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SNTF Immunostaining Reveals Previously Undetected Axonal Pathology in Traumatic Brain Injury
research-article
Author(s):
Victoria E. Johnson
1 ,
William Stewart
2
,
3 ,
Maura T. Weber
1 ,
D. Kacy Cullen
1 ,
Robert Siman
1 ,
Douglas H. Smith
1
Publication date (Electronic):
20 November 2015
Journal:
Acta neuropathologica
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Abstract
Diffuse axonal injury (DAI) is a common feature of severe traumatic brain injury (TBI) and may also be a predominant pathology in mild TBI or “concussion”. The rapid deformation of white matter at the instant of trauma can lead to mechanical failure and calcium-dependent proteolysis of the axonal cytoskeleton in association with axonal transport interruption. Recently, a proteolytic fragment of alpha-II spectrin, “SNTF”, was detected in serum acutely following mild TBI in patients and was prognostic for poor clinical outcome. However, direct evidence that this fragment is a marker of DAI has yet to be demonstrated in either humans following TBI or in models of mild TBI. Here we used immunohistochemistry (IHC) to examine for SNTF in brain tissue following both severe and mild TBI. Human severe TBI cases (survival <7d; n=18) were compared to age-matched controls (n=16) from the Glasgow TBI archive. We also we examined brains from an established model of mild TBI at 6h, 48h and 72h post-injury versus shams. IHC specific for SNTF was compared to that of amyloid precursor protein (APP), the current standard for DAI diagnosis and other known markers of axonal pathology including non-phosphorylated neurofilament-H (SMI-32), neurofilament-68 (NF-68) and compacted neurofilament-medium (RMO-14) using double and triple immunofluorescent labelling. Supporting its use as a biomarker of DAI, SNTF immunoreactive axons were observed at all time-points following both human severe TBI and in the model of mild TBI. Interestingly, SNTF revealed a subpopulation of degenerating axons, undetected by the gold-standard marker of transport interruption, APP. While there was greater axonal co-localization between SNTF and APP after severe TBI in humans, a subset of SNTF positive axons displayed no APP accumulation. Notably, some co-localization was observed between SNTF and the less abundant neurofilament subtype markers. Other SNTF positive axons, however, did not co-localize with any other markers. Similarly, RMO-14 and NF-68 positive axonal pathology existed independent of SNTF and APP. These data demonstrate that multiple pathological axonal phenotypes exist post-TBI and provide insight into a more comprehensive approach to the neuropathological assessment of DAI.
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Most cited references65
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Chronic neuropathologies of single and repetitive TBI: substrates of dementia?
Traumatic brain injury (TBI) has long been recognized to be a risk factor for dementia. This association has, however, only recently gained widespread attention through the increased awareness of 'chronic traumatic encephalopathy' (CTE) in athletes exposed to repetitive head injury. Originally termed 'dementia pugilistica' and linked to a career in boxing, descriptions of the neuropathological features of CTE include brain atrophy, cavum septum pellucidum, and amyloid-β, tau and TDP-43 pathologies, many of which might contribute to clinical syndromes of cognitive impairment. Similar chronic pathologies are also commonly found years after just a single moderate to severe TBI. However, little consensus currently exists on specific features of these post-TBI syndromes that might permit their confident clinical and/or pathological diagnosis. Moreover, the mechanisms contributing to neurodegeneration following TBI largely remain unknown. Here, we review the current literature and controversies in the study of chronic neuropathological changes after TBI.
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Diffuse axonal injury in head injury: definition, diagnosis and grading.
J. Hume Adams, D. Doyle, I Ford … (1989)
Diffuse axonal injury is one of the most important types of brain damage that can occur as a result of non-missile head injury, and it may be very difficult to diagnose post mortem unless the pathologist knows precisely what he is looking for. Increasing experience with fatal non-missile head injury in man has allowed the identification of three grades of diffuse axonal injury. In grade 1 there is histological evidence of axonal injury in the white matter of the cerebral hemispheres, the corpus callosum, the brain stem and, less commonly, the cerebellum; in grade 2 there is also a focal lesion in the corpus callosum; and in grade 3 there is in addition a focal lesion in the dorsolateral quadrant or quadrants of the rostral brain stem. The focal lesions can often only be identified microscopically. Diffuse axonal injury was identified in 122 of a series of 434 fatal non-missile head injuries--10 grade 1, 29 grade 2 and 83 grade 3. In 24 of these cases the diagnosis could not have been made without microscopical examination, while in a further 31 microscopical examination was required to establish its severity.
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Neuroimaging after mild traumatic brain injury: Review and meta-analysis☆
Cyrus Eierud, R Cameron Craddock, Sean Fletcher … (2014)
1 Introduction Two important points about today's neuroimaging clinical standard of care of mTBI may be surprising. First, even though MRI is a premier modality for imaging the brain, when used in conventional clinical modes (e.g. T2- and T1-weighted structural scans) it adds little to clinical diagnoses beyond what is provided by CT (computed tomography). Thus CTs, which are faster and more cost-effective (Holmes et al., 2012; Stein et al., 2006), are routinely used by the emergency department, while MRIs, which do not pose a health risk from repeated ionizing radiation exposure, are virtually never utilized for mTBIs. Second, imaging is not used to diagnose mTBI itself, but to test for hematomas as well as to rule out head injury complications from more severe trauma. Various guidelines for diagnosing mTBI exist, most of which rely on the Glasgow Coma Scale (GCS) (Teasdale and Jennett, 1974) and details of the injury (such as self and witness reported descriptions of the accident, loss of consciousness, and evaluation of sustained trauma) (Ruff et al., 2009). The GCS assesses motor, verbal and eye responses; while there is some variability in the categories, a GCS from 13 to 15 is often designated as mild TBI, 8 or below is considered severe, and 9 to 12 is considered a moderate TBI (Jennett, 1998; Parikh et al., 2007). Ultimately, the diagnosis of TBI and its severity is made by a clinician. Approximately 1.4 million Americans receive TBI (Langlois et al., 2004), with most of these categorized as mTBIs (Cassidy et al., 2004). Although mTBI has long been considered a noncritical injury, serious short and long term effects have been documented. Additionally, there is broad acceptance that multiple mTBIs can have serious long-term consequences (Guskiewicz et al., 2003). There are two common conjectures regarding the etiology of mTBI. The first is that the frontal and anterior cortices are vulnerable to neural contusion (Adams et al., 1980; Beaumont and Gennarelli, 2006; Brandstack et al., 2006; Levin et al., 1992). The second is that linear and rotational forces act on axon bundles, leading to axonal injury (Buki and Povlishock, 2006; Gennarelli et al., 1982; Meythaler et al., 2001; Povlishock et al., 1992). After initial injury, secondary mechanisms elicit biochemical, metabolic, and cellular changes in the time frame of minutes, days and months (Giza and Hovda, 2001; Loane and Faden, 2010; Xiong et al., 1997). Within the first fifteen minute post-injury, there is an extreme dip in neuropsychological performance (McCrea et al., 2002) and deficits can often linger for a week or longer (McCrea et al., 2003). The definition of the acute time frame varies across publications and some studies report acute periods of up to 1 month post-injury (Landre et al., 2006). Our study uses the term acute mTBI up to two weeks post-injury. Using the term acute or “semi-acute” for time periods up to 2 weeks post-injury is common in the literature (Gasparovic et al., 2009; Mac Donald et al., 2011; Mayer et al., 2010; Messe et al., 2011). Most mTBI patients recover, but a substantial minority have persistent disabling problems (Alexander, 1995; Kushner, 1998), known as post-concussion syndrome (PCS). Although criteria have been established by the Diagnostic and Statistical Manual of Mental Disorders IV (American Psychiatric Association, 2000) and International Statistical Classification of Diseases and Related Health Problems (ICD-10), PCS is difficult to diagnose and its symptoms are nonspecific. PCS also manifests symptoms similar to other disorders such as major depression (Iverson, 2006; Iverson and Lange, 2003), chronic pain (Smith-Seemiller et al., 2003) and other diseases such as somatization disorder. Indeed, neuropsychological testing in chronic stages of mTBI (even on the time scale of months) has been criticized as non-specific and insensitive (Iverson, 2005; McCrea and American Academy of Clinical Neuropsychology, 2008), and several studies have questioned the ecological validity of these assessments (Satz et al., 1999; Silver, 2000) and proposed improved approaches for detecting persisting cognitive deficits and linking these to neuroimaging results (Geary et al., 2010). Heterogeneity of injury and current limitations in the sensitivity of imaging are challenges to developing diagnostic tools as well as predictors of recovery. Some of the major complicating factors include: 1) the fact that mTBI is a heterogeneous injury, with complicated dependencies on the mechanism of injury (e.g. an automobile accident vs. a military blast exposure) and the directional and temporal profiles of the forces impacting the skull and body; 2) mTBI lesions are diffuse and microscopic; and 3) the expected outcome of most patients is an eventual recovery. Thus, the physical size and heterogeneous distribution of injury in the brain make detection in an individual challenging and further make reliance on group averages problematic. In addition, since the time course of the injury leads to lingering post-concussive symptoms in a small number or injuries (Alexander, 1995; Kushner, 1998), it is a statistically challenging goal to try to predict which individuals will not recover fully. Finally, longitudinally, the presence or absence of CT findings does not correlate with long-term outcomes such as PCS (Hanlon et al., 1999; Huynh et al., 2006; Kurca et al., 2006; Lee et al., 2008; McCullagh et al., 2001; Tellier et al., 2009). To summarize, imaging is challenging at both acute and chronic stages of mTBI, and attempting to characterize the full time course compounds the level of complexity. Despite the challenges, there has been a growing research effort to characterize structural and functional effects of mTBI. As shown in this paper, the full range of neuroimaging technologies have been brought to bear on this issue, including CT, positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetoencephalography (MEG), electroencencephalography (EEG), and 12 subtypes of MRI, such as diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI). Moreover, collectively, these studies have examined mTBI at both acute and chronic stages of the injury. In reviewing the literature, it is important to note that the time post-injury of a study can affect its participant exclusion criterion, leading to prospective and symptomatic mTBI groups (Dikmen et al., 1992). In prospective mTBI studies, the exclusion criteria are independent from mTBI (e.g. specific age ranges or drug dependences). Symptomatic mTBI studies recruit chronic participants. Based on estimated recovery rates, this corresponds to effectively excluding the majority of those who sustain mTBIs. In other words, studies of symptomatic groups enroll participants because they have lingering complaints caused (presumably) by their head injury, whereas prospective studies recruit based on mTBI records at the time of concussion (before any chronic mTBI is known). This paper broadly reviews mTBI neuroimaging studies of structure and function to highlight the tremendous effort that has taken place to investigate the spectrum of acute to chronic time scales. We additionally provide meta-analyses to examine the current utility of MRI for studying both structure and function. In terms of structure, some reports claim that MRI is more sensitive to detect complicated mTBI than CT (Mittl et al., 1994). Similar to other authors, we use complicated mTBI to include the broad range of abnormalities that lead to non-negative imaging results (Arciniegas et al., 2005; Iverson, 2005; Williams et al., 1990). It should be pointed out that definitions of ‘mild’ vary widely among both clinicians and researchers. Thus while many studies exclude participants with imaging findings, this is not universally the case. Among the other neuroimaging methods, it is also unique in that it can be used to study both structure and function. Many physical parameters provide MRI with a wide range of contrast mechanisms, enabling “traditional” T1- and T2-weighted structural scans, neural correlates of brain function using fMRI, white-matter microstructure by diffusion MRI, and biochemistry through MR spectroscopy. Thus our meta-analyses focus on three areas of MRI study. The first meta-analysis is motivated by the heterogeneity of fMRI findings and focuses on the question of anatomical consistency for fMRI. Similarly, the second analysis examines the issue of white matter vulnerability to mTBI. Looking at anatomically localized findings, previous neuroimaging data suggest that anterior regions of the brain are more vulnerable to abnormalities (Hashimoto and Abo, 2009; Lipton et al., 2009; McAllister et al., 1999; Niogi et al., 2008a). However, published reports are highly heterogeneous in their findings of regional white matter changes. Thus we examined whether anatomical consistency in mTBI lesions exists in the literature. Our third meta-analysis examines the apparent inconsistency in diffusion-based anisotropy findings across studies that has led to debates about whether or not anisotropy values increase, decrease, or even change at all after mTBI (Lange et al., 2012) as well as whether anisotropy levels positively or negatively correlate with performance levels in neuropsychological assessments (FitzGerald and Crosson, 2011). Recent reports suggest that it is important to consider the time post injury in diffusion weighted imaging (Mayer et al., 2011; Niogi and Mukherjee, 2010). For example, Niogi and Mukherjee (2010) suggest that anisotropy is increased in the acute phase and decreased in the chronic phase in symptomatic TBI patients. Similarly Mayer et al. (2011) note that anisotropy values can be either reduced or increased in semi-acute time points, but tend to be decreased in later, chronic stages of symptomatic mTBI. Based on these considerations, we tested the hypotheses that anisotropy is increased in the acute phase and decreased in the chronic phase. Specifically, we performed a meta-analysis that considered the time post-injury of each study's mTBI cohort. Our results support the hypothesis that acute mTBI is associated with elevated anisotropy values and chronic mTBI complaints are correlated with depressed anisotropy. 2 Methods 2.1 Data collection We queried PubMed (http://www.ncbi.nlm.nih.gov/pubmed) for articles on imaging-based mTBI studies. The authors extracted key information from these articles and entered items into a LimeSurvey database (http://www.limesurvey.org) that we developed for this study. LimeSurvey is an open source web-based survey application that features an unlimited number of participants in a survey (in our case, each paper constituted a survey participant) and flexible export functions to multiple text and software formats. The LimeSurvey web interface enabled the authors to collaborate from different locations. Data from the articles were manually entered into LimeSurvey and these data were examined and summarized using custom-built Python parsing scripts. See Supplementary Materials for the actual survey we used for each paper. We examined publications spanning 21 years from 1990 to 2011 using a query focused on mTBI and neuroimaging: (mTBI or “mild traumatic brain injury” or “post concussive syndrome” or “post concussion syndrome” or “postconcussion syndrome” or “postconcussive syndrome”) and (neuroimaging or “magnetic resonance imaging” or MRI or “positron emission tomography” or PET or magnetoencephalography or MEG or electroencephalography or EEG or “functional magnetic resonance imaging” or fMRI or “diffusion tensor imaging” or DTI or T2 or “diffusion spectrum imaging” or DSI or “diffusion weighted imaging” or DWI or SWI or “susceptibility weighted imaging” or “T2*” or “CT” or “computed tomography” or FLAIR or “diffusion kurtosis imaging” or “diffusional kurtosis imaging” or DKI or “single photon emission computed tomography” or SPECT or NIRS or “near-infrared spectroscopy” or fNIRS or “functional near-infrared spectroscopy” or “resting state” or “functional connectivity” or “default mode network”) and (“1990” [Publication Date]: “2011” [Publication Date]). Our search date was November 11, 2011. This query resulted in 298 publications from which we excluded 176, using seven exclusion criteria. Specifically, we excluded 85 review articles, 47 non-imaging articles (most of which mentioned CT in relation to subjects' TBI status, but without any association to a statistical variable), 13 case studies, 12 animal studies, 9 articles that mixed mTBI with moderate/severe TBI or PTSD, 7 articles that were not in English, and 3 others (a video article, a fatigue article, and a mold exposure article). See Table S1 for a full listing of the 122 publications that we included We extracted the following into the LimeSurvey database: 1) Time after concussion (the median/mean time between injury and neuroimaging). If studies were longitudinal or had multiple groups of subjects, multiple time points were reported. 2) Range of time after concussion (the minimum and maximum time between injury and neuroimaging). In publications where only standard deviations were provided, we approximated the range as the median ± 1.6 standard deviations. Note that this is analogous to the 90% confidence interval. 3) Number of subjects (sum of mTBI and control groups). Note that some publications (such as longitudinal studies) included multiple separated time points. 4) Imaging modality (CT, 12 subtypes of MRI including DTI and fMRI, PET, SPECT, MEG, EEG and near infrared spectrometry). 5) Hypothesized effect of mTBI motivating the imaging study, which we termed the “theme.” For example, several studies hypothesized that mTBI would lead to white matter abnormalities, and thus to characterize this they measured anisotropy. In contrast, studies that hypothesized that connectivity would be affected, could address this research “theme” not only by using diverse measures including fMRI but also diffusion imaging-based tractography. 6) Other imaging results (anatomical coordinates, ROIs with significant anisotropy, and other parameters). 2.2 Statistical analysis The fMRI meta-analysis used Ginger ALE (Eickhoff et al., 2009, 2012; Turkeltaub et al., 2012). All fMRI coordinates were converted into Talairach space. We used the default Ginger ALE parameters (Eickhoff et al., 2009), and additionally added the number of subjects per experiment. We chose a false discovery rate threshold level of 0.05. We used all available fMRI publications of mTBI, most of which used working memory tasks, but the tasks also included resting state fMRI, an auditory odd-ball task, and a spatial navigation task (Chen et al., 2007; Krivitzky et al., 2011; Mayer et al., 2009, 2011; McAllister et al., 2001, 2011; Slobounov et al., 2010; Witt et al., 2010). Only coordinates from contrast images using two-sample test (mTBI and control) were included. AFNI (Cox, 1996) was used to present fMRI meta-analysis images in Talairach space. For the DTI meta-analyses, each reported region of interest (ROI) was coded using the ICBM-81 atlas (Mori et al., 2008). ROIs that were outside of this atlas were only used in the analyses involving time post-injury and omitted for the spatial DTI analysis. Most publications reported FA values, however we also included studies that reported relative anisotropy (RA). Publications found anisotropy differences in two predominant ways — either categorically (by examining anisotropy means between mTBI and control groups), or parametrically (through regression analyses correlating anisotropy values with neuropsychological performance). AFNI (Cox, 1996) was used to present DTI meta-analysis results in Montreal Neurological Institute (MNI-152) space. As in other publications that account for diverse sets of assessment scores (Bazarian et al., 2007), we normalized neuropsychological results using algebraic negation (multiplying the original score with a negative one) such that better performance corresponds to positive scores. For example, we did not transform results from the California verbal learning test in which scores should be higher for normal performance than for impaired performance, but we did transform (negate) completion time for the Trail making A test (in this case, better raw scores are lower, corresponding to faster completion times). Our motivation for doing this was that it enables us to unambiguously discuss negative and positive correlations between neuropsychological results with changes in imaging measures. 3 Results 3.1 Neuroimaging literature The number of mTBI publications has dramatically increased over the past two decades. Fig. 1A shows the number of publications with respect to time for the neuroimaging articles we analyzed in the context of mTBI research as a whole. The more general mTBI PubMed search in Fig. 1A excluded our neuroimaging keywords (to include both imaging and non-imaging studies). Specifically, we used (mTBI or “mild traumatic brain injury” or “post concussive syndrome” or “post concussion syndrome” or “postconcussion syndrome” or “postconcussive syndrome”) and (“1990”[Publication Date]: “2011”[Publication Date]). The figure shows that the rate of mTBI publications has been increasing over time, with a notable upsurge occurring around 2007. Fig. 1B classifies imaging modalities of the publications we analyzed over time. Most of the imaging modalities are MRI-based. The number of studies relying (at least in part) on CT is relatively large, primarily because it is used ubiquitously (and almost exclusively) in emergency room settings. Thus, CT was used as part of the clinical characterization of the subjects in many studies. The functional modalities (EEG/MEG, fMRI, SPECT/PET, and ASL) have been steadily used during the time period shown. Figs. 2 and 3 show that structural and functional neuroimaging studies have examined mTBI across acute to chronic time scales with a wide range of imaging modalities. It is important to note that the number of subjects in these publications represents the total number of mTBI and control participants for each experiment, which could include different participant cohorts. Note too, that many experiments have large variability for subjects' post-injury data collection times. Also, while the number of subjects includes control participants, for obvious reasons these controls did not contribute to the post-injury collection times in these Figs. 2 and 3. Focusing on some observations from each figure, Fig. 2 shows that CT studies have been performed on both small and large cohorts, but the majority of these studies occur soon after injury, usually using the subjects' clinical CTs. DTI and the other structural modalities have a much better coverage of the time post-injury. For Fig. 3, one observation is that many EEG studies also occur soon after injury but in contrast to CT, EEG has also been broadly applied to the entire time course post-injury and to a large number of subjects. Fig. 3 also shows that task-based fMRI has been similarly broadly applied, while resting state fMRI studies have predominantly focused on acute and semi-acute time ranges. PET, ASL, CE-MRI and MEG seem to be rarely used. In addition, there is a lack of PET studies of acute mTBI and both PET and MRS publications used groups that were smaller than 40 subjects. The research areas of neuroimaging studies tend to follow distinct structural and functional categories as shown in Fig. 4. As mentioned previously, CT was used as part of the clinical characterization of the subjects in many studies. In papers where CT was a research focus, it was used to examine structural abnormalities arising from mTBI. Although the category of “mild” TBI sometimes includes the stipulation of negative findings, CTs can nonetheless be used to detect abnormalities from a wide range of trauma severities that appear as hyper- or hypo-intensities observed in gray matter or white matter regions as well as hematomas, cerebral swelling, and intraventricular hemorrhage. Gray matter is composed largely of neuronal cell bodies, glial cells, and the capillaries. White matter abnormalities are thought to reflect stretched or sheared axon bundles or abnormal cellular microstructures. Abnormalities not clearly falling into the categories of hematomas or white/gray matter findings have been designated as “complicated mTBI” in Fig. 4. Complicated mTBI was examined fifty-six times in our publication sample, mainly using CT and MRI. Gray matter (GM) abnormalities were only reported in one T1 and one CT study. Abnormal blood brain barrier (BBB) permeabilities were reported by three publications using SPECT and CT (CT studies also detected tau/S100b proteins in blood). White matter (WM) abnormalities were examined thirty-eight times (primarily with DTI). Hematomas were only reported in two CT-based studies. Metabolites were analyzed by a PET, a SPECT and five MRS publications. Perfusion was examined by one PET, one ASL and twelve SPECT publications. Task-based imaging was used in seventeen fMRI and fifteen EEG studies, often linking tasks to brain activity. Connectivity analyses were examined twenty-seven times, detecting low frequency activity, connectivity differences between neural regions, EEG pathologies, altered resting state networks (using fMRI), and connectivity maps (using white matter tractography). Neuropsychological assessments were used in seventy-two publications and were the one theme of investigation (despite not relying on any particular imaging modality itself) that spanned all structural and functional modalities. 3.2 fMRI meta-analysis Figs. 5–7 display results from our MRI meta-analyses (see also downloadable image files in Supplemental data). Fig. 5 shows results from a Ginger ALE analysis of the fMRI publications summarized in Table 1. Table 1 also examines how many independent publications support each ALE peaks. The analysis produced six regions that were consistently more active in mTBI compared to control groups and seven regions with lower activity for mTBI. Spatially, these regions suggest an anterior-to-posterior pattern in which activity is reduced in anterior regions and increased in posterior regions. Of the seven regions with decreased mTBI activity, six were in the frontal lobe or anterior cingulate, with the remaining one being relatively posterior (temporal lobe/BA 39). The regions with increased mTBI activity consisted of two coordinates in the cerebellum, two insula regions, and two foci in the parietal lobe (BA 40). The mean Talairach anterior-to-posterior coordinate for regions with reduced activity was Y = 15 mm, compared to Y = − 23 for increased mTBI activity, and a two-sample t-test of Y between the decreased and increased regions was significant (p = 0.05, two tailed). 3.3 Anatomical frequency of anisotropy findings Close examination of the number and anatomical locations of publications reporting white matter abnormalities revealed significant anatomical heterogeneity as shown in Fig. 6. See Table 2 for white matter regions in the ICBM-81 atlas as well as their abbreviations and center-of-mass coordinates. It is important to note that several ICBM-81 regions have not been reported by the publications we examined. These include the pontine crossing tract, medial lemniscus, inferior cerebellar peduncle, cerebral peduncle, posterior thalamic radiation, cingulum to hippocampus, superior fronto-occipital fasciculus, tapetum, and inferior longitudinal fasciculi. These unreported regions were coded in two ways; both leading to statistically significant correlations between anatomical location, along anterior-to-posterior gradient, and the number of abnormalities. Specifically, we calculated a Spearman correlation 1) omitting these regions from the statistical test (using only regions that had reported abnormal findings in the literature) resulting in a ρ2 = 0.26 (p 2 weeks). In Fig. 7A, a two-sample t-test confirms that anisotropy is greater in the acute phase (one-tailed, 14 DOF, p = 0.02). In Fig. 7B, acute phase anisotropy was also significantly anti-correlated with neuropsychological performance compared to a predominant positive correlation in the post acute phase (one-tailed, 7 DOF, p < 0.006). Finally, to put these results into the context of the literature, it is important to note that the participant populations were recruited using different criteria in the acute and chronic periods. Specifically, all of the acute studies recruited prospective mTBI subjects, the majority of whom would be expected to recover from their injuries. Conversely, many of the post-acute mTBI studies of anisotropy selected for symptomatic subjects. 4 Discussion An obvious key limitation of this paper (and any review that attempts to report a snapshot of a vibrant field) is that components of it are immediately outdated. Although we believe that the conclusions we draw here will be qualitative accurate for the next few years, the literature we sampled just barely supported any meaningful fMRI meta-analyses, and the issue of the anisotropy values' dependence on time post-injury is far from conclusive. Part of what we have hoped to accomplish here is an assessment of the modalities, questions, and times post-injury that recent publications have examined. Based on this we hope that research teams can use this to strategically bolster areas of this field that would most benefit from additional effort. We are optimistic that we and/or others will improve our literature analysis approaches in the future to continue what has the potential to become an ongoing, reflective process. Our review of the mTBI literature highlights the fact that currently about 1/10th of published studies have a human neuroimaging component and that several small and large studies spanning acute to chronic time points have been conducted. These studies have examined both structural and functional changes with mTBI, using virtually every available medical imaging modality. Two key commonalities have been used across the majority of imaging studies. The first is the comparison between mTBI and control populations. The second is the attempt to link imaging results with neuropsychological assessments. Indeed, in addition to highlighting the fact that research topics have largely (but not exclusively) fallen into either structural or functional categories and providing a sense of the proportion of work that has been devoted to the two, Fig. 4 strikingly shows that neuropsychology appears to be the predominant commonality across the imaging literature. This underscores the fact that mTBI neuroimaging efforts have prioritized attempting to link brain measurements with neuropsychological assessments. Despite the large efforts to date, neuroimaging methods still lack the individual patient-level sensitivity and specificity to serve as a diagnostic tool for mTBI. Similar to other reviews in this area (Belanger et al., 2007; Hunter et al., 2012; Niogi and Mukherjee, 2010; Prabhu, 2011; Pulsipher et al., 2011), we believe that ultimate motivations for further neuroimaging work are to provide data to predict an individual's recovery, measure her/his transient and persistent cognitive deficits at a level that compliments or exceeds the sensitivity of neuropsychiatric testing, and quantifies the success of cognitive and drug-based interventions (McAllister et al., 2011). In the meantime, the field needs further effort to provide stronger correlative understanding of the relationships between neuroimaging and neuropsychiatry, and simultaneously both types of measures also need improvement. Our fMRI and DTI meta-analyses show, however, that there is hope for consistent neuroimaging markers of structure and function. The fMRI meta-analysis results in Fig. 5 and Table 1 show a frontal vulnerability in mTBI, demonstrated by decreased signal compared to controls. fMRI studies consistently report decreased activity in mTBI in frontal regions such as right MFG, ACC, and right precentral gyrus. Our finding of bilateral decreases in DLPFC is consistent with three published studies (Chen et al., 2007; Mayer et al., 2011; McAllister et al., 2011). The DLPFC may be related with working memory. Specific localization of working memory intense regions was found in non-human research (Funahashi et al., 1989) and in humans using fMRI (D'Esposito et al., 1995). Even though DLPFC responds to working memory it is a very complex region and serves a multitude of cognitive functions (Kane and Engle, 2002). Interestingly, among the most significant mTBI increases, right cerebellar tonsil and left culmen, were noted in four independent publications (Chen et al., 2007; Mayer et al., 2011; McAllister et al., 2011; Witt et al., 2010). Krivitzky et al. (2011), has noted that the cerebellum has been implicated for regulating behavior, working memory, and other aspects of executive control. In addition, it has long been recognized that cerebellar lesions can lead to postural deficits (Horak and Diener, 1994), which is a prominent issue in mTBI (Guskiewicz et al., 1996, 2007; McCrea et al., 2003). Finally, it is important to note that the well-known fMRI issue of multiple comparisons correction may be a limitation of our Ginger ALE analysis. Most of the publications that we used corrected their findings for multiple comparisons. However, notable exceptions include McAllister et al. (2001) and Witt et al. (2010). Although the predominance of anterior fMRI and DTI findings suggests that frontal areas are more vulnerable to injury, we have not ruled out other possible explanations with this current study (for example, it is possible that there is a sampling bias in the literature, which favors injury mechanisms that lead to more injuries in prefrontal cortex). Another limitation of this fMRI meta-analysis is that, unlike the diffusion-weighted literature, the number of fMRI studies published did not have power enough for an fMRI analysis using time post-injury. We feel that “time-resolving” fMRI studies could be an important area for future investigation. Structurally, diffusion weighted imaging is currently one of the most promising techniques for characterizing the subtle and heterogeneous changes that occur with mTBI. Note Supplemental Table 2 lists specific details about the studies used to generate Figs. 6 and 7. Fig. 6 demonstrates that differences in the frequency of reported mTBI white matter anisotropy values have an anterior to posterior gradient. Taken independently, each DTI study shows white matter differences that seem to be sporadic. When integrating across all reported findings, though, these frequencies of abnormalities have a systematic spatial distribution corroborating previous suggestions that anterior regions of the brain are more vulnerable to abnormalities (Hashimoto and Abo, 2009; Lipton et al., 2009; McAllister et al., 1999; Niogi et al., 2008a). Anterior regions may also be related with executive/cognitive function disabilities in mTBI (McCrea et al., 2003). Using the existing literature to time-resolve results, Fig. 7 suggests that white matter anisotropy can serve as an important potential marker of both chronic symptoms as well as of an acute response to secondary injuries. Taken together, the literature demonstrates that elevated anisotropy values (and negative correlations with neuropsychological performance) are more frequently reported for studies of acute mTBI, while depressed anisotropy findings (and positive correlations with neuropsychological performance) are reported more frequently for chronic studies. Our analyses show that elevated anisotropy values are more frequently reported for studies of acute mTBI, while depressed anisotropy findings are reported more frequently for post-acute studies, corroborating conjectures of Mayer et al. (2011) and Niogi and Mukherjee (2010). Thus, Fig. 7 helps to clarify an apparent inconsistency in the literature about anisotropy changes after mTBI by “time resolving” the results. In addition, Fig. 7B raises important new questions about the interactions of anisotropy, neuropsychological performance, and time post-injury. In particular, the fact that cognitive performance is negatively correlated with anisotropy during acute stages, but positively correlated with anisotropy chronically is puzzling. At least two issues remain outstanding with the results in Fig. 7. The first is the inconsistency in Fig. 7A of the results for paper XXI (Lo et al., 2009). In discussing their results, Lo et al. (2009) are careful to point out that the small sample size of their study calls for more future work. However, they also suggest potential explanations for their findings, including the possibility that the observed increases in FA could reflect recovery from injury; or that specific degradation mechanisms could target subsets of fibers, leading to enhanced FA; or, finally, that the increases in FA are compensatory for other observed decreases in other brain regions. The second issue relates to the heterogeneity of the neuropsychological testing in the papers used to generate Fig. 7B. Thus a limitation in the literature (and in our results) is the lack of consistency of neuropsychological assessment. As previously mentioned, we have “normalized” the scores such that better performance corresponds to a positive score, regardless of what test was used. We note, however, in light of the literature's “uncontrolled assessment variance”, that our results are conservative as well as robust across cognitive domains and testing variability. Undoubtedly, though, more sensitive, standardized assessments that are specifically adapted to mTBI are needed to further reduce the limitations of these types of studies. The results of Fig. 7 and the outstanding issues that we have raised suggest that more studies focused on mTBI and anisotropy are warranted and necessary. At this point we can only conjecture on possible explanations for our meta-analysis results. One possibility for the increase in FA acutely is that MR diffusion-based measurements are not purely sensitive to white matter microstructure, but additionally are inflated by acute stage secondary injury or compensatory mechanisms (Mayer et al., 2011; Niogi and Mukherjee, 2010). The idea behind this is that inflammation and secondary injury factors (including ischemia, cerebral hypoxia, and cerebral edema) may increase anisotropy in acute phase, but that these factors do not contribute in the chronic phase. Chronically, though, residual damage of white matter could lead to a decreased anisotropy signal. A second possible explanation of Fig. 7B, however, is that there is not a simple causal relationship between anisotropy and cognitive performance. Whatever the mechanism, Fig. 7B indicates that poor neuropsychological performance is associated with high anisotropy scores immediately after injury and with low anisotropy in the chronic phase. From a statistical point of view, Fig. 7A demonstrates a main effect between anisotropy and time post-injury, but Fig. 7B additionally indicates an interaction with cognitive performance. We have previously mentioned that there is controversy surrounding anisotropy changes after TBI. Nonetheless FitzGerald and Crosson (2011) note in their review of the literature a general acceptance that decreases in FA values are expected after TBI induced axonal injury. However, they also point out that mechanisms for increases in FA are also possible (e.g. unequal injury in one fiber in voxels where two or more fibers cross). Furthermore, there are examples in other disorders (such as autism) where widespread differences have been noted in the relationship of FA with cognitive function (Ellmore et al., 2013). Better understanding of the underlying mechanisms of anisotropy changes following TBI will likely need to rely on methods and validation studies in animal models such as those of Budde et al. (2011) and Mac Donald et al. (2011) to relate pathology to diffusion tensor imaging findings. Finally, the majority of the literature focuses on FA, but some publications report radial diffusion (RD), axial diffusion (AD) and mean diffusion (MD) diffusion as well. The four measures are related to each other and usually FA and AD are significant in the same direction while MD and RD tend to be significant in the opposite direction. Results of these measures are found in supplemental Table S2. To summarize, this review and meta-analysis have demonstrated several important points about the ongoing use of neuroimaging to understand the functional and structural changes that occur throughout the time course of mTBI recovery. A broad range of neuroimaging modalities are being applied to this problem across the range of post-injury time scales and an important component of many studies is the effort to link imaging measurements to neuropsychological assessments of cognitive deficits. Upon closer scrutiny of two MRI-based modalities, we demonstrated the importance of interpreting white-matter measures of anisotropy in the context of time post-injury and for both structure and function we evaluated whether or not consistent findings were available for evaluating anatomical vulnerability to mTBI. The fact that we have been able to produce statistically significant results in this area is encouraging. Based on the complexity of the injury, however, much more work in this area is required. Future publications focused in depth for each image modality would greatly complement this generic analysis and the field of mTBI. Here we have been able to preliminarily examine important factors related to anatomical vulnerability and time post-injury, but these issues need to be refined with future data and many other factors remain to be studied (e.g. injury mechanism differences, number and timing of previous concussions, and psychological comorbidities including major depression and post-traumatic stress) (Iverson and Lange, 2003; Rosenbaum and Lipton, 2012). Ultimately a major driving motivation for continued study is the further efficacy of neuroimaging for clinical diagnosis at both acute and chronic stages and the synergistic improvement with neuropsychological assessments and rehabilitation strategies.
Author and article information
Journal
Journal ID (nlm-journal-id): 0412041
Journal ID (pubmed-jr-id): 133
Journal ID (nlm-ta): Acta Neuropathol
Journal ID (iso-abbrev): Acta Neuropathol.
Title:
Acta neuropathologica
ISSN
(Print):
0001-6322
ISSN
(Electronic):
1432-0533
Publication date Nihms-submitted: 19
February
2016
Publication date
(Electronic):
20
November
2015
Publication date
(Print):
January
2016
Publication date PMC-release: 01
January
2017
Volume: 131
Issue: 1
Pages: 115-135
Affiliations
[1
] Department of Neurosurgery, Penn center for Brain Injury and Repair, Perelman School
of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Author notes
Corresponding Author: Douglas H. Smith, M.D., The Robert F. Groff Professor of Neurosurgery, Vice Chairman for Research and Education,
Department of Neurosurgery, Director of Penn Center for Brain Injury and Repair, 105
Hayden Hall, 3320 Smith Walk, University of Pennsylvania, Philadelphia, PA19104, USA
Tel. +1 215 898 0881,
Fax. +1 215 573 3808,
smithdou@
123456mail.med.upenn.edu
Article
Accession ID: PMC4780426 Pmcid ID: PMC4780426 Pmc-uid ID: 4780426 Manuscript ID: nihpa740164DOI: 10.1007/s00401-015-1506-0
PMC ID: 4780426
PubMed ID: 26589592
SO-VID: dd1ac2ab-b45f-4683-95fc-398a0ecb7641
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