Elsevier

NeuroImage

Volume 39, Issue 3, 1 February 2008, Pages 1014-1026
NeuroImage

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

https://doi.org/10.1016/j.neuroimage.2007.10.005Get rights and content

Abstract

Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid- and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume > 1.5 cm3). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain–behavior relationships in the TBI population.

Introduction

Each year, approximately 1.5 million people sustain traumatic brain injury (TBI) in the United States alone, causing billions of dollars of economic cost. Among the survivors, 80,000 to 90,000 individuals are left with significant long-term cognitive and motor disabilities (Jacobs, 1988, Max et al., 1990, McKinlay et al., 1981, Rutland-Brown et al., 2006, Thomsen, 1984, Thurman et al., 1999). However, efforts to identify the neuropathologic correlates of these deficits have gained only limited success to date (Bigler, 2001a, Levine et al., 2006). As Levine et al., 2002, Levine et al., 2006 appropriately pointed out, using more sensitive and reliable in vivo neuroimaging protocols may facilitate the identification of specific brain–behavior relationships in the TBI population.

Developing an imaging protocol that allows precise quantification of TBI-induced structural changes has proven to be challenging. The complicated nature of traumatic brain injuries typically involves a combination of focal and diffuse injury mechanisms (Gennarelli and Graham, 2005). While it is relatively easy to locate and quantify focal lesions such as contusions or hematomas through visual inspection and manual tracing, diffuse lesions such as diffuse axonal injury (DAI) have not been directly amenable to this traditional volumetric measurement. Instead, generalized atrophy is typically assessed using indirect measures such as total brain volume, ventricular enlargement, or ventricle to brain ratio. For more localized volume losses, individual structures hypothesized to be vulnerable to atrophy have been targeted as regions of interest (ROIs) for subsequent volumetric analysis. However, methodological limitations of these traditional volumetric approaches (cf. Bermudez and Zatorre, 2001, Dubb et al., 2005) are likely to have undermined the accuracy and sensitivity of previous studies in identifying the common areas of volume loss in TBI. First, using gross volume or length measures of an anatomical structure fails to capture more localized shape variations within the region, decreasing the sensitivity of the measure. Second, selecting a small set of structures a priori leaves out other potentially relevant areas. This may be a particularly inefficient strategy for TBI, considering the diffuse nature of the injury. Third, manual slice-by-slice delineation makes it very hard to make ROIs where no clear boundary between two structures exists, limiting the analysis to clearly definable structures (e.g., lateral ventricles).

Reflecting these difficulties, previous volumetric neuroimaging studies have not been able to provide a definitive picture of TBI-induced structural changes. For example, Bigler (Bigler, 2001b, Bigler, 2005) summarized the results from a large number of volumetric studies conducted over the last two decades and classified the degree of atrophy reported by each study into three categories: “major” atrophy was reported in the total brain volume, the lateral ventricles, and the corpus callosum; “moderate” volume changes were found in the third ventricle, the amygdala, and the hippocampus; “minimal” atrophy was found in the basal ganglia, the thalamus, the fornix, the mammillary body, the cingulate gyrus, the midbrain, the cerebellum, the internal capsule, and the corticospinal tracts. However, since different regions were measured in separate studies with different participants and imaging protocols, it is hard to know whether a subset of regions is more affected than others or there is only a generalized, non-specific pattern of atrophy in TBI.

This limitation of previous volumetric approaches in providing a more comprehensive and unbiased picture of structural changes motivated some TBI researchers to adopt a fully automated, whole brain image processing pipeline such as voxel-based morphometry (VBM; Gale et al., 2005, Salmond et al., 2005, Tomaiuolo et al., 2005). In VBM, individual structural images are normalized to a common sterotaxic space, segmented based on intensity (into gray matter, white matter, and CSF), and smoothed to calculate tissue composition maps. These maps are then statistically compared voxel-wise to detect group differences (Ashburner and Friston, 2000, Good et al., 2001). This approach enables researchers to examine gray and/or white matter concentrations over the whole brain simultaneously without any a priori hypotheses. To date, only three studies have used this procedure in an attempt to characterize the structural consequences of TBI. Tomaiuolo and colleagues (2005) compared 19 TBI patients and 19 control subjects in terms of white matter density throughout the whole brain except the brainstem and the cerebellum. They observed white matter reduction in the corpus callosum, the fornix, the para-hippocampal gyrus, the optic radiation, the optic chiasm, the internal capsule, and the superior frontal gyrus. Gale and colleagues (2005) examined the gray matter density in nine patients and nine controls. They reported reduced density in a widespread area of gray matter including the subcortical gray matter, the cingulate gyrus, the frontal and temporal cortices, and the cerebellum. Salmond and colleagues (2005) evaluated both gray and white matter density in 22 patients and 23 controls. Compared to controls, patients were reported to have reduced density of gray matter in the basal forebrain, the hippocampal formation, the insula, the thalamus, the cerebellum, and the areas of neocortex (temporal, occipital, and parietal lobes). Less marked white matter density reduction was found in the lateral capsular pathway and the corpus callosum.

Although the results from these VBM studies are encouraging, they still do not provide a detailed, reliable picture of the pattern of volume loss associated with TBI for the following reasons. First, the biggest concern is related to the step of spatially registering each brain to a reference template. Since the tissue density calculation of the VBM procedure is based on the assumption of successful normalization, any misregistration during the normalization process can potentially lead researchers to falsely identify registration errors as true anatomic differences (Bookstein, 2001). In fact, achieving accurate normalization with minimal registration error is most critical in any voxel-based structural MRI study designed for a between-group comparison. However, the brains of TBI survivors present a great challenge to this process because they can exhibit severe global and focal atrophy. These structural abnormalities manifested in the brains of TBI subjects violate the basic assumptions of small deformations and/or simple intensity relationships used in many existing image registration methods (cf. Studholme et al., 2004). Unfortunately, none of the previous VBM studies of TBI used deformation models that are able to capture the expansive, large deformation atrophy induced by TBI. Because previous VBM studies all used a linear or coarse-resolution spatial normalization, results from those studies are bound to be ambiguous. The next step of VBM involves segmenting the normalized brain into different tissue types. Since segmentation requires a good separation of intensities, hypointense lesion areas of TBI subjects pose another challenge for the VBM method. Thus, the results of prior VBM studies may be confounded by unreliability in segmenting affected brain regions. The smoothing process is another limitation. In the VBM procedure, spatial smoothing of the segmented map is required to obtain normally distributed tissue concentration values for each voxel. However, image blurring caused by the smoothing further prevents one from doing a fine-grained analysis on localized atrophy. Lastly, previous VBM studies, except the one by Tomaiuolo et al. (2005), did not control for the effects of macroscopic focal lesions on the preprocessing steps of VBM such as segmentation and normalization. A subgroup analysis excluding participants with macroscopic focal lesions should be done to rule out the confounding effects of large focal lesions on the pattern of volume loss.

The purpose of the present study was to delineate the pattern of diffuse volume changes after TBI with more detail and certainty by using a processing protocol that is more optimally suited to TBI data. Compared to prior VBM studies, the following methodological improvements have been made in the current study. First, tensor-based morphometry (TBM; Ashburner et al., 1998, Chung et al., 2001, Davatzikos et al., 2001, Gaser et al., 1999, Studholme et al., 2004, Thompson et al., 2000) was used. TBM methods utilize information from high resolution deformation tensor fields obtained from the non-linear transformations of individual images to the template. Anatomic differences can be directly characterized from the properties of these deformation tensor fields. In addition, since the TBM method does not require a segmentation step, one can avoid the difficult issue of accurate tissue classification complicated, in particular, by unpredictable changes in tissue appearance due to TBI. TBM has been validated against an expert tracing method (Gaser et al., 2001) and also used to detect group differences between healthy controls and various patient populations including schizophrenia (Gaser et al., 1999), dementia (Studholme et al., 2004), and HIV/AIDS (Chiang et al., 2007). It has been recently shown that, compared to the traditional volumetric method, TBM yields more statistical power to associate structure with other biological and demographic variables (Lee et al., 2007). It was also demonstrated that the method is well suited to track longitudinal changes of individual brains (e.g., Brambati et al., 2007, Cardenas et al., 2007, Leow et al., 2006).

Second, to further enhance the accuracy and sensitivity of TBM protocol, a novel algorithm, symmetric normalization (SyN; Avants et al., in press, Avants et al., 2006) was employed for the inter-subject image normalization. SyN is a recently developed, high-resolution diffeomorphic image registration algorithm that uses large deformation capabilities to maximize the sensitivity of neuroimaging studies (for diffeomorphic image matching technique, see Avants and Gee, 2004, Miller, 2004). Its ability to capture large deformation shape change minimizes the well-known shortcomings of SPM2 (Ashburner et al., 1998), which underestimates the shape transformations required when analyzing atypical brains such as those of TBI patients. This technique has been successfully used to quantify spatial and longitudinal atrophy patterns of neurodegenerative disorders (Avants et al., in press, Avants et al., 2005).

Third, the current study was performed with an optimal, population-specific template that fairly represents both controls and TBI patients. To construct such a template, symmetric normalization uses a shape and appearance averaging technique (Avants and Gee, 2004, Avants et al., 2006) to estimate the most representative brain for a population of images. Our approach weighted each individual in such a way that controls and patients contributed equally to the final template. The resulting custom template contains sharp features, shared across the population, that are necessary for successful high-resolution image normalization. This custom template also enables statistically fair comparisons between the two groups while guaranteeing the ability to capture the finest shape differences. It has been previously shown that normalization to a custom template improves localization accuracy, reduces bias in statistical testing, and ultimately yields more biologically plausible results (Kochunov et al., 2005, Kochunov et al., 2001, Leow et al., 2006, Senjem et al., 2005, Woods, 2003). Lastly, the present study also attempted to control for the effects of the macroscopic focal lesions on the group differences by conducting a subgroup analysis after excluding subjects with such lesions. These methodological improvements are expected to help in revealing more reliable and fine-grained patterns of structural consequences resulting from TBI.

Section snippets

Participants

The data were collected as part of a larger study investigating the neural correlates of attention deficits and treatment responses of various psychoactive drugs in the survivors of TBI (principal investigator: J.W.). Thirty individuals with TBI and 20 healthy volunteers were recruited. We planned to recruit more TBI participants because data of TBI survivors are more likely to be discarded in a typical functional neuroimaging study due to movements in the scanner and poor behavioral

Results

Fig. 2 shows the profile of local volume differences between individuals with TBI (N = 29) and healthy controls (N = 20). Compared to controls, survivors of TBI showed volume reductions in widespread areas of both gray and white matter. Table 2 reports the details of the regions where significant volume losses were found in TBI survivors. The largest area of volume loss was found in the thalamus clusters (mediodorsal nucleus and pulvina), followed by the midbrain clusters (cerebral peduncle and

Discussion

The present study demonstrates that a large deformation image registration technique (SyN) can be successfully combined with a TBM method to delineate a 3D pattern of TBI-induced volume changes with greater precision. Localized volume losses were found mainly in the deep nuclei and the white matter regions including the thalamus, the midbrain, the cingulate cortex (white matter portion), the corpus callosum, and the caudate. Significant volume loss clusters were also detected in the cerebellum

Acknowledgments

The authors wish to thank Monica Vaccaro, M.S., Patricia Grieb-Neff, M.A., Kathy Z. Tang, B.A., and Chris Weber for their help in subject recruitment and data analysis. The assistance of MRI technicians Doris Cain, Patricia O'Donnell, and Norman Butler is also gratefully acknowledged. This study is supported by grant R24HD39621 from the NCMRR, NICHD, NIH, H133G050219 from the NIDRR, U.S. Department of Education, and P30NS045839 from the NINDS, NIH. This project is also funded, in part, under a

References (64)

  • M.K. Chung et al.

    A unified statistical approach to deformation-based morphometry

    NeuroImage

    (2001)
  • C. Davatzikos

    Why voxel-based morphometric analysis should be used with great caution when characterizing group differences

    NeuroImage

    (2004)
  • C. Davatzikos et al.

    Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy

    NeuroImage

    (2001)
  • A. Dubb et al.

    Characterization of brain plasticity in schizophrenia using template deformation

    Acad. Radiol.

    (2005)
  • C. Gaser et al.

    Detecting structural changes in whole brain based on nonlinear deformations—application to schizophrenia research

    NeuroImage

    (1999)
  • C. Gaser et al.

    Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI

    NeuroImage

    (2001)
  • C.R. Genovese et al.

    Thresholding of statistical maps in functional neuroimaging using the false discovery rate

    NeuroImage

    (2002)
  • C.D. Good et al.

    A voxel-based morphometric study of ageing in 465 normal adult human brains

    NeuroImage

    (2001)
  • D.I. Graham et al.

    Novel aspects of the neuropathology of the vegetative state after blunt head injury

    Prog. Brain Res.

    (2005)
  • A.D. Lee et al.

    3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry

    NeuroImage

    (2007)
  • A.D. Leow et al.

    Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

    NeuroImage

    (2006)
  • M.I. Miller

    Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms

    NeuroImage

    (2004)
  • R.C. Oldfield

    The assessment and analysis of handedness: the Edinburgh inventory

    Neuropsychologia

    (1971)
  • M.L. Senjem et al.

    Comparison of different methodological implementations of voxel-based morphometry in neurodegenerative disease

    NeuroImage

    (2005)
  • C. Studholme et al.

    Deformation tensor morphometry of semantic dementia with quantitative validation

    NeuroImage

    (2004)
  • J.P. Thirion

    Image matching as a diffusion process: an analogy with Maxwell's demons

    Med. Image Anal.

    (1998)
  • D.J. Tisserand et al.

    Regional frontal cortical volumes decrease differentially in aging: an MRI study to compare volumetric approaches and voxel-based morphometry

    NeuroImage

    (2002)
  • D.C. Van Essen et al.

    Mapping visual cortex in monkeys and humans using surface-based atlases

    Vision Res.

    (2001)
  • R.P. Woods

    Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation

    NeuroImage

    (2003)
  • P.A. Yushkevich et al.

    User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability

    NeuroImage

    (2006)
  • J.H. Adams et al.

    Diffuse axonal injury in head injury: definition, diagnosis and grading

    Histopathology

    (1989)
  • J.H. Adams et al.

    The neuropathology of the vegetative state after an acute brain insult

    Brain

    (2000)
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