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Publication: Traumatic Brain Injury and Personalized Outcome Prediction

posted Sep 20, 2012, 1:53 PM by Stephen Aylward   [ updated Sep 20, 2012, 1:59 PM ]
We are please to have collaborated with UCLA, Univ of Utah, and others on the following publication.  It mentions some of our research with Marc Niethammer at UNC on Geometric Metamorphosis for longitudinal assessment of lesions.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction
Andrei Irimia, Bo Wang, Stephen R. Aylward, Marcel W. Prastawa, Danielle F. Pace, Guido Gerig, David A. Hovda, Ron Kikinis, Paul M. Vespa, John D. Van Horn
NeuroImage: Clinical,  Vol 1, Issue 1, 2012

Abstract

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

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