Abstract
A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.
Highlights
Modern science is marked by an accumulation of massive amounts of data and neuroscience is no exception
The different neuroimaging modalities, such as diffusion tensor imaging (DTI), functional magnetic resonance imaging, structural MRI, electroencephalography (EEG), positron emission tomography, or magnetoencephalography, each produce a huge amount of data that when combined with genetic information, psychological assessment results, and socio-demographics makes it impossible for researchers to draw conclusions without sophisticated storage, recall, and inference methods
The MRN Clinical Imaging Consortium (MCIC) project is composed of sources from more than 400 human research volunteers that have had comprehensive baseline and longitudinal neuroimaging, genetic, clinical, socio-demographic, and neuropsychological assessments
Summary
Modern science is marked by an accumulation of massive amounts of data and neuroscience is no exception. The different neuroimaging modalities, such as diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), structural MRI (sMRI), electroencephalography (EEG), positron emission tomography, or magnetoencephalography, each produce a huge amount of data that when combined with genetic information, psychological assessment results, and socio-demographics makes it impossible for researchers to draw conclusions without sophisticated storage, recall, and inference methods. Neuroinformatics (NI) aims to solve these problems and increase the effectiveness of researchers through intelligent use of data storage, data analysis, and data presentations. NI makes storage and retrieval of data easy and transparent to researchers, and assists them by supplying only the data that is relevant to their needs (Toga, 2002). Combining these services with data repositories enables easy sharing and reduces the difficulties of scanning enough subjects to draw meaningful conclusions
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