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Review
. 2012 Aug 24;1(1):1-17.
doi: 10.1016/j.nicl.2012.08.002. eCollection 2012.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Andrei Irimia  1 , 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
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Free PMC article
Review

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Andrei Irimia et al. Neuroimage Clin. .
Free PMC article
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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.

Keywords: 3D, three-dimensional; AAL, Automatic Anatomical Labeling; ADC, apparent diffusion coefficient; ANTS, Advanced Normalization ToolS; BOLD, blood oxygen level dependent; CC, corpus callosum; CT, computed tomography; DAI, diffuse axonal injury; DSI, diffusion spectrum imaging; DTI, diffusion tensor imaging; DWI, diffusion weighted imaging; Diffusion tensor; FA, fractional anisotropy; FLAIR, Fluid Attenuated Inversion Recovery; FSE, Functional Status Examination; GCS, Glasgow Coma Score; GM, gray matter; GOS, Glasgow Outcome Score; GRE, Gradient Recalled Echo; HARDI, high-angular-resolution diffusion imaging; IBA, Individual Brain Atlas; LDA, linear discriminant analysis; MRI, magnetic resonance imaging; MRI/fMRI; NINDS, National Institute of Neurological Disorders and Stroke; Neuroimaging; Outcome measures; PCA, principal component analysis; PROMO, PROspective MOtion Correction; SPM, Statistical Parametric Mapping; SWI, Susceptibility Weighted Imaging; TBI, traumatic brain injury; TBSS, tract-based spatial statistics; Trauma; WM, white matter; fMRI, functional magnetic resonance imaging.

Figures

Fig. 1
Axial views of acute and chronic TBI in a sample subject.
Fig. 2
Overview of semi-automatic segmentation using personalized atlas construction.
Fig. 3
Construction of a personalized spatiotemporal atlas using diffeomorphic and non-diffeomorphic components. The diffeomorphic component is the temporally global atlas P¯ that is mapped to each time point while preserving atlas topology. The non-diffeomorphic components are the temporally local probability density functions Qt at each time point t that may change the topology between different time points. Regions that change diffeomorphically are colored in green, while regions that change topology are colored in red.
Fig. 4
Segmentation of lesions in both acute and chronic images and visualization of the deformation field via the Jacobian determinant. (a) 3D lesion segmentation of acute images, blue color indicates edema, brown color indicates bleeding, and the transparent color indicates white matter. (b) 3D lesion segmentation of chronic images, purple color indicates necrosis, and the transparent color indicates white matter. (c) Visualization of the deformation field via the determinant of the Jacobian, red color indicates tissue compression, green color indicates no change, blue color indicates tissue expansion. (d) Axial view of lesion segmentation of acute images. (e) Axial view of lesion segmentation of chronic images. (f) Axial view of visualization of the deformation field via the determinant of the Jacobian matrix.
Fig. 5
Comparison of acute (left) and chronic (right) MRI scans. Large ovals indicate the TBI lesion. Small ovals in bottom row indicate a brain structure that is deformed as the lesion heals. The blue region in the lower left image indicates tissue which has recovered during the healing process. Geometric metamorphosis automatically detects those regions in which tissue conversion (e.g., from lesion to healthy) has occurred.
Fig. 6
Connectograms for three sample TBI subjects. For complete details on how to interpret the connectogram, the reader is referred to Irimia et al. (2012b). The outermost ring shows the various brain regions arranged by lobe (fr—frontal; ins—insula; lim—limbic; tem—temporal; par—parietal; occ—occipital; nc—non-cortical; bs—brain stem; CeB—cerebellum) and ordered anterior-to-posterior. The color map of each region is lobe- and ROI-specific. The set of five rings (from the outside inward) reflects volumetric and morphometric measures. For non-cortical regions, only average regional volume is shown. Links represent the computed degrees of connectivity between segmented brain regions. In the top row, links represent connections that have been affected by primary TBI. In the bottom row, links represent connections that have suffered an appreciable degree of atrophy six months after injury.
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