Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy

Mapping Connectivity Damage in the Case of Phineas Gage

White matter (WM) mapping of the human brain using neuroimaging techniques has gained considerable interest in the neuroscience community. Using diffusion weighted (DWI) and magnetic resonance imaging (MRI), WM fiber pathways between brain regions may be systematically assessed to make inferences concerning their role in normal brain function, influence on behavior, as well as concerning the consequences of network-level brain damage. In this paper, we investigate the detailed connectomics in a noted example of severe traumatic brain injury (TBI) which has proved important to and controversial in the history of neuroscience. We model the WM damage in the notable case of Phineas P. Gage, in whom a “tamping iron” was accidentally shot through his skull and brain, resulting in profound behavioral changes. The specific effects of this injury on Mr. Gage's WM connectivity have not previously been considered in detail. Using computed tomography (CT) image data of the Gage skull in conjunction with modern anatomical MRI and diffusion imaging data obtained in contemporary right handed male subjects (aged 25–36), we computationally simulate the passage of the iron through the skull on the basis of reported and observed skull fiducial landmarks and assess the extent of cortical gray matter (GM) and WM damage. Specifically, we find that while considerable damage was, indeed, localized to the left frontal cortex, the impact on measures of network connectedness between directly affected and other brain areas was profound, widespread, and a probable contributor to both the reported acute as well as long-term behavioral changes. Yet, while significantly affecting several likely network hubs, damage to Mr. Gage's WM network may not have been more severe than expected from that of a similarly sized “average” brain lesion. These results provide new insight into the remarkable brain injury experienced by this noteworthy patient

ANALYZING IMAGING BIOMARKERS FOR TRAUMATIC BRAIN INJURY USING 4D MODELING OF LONGITUDINAL MRI

Quantitative imaging biomarkers are important for assessment of impact, recovery and treatment efficacy in patients with traumatic brain injury (TBI). To our knowledge, the identification of such biomarkers characterizing disease progress and recovery has been insufficiently explored in TBI due to difficulties in registration of baseline and followup data and automatic segmentation of tissue and lesions from multimodal, longitudinal MR image data. We propose a new methodology for computing imaging biomarkers in TBI by extending a recently proposed spatiotemporal 4D modeling approach in order to compute quantitative features of tissue change. The proposed method computes surface-based and voxel-based measurements such as cortical thickness, volume changes, and geometric deformation. We analyze the potential for clinical use of these biomarkers by correlating them with TBI-specific patient scores at the level of the whole brain and of individual regions. Our preliminary results indicate that the proposed voxel-based biomarkers are correlated with clinical outcomes. Index Terms — Imaging biomarkers, longitudinal MRI, correlation analysis, clinical outcome

The distribution characteristics of affected white matter pathways.

<p>WM fiber pathways intersected by the rod were pooled across all N = 110 subjects and examined for a) the relative lengths (w_ij) of affected pathways and b) the relative percentages of lost fiber density (g_ij); c) the bivariate distribution of g_ij versus w_ij indicating that local fiber pathways were affected, <i>e.g.</i> relatively short pathways proximal to the injury site, as well as damaging dense, longer-range fiber pathways, <i>e.g.</i> innervating regions some distance from the tamping iron injury (see “<i>Calculation of Pathology Effects upon GM/WM Volumetrics</i>” for further details).</p

Mean connectivity affected by the presence of the tamping iron combined across subjects.

<p>The lines in this connectogram graphic represent the connections between brain regions that were lost or damaged by the passage of the tamping iron. Fiber pathway damage extended beyond the left frontal cortex to regions of the left temporal, partial, and occipital cortices as well as to basal ganglia, brain stem, and cerebellum. Inter-hemispheric connections of the frontal and limbic lobes as well as basal ganglia were also affected. Connections in grayscale indicate those pathways that were completely lost in the presence of the tamping iron, while those in shades of tan indicate those partially severed. Pathway transparency indicates the relative density of the affected pathway. In contrast to the morphometric measurements depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037454#pone-0037454-g002" target="_blank">Fig. 2</a>, the inner four rings of the connectogram here indicate (from the outside inward) the regional network metrics of betweenness centrality, regional eccentricity, local efficiency, clustering coefficient, and the percent of GM loss, respectively, in the presence of the tamping iron, in each instance averaged over the N = 110 subjects.</p

Estimates of the Damage to Gage's Brain.

<p>Estimates of the Damage to Gage's Brain.</p

Gage Skull Measurements.

<p>Gage Skull Measurements.</p

The circular representation of cortical anatomy and WM connectivity from N = 110 normal right-handed males (age 25–36).

<p>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 further ordered anterior-to-posterior based upon the centers-of-mass of these regions in the published Destrieux atlas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037454#pone.0037454-Destrieux1" target="_blank">[72]</a> (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037454#pone-0037454-t006" target="_blank">Table 6</a> for complete region names, abbreviations, and FreeSurfer IDs, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037454#pone-0037454-t007" target="_blank">Table 7</a> for the abbreviation construction scheme). The left half of the connectogram figure represents the left-hemisphere of the brain, whereas the right half represents the right hemisphere with the exception of the brain stem, which occurs at the bottom, 6 o'clock position of the graph. The lobar abbreviation scheme is given in the text. The color map of each region is lobe-specific and maps to the color of each regional parcellation as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037454#pone.0037454.s002" target="_blank">Fig. S2</a>. The set of five rings (from the outside inward) reflect average i) regional volume, ii) cortical thickness, iii) surface area, and iv) cortical curvature of each parcellated cortical region. For non-cortical regions, only average regional volume is shown. Finally, the inner-most ring displays the relative degree of connectivity of that region with respect to WM fibers found to emanate from this region, providing a measure of how connected that region is with all other regions in the parcellation scheme. The links represent the computed degrees of connectivity between segmented brain regions. Links shaded in blue represent DTI tractography pathways in the lower third of the distribution of fractional anisotropy, green lines the middle third, and red lines the top third. Circular “color bars” at the bottom of the figure describe the numeric scale for each regional geometric measurement and its associated color on that anatomical metric ring of the connectogram.</p