A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury

Clinical Neuroscience Laboratory, Department of Psychiatry, VA Boston Healthcare System, Brockton, MA, USA.
Brain Imaging and Behavior (Impact Factor: 4.6). 03/2012; 6(2). DOI: 10.1007/s11682-012-9156-5
Source: PubMed


Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.

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Available from: Martha E Shenton
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    • "(Corouge et al., 6 2006; Hagmann et al., 2007; Malcolm et al., 2010; Sporns et al., 2005; Wang 7 et al., 2014), and to investigate many brain disorders such as Alzheimer's 8 disease, schizophrenia, and mild traumatic brain injury, see e.g. (Shenton 9 et al., 2012; Shi et al., 2013; Thomason and Thompson, 2011). "
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    ABSTRACT: Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements , a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SParse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimag-ing study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.
    Full-text · Article · Dec 2015 · Medical image analysis
    • "One potential biomarker that has shown promise in mild traumatic brain injury (mTBI) is diffusion tensor imaging (DTI). Evidence from work in animals [Mac Donald et al., 2007; Spain et al., 2010] and humans [Dodd et al., 2014; Shenton et al., 2012] demonstrates that white matter abnormalities are a common consequence of mTBI. One commonly assessed DTI metric is fractional anisotropy (FA), which measures the direction of water diffusion along white matter tracts and is thought to reflect axonal microstructure [Beaulieu, 2002]. "
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    ABSTRACT: There is great interest in developing physiological-based biomarkers such as diffusion tensor imaging to aid in the management of concussion, which is currently entirely dependent on clinical judgment. However, the time course for recovery of white matter abnormalities following sports-related concussion (SRC) is unknown. We collected diffusion tensor imaging and behavioral data in forty concussed collegiate athletes on average 1.64 days (T1; n = 33), 8.33 days (T2; n = 30), and 32.15 days post-concussion (T3; n = 26), with healthy collegiate contact-sport athletes (HA) serving as controls (n = 46). We hypothesized that fractional anisotropy (FA) would be increased acutely and partially recovered by one month post-concussion. Mood symptoms were assessed using structured interviews. FA differences were assessed using both traditional and subject-specific analyses. An exploratory analysis of tau plasma levels was conducted in a subset of participants. Results indicated that mood symptoms improved over time post-concussion, but remained elevated at T3 relative to HA. Across both group and subject-specific analyses, concussed athletes exhibited increased FA in several white matter tracts at each visit post-concussion with no longitudinal evidence of recovery. Increased FA at T1 and T3 was significantly associated with an independent, real-world outcome measure for return-to-play. Finally, we observed a nonsignificant trend for reduced tau in plasma of concussed athletes at T1 relative to HA, with tau significantly increasing by T2. These results suggest white matter abnormalities following SRC may persist beyond one month and have potential as an objective biomarker for concussion outcome. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
    No preview · Article · Dec 2015 · Human Brain Mapping
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    • "Tumor segmentation methods often borrow ideas from other brain tissue and other brain lesion segmentation methods that have achieved a considerable accuracy [8]. Brain lesions resulting from traumatic brain injuries [9], [10] and stroke [11], [12] are similar to glioma lesions in terms of size and multimodal intensity patterns, but have attracted little attention so far. Most automated algorithms for brain lesion segmentation rely on either generative or discriminative probabilistic models at the core of their processing pipeline. "
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    ABSTRACT: We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypoor hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generativediscriminative model to be one of the top ranking methods in the BRATS evaluation.
    Preview · Article · Nov 2015 · IEEE Transactions on Medical Imaging
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