TY - JOUR
T1 - Mining the Mind Research Network
T2 - A novel framework for exploring large scale, heterogeneous translational neuroscience research data sources
AU - Bockholt, Henry J.
AU - Scully, Mark
AU - Courtney, William
AU - Rachakonda, Srinivas
AU - Scott, Adam
AU - Caprihan, Arvind
AU - Fries, Jill
AU - Kalyanam, Ravi
AU - Segall, Judith M.
AU - de la Garza, Raul
AU - Lane, Susan
AU - Calhoun, Vince D.
PY - 2010/4/21
Y1 - 2010/4/21
N2 - 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 diffi cult. 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 effi cient 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 effi cient, 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.
AB - 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 diffi cult. 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 effi cient 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 effi cient, 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.
KW - Data mining
KW - Magnetic resonance imaging
KW - Mind clinical imaging consortium
KW - XCEDE
KW - XML
UR - http://www.scopus.com/inward/record.url?scp=79957625012&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79957625012&partnerID=8YFLogxK
U2 - 10.3389/neuro.11.036.2009
DO - 10.3389/neuro.11.036.2009
M3 - Article
C2 - 20461147
AN - SCOPUS:79957625012
SN - 1662-5196
VL - 3
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
IS - APR
M1 - 36
ER -