, Integrated Neuroimaging Cloud; MRI, magnetic resonance imaging.

Figure 2

The web interface of the neuroimaging computing platform, showing functional modules available to users.

Figure 3

The query interface of the brain feature library (A) and the query results (B).

Figure 4

Examples of research that can be carried out based on INCloud. (A) Cross-disease investigation of a certain brain region. (B) Covariation analysis of brain regions across different diseases (a), effect size analysis (b), multivariate pattern analyses (c) and disease similarity analyses (d). (C) Transdiagnostic analysis of dimensions of brain deficits. (D) Development of computer-aided diagnosis system. INCloud, Integrated Neuroimaging Cloud; MR, magnetic resonance.