Secondary Logo

Journal Logo

Critical Review

Neuroimaging in the Diagnosis of Chronic Traumatic Encephalopathy

A Systematic Review

Sparks, Philip BSc*; Lawrence, Tim FRCS; Hinze, Stephan MRCP*

Author Information
Clinical Journal of Sport Medicine: March 2020 - Volume 30 - Issue - p S1-S10
doi: 10.1097/JSM.0000000000000541
  • Free



Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative condition associated with repeated mild traumatic brain injury (rmTBI). Repeated mild traumatic brain injury includes both subconcussive and concussive head injury.1 Chronic traumatic encephalopathy was first described in 1928 in boxers2; it has since been reported in various contact sports, domestic abuse victims, military veterans, and in cases of self-inflicted head banging.3 Currently, a definitive diagnosis of CTE can only be made at postmortem.

The clinical features of CTE typically begin insidiously 8 to 10 years after exposure to rmTBI and include memory loss, aggression, impulsivity, depression, and suicidality.3 Progression of the disease leads to motor symptoms such as parkinsonism and speech abnormalities.3 In advanced disease, the clinical presentation may be similar to Alzheimer disease (AD) or frontotemporal dementia.4

Four progressive stages of pathology have been described,5 and recently, consensus criteria have been developed.6 The pathognomonic feature of CTE is accumulation of abnormal hyperphosphorylated tau in neurons and astroglia in a specific distribution around small blood vessels at the depths of cortical sulci.5,6

Despite these recent advances in the neuropathological diagnosis of CTE, definitive in vivo diagnosis is not yet possible. In vivo diagnosis is the major focus of current CTE research because it provides the key to understanding the pathophysiology, prevalence, clinical features, course, risk factors, and management of CTE. A variety of diagnostic approaches have been investigated, including the development of clinical criteria,7–9 identification of cerebrospinal fluid (CSF) and blood biomarkers,10,11 and use of various neuroimaging techniques.12

Recently, there has been a wealth of research investigating the use of neuroimaging to diagnose CTE due to the development of novel neuroimaging techniques such as tau-selective radioligands.13 A systematic review of studies published before December 1, 2014 identified 7 studies using a range of imaging modalities.12 The authors of the review concluded that although some of these modalities showed great promise for revealing highly sensitive biomarkers of CTE, there was a need for further longitudinal, multicentre, and multimodal investigation.

The present work evaluates the utility of neuroimaging in the diagnosis of CTE by systematically reviewing recent evidence for changes in neuroimaging biomarkers in suspected cases of CTE compared with controls.


We searched for articles published between December 2, 2014, and July 13, 2016. These dates were chosen to provide an update on a previous systematic review of articles published until December 1, 2014.12 We searched PubMed using the keywords neuroimaging or imaging AND repetitive traumatic brain injury or repetitive brain trauma or chronic traumatic encephalopathy. Studies were eligible for review if (1) neuropsychological assessment of study subjects showed clinical symptoms or signs associated with CTE, including memory loss, aggression, depression, headache, confusion, impaired judgment, and impaired impulse control. These symptoms and signs are broadly based on previously described clinical criteria.7–9 Additional eligibility criteria: (2) repetitive brain trauma was explicitly stated, and it was clear that subjects had sustained more than 1 concussion; or (3) repetitive brain trauma was explicitly stated, and it was clear that there was more than 1 subconcussive impact to the head; or (4) participants of the study were contact sport athletes who experienced repetitive brain trauma through direct impact to the head. Exclusion criteria were case studies, review articles, and articles focusing on repetitive head trauma from military service, head banging, epilepsy, physical abuse, or animal models.

The literature search and data collection were performed by 1 unmasked reviewer. Article titles and abstracts were used to screen retrieved articles for eligibility/exclusion. Full-text articles of potentially eligible studies were accessed to identify those meeting the review criteria. Variables sought from included articles were participant characteristics (number, sex, mean age, and retirement status), matching of controls, nature of exposure to head injury, clinical evidence of CTE, modality sequence, and analysis technique. Risk of bias within and between studies was not formally assessed. The principal outcome measure was differences in imaging metrics between suspected cases of CTE and controls. The secondary outcome measure was direct association between imaging metrics and symptom severity in suspected cases of CTE.


Results of the Search

A total of 298 articles were retrieved by the search. No duplicates were identified. Screening excluded 271 articles. Full-text assessment of eligibility excluded 20 articles. Of these, 12 articles were excluded because they did not satisfy review criteria 2, 3, or 4,14–25 7 articles did not satisfy criteria 1,26–32 and the full text of 1 article could not be accessed.33 In total, 7 articles met the review criteria.34–40 A flowchart of the literature search results is shown in Figure 1. Summaries of included studies are given in Table 1.

Figure 1
Figure 1:
Study flow diagram.
TABLE 1-a:
Summary of Search Results*
TABLE 1-b:
Summary of Search Results*
TABLE 1-c:
Summary of Search Results*

To facilitate comparison of results, data are presented under the categories of structural magnetic resonance imaging (MRI), diffusion MRI, and radionuclide studies. Where relevant, other publications are also discussed to facilitate interpretation of results.

Structural Magnetic Resonance Imaging

Although conventional MRIs are typically unable to detect CTE,41 a number of groups have looked for specific features that may distinguish CTE. One such feature is the Evans index (EI), which indicates the ventricular size by comparing the maximum width of anterior horns of the lateral ventricles to the maximal width of the internal skull diameter. Increased EI indicates ventricular enlargement, which may occur because of cerebral atrophy.42,43 Both ventricular enlargement and cerebral atrophy are characteristic features of CTE at postmortem.3 Increased EI was reported in a group of 10 boxers compared with 9 participants in noncombative sports (P = 0.05, Cohen f = 0.531).40 However, given that ventricular enlargement is reported in just 53% of neuropathologically verified CTE cases,3 this biomarker is unlikely to have the sensitivity required for clinical use.

In a study comparing 5 retired professional boxers with 4 age-matched controls, no significant differences in the absolute volumes of gray matter (GM), white matter (WM), CSF and whole brain (P > 0.05, actual values not reported), or amygdala (P = 0.28) and hippocampus (P = 0.41) were reported.35

One study investigated the presence of a cavum septum pellucidum (CSP) as a marker of CTE.37 The CSP typically closes during brain development but persists as a normal anatomical variant in around 10% of adults.44 Repeated head injury causes an increase in the size and extent of CSP, perhaps because of repeated sudden increases in intracranial pressure which force CSF through small defects in the septal leaflets.45 Cavum septum pellucidum has been identified in 69% of neuropathologically confirmed cases of CTE.3 These neuropathological findings are consistent with those using high-resolution MRI in a cohort of 72 retired National Football League (NFL) players.37 Compared with 14 former professional noncontact sport athletes, NFL players had a higher rate of CSP (92% vs 57%, P = 0.0006), greater length of CSP (7.7 vs 4.4 mm, P = 0.03), and greater ratio of CSP length to septum length (0.15 vs 0.08, P = 0.03).37 Greater CSP length (>6 mm) was associated with reduced performance on a list-learning task (P = 0.04) and a reduced score on a measure of verbal intelligence (P = 0.02) compared with shorter CSP length (<3 mm) among NFL players.37 Based on this finding, the authors suggested that CSP lengths ≥6 mm could possibly be used in clinical practice to identify those patients at high risk of having or developing CTE.37 However, it is currently unclear whether greater CSP length is indicative of exposure to rmTBI or a biomarker of CTE.

Diffusion Magnetic Resonance Imaging

Diffuse axonal injury of WM tracts is characteristic of rmTBI in the short to medium term46,47 and is best measured using diffusion MRI techniques, most commonly diffusion tensor imaging (DTI). The acquisition, analysis, and interpretation of DTI images is comprehensively described elsewhere48,49; what follows is a short primer on DTI to facilitate interpretation of subsequent results.

Diffusion tensor imaging is based on the principle that the diffusion properties of water vary between tissues. In unstructured tissue (ie, CSF), water is free to diffuse in all directions equally; this is called isotropy. In highly structured tissue (ie, Corpus callosum), water diffusion is more restricted in certain directions by cellular structures including the cell membrane and myelin sheath. Therefore, water tends to diffuse along the axon; this directionality of diffusion is called anisotropy. Diffusion tensor imaging computes the 3D trajectory of water diffusion in each voxel, which can be visualized as an ellipsoid. The primary measurement of the shape of the ellipsoid is fractional anisotropy (FA) which ranges from zero to 1. In relatively unstructured tissue (ie, CSF and GM), FA approaches zero. In relatively structured tissue (ie, WM), FA approaches 1. The primary measurement of the size of the ellipsoid is mean diffusivity (MD) which is inversely related to FA. Impaired WM integrity is believed to be reflected by decreases and increases in FA and MD, respectively.

Fractional anisotropy and MD are generally considered to be highly sensitive but not very specific markers of WM injury, because of their inability to differentiate between demyelination and axonal injury. One solution to this problem was the introduction of 2 additional metrics, axial diffusivity (AxD) and radial diffusivity (RD). Axial diffusivity is the magnitude of longest axis of the diffusion ellipsoid. Radial diffusivity is the average of the magnitude of the 2 perpendicular axes. Reduced AxD and increased RD have been associated, respectively, with axonal and myelin injury.50,51 This interpretation was based on changes in these metrics in mouse models of axonal loss and demyelination which corresponded to histological evidence of axonal50 and myelin injury.51 However, more recent work using mouse models of spinal cord injury and multiple sclerosis showed that changes in these metrics do not always correspond to histological evidence of axonal and myelin integrity.52 This has led to an ongoing debate concerning the interpretation and validity of AxD and RD as markers of WM injury.53,54 A summary of the origin and interpretation of FA, MD, RD, and AxD is given in Table 2.

The Origin and Interpretation of Traditional DTI Metrics

An additional ongoing debate in DTI concerns the most appropriate method of normalizing DTI images to allow comparison between subjects.38,55 Initial methods included region of interest (ROI) and voxel-based morphometry (VBM) style approaches. The VBM style approach has been criticized for alignment inaccuracies and the absence of a method to determine smoothing extent.55,56 The ROI approach has been criticized because the whole brain cannot be investigated, and it requires operator intervention to define the tracts to be analyzed.55 These criticisms led to the development of tract-based spatial statistics (TBSS).55 This method may improve the sensitivity, objectivity, and interpretability of the results.55 However, there is currently a lack of consensus on which method is most appropriate.

Evidence that changes in DTI metrics are associated with cognitive dysfunction in suspected cases of CTE is substantial. Using TBSS in a cohort of 18 retired Canadian football players, reduced FA in the right superior longitudinal fasciculus (SLF) correlated significantly with reduced visual learning ability (r = 0.5, P = 0.002).39 Given that the SLF is believed to be involved in visuospatial memory,57 impairment of the WM integrity may partially explain symptoms of memory impairment described in CTE. Using an ROI approach to quantitative tractography in a study of 10 boxers, increased MD in the left ventral striatum was negatively correlated with performance in a declarative memory task (r = −0.74, P = 0.02) and reaction time to a repeating number sequence (r = 0.7, P = 0.04).40 This association is consistent with evidence that interaction between the ventral striatum and the hippocampus is important during learning.58

Evidence that changes in DTI metrics can distinguish suspected cases of CTE from healthy controls is equivocal. Comparison of DTI metrics using an ROI approach to quantitative tractography in 10 boxers and 9 participants in noncombative sports found no significant difference in FA (P > 0.1) or MD (P > 0.1) in the cerebral peduncle, inferior longitudinal fasciculus, uncinate fasciculus, or ventral striatum bilaterally.40 However, the large range in age (32 years) and boxing exposure (35 years) in this small sample of boxers limits the power of this study to detect significant differences in DTI metrics between groups. Using TBSS to compare DTI metrics in 18 retired Canadian football league players with 17 healthy controls, no regional changes in FA, RD, or MD (P values not reported) were identified.39 However, AxD was significantly increased in the right hemisphere in the SLF, anterior thalamic radiation (ATR), and corticospinal tract (CST) in players compared with controls (P < 0.05).39 Although the interpretation of changes in AxD is controversial,52–54 the observed increase in AxD may be a consequence of axonal injury.50 Given that the SLF, ATR, and CST play a role in visuospatial memory, memory encoding, and voluntary movement, respectively,57,59 axonal injury in these WM tracts may lead to the memory deficits and motor symptoms described in CTE.

Evidence discussed so far suggests that although regional changes in DTI metrics may be associated with symptoms of CTE, these changes are not able to consistently distinguish between suspected cases of CTE and healthy controls. There are 3 possible explanations for this observation. The first is that changes in DTI metrics are indicative of a pathological process which causes cognitive impairment but is not related to CTE. The second is that the severity of disease in the suspected cases of CTE in these studies was not sufficient for DTI metrics to change significantly. The third is that traditional DTI metrics are insensitive to subtle WM changes occurring in CTE.

Traditional DTI metrics assume a Gaussian distribution of diffusion displacement60 and therefore do not specifically measure individual water compartment fractions.61 This is significant because changes in FA could reflect various pathophysiological processes (ie, edema, demyelination, and microtubule damage) which affect the intra/extracellular fluid compartments to varying degrees. These compartments can be individually measured using a novel geometric diffusion MRI model (neurite orientation dispersion and density imaging, NODDI). Neurite orientation dispersion and density imaging was validated in a cohort of 13 active mixed martial artists (MMA) and 14 healthy controls by showing that FA changes in healthy controls and in groupwise comparison have a strong negative relationship with changes in the orientation dispersion index (an NODDI metric which reflects WM integrity) (adjusted r2 ≥  0.64).38 Using NODDI, they showed that only a moderate relationship exists between estimates of free/intracellular water fractions and traditional DTI metrics, thus suggesting that NODDI provides differential information about cellular microstructure. Indeed, NODDI revealed significantly increased (P < 0.005) free water in WM tracts (mainly SLF and anterior corona radiata) and significantly increased (P < 0.005) intracellular water fractions in the brainstem, cerebellum, and striatum in MMA compared with controls, findings which were not replicated by traditional DTI metrics. These findings highlight the utility of specifically measuring free/intracellular water fractions in diffusion MRI and reveal a possible role of edema in the pathophysiology of CTE. Given that these findings were reported in active MMA, combined with evidence of edema in the acute phase of mTBI in both animals62 and humans,63 it is likely that edema plays a significant role in the acute (rather than chronic) phase of mTBI and may lead to neurodegeneration through cytotoxic effects. This finding also highlights the issue that imaging findings in CTE are likely to vary with time since exposure to rmTBI.

Radionuclide Studies

In total, 3 studies using radionuclide imaging met the review criteria. The first investigated differences in glucose metabolism [using 18F-fludeoxyglucose (FDG) positron emission tomography (PET)] and GABA receptor expression [using 18F-flumazenil (FMZ)] in 5 retired professional boxers compared with 4 healthy controls.35 Reduced glucose metabolism (FDG signal) and FMZ uptake are believed to represent functional abnormalities and neuronal loss,64 respectively. Compared with controls, boxers showed impaired glucose metabolism in the bilateral dorsolateral prefrontal cortices (DLPFC) and right middle orbitofrontal cortex (mOFC) (P < 0.005). Moreover, there was a significant positive correlation between the higher delayed visuospatial recall score and higher FDG signal in the right mOFC (r2 = 0.496, P < 0.05) and DLPFC (r2 = 0.524, P < 0.05) within boxers. Given that these areas participate in executive function and inhibitory control of decision making,65,66 impaired glucose metabolism (indicative of functional abnormality) in these areas may contribute to the clinical features of CTE. However, given that impaired frontal glucose metabolism is a hallmark of AD,67 which is an important differential in the diagnosis of CTE,5 this biomarker is unlikely to be specific enough for clinical use.

In boxers, FMZ uptake was reduced in the temporal cortical regions and the angular gyrus (P < 0.005). Selective neuronal loss in these areas is consistent with evidence of marked atrophy of the hippocampus, entorhinal cortex, and amygdala in CTE brains at postmortem.3 However, FMZ was also increased in the right postcentral gyrus, precentral gyrus, superior occipital cortex, and inferior parietal cortex in boxers compared with controls35 (P < 0.005). The authors explained these divergent findings by suggesting a compensatory mechanism involving either upregulation of benzodiazepine-binding sites or posttraumatic cortical neurogenesis.35 However, this explanation is largely speculative, and these findings could also be explained by the very small sample size. Within boxers, there was also a significant positive correlation between the higher motor assembly score and higher FMZ uptake in the left OFC (r2 = 0.499, P < 0.05) and right cerebellum (r2 = 0.643, P < 0.05). This is consistent with a role for the OFC and GABAergic cerebellar cells in activation and task maintenance68 and motor coordination,69 respectively.

The second study compared brain perfusion using single-photon emission computed tomography (SPECT) in 161 retired and current NFL players with 124 healthy controls.34 Cerebral perfusion was reduced in NFL players in 36 brain regions (P < 0.01). Using both discriminant and automatic linear regression predictive models, NFL players could be distinguished from controls with 90% sensitivity, 86% specificity, and 94% accuracy (95% confidence interval, 95-99). The most sensitive regions in distinguishing NFL players from controls were the anterior superior temporal lobes, rolandic operculum, insula, superior temporal lobes, precuneus, and cerebellar vermis. Importantly, some of these structures are particularly susceptible to damage after mTBI, which may explain why they represent particularly sensitive markers of CTE. For example, a systematic review of common SPECT abnormalities in mTBI identified the temporal lobes as one of the most common regions affected.70

The third study compared 18F-FDDNP PET imaging in 14 retired NFL players, 28 healthy controls, and 24 patients with AD.36 As a tau-binding radionuclide, 18F-FDDNP binds to neurofibrillary tangles (NFTs), which are the pathological hallmark of CTE, in vitro and in vivo.71 In NFL players, 4 patterns of 18F-FDDNP signal were identified involving brainstem WM tracts, subcortical, limbic, and cortical areas. Importantly, these patterns were consistent with the 4 stages of NFT distribution described at postmortem,5 and the areas affected were consistent with the mood and cognitive symptoms reported in CTE.36 They went on to show that increased 18F-FDDNP signal in cortical, subcortical, and limbic regions distinguished NFL players from controls (P < 0.0001). Moreover, increased 18F-FDDNP signal in subcortical and limbic areas distinguished NFL players from patients with AD (P < 0.0001). The main limitation of 18F-FDDNP PET in the diagnosis of CTE is the lack of specific binding of 18F-FDDNP to NFTs. 18F-FDDNP also binds to amyloid-beta72 and TDP-43,73 both of which have been observed in pathologically confirmed cases of CTE, particularly in severe disease.5 Amyloid-beta and TDP-43 are the pathological hallmarks of AD74 and FTD,75 respectively. Given that AD and FTD are important differentials in the diagnosis of CTE,5 the specificity of 18F-FDDNP PET may not be sufficient for clinical use, particularly in cases of severe suspected CTE. A more specific tau ligand (18F-T807) which has been recently developed76 is currently being evaluated for the diagnosis of CTE in an ongoing clinical trial (NCT02191267) due for completion in October 2016.


This systematic review highlights the rapid pace at which imaging biomarkers for CTE diagnosis are being developed; the number of articles published in the past 18 months is equal to the number published in the entire preceding period.12 Several promising biomarkers have been identified, and in Table 3, these have been aligned with comparable imaging findings reported in AD77,78 to help view these results in a wider context. Several important limitations of the included studies are now discussed.

Comparison of Remarkable Imaging Findings Reported in CTE With Those Reported in AD

The most significant difficulty in researching diagnostic imaging biomarkers of CTE is the need to validate any potential biomarker by correlation with clinical features and with a definitive postmortem diagnosis. This is particularly important, given that all included studies identified suspected cases of CTE with broad and nonspecific clinical criteria. These criteria varied significantly between studies; this is apparent in the “clinical evidence of CTE” column in Table 1. CTE diagnosis requires a history of rmTBI which was largely self-reported and therefore of questionable reliability. Moreover, the symptoms and signs of suspected cases of CTE in all studies overlap with clinical features of other neurodegenerative conditions, such as AD, whose prevalence is increased in CTE.79 In addition, the symptoms and signs may also reflect various aspects of an athlete's medical history, lifestyle factors (such as bankruptcy or divorce), alcohol, and recreational drug use. Therefore, the suspected cases of CTE are identified with a low specificity. Indeed, a study of 6 Canadian Football League players with a history of progressive cognitive, psychiatric, and/or motor symptom decline found postmortem evidence of CTE in just 50% of cases.80 Therefore, although the most promising imaging biomarkers may effectively distinguish “suspected CTE” from controls, the specificity of these tests in the diagnosis of CTE remains uncertain. To increase the specificity of neuroimaging findings in the diagnosis of CTE, future research should identify suspected cases of CTE using published clinical criteria.7–9

An inherent limitation in investigating diagnostic methods for CTE is the practical and ethical barriers to conducting a randomized controlled trial. The control groups in the included studies are therefore healthy controls which are matched to age, sex, and education to varying degrees. Two studies37,40 included control groups who were participants in noncontact sports. This latter approach is preferable because it reduces the probability that imaging changes are caused by an athletic lifestyle, rather than CTE itself.

An additional limitation is the small sample size in most of the included studies. Of the 7 included studies, only 2 had a sample size greater than 20. Therefore, although the results of the smaller studies may provide some clues as to useful imaging markers, these findings must be followed up in larger cohorts.

In principle, efforts to develop specific biomarkers of CTE should focus on aspects of the pathophysiological process which are specific to the condition. For this reason, imaging of the regional distribution of tau is particularly promising, as it is the defining feature at postmortem.5 However, traditional DTI metrics are unlikely to have the specificity to be clinically useful because these also change after a single concussion81 and rmTBI which is not associated with cognitive decline.47 It is possible that novel diffusion MRI techniques (ie, NODDI) will provide unique insight into the pathophysiology of CTE, thus revealing unique biomarkers.

This review also highlights that most recent research has been conducted in professional American football players. Although this is understandable, given the high levels of participation and exposure to head injury in the sport, it poses 2 limitations. The first is that findings in this population may not be generalizable to other contact sports (ie, boxing or ice hockey), where the biomechanics of head impacts, and possibly disease process, may differ. The second is that findings may not be generalizable to nonprofessional contact sport athletes, who differ in exposure to head injury and lifestyle. This is particularly important, given that this group are far greater in number than professional contact sport athletes and that just 1 season of college American football can lead to long-term WM changes even in the absence of concussion,46 suggesting that this group are also at risk of later developing CTE.

Efforts to diagnose CTE in vivo must acknowledge that the pathophysiology dynamically evolves over decades and is probably triggered by rmTBI.4 Therefore, investigation of imaging biomarkers in the acute and subacute phase of a single mTBI may provide crucial diagnostic and prognostic information, which would allow individuals at high risk of developing CTE to be identified, leading to early diagnosis and perhaps opening a window of opportunity for intervention. Recent advances in the development of such biomarkers use a range of additional modalities and measurements to those discussed here, including susceptibility-weighted imaging, functional MRI, MR spectroscopy, arterial spinal labeling, and dynamic susceptibility contrast MRI.82 In the progression from acute to subacute to chronic phases of traumatic encephalopathy, different imaging modalities will better reveal the underlying pathophysiology at different times. Moreover, simultaneous multimodal imaging will improve the specificity of diagnosis by assessing various aspects of the pathological cascade. For example, structural MRI combined with 18F-FDDNP PET imaging would allow simultaneous assessment of CSP length and tau distribution. These measurements, respectively, would provide evidence of repeated head injury37 and downstream changes5,36 which in combination support a diagnosis of CTE more than either factor alone. In combination with the clinical history and examination, it is likely that the role of imaging biomarkers in the diagnosis of CTE will be similar to the role they currently play in the diagnosis of AD, whereby positive imaging findings increase the likelihood of a probable AD diagnosis but do not confirm it.83


The purpose of this systematic review was to evaluate the utility of neuroimaging in the diagnosis of CTE. Seven studies met the review criteria, highlighting the extent of recent progress. The most promising study differentiated suspected CTE from controls and patients with AD using the PET signal from a tau-binding radionuclide. Important limitations include low specificity in identification of suspected cases of CTE across studies, the need for postmortem validation, and a lack of generalizability to nonprofessional athletes. Recommendations for future research include the use of published clinical criteria to identify suspected CTE. The future role of imaging biomarkers in the diagnosis of CTE is likely to reflect their current role in AD, whereby positive findings increase the likelihood, but do not confirm, a probable diagnosis.


1. Baugh CM, Stamm JM, Riley DO, et al. Chronic traumatic encephalopathy: neurodegeneration following repetitive concussive and subconcussive brain trauma. Brain Imaging Behav. 2012;6:244–254.
2. Martland HS. Punch drunk. J Am Med Assoc. 1928;91:1103–1107.
3. McKee AC, Cantu RC, Nowinski CJ, et al. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J Neuropathol Exp Neurol. 2009;68:709–735.
4. Gavett BE, Stern RA, McKee AC. Chronic traumatic encephalopathy: a potential late effect of sport-related concussive and subconcussive head trauma. Clin Sports Med. 2011;30:179–188. xi.
5. McKee AC, Stein TD, Nowinski CJ, et al. The spectrum of disease in chronic traumatic encephalopathy. Brain. 2013;136:43–64.
6. McKee AC, Cairns NJ, Dickson DW, et al. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol. 2016;131:75–86.
7. Jordan BD. The clinical spectrum of sport-related traumatic brain injury. Nat Rev Neurol. 2013;9:222–230.
8. Victoroff J. Traumatic encephalopathy: review and provisional research diagnostic criteria. NeuroRehabilitation. 2013;32:211–224.
9. Montenigro PH, Baugh CM, Daneshvar DH, et al. Clinical subtypes of chronic traumatic encephalopathy: literature review and proposed research diagnostic criteria for traumatic encephalopathy syndrome. Alzheimers Res Ther. 2014;6:68.
10. Neselius S, Brisby H, Theodorsson A, et al. CSF-biomarkers in Olympic boxing: diagnosis and effects of repetitive head trauma. PLoS One. 2012;7:e33606.
11. Zetterberg H, Blennow K. Fluid markers of traumatic brain injury. Mol Cell Neurosci. 2015;66:99–102.
12. Koerte IK, Lin AP, Willems A, et al. A review of neuroimaging findings in repetitive brain trauma. Brain Pathol. 2015;25:318–349.
13. Dani M, Brooks DJ, Edison P. Tau imaging in neurodegenerative diseases. Eur J Nucl Med Mol Imaging. 2016;43:1139–1150.
14. Li L, Sun G, Liu K, et al. White matter changes in posttraumatic stress disorder following mild traumatic brain injury: a prospective longitudinal diffusion tensor imaging study. Chin Med J. 2016;129:1091–1099.
15. Killgore WD, Singh P, Kipman M, et al. Gray matter volume and executive functioning correlate with time since injury following mild traumatic brain injury. Neurosci Lett. 2016;612:238–244.
16. Ting WK, Schweizer TA, Topolovec-Vranic J, et al. Antisaccadic eye movements are correlated with corpus callosum white matter mean diffusivity, stroop performance, and symptom burden in mild traumatic brain injury and concussion. Front Neurol. 2015;6:271.
17. Han K, Chapman SB, Krawczyk DC. Altered amygdala connectivity in individuals with chronic traumatic brain injury and comorbid depressive symptoms. Front Neurol. 2015;6:231.
18. Veeramuthu V, Narayanan V, Kuo TL, et al. Diffusion tensor imaging parameters in mild traumatic brain injury and its correlation with early neuropsychological impairment: a longitudinal study. J Neurotrauma. 2015;32:1497–1509.
19. Sours C, Chen H, Roys S, et al. Investigation of multiple frequency ranges using discrete wavelet decomposition of resting-state functional connectivity in mild traumatic brain injury patients. Brain Connect. 2015;5:442–450.
20. Tyler CW, Likova LT, Mineff KN, et al. Deficits in the activation of human oculomotor nuclei in chronic traumatic brain injury. Front Neurol. 2015;6:173.
21. Astafiev SV, Shulman GL, Metcalf NV, et al. Abnormal white matter blood-oxygen-level-dependent signals in chronic mild traumatic brain injury. J Neurotrauma. 2015;32:1254–1271.
22. Astafiev SV, Zinn KL, Shulman GL, et al. Exploring the physiological correlates of chronic mild traumatic brain injury symptoms. Neuroimage Clin. 2016;11:10–19.
23. Sours C, Zhuo J, Roys S, et al. Disruptions in resting state functional connectivity and cerebral blood flow in mild traumatic brain injury patients. PLoS One. 2015;10:e0134019.
24. Cole JH, Leech R, Sharp DJ. Initiative AsDN. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol. 2015;77:571–581.
25. Lu L, Cao H, Wei X, et al. Iron deposition is positively related to cognitive impairment in patients with chronic mild traumatic brain injury: assessment with susceptibility weighted imaging. Biomed Res Int. 2015;2015:470676.
26. Svaldi DO, McCuen EC, Joshi C, et al. Cerebrovascular reactivity changes in asymptomatic female athletes attributable to high school soccer participation. Brain Imaging Behav. 2016;11:98–112.
27. Koerte IK, Lin AP, Muehlmann M, et al. Altered neurochemistry in former professional soccer players without a history of concussion. J Neurotrauma. 2015;32:1287–1293.
28. Coughlin JM, Wang Y, Munro CA, et al. Neuroinflammation and brain atrophy in former NFL players: an in vivo multimodal imaging pilot study. Neurobiol Dis. 2015;74:58–65.
29. Abbas K, Shenk TE, Poole VN, et al. Effects of repetitive sub-concussive brain injury on the functional connectivity of Default Mode Network in high school football athletes. Dev Neuropsychol. 2015;40:51–56.
30. Wang Y, West JD, Bailey JN, et al. Decreased cerebral blood flow in chronic pediatric mild TBI: an MRI perfusion study. Dev Neuropsychol. 2015;40:40–44.
31. Poole VN, Breedlove EL, Shenk TE, et al. Sub-concussive hit characteristics predict deviant brain metabolism in football athletes. Dev Neuropsychol. 2015;40:12–17.
32. List J, Ott S, Bukowski M, et al. Cognitive function and brain structure after recurrent mild traumatic brain injuries in young-to-middle-aged adults. Front Hum Neurosci. 2015;9:228.
33. Guild EB, Levine B. Functional correlates of midline brain volume loss in chronic traumatic brain injury. J Int Neuropsychol Soc. 2015;21:650–655.
34. Amen DG, Willeumier K, Omalu B, et al. Perfusion neuroimaging abnormalities alone distinguish national football league players from a healthy population. J Alzheimers Dis. 2016;53:237–241.
35. Bang SA, Song YS, Moon BS, et al. Neuropsychological, metabolic, and GABAA receptor studies in subjects with repetitive traumatic brain injury. J Neurotrauma. 2016;33:1005–1014.
36. Barrio JR, Small GW, Wong KP, et al. In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proc Natl Acad Sci USA. 2015;112:E2039–E2047.
37. Koerte IK, Hufschmidt J, Muehlmann M, et al. Cavum septi pellucidi in symptomatic former professional football players. J Neurotrauma. 2016;33:346–353.
38. Mayer AR, Ling JM, Dodd AB, et al. A prospective microstructure imaging study in mixed-martial artists using geometric measures and diffusion tensor imaging: methods and findings. Brain Imaging Behav. 2016;11:698–711.
39. Multani N, Goswami R, Khodadadi M, et al. The association between white-matter tract abnormalities, and neuropsychiatric and cognitive symptoms in retired professional football players with multiple concussions. J Neurol. 2016;263:1332–1341.
40. Wilde EA, Hunter JV, Li X, et al. Chronic effects of boxing: diffusion tensor imaging and cognitive findings. J Neurotrauma. 2016;33:672–680.
41. Sundman M, Doraiswamy PM, Morey RA. Neuroimaging assessment of early and late neurobiological sequelae of traumatic brain injury: implications for CTE. Front Neurosci. 2015;9:334.
42. Ambarki K, Israelsson H, Wåhlin A, et al. Brain ventricular size in healthy elderly: comparison between Evans index and volume measurement. Neurosurgery. 2010;67:94–99; discussion 99.
43. Hamidu AU, Olarinoye-Akorede SA, Ekott DS, et al. Computerized tomographic study of normal Evans index in adult Nigerians. J Neurosci Rural Pract. 2015;6:55–58.
44. Saba L, Anzidei M, Raz E, et al. MR and CT of brain's cava. J Neuroimaging. 2013;23:326–335.
45. Aviv RI, Tomlinson G, Kendall B, et al. Cavum septi pellucidi in boxers. Can Assoc Radiol J. 2010;61:29–32; quiz 21–22.
46. Bazarian JJ, Zhu T, Zhong J, et al. Persistent, long-term cerebral white matter changes after sports-related repetitive head impacts. PLoS One. 2014;9:e94734.
47. Herweh C, Hess K, Meyding-Lamadé U, et al. Reduced white matter integrity in amateur boxers. Neuroradiology. 2016;58:911–920.
48. Kou Z, Wu Z, Tong KA, et al. The role of advanced MR imaging findings as biomarkers of traumatic brain injury. J Head Trauma Rehabil. 2010;25:267–282.
49. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906.
50. Song SK, Sun SW, Ju WK, et al. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20:1714–1722.
51. Song SK, Yoshino J, Le TQ, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage. 2005;26:132–140.
52. Budde MD, Kim JH, Liang HF, et al. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn Reson Med. 2007;57:688–695.
53. Wheeler-Kingshott CA, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med. 2009;61:1255–1260.
54. Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage. 2013;73:239–254.
55. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487–1505.
56. Jones DK, Symms MR, Cercignani M, et al. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage. 2005;26:546–554.
57. Thiebaut de Schotten M, Dell'Acqua F, Forkel SJ, et al. A lateralized brain network for visuospatial attention. Nat Neurosci. 2011;14:1245–1246.
58. Mattfeld AT, Stark CE. Functional contributions and interactions between the human hippocampus and subregions of the striatum during arbitrary associative learning and memory. Hippocampus. 2015;25:900–911.
59. Mamah D, Conturo TE, Harms MP, et al. Anterior thalamic radiation integrity in schizophrenia: a diffusion-tensor imaging study. Psychiatry Res. 2010;183:144–150.
60. Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–1440.
61. Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000–1016.
62. Lewén A, Li GL, Nilsson P, et al. Traumatic brain injury in rat produces changes of beta-amyloid precursor protein immunoreactivity. Neuroreport. 1995;6:357–360.
63. Mayer AR, Ling J, Mannell MV, et al. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology. 2010;74:643–650.
64. Shiga T, Ikoma K, Katoh C, et al. Loss of neuronal integrity: a cause of hypometabolism in patients with traumatic brain injury without MRI abnormality in the chronic stage. Eur J Nucl Med Mol Imaging. 2006;33:817–822.
65. Elliott R. Executive functions and their disorders. Br Med Bull. 2003;65:49–59.
66. Damasio H, Grabowski T, Frank R, et al. The return of Phineas Gage: clues about the brain from the skull of a famous patient. Science. 1994;264:1102–1105.
67. Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer's disease. FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging. 2005;32:486–510.
68. Ridderinkhof KR, van den Wildenberg WP, Segalowitz SJ, et al. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. 2004;56:129–140.
69. Lalonde R, Strazielle C. Motor coordination, exploration, and spatial learning in a natural mouse mutation (nervous) with Purkinje cell degeneration. Behav Genet. 2003;33:59–66.
70. Raji CA, Tarzwell R, Pavel D, et al. Clinical utility of SPECT neuroimaging in the diagnosis and treatment of traumatic brain injury: a systematic review. PLoS One. 2014;9:e91088.
71. Harada R, Okamura N, Furumoto S, et al. Characteristics of tau and its ligands in PET imaging. Biomolecules. 2016;6:7.
72. Harada R, Okamura N, Furumoto S, et al. Comparison of the binding characteristics of [18F]THK-523 and other amyloid imaging tracers to Alzheimer's disease pathology. Eur J Nucl Med Mol Imaging. 2013;40:125–132.
73. Robinson JL, Geser F, Stieber A, et al. TDP-43 skeins show properties of amyloid in a subset of ALS cases. Acta Neuropathol. 2013;125:121–131.
74. Bloom GS. Amyloid-β and tau: the trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurol. 2014;71:505–508.
75. Hu WT, Grossman M. TDP-43 and frontotemporal dementia. Curr Neurol Neurosci Rep. 2009;9:353–358.
76. Chien DT, Bahri S, Szardenings AK, et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis. 2013;34:457–468.
77. Jobst KA, Smith AD, Barker CS, et al. Association of atrophy of the medial temporal lobe with reduced blood flow in the posterior parietotemporal cortex in patients with a clinical and pathological diagnosis of Alzheimer's disease. J Neurol Neurosurg Psychiatry. 1992;55:190–194.
78. Medina D, DeToledo-Morrell L, Urresta F, et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging. 2006;27:663–672.
79. McKee AC, Stein TD, Kiernan PT, et al. The neuropathology of chronic traumatic encephalopathy. Brain Pathol. 2015;25:350–364.
80. Hazrati LN, Tartaglia MC, Diamandis P, et al. Absence of chronic traumatic encephalopathy in retired football players with multiple concussions and neurological symptomatology. Front Hum Neurosci. 2013;7:222.
81. Virji-Babul N, Borich MR, Makan N, et al. Diffusion tensor imaging of sports-related concussion in adolescents. Pediatr Neurol. 2013;48:24–29.
82. Koerte I, Hufschmidt J, Muehlmann M, et al. Advanced neuroimaging of mild traumatic brain injury. In: Laskowitz D, Grant G, eds. Translational Research in Traumatic Brain Injury. Boca Raton (FL): CRC Press/Taylor and Francis Group; 2016. Chapter 13.
83. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Demen. 2011;7:263–269.

traumatic brain injury; repeated head injury; dementia pugilistica

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.