Database integration of protocol-specific neurological imaging datasets

For many years now, Magnetic Resonance Innovations (MR Innovations), a magnetic resonance imaging (MRI) software development, technology, and research company, has been aggregating a multitude of MRI data from different scanning sites through its collaborations and research contracts. The majority of the data has adhered to neuroimaging protocols developed by our group which has helped ensure its quality and consistency. The protocols involved include the study of: traumatic brain injury, extracranial venous imaging for multiple sclerosis and Parkinson's disease, and stroke. The database has proven invaluable in helping to establish disease biomarkers, validate findings across multiple data sets, develop and refine signal processing algorithms, and establish both public and private research collaborations. Myriad Masters and PhD dissertations have been possible thanks to the availability of this database. As an example of a project that cuts across diseases, we have used the data and specialized software to develop new guidelines for detecting cerebral microbleeds. Ultimately, the database has been vital in our ability to provide tools and information for researchers and radiologists in diagnosing their patients, and we encourage collaborations and welcome sharing of similar data in this database.

[1]  Satrajit S. Ghosh,et al.  Data sharing in neuroimaging research , 2012, Front. Neuroinform..

[2]  F. Salvi,et al.  Chronic cerebrospinal venous insufficiency in patients with multiple sclerosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[3]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..

[4]  W. Koroshetz,et al.  Toward an international initiative for traumatic brain injury research. , 2013, Journal of neurotrauma.

[5]  M. Dake,et al.  Chronic cerebrospinal venous insufficiency in multiple sclerosis: a historical perspective. , 2011, Functional neurology.

[6]  David Hubbard,et al.  Patients with multiple sclerosis with structural venous abnormalities on MR imaging exhibit an abnormal flow distribution of the internal jugular veins. , 2012, Journal of vascular and interventional radiology : JVIR.

[7]  E. Mark Haacke,et al.  Characteristics of flow through the internal jugular veins at cervical C2/C3 and C5/C6 levels for multiple sclerosis patients using MR phase contrast imaging , 2012, Neurological research.

[8]  E. Haacke,et al.  Patterns of chronic venous insufficiency in the dural sinuses and extracranial draining veins and their relationship with white matter hyperintensities for patients with Parkinson's disease. , 2015, Journal of vascular surgery.

[9]  E. Haacke,et al.  The role of venous abnormalities in neurological disease. , 2012, Reviews on recent clinical trials.

[10]  E. Haacke,et al.  A comparative study of magnetic resonance venography techniques for the evaluation of the internal jugular veins in multiple sclerosis patients. , 2013, Magnetic resonance imaging.

[11]  E. Haacke,et al.  Magnetic resonance imaging signatures of vascular pathology in multiple sclerosis , 2012, Neurological research.

[12]  E. Haacke,et al.  Quantitative flow measurements in the internal jugular veins of multiple sclerosis patients using magnetic resonance imaging. , 2012, Reviews on recent clinical trials.

[13]  Naftali Raz,et al.  Jugular Venous Flow Abnormalities in Multiple Sclerosis Patients Compared to Normal Controls , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[14]  E. Haacke.  Chronic cerebral spinal venous insufficiency in multiple sclerosis , 2011, Expert review of neurotherapeutics.

[15]  David Hubbard,et al.  Using magnetic resonance imaging as a means to study chronic cerebral spinal venous insufficiency in multiple sclerosis patients. , 2012, Techniques in vascular and interventional radiology.

[16]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[17]  M. Wintermark,et al.  Common data elements in radiologic imaging of traumatic brain injury. , 2010, Archives of physical medicine and rehabilitation.