2.2. Pathology identification

To date, the number of studies that use neuroimaging volumetrics and morphometrics to identify outcome markers has been disappointingly low, in part because manual segmentation of TBI volumes is laborious and resource-demanding when large sample sizes are involved. Generally, computational methods for volumetric and morphometric analysis (such as FreeSurfer; Dale et al., 1999; Desikan et al., 2006; Destrieux et al., 2010; Fischl et al., 1999a) are preferable to manual ones because of the reduced cost of the former and of the smaller amount of time that is required for their application. Many automatic methods can achieve an accuracy level that is comparable to that of manual methods, although this is most often the case for healthy populations or for disease groups whose anatomies do not differ appreciably from health. In the case of TBI, automated methods can fail, and the development and dissemination of accurate and reliable automatic segmentation and morphometry methods that are tailored for TBI remain goals of central importance to future progress in this area.

CT and structural MRI have been and remain techniques of key importance for the purpose of TBI multimodal neuroimaging. Within relatively short scan times, both T₁- and T₂-weighted MR imaging can offer highly accurate visual descriptions of water and fat distribution in both healthy-appearing and pathological tissues at high spatial resolutions. Similarly, CT has been very valuable for structural imaging of TBI, and more so than MRI in the first few days after injury. For example, pathology documented using CT has been found to be a clinically important risk factor in determining post-traumatic neurological deficits (Asikainen et al., 1999), and in categorizing CT abnormalities based on mesencephalic cistern status, midline shift, presence of surgical masses has helped to predict mortality in head injury cases (Englander et al., 2003). CT has also been useful to show that hypoxia in the pre-hospital setting significantly increases the odds of mortality after TBI controlled for multiple variables (Chi et al., 2006). A study by Lehtonen et al. (2005) examined the relationship between cortical lesion location observed via CT and brain injury outcome to conclude that frontal and fronto-temporal lesions detected acutely using CT were associated with poorer performance on neuropsychological measures of executive function and memory at rehabilitation discharge.

Information on TBI obtained from MRI/CT allows clinicians and researchers to localize and quantify focal lesions straightforwardly and to evaluate lesion loads. Additional MR sequence types such as Fluid Attenuated Inversion Recovery (FLAIR), Gradient-Recalled Echo (GRE) T2-weighted imaging and Susceptibility Weighted Imaging (SWI) can increase the descriptive power of MRI by allowing researchers to distinguish between various types of lesions. FLAIR, a pulse sequence which uses inversion recovery to nullify cerebrospinal fluid (CSF) signal, has been widely used to associate hyperintensities in this modality with edema. GRE imaging and SWI, on the other hand, are commonly used to identify hemorrhages, which appear hypointense in these modalities. The use of these three sequences is very common in TBI because of their abilities to isolate pathology, and a combination of T₁, T₂, FLAIR and SWI imaging has already been successfully used by Irimia et al. (2011) to obtain segmentations and 3D models of edema, hemorrhaging tissue, as well as healthy-appearing white matter (WM) and gray matter (GM).

2.3. Tissue classification

One significant methodological issue that must be taken into account when designing automatic TBI segmentation methods is the fact that TBI characterization from MRI often requires the combined use of several image channels in order to identify pathology. Thus, TBI neuroimaging is multimodal par excellence. Because MR volumes of TBI often contain skull fractures, multiple lesion types and associated tissue deformations, multi-channel segmentation of TBI volumes bears significant challenges, especially when such abnormalities are characterized by having a complex structure. Examples of both acute and chronic multi-channel MR image patient are shown in Fig. 1, where the challenging nature of TBI-related pathology is demonstrated. As this figure illustrates, TBI presents significant segmentation challenges due to the need to account for tissue classes other than healthy-appearing GM and WM. Depending on imaging sequence type, these tissues associated with pathology can have distinct intensities and spatial configurations. Moreover, ascertaining their physical and chemical content can pose substantial interpretative dilemmas even for experienced health care providers, which makes the development of robust image processing methods for TBI even more difficult.

Segmentation algorithms for MR images of healthy-appearing brains have been developed by a large number of investigators (see, for example, Van Leemput et al., 1999 and Zhang et al., 2001 for two early developments), and software packages for this task are both widely and freely available. Most such algorithms, however, are not designed to address pathology, which presents significant challenges because the locations and shapes of pathological structures are not easily predictable and, in certain MR modalities, some pathology patterns present image intensities and appearance that are similar to those of normal tissues. Generally, developers of TBI segmentation algorithms have inspired themselves from methods for the MR analysis of brain sclerosis and tumors, which present similar problems compared to TBI. In the case of sclerosis, Van Leemput et al. have proposed a method where regions affected by pathology are treated as outliers from healthy anatomy (Van Leemput et al., 2001), whereas Wu et al. (2006) introduced a k-nearest neighbor (kNN) method that uses multichannel MRI to differentiate between abnormal and healthy-appearing tissues. Recently, Geremia et al. (2011) proposed a method based on decision forests and, in the case of tumors, Prastawa et al. (2004, 2003) developed a method based on outlier detection and subject-specific modification of atlas priors. Similarly, Clark et al. (1998) introduced an automatic method for pathology segmentation that uses knowledge-based techniques. A level-set based tumor segmentation method has been developed by Ho et al., 2002, whereas Menze et al. (2010) have presented a generative model for brain tumor segmentation using multi-modal MR images. For the express purpose of TBI image analysis, Thatcher et al. (1997) have used fuzzy C-means, kNN and manual classification to segment 3D MR images of TBI patients, and Wang et al. (2012a, 2012b) have proposed the use of a personalized atlas for the segmentation of longitudinal TBI data. The essential ideas behind the latter method are to jointly segment images acquired at the acute and chronic stages, as well as to describe anatomical changes due to therapeutic intervention and recovery.

In traditional image processing approaches, individual images of longitudinal series are treated independently by separate segmentations. A notable innovation suitable for TBI is that of Wang et al., who use information from all time points to improve segmentation and to additionally describe changes in healthy tissue and pathology (Fig. 2). Their segmentation method iteratively estimates the image appearance model as well as the spatial anatomical model that undergoes diffeomorphic deformation and non-diffeomorphic/topological changes. In this approach, the initialization step of the algorithm consists of manually selecting one or several primary lesion sites and then affinely registering normal brain atlas to the image at each time point. The initial coarse segmentation is then refined via a joint approach composed of Bayesian segmentation and of personalized atlas construction. This latter step estimates the average of the posteriors obtained from Bayesian segmentation at each time point, whereafter the estimated average is warped back to each time point so as to provide the updated priors for the next iteration of Bayesian segmentation. Once the user has performed the manual initialization (for example, by placing spheres at major lesion sites), the method automatically segments healthy structures (WM, GM, CSF) as well as different lesion types including hemorrhagic lesions, edema and chronic pathology.

Fig. 3 illustrates the construction of a personalized spatiotemporal atlas using the method of Wang et al. The longitudinal segmentation method makes use of information from multiple MR channels and from all time points to achieve a robust segmentation (Wang et al., 2012a, 2012b). The spatial transformations between any time point and the average space are obtained through the estimation of a subject-specific atlas with associated nonlinear deformations, and the tissue deformation between time points is made available by composition of the individual transformations or of their inverses. By means of a procedure such as this, a segmentation method used longitudinally can be modified to provide not only tissue and lesion segmentation but also information related to the amount and direction of deformation between tissues as measured at pairs of time points. The results of this type of process are clinically relevant because they provide quantitative measurements of lesions for each time point, as well as additional information on how tissues and/or pathology shrink or expand in time as a result of recovery.

Segmentations of lesions as imaged at two time points (acute and chronic) and visualization of the deformation field are shown in Fig. 4 for a sample subject. In this case, the deformation field specifies the direction and magnitude of displacement between time points and can be used to determine and evaluate structural changes in brain anatomy. A significant advantage of this type of framework is that it can handle different sets of modalities at each time point, thus providing flexibility in the analysis of clinical scans. Results on a range of subjects (Wang et al., 2012a, 2012b) have demonstrated that joint analysis of TBI volumes acquired at different time points yields improved segmentation compared to independent analysis of the time points. Joint longitudinal segmentation methods such as that of Wang et al. are also important because they provide the ability to assess the value of novel outcome measures by means of Bayesian estimation and predictor–corrector methods. Such methods can allow one to predict outcome using neuroimaging metrics associated with the acute time point, and then to modify, correct or otherwise improve the predictive value of those metrics based on the evolution of the injury. Subsequently, outcome measures identified in this way can in theory be applied prospectively for further validation.

2.4. Morphometric and volumetric calculations

In addition to new and improved methods for volumetric analysis of TBI based on MRI, adaptable brain morphometry tools are also needed to explore outcome prediction hypotheses, if only because it is conceivable that TBI-induced atrophy and/or regeneration can modify the shape of the cortex in ways that can forecast outcome. Morphometric methods frequently make use of MRI volume segmentations to fit a mesh of points to the surface of the brain and then parcellate its structures into regions using a population atlas as a structural prior and based on knowledge of the local curvature (Fischl and Dale, 2000; Fischl et al., 2001; Fischl et al., 2002; Fischl et al., 1999b; Fischl et al., 2004). Subsequently, volumetrics (cortical thickness, GM and WM volume, etc.) and morphometrics (curvature, folding index) can be computed for each cortical region, as has been done extensively in studies of aging (Salat et al., 2004) or disease (Kuperberg et al., 2003; Rosas et al., 2002). Although automatic cortical parcellation methods have been applied to TBI in the past, the caveat remains that errors due to the application of probabilistic tissue classification can frequently occur whenever TBI anatomy differs appreciably from health. Some studies where automatic parcellation was applied to TBI volumes have reported major topological defects, failures to fit cortical surfaces, as well as subcortical segmentation errors (Strangman et al., 2010). Consequently, further methodological improvements in this area are needed.

With the extension of conventional morphometry methods to TBI analysis come numerous pitfalls and technological difficulties. Whereas sequences such as FLAIR, T₂ and SWI can aptly localize focal pathology, one segmentation task that continues to remain problematic is that of identifying the boundary between WM from GM when tissues on both sides of it have been affected by trauma. In chronic TBI, scar tissue can also lead to overestimation of GM volume and/or underestimation of WM volume. Unfortunately, these issues bear relevance upon the accuracy of both volumetric and morphometric measures extracted from structural MRI because, on the one hand, improper segmentation of the WM/GM boundary can result in the inaccurate calculation of GM and WM volumes. On the other hand, errors of this kind can dramatically affect computed morphometric measures such as local curvature and the folding index of the cortical surface.

3.2. Personalized connectomic analysis

Although diffusion methods are suitable for the longitudinal study of WM connectivity, this topic has been insufficiently explored in TBI patients. Nonetheless, several studies have used diffusion imaging measurements to reveal that WM abnormalities can appear quickly after injury and then evolve dynamically over time (Mac Donald et al., 2007; Sharp and Ham, 2011) as a consequence of axonal injury and demyelination (Beaulieu et al., 1996; Song et al., 2002; Sun et al., 2008). One limitation of diffusion techniques is that, although diffusion imaging is very suitable for investigating the longitudinal evolution of brain connectivity, DTI scans can capture only snapshots of cerebral reorganization prompted by injury. Due to high attrition rates in many longitudinal TBI studies (Corrigan et al., 2003) and to TBI heterogeneity, investigators must often rely on small sample sizes to perform this type of research, which can appreciably curtail the predictive power of their statistical analyses. Consequently, it is important that more studies be undertaken where DTI scans are acquired at a number of time points after injury in a large patient population so that the acute effects of TBI upon brain network topology can be better understood. In particular, the acute period after TBI should be targeted because this is when important changes in brain connectivity occur. Typically, the first 4–6 h after injury is associated with cytoskeletal disruption followed by axonal disconnection between 1 and 7 days after the traumatic event (Gaetz, 2004). Because such damages to brain connectivity can result in deterioration of cognitive function that may persist for years (Povlishock and Katz, 2005), further efforts should be dedicated to the longitudinal use of DTI in the acute phase of TBI in order to understand the relationship between the structural remodeling of the brain, on the one hand, and long-term improvements or deterioration in motor and cognitive function, on the other hand.

To address the need for methods that allow one to investigate personalized profiles of WM atrophy in TBI, Irimia et al. (2012a) used DTI to introduce a patient-tailored approach to the graphical representation of WM change over time. These authors' method allows one to visualize brain connections affected by pathology and to relate patient injury profiles to the existing body of scientific and clinical knowledge on affected cortical structure function. The approach provides the ability to quantify WM atrophy for personalized connectomics and allows one to integrate such knowledge with other clinical case information to provide a more insightful picture on the neuroplasticity and neuro-degeneration patterns that occur in the TBI brain. The authors also introduced a circular representation wherein the parcellated gyral and sulcal structures of the cortex are displayed as a circle of radially aligned elements called a “connectogram” (Fig. 6). To calculate inter-region connectivity in the approach of Irimia et al., each fiber tract extremity is first identified and associated with the pair of parcellated regions which it connects. In the second step, the percentage change in the density of fibers previously selected is computed for each connection, using the formula Δ = [D (t₂) − D (t₁)] / D (t₁). Finally, those fibers that have computed changes in fiber density with absolute values greater than 20% (|Δ| > 20%, i.e. the top four fifths of the distribution of percentage changes) are displayed on a separate connectogram. For each pair of cortical regions, the change Δ in the fiber density D between successive time points t₁ and t₂ is computed as a percentage of the fiber count at acute baseline based on the multimodal imaging data acquired at the two time points. A combination of conservative restrictions upon the selection of atrophied fibers (see Irimia et al., 2012a for details) can then allow one to confidently identify fibers undergoing a large amount of atrophy in a particular patient.

Personalized atrophy profiles in the fashion of those created by Irimia et al. can be used to identify WM connections that have suffered from appreciable atrophy between acute and chronic time points. Such representations may be of interest to clinicians and to other medical professionals to gain insight on the effect of TBI upon a patient's clinical picture as well as to examine atrophy trajectories. Additionally, connectogramic display of atrophy patterns allows one to identify studies from the current literature that have possible relevance to improving and tailoring patient rehabilitation protocols. Such studies describe cognitively demanding exercises that involve stimulus–response selection in the face of competing streams of information, including divided-attention tasks, verbal- and motor-response selection tasks that challenge faculties commonly affected by TBI.

3.3. Network-theoretic methods

Much of the promise that diffusion imaging techniques hold for the purpose of TBI outcome prediction stems from the latter's ability to investigate changes in brain network topology. Motor and cognitive functions such as attention, for instance, are frequently affected by TBI in ways which are difficult to quantify based on volumetric and morphometric measures alone. Instead, because these functions are dependent upon the integration and segregation properties of large-scale brain networks, the study of TBI and the formulation of treatments for this condition should incorporate knowledge of how these networks are impaired by trauma (Sharp and Ham, 2011). Currently, DTI- and MRI-based diagnosis and assessment of TBI is often primarily qualitative and performed ‘by eye’ due to the unavailability of clinical tools for studying brain network properties and for elucidating changes to the structural connectivity of the brain (Kuceyeski et al., 2011). Thus, although there is considerable interest in how lesion location and size influence disability type and severity via alterations to WM connectivity patterns, the quantitative study of this relationship is in its infancy and requires further effort.

The understanding that brain network topology and dynamics modulate a vast array of brain functions that are affected by disease has prompted an increasing interest in the theoretical aspects of network analysis across the entire spectrum of neuroscience research (Dimitriadis et al., 2010). In TBI, there is growing demand for time-dependent network analysis methods that are able to capture and quantify the dynamic changes that brain connections undertake acutely as a result of primary TBI and sub-acutely in response to treatment and recovery. Thus, whereas brain network topologies were previously explored using static graphs, advances in the field have led to the development of methodologies that account for the continuous formation and dissolution of structural and functional links over multiple time scales. Dimitriadis et al. (2010), for example, proposed the concept of time-dependent network analysis based on weighted graphs using metrics reflecting network segregation (clustering coefficient, local efficiency) and integration (characteristic path length). Using these network metrics in TBI research makes sense because previous studies have found that significant differences in these measures exist when comparing healthy adults to schizophrenics (Zalesky et al., 2011), AD patients (Lo et al., 2010) and normal aging (Wen et al., 2011). For these reasons, the use of network theory to explore pathology is particularly appealing, especially because this could reveal novel biomarkers of TBI outcome.

Research into how brain connectivity is affected by trauma has spurned renewed interest into particular aspects of network theory, such as the development and application of theoretical concepts that can address and model network properties and phenomena that occur in TBI, including cortical reorganization and resilience to injury. Because structure entails function, a structural description of brain connectivity will likely help to understand cortical function and to provide insight into brain network robustness and recovery from damage (Kaiser et al., 2007). The study of network properties such as clustering and hierarchical organization is useful for determining how brain rewiring can occur as a result of trauma, and which regions of the brain are most sensitive to injury.

Whereas appreciable WM loss can sometimes be compensated for by large-scale rewiring, focal damage to highly specialized areas (e.g. auditory cortex or language areas) can lead to significant decline in day-to-day functioning. Kaiser et al. (2007) and the references therein used theoretical network models to conclude that the type of injuries that are likely to result in appreciable deficits is that involves highly-connected hub nodes and bottleneck connections. A study by Varier et al. (2011) used a network model to reproduce known findings according to which (1) lesion effects to brain networks are greater for larger and multifocal lesions and (2) early lesions cause qualitative changes in system behavior that emerge after a delay during which effects are latent. Kuceyeski et al. (2011) used structural and diffusion MRI from 14 healthy controls to create spatially unbiased WM ‘connectivity importance’ maps that quantify the amount of brain network disruption that would occur if any particular brain region was lesioned. The authors then validated the maps by investigating the correlations of the importance of maps' predicted cognitive deficits in a group of 15 TBI patients with their cognitive test scores of memory and attention.

3.4. Functional connectomics

Although structural neuroimaging can reveal a wealth of relevant information which can be critical to the process of TBI clinical care and rehabilitation, the fact remains that next-generation methods for the study of this condition will require a synthesis of both neuroanatomical (CT, MRI, DTI) as well as functional imaging methods such as fMRI. Thus, although the fMRI literature on TBI has been thoroughly reviewed elsewhere (Belanger et al., 2007; Hillary et al., 2002), it is nevertheless useful to indicate here how this technique can be useful for the purpose of investigating connectomic changes associated with brain injury.

A potential impediment associated with the application of fMRI to the study of TBI is the necessity to examine the effects of collecting or loose blood, including subarachnoid hemorrhage and subdural hematomas, as well as that of factors which may alter hemodynamic responses, including increased intracranial pressure (Hillary et al., 2002). Despite such difficulties associated with the quantification and interpretation of blood oxygen-level dependent (BOLD) signals, fMRI is likely to become increasingly beneficial for the purpose of illuminating how the brain overcomes the effects of injury by means of developing compensatory neural networks (NIH, 1998). For example, one study which examined brain activations while TBI patients performed a working memory task found that TBI patients displayed cerebral activation patterns which were more regionally dispersed and more lateralized to the right hemisphere (Christodoulou et al., 2001). Similarly, a TBI case study (Scheibel et al., 2003) found increased frontal activation under a 2-back relative to a 1-back condition of working memory, with more extensive activation in two TBI subjects compared to controls. To measure improvements in cognitive ability following rehabilitation, Laatsch and Krisky (2006) used fMRI to investigate task performance in the context of a cognitive rehabilitation model and concluded that individuals with severe TBI can demonstrate improvements in neuropsychological testing even many years after injury. Another useful study by Karunanayaka et al. investigated covert verb generation in a pediatric TBI group and found significant differences in BOLD signal activation in peri-sylvian language areas between the TBI group and a control group, as well as significant associations between BOLD signal activation and performance on language-specific neuropsychological tests (Karunanayaka et al., 2007). Finally, an important study by Monti et al. (2010) used fMRI to show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. All of these studies have indicated the usefulness of fMRI for investigating neuronal network reorganization after injury and, although the application of this technique to the study of TBI is still in its infancy, the findings listed above do indicate the potential utility of fMRI for the purpose of studying how the functional connectome changes with injury.

In addition to working memory and language, attention can also be highlighted as an aspect of brain function whose study is critical for understanding the effects of TBI. For example, Kramer et al. (2008) found that pediatric TBI patients exhibited attention task-related activations of frontal and parietal areas which were significantly greater than in healthy controls. Interestingly, the authors suggested that such hyper-activation of attention networks in TBI contrasted with the hypo-activation of attention networks which has been reported for attention-deficit/hyperactivity disorder. Interestingly, whereas Kramer et al. found over-activation of attention-related networks, Sanchez‐Carrion et al. (2008) found that TBI patients had a hypo-activation of frontal lobe networks in several n-back working memory tasks, which indicates that the effects of TBI upon cognition likely consist of both hyper- and hypo-activation in response to exogenous stimuli, depending upon which brain function is being activated as well as upon the nature of the functional and structural networks involved.

4.2. The role of CT in outcome prediction

As previously detailed, CT can often be more sensitive than MRI for the detection and quantification of pathology within the first few days after injury (see Maas et al., 2007 and the references therein). Partly for this reason, the ability to predict outcome early after injury based on CT alone is a particularly attractive goal which has attracted appreciable efforts, though with mixed results. An early study by Ichise et al. (1994), for example, found that the antero-posterior ratio as computed from CT images was correlated with six tests of neuropsychological outcome, though the ventricle-to-brain ratio was correlated with only two such tests, despite being known to be a structural index of poor outcome. Englander et al. (2003) studied the association between early CT findings and the need for assistance for ambulation, activities of daily living and for supervision at the time of rehabilitation discharge. These authors found that individuals with midline shifts in excess of 5 mm were more likely to require assistance at discharge, and that 57% of such patients needed home supervision compared to fewer than 40% of patients with midline shifts of less than 5 mm. Patients with bilateral cortical contusions as revealed by CT were found to require more global supervision at rehabilitation discharge but not for ambulation. Importantly, individuals with mass lesions in excess of 15 cm³ and who had inflammation with structural shifts of over 3 mm were found to have mortality rates greater than 50%. A study by Temkin et al. (2003) proposed the Functional Status Examination (FSE) as an outcome measure based on CT findings and found that it could dichotomize patients well based on recovery from one month to five years after injury. Additionally, this measure was found to have the ability of identifying individuals with functional problems as well as associated neuropsychological and emotional impairments as late as 5 years after injury. Maas et al. examined the predictive value of the Marshall CT classification with alternative CT models by means of logistic regression and recursive partitioning with bootstrapping techniques and found that the former classification indicated reasonable discrimination for the purpose of outcome prediction. The authors also found that discrimination could be improved by including intraventricular and traumatic subarachnoid hemorrhage and by detailed differentiation of mass lesions and basal cisterns, although individual CT predictors were preferable to the Marshall classification for prognostic purposes. An important contribution to the task of outcome prediction using CT is that of Yuh et al. (2008), who developed a computer algorithm for automatic detection of intracranial hemorrhage and mass effect in patients with suspected TBI. The authors found that their method was excellent for detection of these two phenomena in addition to that of midline shift, while maintaining intermediate specificity. In particular, software detection of the presence of at least one non-contrast CT feature of acute TBI demonstrated high sensitivity of 98% and high negative predictive value of 99%.

4.3. Prognostication via diffusion imaging

It has long been known that patients with focal injuries to a specific part of the brain can experience long-term deficits related to cognitive functions that are localized in very different brain areas. Some modeling studies have indicated that cortical areas along the midline, including cingulate cortex, are particularly susceptible to DAI even in the absence of focal injuries to those areas, presumably due to the network topology of inter-hemispheric WM fibers (Alstott et al., 2009). This finding is even more interesting in light of the fact that autopsies of mild TBI patients indicate that the corpus callosum (CC), a region in the immediate vicinity of cingulate cortex, is quite frequently affected by DAI (Blumbergs et al., 1995). Galloway et al. (2008) attempted to predict TBI outcome using DWI and found that the mean apparent diffusion coefficient (ADC) values associated with healthy-appearing WM could be used as an outcome predictor in pediatric cases of severe TBI. Other findings obtained using diffusion techniques—such as water diffusion abnormalities in contusion cases—have been associated with Glasgow Coma Score (GCS) values and with Rankin scores at patient discharge (Huisman et al., 2004), while reduced fractional anisotropy (FA) in the splenium has been correlated with cognitive dysfunction over one year after injury (Nakayama et al., 2006). In a related study, Wang et al. (2011) studied a cohort of 28 patients with mild to severe TBI to conclude that DTI tractography is a valuable tool for identifying longitudinal structural connectivity changes and for predicting patients' long term outcome. These authors found that, in agreement with the diverse outcomes of their study cohort, WM changes in patients were heterogeneous, ranging from improvements to deteriorations in structural connectivity. Another study by Bazarian et al. (2007) used DTI to detect clinically important axonal damage in cerebral WM within 72 h after injury using ROI analysis of FA values. The authors found that, compared to control subjects, mild TBI patients had WM voxel DTI trace values that were significantly lower in the left anterior internal capsule as well as maximum ROI-specific median FA values which were significantly higher in the posterior CC. These FA values were found to be correlated with 72-h post-concussive symptom (PCS) score and with neurobehavioral tests of visual motor speed as well as impulse control.

The usefulness of DTI to predict injury severity has received a large amount of scrutiny in the TBI community. Benson et al. (2007), for example, hypothesized that a global WM analysis of DTI data would be sensitive to DAI across a spectrum of TBI severity and injury-to-scan interval. The authors found that FA empirical distribution parameters (mean, standard deviation, kurtosis, skewness) were globally decreased in mild TBI compared to healthy controls, and furthermore that the statistical properties of FA distributions were correlated with injury severity as indexed by GCS and post-traumatic amnesia. Increased diffusion in the short DTI axis dimension, likely reflecting dysmyelination and axonal swelling, was found to account for most decreases in FA. The conclusions of the study were that (1) FA is globally decreased in mild TBI, possibly reflecting widespread effects of injuries, and that (2) FA changes seem to be correlated with injury severity, suggesting a potential role of DTI in the early diagnosis and prognosis of TBI. Another important study by Kraus et al. (2007) indicated that WM load was negatively correlated with performance in all cognitive domains and that DTI provides an objective means for determining the relationship of cognitive deficits to TBI, even in cases where the injury was sustained years prior to the evaluation. Similar findings were obtained by Wozniak et al. (2007), who showed that children with TBI showed slower processing speed, working memory and executive deficits, as well as greater behavioral dysregulation, and that supracallosal FA was correlated with motor speed and behavioral ratings in such patients. Ewing-Cobbs additionally found disruptions in callosal microstructure, and significant correlations between radial diffusion and/or FA in the isthmus, on the one hand, and working memory as well as motor and academic skills, on the other hand (Ewing-Cobbs et al., 2008). Another study involving a pediatric population found that DTI was superior to conventional MR in detecting WM injury at 3 months after injury in moderate to severe TBI. DTI measures were also found to be related to global outcome, cognitive processing speed, and speed of resolving interference in children with moderate to severe TBI. By contrast, in adults, Bendlin et al. found that neuropsychological function improved throughout the first year post-injury despite TBI affecting virtually all major fiber bundles in the brain including the CC, cingulum and uncinate fasciculus (Bendlin et al., 2008). Niogi et al. (2008) found that, in postconcussive syndrome, WM injury is correlated with impaired cognitive reaction time and that the most frequently affected WM structures in the adult population investigated were the anterior corona radiata, uncinate fasciculus, genu of the CC, inferior longitudinal fasciculus, and cingulum bundle. These findings were largely replicated by Rutgers et al. (2008), who additionally found that supratentorial projection fiber bundles and fronto-temporo-occipital association bundles were also frequently affected in adult TBI patients. In conclusion, given that cognitive recovery from TBI correlates with the restoration of WM integrity, DTI as a neuroimaging technique is uniquely positioned to predict recovery in TBI patients (Belanger et al., 2007; Terayama et al., 1993) and should be used more widely in studies that aim to identify markers of TBI outcome.

A significant recent study which combines DTI with fMRI is that of Bonnelle et al. (2011), who showed that sustained attention impairments in TBI patients are associated with an increase in default mode network activation, particularly in the precuneus and posterior cingulate cortex. Additionally, these authors found that the functional connectivity of the former structure with the rest of the network at the beginning of an attention task was predictive of which patients would go on to exhibit impairments. This predictive information was present before the patients exhibited any behavioral evidence of sustained attention impairment, and the relationship was also identified in a subgroup of patients without focal brain damage. In another important study, Mayer et al. (2011) investigated whether functional connectivity inferred using DTI and resting-state fMRI could provide objective markers of injury as well as predict cognitive, emotional and somatic deficits in mild TBI patients semi-acutely and in late recovery. These authors found that their patient cohort demonstrated decreased functional connectivity within the default mode network and increased connectivity between the latter and lateral prefrontal cortex, with functional connectivity measures having high sensitivity and specificity for patient classification and deficit prediction.

This work was supported by (1) the National Alliance for Medical Image Computing (NA-MIC) (www.na-mic.org), under the National Institutes of Health Roadmap Initiative (2U54EB005149 to R. K., sub-award to J. D. V. H.) and by (2) the National Institute of Neurological Disorders and Stroke (P01NS058489 to P. M. V. and D. A. H.). The authors would also like to thank S.-Y. Matthew Goh, Carinna M. Torgerson and the dedicated staff in the Laboratory of Neuro Imaging at the University of California, Los Angeles for their support and assistance.