This paper presents a novel methodology to use DTI (Diffusion Tensor Imaging) in the detection and quantification of traumatic brain injury (TBI) and brain pathways (tracts) affected by the injury. TBI is a growing health problem in the US, commonly attributed to motor vehicular accidents and sports injuries but recently also to war-related injuries sustained near an explosion. One of the serious consequences of TBI is diffuse axonal injury (DAI), or white matter injuries, induced by sudden acceleration/deceleration and/or rotational/vibrational forces causing a shearing of nerve fibers [1
]. In addition to diffuse injury, local shearing of axons at the gray/white interface is also possible. Diffuse and local axonal shearing both disrupt axonal connections critical to brain function, and DAI commonly refers to both types of injury. DAI has been identified as one of the key reasons for permanent disability or death. In general, DAI can be very debilitating, leading to a wide range of neurological impairments from mild memory deficits to persistent vegetative states. Though CT and MRI are routinely employed to evaluate trauma and DAI, several reports suggest that CT and commonly used T1- and T2-weighted MRI protocols are unable to detect the full extent of injury and likely to underestimate the consequences of DAI, resulting in a poor correlation between diagnosis and final outcome [5
]. Typically these patients exhibit a high rate of morbidity without any evidence of lesions on CT or MRI. It has even been suggested that “DAI can only be definitely diagnosed postmortem” [7
Histologically DAI is characterized by disruption of the cytoskeletal network and axonal membranes, leading to impaired axonal transport [3
]. Thus a thorough evaluation of DAI requires an imaging method able to quantify the integrity of axons and the network of connections required to support normal brain function. For example, a person may look fully recovered months after surviving a nearby explosion, but cognitive function may be abnormal due to loss of connectivity among key brain regions. Some of the sites frequently damaged in TBI include the corpus callosum, fornix and cingulum [8
]. Because of these locations, memory and language deficits are common as are certain prefrontal syndromes such as dysexecutive, disinhibited and apathetic behavior. Often these deficits are present without any gross lesions on the CT scan and the etiology is attributed to DAI. Moreover, approaches to identify DAI through antibodies targeting amyloid precursor protein (APP) [9
] may not always succeed because some axons demonstrate cytoskeletal alteration and detachment without axonal swelling and are thus not identifiable by markers of axonal swelling such as APP [10
DTI has been used over the past several years to detect injured regions in TBI (e.g., [3
]). DTI is sensitive to the biological diffusion of water molecules, hindered by extracellular and restricted by intracellular components [15
]. When there is no obstruction to diffusion, the diffusion tensor is isotropic. However, in the presence of axons and their myelinated sheath, diffusion becomes anisotropic and quantifiable, which reveals the direction and integrity of axons. Though myelin is not necessary, it influences several diffusion anisotropy metrics. Thus damage to the myelin sheath and/or axons is potentially detectable by DTI. Maps of several diffusion anisotropy metrics, e.g., Fractional Anisotropy (FA) -- the normalized differences among the three eigen values of the tensor; Mean Diffusivity (MD) -- the average of the three eigen values; Axial Diffusivity (DA) -- the first eigen value oriented along the long axis of the axons; Radial Diffusivity (DR) -- the average of second and third eigen values; Volume Ratio (VR) -- the normalized product of the three eigen values, and the ratio DA/DR have been evaluated in human and animal DTI studies [16
The exact model of axonal damage leading to changes in the diffusivity and resulting diffusion anisotropy measures such as FA is not well-understood. There is converging opinion that DAI represents a progressive injury, beginning with local swelling of axons, followed by cytoskeletal perturbations including misalignment of fibers and eventual disconnection [3
]. It has been hypothesized [3
] that the consequences of TBI on DTI would be a decrease in FA and an increase in MD, attributed mainly to an increase in DR and a decrease in diffusivity along the principal direction (i.e., a reduction in DA). Indeed, significant FA reductions but less significant changes in MD have been reported in the internal capsule and corpus callosum during the first 24 hours of injury [3
]. Changes in FA, however, were significantly less at one month, suggesting the dynamic nature of the injury. A 20-subject study of changes in the FA maps in DAI [4
] with significant correlation with the Glasgow Coma Scale or GCS (r = 0.65−0.74, p < 0.001) also concluded that FA values were significantly reduced within the internal capsule and splenium. MD was not analyzed in that study but no significant changes were found in the Apparent Diffusion Coefficient (ADC), which is a less sensitive marker of diffusivity. A previous study also reported an increase in MD but unchanged FA [23
], and another study found reduced FA in the corpus callosum, internal capsule, and centrum semiovale, with significant increase in MD in the corpus callosum and internal capsule [12
]. Similar results have also been reported very recently [24
] for the fornix body, all sub-regions of the corpus callosum and peduncular projections, with the additional observation that not only did DTI detect loss of white matter integrity at the beginning of the injury process but the DTI metrics also correlate strongly to functional outcome.
A recent study also compared mild TBI to moderate-to-severe TBI and found that the FA in moderate-to-severe TBI was reduced in the posterior corona radiata, cortico-spinal tracts, cingulum, external capsule, forceps minor and major, genu, body and splenium of the corpus callosum, inferior fronto-occipital fasciculus, superior longitudinal fasciculus and sagittal stratum. FA reduction in mild TBI, however, was detected only in the cortico-spinal tract, sagittal stratum and the superior longitudinal fasciculus [25
]. This study also examined DR and DA and found that both DR and DA were increased in moderate-to-severe TBI, which could explain the decrease in FA. However only DA was found to increase in some regions in mild TBI while DR did not show any significant increase in any region. It is not clear how the FA would decrease under these circumstances as FA is likely to increase if DA increases and DR remains unchanged. This specific issue was not addressed by the authors, but the authors suggest that their results are consistent with the notion that damage to myelin in mild TBI is less common, whereas both the axons and myelin are likely to be damaged in moderate to severe injury.
Contrary to the above studies, an increase in mean diffusivity and an increase in FA in an infant with severe TBI has also been reported [26
], though the authors suggest that the increase in FA is probably a transient effect due to an orderly disruption of cellular membrane in combination with cellular and vasogenic edema that could temporarily increase both the FA and diffusivity. Also, an increase in FA and a decrease in MD have been reported very recently [14
] in a 6-subject study of acute mild TBI. A significant increase in FA in the posterior corpus callosum and significant decrease in MD in the left anterior internal capsule within 72 hours of injury was observed and though these results were highly correlated with post-concussive symptoms (PCS) and neurobehavioral tests, the authors [14
] acknowledged that their results were not consistent with previous studies of mild TBI [3
]. The explanation offered by the authors was that as most DTI measurements observe diffusion in water contained in the space between axons (intercellular space) rather than the axons themselves (intracellular space), the swelling of axons in the acute injury phase would constrict the intercellular space, leading to a decrease in MD and an increase in FA. This model is plausible but awaits validation with a larger number of patients. Other possible explanations include coregistration/normalization errors and the presence of multiple fibers within a voxel. If more than one fiber were present in a voxel, and one of them was selectively damaged, the diffusion anisotropy around the remaining intact fibers would be enhanced, leading to a higher FA for that voxel. Also a recent longitudinal study of severe TBI found that though the FA was reduced in all investigated regions, mainly due to a decrease in DA and an increase in DR, the FA had increased in the internal capsule and the centrum semiovale at a mean 12-month follow-up [27
]. The increase in FA, which now reached normal to above normal values, primarily in patients with favorable outcome, was attributed to an increase in DA with no change in DR. The FA remained depressed in patients with unfavorable outcome.
Though the model for observed FA changes in TBI is not well-established, it is apparent that maps of anisotropy changes can be, and have been, used to detect injured regions. However these maps alone are inadequate to isolate specific brain pathways disrupted by the injury or to quantify the loss of brain connectivity along affected pathways. DTI-tractography, which creates 3D maps of axonal connections, provides a mechanism to localize and quantify pathways affected by the injury.
Though there are many computational schemes to conduct tractography, the commonly used streamline tractography procedure tracks the direction of the first eigen value of an assumed rank-2 diffusion tensor per voxel until either the FA falls below the threshold or the orientation shows an abrupt angular change exceeding a specified threshold [28
]. A 3D interpolation of the tensor matrix is usually employed to display tracts with sub-voxel resolution. Although human DTI resolution limitations (approximately 2mm) limit tractography to mapping bundles of tightly packed axons (tracts), this is not expected to be a major weakness since DAI suffered in head trauma is likely to disrupt tracts and not merely single axons. This capability to directly visualize axonal connections and quantify connectivity among specified regions is a particular strength of DTI tractography not achievable with any other imaging modality at the present time. When combined with methods to detect voxel-based anisotropy changes, DTI including tractography provides a unique capability to localize and quantify injured regions and brain pathways affected by the injury.
Anisotropy changes are detected by first normalizing the maps of anisotropy metrics (scalars) from all subjects (subject space) to a common space (normalized space) and then performing a t-test or similar statistical analysis. There are numerous publications on voxel-based whole-brain anisotropy comparisons, particularly FA in TBI [8
]. Statistical comparisons of whole-brain tractography, however, are more complex as normalization of tensors or tracts requires additional eigen vector corrections. A previous approach to normalize tractography relied on averaging tensors in normalized space [34
] but this does not fully account for inherent primary eigen vector variations in individuals. The commonly used current approaches to normalize whole-brain tractography incorporate a rotational correction factor that realigns the eigen vectors (or gradients before estimating the eigen vectors) after mapping all images into a standard or normalized space, consistent with the non-linear mapping transformations for every voxel [35
]. However there are several inherent problems with tractography when it is conducted after spatial transformations, namely: a) spatial mapping exacerbates partial volume problems due to the required averaging of neighboring voxels during spatial interpolation, b) it may not maintain tract topography when non-linear normalization is used, and c) frequently known continuous tracts are divided into non-contiguous segments. To avoid performing whole-brain tractography in a common space, an alternative approach is to first identify ROIs in normalized space by, for example a FA comparison [8
], and based on the anatomical locations of these ROIs, or based on a priori hypotheses [24
], subjectively draw these ROIs in each subject's head-space. These ROIs are then used to sort tracts from whole-brain tractography conducted in each subject's native space. However, manually outlining ROIs is subjective and tracts sorted by such ROIs are prone to relatively large errors as even small errors in the location of seed points propagate and accumulate along tracts spanning a large number of voxels.
In an attempt to improve tract normalization and quantification methodology for application to TBI, the primary objectives of this work were to identify injured regions in a TBI patient, quantify the injury in terms of DTI anisotropy metrics, identify brain pathways (tracts) affected by the injury and quantify the effect on impacted pathways. A preliminary study where affected pathways in four TBI subjects were identified in a standard normalized space was reported by us recently at a MRI conference [38
]. However, as neurosurgical or other individualized interventions rely on the anatomy of the patient's own brain, one of the key goals was to quantify injury related tractography changes in the patient's own 3D head space, which requires mapping of whole-brain tractographies of a control group to the 3D brain coordinates of an individual patient. In addition, a prerequisite was not to require any a priori hypotheses or manual outlining of ROIs to reduce errors of subjectivity. A secondary objective was to conduct a similar study comparing a group of TBI patients to a group of control subjects in a common 3D space to detect and quantify patterns of injury and affected pathways in TBI patients, also without the need for any a priori hypotheses or manually drawn ROIs. Both objectives require novel tractography normalization and quantification methods. Details of the methodology and results from a small sample of TBI patients (n=12) and normal subjects (n=10) are presented in this paper.